Sync and disable process replay temporarily

models: add a refresh models button to mici, gate and show progress on both panels

Adds the refresh-models tile to the mici models panel (it was missing there),
factoring the sync-key trigger + in-progress check into refresh_model_list()
and refresh_in_progress() shared with the big UI.

On both UIs the refresh button is now gated on offroad + not-downloading +
not-refreshing (the manager runs offroad-only and its per-tick manifest fetch
sits above a blocking download loop, so a refresh queued in either state would
stick), and shows progress while the manager refetches: mici shows "fetching..."
on the tile, the big UI flips its button from REFRESH to "FETCHING..." to match
its FETCHING.../SELECT/CLEAR label style (as in the OSM panel), replacing the
old fire-and-forget popup.


Claude-Session: https://claude.ai/code/session_01EGMnVnSk5inGTrd7kuDVG9

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: James Vecellio-Grant <159560811+Discountchubbs@users.noreply.github.com>

.codespellignore

@@ -2,5 +2,6 @@ Wen
 REGIST
 PullRequest
 cancelled
+indeces
 FOF
 NoO

.github/CODEOWNERS

@@ -1,11 +0,0 @@
-*       @sunnypilot/dev-internal
-/.github/ @devtekve @sunnyhaibin
-/release/ci/ @devtekve @sunnyhaibin
-/tinygrad_repo @devtekve @Discountchubbs
-/tinygrad/ @devtekve @Discountchubbs
-/selfdrive/controls/lib/longitudinal_planner.py @devtekve @Discountchubbs
-/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py @devtekve @Discountchubbs
-/selfdrive/modeld/ @devtekve @Discountchubbs
-/sunnypilot/model* @devtekve @Discountchubbs
-/sunnypilot/sunnylink/ @devtekve
-/system/athena/ @devtekve

.github/release-drafter.yml

@@ -1,43 +0,0 @@
-exclude-labels:
-  - 'no-changelog'
-categories:
-  - title: '🚀 Features'
-    labels:
-      - 'feature'
-      - 'enhancement'
-  - title: '🐛 Bug Fixes'
-    collapse-after: 5
-    labels:
-      - 'fix'
-      - 'bugfix'
-      - 'bug'
-  - title: '🧰 Maintenance'
-    collapse-after: 5
-    label: 'chore'
-change-template: '- $TITLE @$AUTHOR (#$NUMBER)'
-change-title-escapes: '\<*_&'
-replacers:
-  - search: '/[Ss][Uu][Nn][Nn][Yy][Pp][Ii][Ll][Oo][Tt]/g'
-    replace: 'sunnypilot'
-  - search: '/\b[Ss][Pp]\b/g'
-    replace: 'SP'
-version-resolver:
-  major:
-    labels:
-      - 'major'
-  minor:
-    labels:
-      - 'minor'
-  patch:
-    labels:
-      - 'patch'
-  default: patch
-name-template: 'v$RESOLVED_VERSION 🚀'
-tag-template: 'v$RESOLVED_VERSION'
-version-template: "0.$MAJOR.$MINOR.$PATCH" # The day OP becomes v1, we need to bump this
-tag-prefix: "v0." # The day OP becomes v1, we need to bump this
-prerelease-identifier: "staging"
-template: |
-  ## Changes
-
-  $CHANGES

.github/workflows/build-all-tinygrad-models.yaml

@@ -8,18 +8,23 @@ on:
         required: true
         type: string
       target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
         required: true
         type: choice
         default: 'qcom'
         options:
           - qcom
-          - usbgpu
+          - chestnut
       hf_repo:
         description: 'Hugging Face dataset repository'
         required: false
         type: string
         default: 'sunnypilot/sunnypilot_models_v1'
+      docs_repo:
+        description: 'GitHub repo holding the driving_models JSON on its gh-pages branch'
+        required: false
+        type: string
+        default: 'sunnypilot/sunnypilot-models'
 
 jobs:
   setup:
@@ -34,7 +39,6 @@ jobs:
       - name: Checkout sunnypilot repo
         uses: actions/checkout@v4
         with:
-          repository: sunnypilot/sunnypilot
           path: sunnypilot
           submodules: recursive
 
@@ -47,10 +51,10 @@ jobs:
           echo "tinygrad_ref=$ref" >> $GITHUB_OUTPUT
           echo "tinygrad_ref is $ref"
 
-      - name: Checkout docs repo (sunnypilot-models, gh-pages)
+      - name: Checkout docs repo (gh-pages)
         uses: actions/checkout@v4
         with:
-          repository: sunnypilot/sunnypilot-models
+          repository: ${{ inputs.docs_repo }}
           ref: gh-pages
           path: docs
           ssh-key: ${{ secrets.CI_SUNNYPILOT_DOCS_PRIVATE_KEY }}
@@ -59,7 +63,7 @@ jobs:
         id: get-json
         run: |
           cd docs/docs
-          PREFIX="driving_models_${{ inputs.target_hardware == 'usbgpu' && 'usbgpu_' || '' }}v"
+          PREFIX="driving_models_${{ inputs.target_hardware == 'chestnut' && 'chestnut_' || '' }}v"
           latest=$(ls ${PREFIX}*.json | sed -E "s/${PREFIX}([0-9]+)\.json/\1/" | sort -n | tail -1)
           next=$((latest+1))
           json_file="${PREFIX}${next}.json"
@@ -78,6 +82,7 @@ jobs:
       - name: Get next recompiled dir number
         id: create-recompiled-dir
         env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           HF_REPO: ${{ github.event.inputs.hf_repo }}
         run: |
           pip install huggingface_hub
@@ -117,6 +122,7 @@ jobs:
       json_version: ${{ needs.setup.outputs.json_version }}
       target_hardware: ${{ github.event.inputs.target_hardware }}
       hf_repo: ${{ github.event.inputs.hf_repo }}
+      docs_repo: ${{ inputs.docs_repo }}
       set_min_version: ${{ github.event.inputs.set_min_version }}
       tinygrad_ref: ${{ needs.setup.outputs.tinygrad_ref }}
     secrets: inherit
@@ -161,6 +167,7 @@ jobs:
       target_hardware: ${{ github.event.inputs.target_hardware }}
       artifact_suffix: -retry
       hf_repo: ${{ github.event.inputs.hf_repo }}
+      docs_repo: ${{ inputs.docs_repo }}
       set_min_version: ${{ github.event.inputs.set_min_version }}
       tinygrad_ref: ${{ needs.setup.outputs.tinygrad_ref }}
     secrets: inherit

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

@@ -0,0 +1,522 @@
+name: Build default models
+
+on:
+  workflow_dispatch:
+    inputs:
+      target:
+        description: 'Model target to build'
+        required: true
+        type: choice
+        options:
+          - small
+          - big
+          - dm
+  workflow_call:
+    inputs:
+      target:
+        description: 'Model target to build (small, big, or dm)'
+        required: true
+        type: string
+
+concurrency:
+  group: build-default-models-${{ inputs.target }}
+  cancel-in-progress: false
+
+env:
+  HF_REPO: sunnypilot/sunnypilot_models_v1
+
+jobs:
+  resolve:
+    runs-on: ubuntu-24.04
+    outputs:
+      model_name: ${{ steps.resolve.outputs.model_name }}
+      safe_model_name: ${{ steps.resolve.outputs.safe_model_name }}
+      onnx_ref: ${{ steps.resolve.outputs.onnx_ref }}
+      onnx_path: ${{ steps.resolve.outputs.onnx_path }}
+      hf_defaults_path: ${{ steps.resolve.outputs.hf_defaults_path }}
+      tinygrad_ref: ${{ steps.resolve.outputs.tinygrad_ref }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - id: resolve
+        run: |
+          export PYTHONPATH=${{ github.workspace }}
+
+          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 [ "${{ inputs.target }}" = "dm" ]; then
+            ONNX_PATH="openpilot/selfdrive/modeld/models/dmonitoring_model.onnx"
+            HF_DEFAULTS_PATH="models/defaults/dm"
+            NAME="dmonitoring_model ($(git log -1 --format=%cd --date=format:'%B %d, %Y' -- "$ONNX_PATH"))"
+          else
+            NAME=$(python3 -c "from openpilot.sunnypilot.models.model_name import DEFAULT_MODEL; print(DEFAULT_MODEL)")
+            ONNX_PATH="openpilot/selfdrive/modeld/models/driving_supercombo.onnx"
+            HF_DEFAULTS_PATH="models/defaults/small"
+          fi
+
+          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"
+            exit 1
+          fi
+
+          SAFE_NAME="${NAME// /-}"
+          echo "model_name=${NAME}" >> $GITHUB_OUTPUT
+          echo "safe_model_name=${SAFE_NAME}" >> $GITHUB_OUTPUT
+          echo "onnx_ref=${ONNX_REF}" >> $GITHUB_OUTPUT
+          echo "onnx_path=${ONNX_PATH}" >> $GITHUB_OUTPUT
+          echo "hf_defaults_path=${HF_DEFAULTS_PATH}" >> $GITHUB_OUTPUT
+          echo "tinygrad_ref=${TINYGRAD_REF}" >> $GITHUB_OUTPUT
+
+  build_small_model:
+    needs: resolve
+    if: ${{ inputs.target == 'small' }}
+    runs-on: [self-hosted, tici]
+    env:
+      SMALL_ONNX: openpilot/selfdrive/modeld/models/driving_supercombo.onnx
+      SMALL_PKL: openpilot/selfdrive/modeld/models/driving_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull ONNX via LFS
+        run: git lfs pull -I "${{ env.SMALL_ONNX }}"
+
+      - 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 small model with stock compiler
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          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"
+
+          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: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.SMALL_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked into {len(targets)} files')
+          "
+
+      - name: Prepare output
+        env:
+          MODEL_NAME: ${{ needs.resolve.outputs.safe_model_name }}
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          MODELS_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld/models"
+          OUTPUT_DIR="${{ github.workspace }}/small_output"
+          PKL_BASE="driving_tinygrad.pkl"
+          mkdir -p "$OUTPUT_DIR"
+
+          cp "$MODELS_DIR/${PKL_BASE}".chunk* "$OUTPUT_DIR/"
+          cp "$MODELS_DIR/${PKL_BASE}.chunkmanifest" "$OUTPUT_DIR/"
+
+          python3 "${{ 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 }}"
+
+          echo "model-${MODEL_NAME}-${{ github.run_number }}" > "$OUTPUT_DIR/artifact_name.txt"
+
+      - name: Upload small model artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: model-${{ needs.resolve.outputs.safe_model_name }}-${{ github.run_number }}
+          path: ${{ github.workspace }}/small_output/
+
+      - name: Upload artifact name file
+        uses: actions/upload-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: ${{ github.workspace }}/small_output/artifact_name.txt
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+
+  build_big_model:
+    needs: resolve
+    if: ${{ inputs.target == 'big' }}
+    runs-on: [self-hosted, chestnut]
+    env:
+      BIG_ONNX: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
+      BIG_PKL: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull big ONNX via LFS
+        run: git lfs pull -I "${{ env.BIG_ONNX }}"
+
+      - 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: Wait for chestnut PCIe link
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+          python3 -c "
+          import time
+          from openpilot.system.hardware.chestnut.flash import link_up
+          for i in range(10):
+              if link_up():
+                  print(f'PCIe link up after {i+1} attempt(s)')
+                  break
+              time.sleep(1)
+          else:
+              raise RuntimeError('Chestnut PCIe link not ready after 10 attempts')
+          "
+
+      - name: Compile big model with stock compiler
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
+
+          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: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.BIG_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked into {len(targets)} files')
+          "
+
+      - name: Prepare output
+        env:
+          MODEL_NAME: ${{ needs.resolve.outputs.safe_model_name }}
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          MODELS_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld/models"
+          OUTPUT_DIR="${{ github.workspace }}/big_output"
+          PKL_BASE="big_driving_tinygrad.pkl"
+          mkdir -p "$OUTPUT_DIR"
+
+          cp "$MODELS_DIR/${PKL_BASE}".chunk* "$OUTPUT_DIR/"
+          cp "$MODELS_DIR/${PKL_BASE}.chunkmanifest" "$OUTPUT_DIR/"
+
+          python3 "${{ 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 }}"
+
+          echo "model-${MODEL_NAME}-${{ github.run_number }}" > "$OUTPUT_DIR/artifact_name.txt"
+
+      - name: Upload big model artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: model-${{ needs.resolve.outputs.safe_model_name }}-${{ github.run_number }}
+          path: ${{ github.workspace }}/big_output/
+
+      - name: Upload artifact name file
+        uses: actions/upload-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: ${{ github.workspace }}/big_output/artifact_name.txt
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+
+  upload_defaults:
+    needs: [ resolve, build_small_model, build_big_model, build_dm_model ]
+    if: |
+      ${{
+        !cancelled() &&
+        (inputs.target == 'big' && needs.build_big_model.result == 'success' ||
+         inputs.target == 'small' && needs.build_small_model.result == 'success' ||
+         inputs.target == 'dm' && needs.build_dm_model.result == 'success')
+      }}
+    runs-on: ubuntu-24.04
+    permissions:
+      id-token: write
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Pull ONNX via LFS
+        run: git lfs pull -I "${{ needs.resolve.outputs.onnx_path }}"
+
+      - name: Install huggingface_hub
+        run: pip install --upgrade "huggingface_hub>=0.22.0"
+
+      - name: Download artifact name
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: artifact-name-${{ needs.resolve.outputs.safe_model_name }}
+          path: artifact_name
+
+      - name: Read artifact name
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        id: artifact
+        run: |
+          ARTIFACT_NAME=$(cat artifact_name/artifact_name.txt)
+          echo "artifact_name=$ARTIFACT_NAME" >> $GITHUB_OUTPUT
+
+      - name: Download model artifact
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: ${{ steps.artifact.outputs.artifact_name }}
+          path: output
+
+      - name: Upload model to HF
+        if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
+        run: |
+          rm -f output/artifact_name.txt
+          export PYTHONPATH=$(pwd)
+          python3 release/ci/upload_default_model.py \
+            --hf-repo "${{ env.HF_REPO }}" \
+            --hf-defaults-path "${{ 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 }}"
+
+      - name: Download DM artifact
+        if: ${{ inputs.target == 'dm' }}
+        uses: actions/download-artifact@v4
+        with:
+          name: dm-model-${{ github.run_number }}
+          path: dm_output
+
+      - name: Generate DM metadata and upload to HF
+        if: ${{ inputs.target == 'dm' }}
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          export PYTHONPATH=$(pwd)
+          python3 -c "
+          import json, hashlib
+          from pathlib import Path
+          from datetime import datetime, UTC
+
+          dm_dir = Path('dm_output')
+          manifest = list(dm_dir.glob('*.chunkmanifest'))
+          assert manifest, 'No chunkmanifest found'
+          pkl_name = manifest[0].name.removesuffix('.chunkmanifest')
+          num_chunks = int(manifest[0].read_text().strip())
+
+          chunks = []
+          for i in range(num_chunks):
+              chunk = dm_dir / f'{pkl_name}.chunk{i+1:02d}of{num_chunks:02d}'
+              chunks.append({
+                  'file_name': chunk.name,
+                  'sha256': hashlib.sha256(chunk.read_bytes()).hexdigest()
+              })
+
+          digest = hashlib.sha256()
+          for c in chunks:
+              with open(dm_dir / c['file_name'], 'rb') as f:
+                  while block := f.read(1024*1024):
+                      digest.update(block)
+
+          metadata = {
+              'bundles': [{
+                  'short_name': 'DMMODEL',
+                  'display_name': '${{ needs.resolve.outputs.model_name }}',
+                  'ref': '${{ needs.resolve.outputs.onnx_ref }}',
+                  'runner': 'tinygrad',
+                  'build_time': datetime.now(UTC).strftime('%Y-%m-%dT%H:%M:%SZ'),
+                  'models': [{
+                      'type': 'chunked',
+                      'artifact': {
+                          'file_name': pkl_name,
+                          'download_uri': {'url': '', 'sha256': digest.hexdigest()},
+                          'chunks': chunks
+                      }
+                  }]
+              }]
+          }
+          with open(dm_dir / 'metadata.json', 'w') as f:
+              json.dump(metadata, f, indent=2)
+          print('Generated DM metadata.json')
+          "
+
+          python3 release/ci/upload_default_model.py \
+            --hf-repo "${{ env.HF_REPO }}" \
+            --hf-defaults-path "${{ needs.resolve.outputs.hf_defaults_path }}" \
+            --artifact-name "dm-model-${{ github.run_number }}" \
+            --model-dir dm_output \
+            --onnx-path "${{ 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 }}"
+
+  build_dm_model:
+    needs: resolve
+    if: ${{ inputs.target == 'dm' }}
+    runs-on: [self-hosted, tici]
+    env:
+      DM_ONNX: openpilot/selfdrive/modeld/models/dmonitoring_model.onnx
+      DM_PKL: openpilot/selfdrive/modeld/models/dmonitoring_model_tinygrad.pkl
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+
+      - name: Pull DM ONNX via LFS
+        run: git lfs pull -I "${{ env.DM_ONNX }}"
+
+      - 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 DM model
+        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"
+
+          taskset -c 7 env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/tinygrad_repo/examples/openpilot/compile3.py \
+            ${{ github.workspace }}/${{ env.DM_ONNX }} \
+            ${{ github.workspace }}/${{ env.DM_PKL }}
+
+      - name: Chunk DM pkl
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          export PYTHONPATH=${{ github.workspace }}
+          python3 -c "
+          from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+          import os
+          pkl = '${{ github.workspace }}/${{ env.DM_PKL }}'
+          size = os.path.getsize(pkl)
+          targets = get_chunk_targets(pkl, size)
+          chunk_file(pkl, targets)
+          print(f'Chunked {pkl} into {len(targets)} chunks')
+          "
+
+      - name: Compile DM warp
+        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"
+          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 $(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')"); do
+            WARP_PKL="${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} \
+              --output ${WARP_PKL}
+          done
+
+      - name: Prepare DM output
+        run: |
+          mkdir -p dm_output
+          cp ${{ github.workspace }}/${{ env.DM_PKL }}.chunk* dm_output/
+          cp ${{ github.workspace }}/${{ env.DM_PKL }}.chunkmanifest dm_output/
+          cp ${{ github.workspace }}/openpilot/selfdrive/modeld/models/dm_warp_* dm_output/
+
+      - name: Upload DM artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dm-model-${{ github.run_number }}
+          path: dm_output/
+
+      - name: Re-enable powersave
+        if: always()
+        run: |
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
+          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
+

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

@@ -30,7 +30,7 @@ on:
         type: boolean
         default: true
       target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
         required: false
         type: string
         default: 'qcom'
@@ -39,6 +39,11 @@ on:
         required: false
         type: string
         default: 'sunnypilot/sunnypilot_models_v1'
+      docs_repo:
+        description: 'GitHub repo holding the driving_models JSON on its gh-pages branch'
+        required: false
+        type: string
+        default: 'sunnypilot/sunnypilot-models'
       set_min_version:
         description: 'Minimum selector version'
         required: false
@@ -101,15 +106,20 @@ on:
         default: 'qcom'
         options:
           - qcom
-          - usbgpu
+          - chestnut
       hf_repo:
         description: 'Hugging Face dataset repository'
         required: false
         type: string
         default: 'sunnypilot/sunnypilot_models_v1'
+      docs_repo:
+        description: 'GitHub repo holding the driving_models JSON on its gh-pages branch'
+        required: false
+        type: string
+        default: 'sunnypilot/sunnypilot-models'
 env:
   RECOMPILED_DIR: recompiled${{ inputs.recompiled_dir }}
-  JSON_FILE: docs/docs/driving_models_${{ inputs.target_hardware == 'usbgpu' && 'usbgpu_v' || 'v' }}${{ inputs.json_version }}.json
+  JSON_FILE: docs/docs/driving_models_${{ inputs.target_hardware == 'chestnut' && 'chestnut_v' || 'v' }}${{ inputs.json_version }}.json
 
 jobs:
   build_model:
@@ -136,7 +146,7 @@ jobs:
       - name: Checkout docs repo
         uses: actions/checkout@v4
         with:
-          repository: sunnypilot/sunnypilot-models
+          repository: ${{ inputs.docs_repo }}
           ref: gh-pages
           path: docs
           ssh-key: ${{ secrets.CI_SUNNYPILOT_DOCS_PRIVATE_KEY }}
@@ -146,7 +156,7 @@ jobs:
 
       - name: Validate hf_repo and JSON version
         env:
-          HF_OIDC_RESOURCE: datasets/${{ inputs.hf_repo }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
           if [ ! -f "$JSON_FILE" ]; then
             echo "JSON file $JSON_FILE does not exist!"
@@ -155,13 +165,8 @@ jobs:
           python3 -c "
           import sys
           from huggingface_hub import HfApi
-          try:
-              api = HfApi()
-              api.repo_info(repo_id=sys.argv[1], repo_type='dataset')
-              print(f'Success: Repo {sys.argv[1]} exists.')
-          except Exception as e:
-              print('HF validation failed:', e)
-              sys.exit(1)
+          HfApi().repo_info(repo_id=sys.argv[1], repo_type='dataset')
+          print(f'Success: Repo {sys.argv[1]} exists.')
           " "${{ inputs.hf_repo }}"
 
       - name: Download artifact name file
@@ -192,7 +197,7 @@ jobs:
 
       - name: Upload to Hugging Face
         env:
-          HF_OIDC_RESOURCE: datasets/${{ inputs.hf_repo }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           ARTIFACT_NAME: ${{ steps.read-artifact-name.outputs.artifact_name }}
         run: |
           hf upload ${{ inputs.hf_repo }} \

.github/workflows/download-hf-model-chunks/action.yml

@@ -0,0 +1,73 @@
+name: Download HF model chunks
+description: Resolve and download model chunks from HuggingFace in parallel
+
+inputs:
+  hf_repo:
+    description: HuggingFace dataset repo
+    required: true
+  models:
+    description: 'JSON array of {hf_path, onnx_hash, canonical} objects'
+    required: true
+  dest_dir:
+    description: Destination directory for downloaded chunks
+    required: true
+
+runs:
+  using: composite
+  steps:
+    - name: Download model chunks
+      shell: bash
+      env:
+        HF_REPO: ${{ inputs.hf_repo }}
+        MODELS_JSON: ${{ inputs.models }}
+        DEST_DIR: ${{ inputs.dest_dir }}
+      run: |
+        set -eo pipefail
+        DOWNLOAD_LIST=$(mktemp)
+
+        resolve_chunks() {
+          local HF_PATH="$1" ONNX_HASH="$2" CANONICAL="$3" DEST_DIR="$4"
+          local JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_PATH}/default_models.json"
+          local DEFAULTS BUNDLE ARTIFACT BASE_URL NUM_CHUNKS
+          DEFAULTS=$(curl -fsSL "$JSON_URL")
+          BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)')
+          ARTIFACT=$(echo "$BUNDLE" | jq -r '.models[0].artifact')
+          BASE_URL=$(echo "$ARTIFACT" | jq -r '.download_uri.url' | sed 's|/[^/]*$||')
+          NUM_CHUNKS=$(echo "$ARTIFACT" | jq -r '.chunks | length')
+
+          mkdir -p "$DEST_DIR"
+          while IFS= read -r CHUNK_NAME; do
+            CHUNK_IDX=$(echo "$CHUNK_NAME" | grep -oP 'chunk\K[0-9]+of[0-9]+' || true)
+            if [ -z "$CHUNK_IDX" ]; then
+              echo "::error::Failed to parse chunk index from: $CHUNK_NAME"
+              return 1
+            fi
+            ENCODED_URL=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${BASE_URL}/${CHUNK_NAME}', safe=':/'))")
+            printf '%s\t%s\n' "$ENCODED_URL" "${DEST_DIR}/${CANONICAL}.chunk${CHUNK_IDX}" >> "$DOWNLOAD_LIST"
+          done < <(echo "$ARTIFACT" | jq -r '.chunks[].file_name')
+          echo "$NUM_CHUNKS" > "${DEST_DIR}/${CANONICAL}.chunkmanifest"
+
+          if [ "$CANONICAL" = "dmonitoring_model_tinygrad.pkl" ]; then
+            for warp in dm_warp_1928x1208_tinygrad.pkl dm_warp_1344x760_tinygrad.pkl; do
+              ENCODED_URL=$(python3 -c "import urllib.parse; print(urllib.parse.quote('${BASE_URL}/${warp}', safe=':/'))")
+              printf '%s\t%s\n' "$ENCODED_URL" "${DEST_DIR}/${warp}" >> "$DOWNLOAD_LIST"
+            done
+          fi
+        }
+
+        echo "$MODELS_JSON" | jq -c '.[]' | while IFS= read -r model; do
+          HF_PATH=$(echo "$model" | jq -r '.hf_path')
+          ONNX_HASH=$(echo "$model" | jq -r '.onnx_hash')
+          CANONICAL=$(echo "$model" | jq -r '.canonical')
+          resolve_chunks "$HF_PATH" "$ONNX_HASH" "$CANONICAL" "$DEST_DIR"
+        done
+
+        TOTAL=$(wc -l < "$DOWNLOAD_LIST")
+        echo "Downloading $TOTAL chunks with 8 parallel connections..."
+        xargs -P8 -d'\n' -I{} bash -c '
+          URL="${1%%	*}"
+          DEST="${1#*	}"
+          echo "Downloading $(basename "$DEST")"
+          curl -fsSL --retry 3 --retry-delay 5 -o "$DEST" "$URL"
+        ' _ {} < "$DOWNLOAD_LIST"
+        rm -f "$DOWNLOAD_LIST"

.github/workflows/release-drafter.yml

@@ -1,28 +0,0 @@
-name: Release Drafter
-
-on:
-  push:
-    branches:
-      - master
-    tags:
-      - 'v*'
-  pull_request_target:
-    types: [opened, reopened, synchronize]
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-jobs:
-  update_release_draft:
-    permissions:
-      contents: write
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-      - uses: release-drafter/release-drafter@v6
-        with:
-          config-name: release-drafter.yml
-          prerelease: ${{ !startsWith(github.ref, 'refs/tags/v') }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

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

@@ -31,7 +31,7 @@ on:
         type: string
         default: ''
       target_hardware:
-        description: 'Hardware target to compile for (qcom or usbgpu)'
+        description: 'Hardware target to compile for (qcom or chestnut)'
         required: false
         type: string
         default: 'qcom'
@@ -57,7 +57,7 @@ on:
         type: choice
         options:
           - qcom
-          - usbgpu
+          - chestnut
         default: 'qcom'
 
 
@@ -102,21 +102,26 @@ jobs:
           cat $GITHUB_OUTPUT
       - run: |
           cd ${{ github.workspace }}/openpilot/openpilot
-          if [ "${{ inputs.target_hardware }}" != "usbgpu" ]; then
-            git lfs pull -X "selfdrive/modeld/models/big_*.onnx" -X "selfdrive/modeld/models/dmonitoring_*.onnx"
+          if [ "${{ inputs.target_hardware }}" != "chestnut" ]; then
+            git lfs pull -X "**/selfdrive/modeld/models/big_*.onnx,**/selfdrive/modeld/models/dmonitoring_*.onnx"
             rm -f selfdrive/modeld/models/big_*.onnx selfdrive/modeld/models/dmonitoring_*.onnx
           else
-            git lfs pull -I "selfdrive/modeld/models/big_*.onnx"
+            git lfs pull -I "**/selfdrive/modeld/models/big_*.onnx" -X ""
             find selfdrive/modeld/models -name "*.onnx" ! -name "big_*.onnx" -delete
           fi
+          if grep -lIF "version https://git-lfs.github.com/spec/v1" selfdrive/modeld/models/*.onnx; then
+            echo "::error::the ONNX files above are still LFS pointers, not real models"
+            exit 1
+          fi
       - name: 'Upload Artifact'
         uses: actions/upload-artifact@v4
         with:
           name: models-${{ env.REF }}${{ inputs.artifact_suffix  }}
           path: ${{ github.workspace }}/openpilot/openpilot/selfdrive/modeld/models/*.onnx
+          if-no-files-found: error
 
   build_model:
-    runs-on: [self-hosted, tici]
+    runs-on: [self-hosted, "${{ inputs.target_hardware == 'chestnut' && 'chestnut' || 'tici' }}"]
     needs: get_model
     env:
       MODEL_NAME: ${{ inputs.custom_name || inputs.upstream_branch }} (${{ needs.get_model.outputs.model_date }})
@@ -127,7 +132,6 @@ jobs:
           fetch-depth: 1
           submodules: recursive
 
-      - run: git lfs pull
 
       - name: Set environment variables
         id: set-env
@@ -160,7 +164,7 @@ jobs:
           fi
           source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
-          rm -rf ${{ env.MODELS_DIR }}/*.onnx
+          rm -rf ${{ env.MODELS_DIR }}/*.onnx*
 
       - name: Download model artifacts
         uses: actions/download-artifact@v4
@@ -180,34 +184,48 @@ jobs:
           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}')")
 
-          if [ "${{ inputs.target_hardware }}" == "usbgpu" ]; then
-            echo "USBGPU build"
-            export USBGPU=1
-            TG_FLAGS="DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV=QCOM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2"
+          TG_FLAGS_QCOM="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+          if [ "${{ inputs.target_hardware }}" == "chestnut" ]; then
+            echo "CHESTNUT build"
+            export CHESTNUT=1
+            TG_FLAGS="DEBUG=1 DEV=USB+AMD:LLVM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_OCCUPANCY_OPT=1"
             OUTPUT_PKL="${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl"
           else
             echo "QCOM build"
-            TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+            TG_FLAGS="$TG_FLAGS_QCOM"
             OUTPUT_PKL="${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
           fi
 
           # Generate metadata for all ONNX files
           find "${{ env.MODELS_DIR }}" -maxdepth 1 -name '*.onnx' | while IFS= read -r onnx_file; do
             echo "Generating metadata: $onnx_file"
-            env ${TG_FLAGS} python3 "${{ env.MODELS_DIR }}/../get_model_metadata.py" "$onnx_file" || true
+            env ${TG_FLAGS_QCOM} python3 "${{ env.MODELS_DIR }}/../get_model_metadata.py" "$onnx_file" || true
           done
 
           # Detect model type and build compile args
-          VISION_ONNX="${{ env.MODELS_DIR }}/driving_vision.onnx"
-          POLICY_ONNX="${{ env.MODELS_DIR }}/driving_policy.onnx"
-          OFF_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_off_policy.onnx"
-          ON_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_on_policy.onnx"
+          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"; do
-            if [ -f "$f" ]; then
-              SUPERCOMBO_ONNX="$f"
-              break
-            fi
+          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=""

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

@@ -4,12 +4,8 @@ env:
   BUILD_DIR: "/data/openpilot"
   OUTPUT_DIR: ${{ github.workspace }}/output
   CI_DIR: ${{ github.workspace }}/release/ci
-  SCONS_CACHE_DIR: ${{ github.workspace }}/release/ci/scons_cache
   PUBLIC_REPO_URL: "https://github.com/sunnypilot/sunnypilot"
 
-  # Branch configurations
-  STAGING_SOURCE_BRANCH: 'master'
-
   # Runtime configuration
   SOURCE_BRANCH: "${{ github.head_ref || github.ref_name }}"
 
@@ -40,8 +36,11 @@ jobs:
       publish_concurrency_group: ${{ steps.strategy.outputs.publish_concurrency_group }}
       is_stable_branch: ${{ steps.strategy.outputs.is_stable_branch }}
       build: ${{ steps.strategy.outputs.build }}
+      include_big_model: ${{ steps.strategy.outputs.include_big_model }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
       - name: Extract deploy strategy
         id: strategy
         run: |
@@ -82,6 +81,9 @@ jobs:
             stable_version=$(cat openpilot/sunnypilot/common/version.h | grep SUNNYPILOT_VERSION | sed -e 's/[^0-9|.]//g');
             echo "version=$([ "$is_stable_branch" = "true" ] && echo "$stable_version" || echo "$BUILD")" >> $GITHUB_OUTPUT
             echo "extra_version_identifier=${environment}" >> $GITHUB_OUTPUT
+
+            include_big_model="$(echo "$CONFIG" | jq -r '.include_big_model // false')";
+            echo "include_big_model=$include_big_model" >> $GITHUB_OUTPUT
           fi
           echo "build=$BUILD" >> $GITHUB_OUTPUT
           cat $GITHUB_OUTPUT
@@ -96,6 +98,8 @@ jobs:
       }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
       - name: Wait for Tests
         uses: ./.github/workflows/wait-for-action # Path to where you place the action
         with:
@@ -109,11 +113,6 @@ jobs:
       group: build-${{ github.head_ref || github.ref_name }}
       cancel-in-progress: false
     runs-on: [self-hosted, tici]
-    outputs:
-      new_branch: ${{ needs.prepare_strategy.outputs.new_branch }}
-      version: ${{ needs.prepare_strategy.outputs.version }}
-      extra_version_identifier: ${{ needs.prepare_strategy.outputs.extra_version_identifier }}
-      commit_sha: ${{ github.sha }}
     if: ${{
       (always() && !cancelled() && !failure()) &&
       needs.prepare_strategy.result == 'success' &&
@@ -124,31 +123,14 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
+          fetch-depth: 1
           submodules: recursive
           ref: ${{ env.SOURCE_BRANCH }}
           repository: ${{ github.event.pull_request.head.repo.fork && github.event.pull_request.head.repo.full_name || github.repository }}
       - run: git lfs pull
 
-      - name: Cache SCons
-        uses: actions/cache@v4
-        with:
-          path: ${{env.SCONS_CACHE_DIR}}
-          key: scons-${{ runner.os }}-${{ runner.arch }}-${{ env.SOURCE_BRANCH }}-${{ github.sha }}
-          # Note: GitHub Actions enforces cache isolation between different build sources (PR builds, workflow dispatches, etc.)
-          #   for security. Only caches from the default branch are shared across all builds. This is by design and cannot be overridden.
-          restore-keys: |
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.SOURCE_BRANCH }}
-            scons-${{ runner.os }}-${{ runner.arch }}-${{ env.STAGING_SOURCE_BRANCH }}
-            scons-${{ runner.os }}-${{ runner.arch }}
-
       - name: Set environment variables
-        id: set-env
         run: |
-          echo "new_branch=${{ needs.prepare_strategy.outputs.new_branch }}" >> $GITHUB_OUTPUT
-          echo "version=${{ needs.prepare_strategy.outputs.version }}" >> $GITHUB_OUTPUT
-          echo "extra_version_identifier=${{ needs.prepare_strategy.outputs.extra_version_identifier }}" >> $GITHUB_OUTPUT
-          echo "commit_sha=${{ github.sha }}" >> $GITHUB_OUTPUT
-
           # Set up common environment
           source /etc/profile;
           export UV_PROJECT_ENVIRONMENT=${HOME}/venv
@@ -157,9 +139,6 @@ jobs:
           export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
           uv sync
           printenv >> $GITHUB_ENV
-          if [[ "${{ runner.debug }}" == "1" ]]; then
-            cat $GITHUB_OUTPUT
-          fi
 
       - name: Setup build environment
         run: |
@@ -168,7 +147,7 @@ jobs:
           echo "Starting build stage..."
           echo "BUILD_DIR: ${BUILD_DIR}"
           echo "CI_DIR: ${CI_DIR}"
-          echo "VERSION: ${{ steps.set-env.outputs.version }}"
+          echo "VERSION: ${{ needs.prepare_strategy.outputs.version }}"
           echo "UV_PROJECT_ENVIRONMENT: ${UV_PROJECT_ENVIRONMENT}"
           echo "VIRTUAL_ENV: ${VIRTUAL_ENV}"
           echo "-------"
@@ -180,61 +159,44 @@ jobs:
 
       - name: Build Main Project
         run: |
-          export PYTHONPATH="$BUILD_DIR"
-          ./tools/release/release_files.py | sort | uniq | rsync -rRl${RUNNER_DEBUG:+v} --files-from=- . $BUILD_DIR/
+          export PYTHONPATH="$BUILD_DIR:$BUILD_DIR/msgq_repo:$BUILD_DIR/opendbc_repo:$BUILD_DIR/rednose_repo:$BUILD_DIR/teleoprtc_repo:$BUILD_DIR/tinygrad_repo"
+          ./tools/release/release_files.py | xargs -0 cp -pR --parents -t "$BUILD_DIR" --
+          # outside the checkout, which is wiped each run. /data/scons_cache is the device's, not ours.
+          SCONS_CACHE="$RUNNER_WORKSPACE/scons_cache"
+          mkdir -p "$SCONS_CACHE"
           cd $BUILD_DIR
-          ln -sfn msgq_repo/msgq msgq
-          ln -sfn opendbc_repo/opendbc opendbc
-          ln -sfn rednose_repo/rednose rednose
-          ln -sfn teleoprtc_repo/teleoprtc teleoprtc
-          ln -sfn tinygrad_repo/tinygrad tinygrad
-          sed -i '/from .board.jungle import PandaJungle, PandaJungleDFU/s/^/#/' panda/__init__.py
-          echo "Building sunnypilot's modeld_v2..."
-          scons -j$(nproc) cache_dir=${{env.SCONS_CACHE_DIR}} --minimal openpilot/sunnypilot/modeld_v2
-          echo "Building sunnypilot's locationd..."
-          scons -j2 cache_dir=${{env.SCONS_CACHE_DIR}} --minimal openpilot/sunnypilot/selfdrive/locationd
-          echo "Building openpilot's locationd..."
-          scons -j1 cache_dir=${{env.SCONS_CACHE_DIR}} --minimal openpilot/selfdrive/locationd
+          echo "Building locationd..."
+          # -j1: parallel rednose generators OOM the device
+          scons -j1 cache_dir="$SCONS_CACHE" --minimal \
+            openpilot/selfdrive/locationd openpilot/sunnypilot/selfdrive/locationd
           echo "Building rest of sunnypilot"
-          scons -j$(nproc) cache_dir=${{env.SCONS_CACHE_DIR}} --minimal
+          SKIP_TINYGRAD_COMPILE=1 /usr/bin/time -v scons -j$(nproc) cache_dir="$SCONS_CACHE" --minimal
           touch ${BUILD_DIR}/prebuilt
           if [[ "${{ runner.debug }}" == "1" ]]; then
             ls -la ${BUILD_DIR}
           fi
 
-      - name: Prepare Output
+      - name: Strip release tree
         run: |
-          sudo rm -rf ${OUTPUT_DIR}
-          mkdir -p ${OUTPUT_DIR}
-          rsync -am${RUNNER_DEBUG:+v} \
-            --exclude='.sconsign.dblite' \
-            --exclude='*.a' \
-            --exclude='*.o' \
-            --exclude='*.os' \
-            --exclude='*.pyc' \
-            --exclude='moc_*' \
-            --exclude='__pycache__' \
-            --exclude='Jenkinsfile' \
-            --exclude='**/release/' \
-            --exclude='**/.github/' \
-            --exclude='**/openpilot/selfdrive/ui/replay/' \
-            --exclude='**/__pycache__/' \
-            --exclude='${{env.SCONS_CACHE_DIR}}' \
-            --exclude='**/.git/' \
-            --exclude='**/SConstruct' \
-            --exclude='**/SConscript' \
-            --exclude='**/.venv/' \
-            --exclude='openpilot/selfdrive/modeld/models/*.onnx*' \
-            --exclude='openpilot/sunnypilot/modeld*/models/*.onnx*' \
-            --exclude='openpilot/third_party/*x86*' \
-            --exclude='openpilot/third_party/*Darwin*' \
-            --delete-excluded \
-            --chown=comma:comma \
-            ${BUILD_DIR}/ ${OUTPUT_DIR}/
+          cd $BUILD_DIR
+          find . -name '*.a' -delete
+          find . -name '*.o' -delete
+          find . -name '*.os' -delete
+          find . -name '*.pyc' -delete
+          find . -name 'moc_*' -delete
+          find . -name '__pycache__' -type d -exec rm -rf {} +
+          find . -name 'SConstruct' -delete
+          find . -name 'SConscript' -delete
+          rm -rf .sconsign.dblite Jenkinsfile tools/release/ release/
+          rm -f openpilot/selfdrive/modeld/models/*.onnx*
+          rm -f openpilot/sunnypilot/modeld*/models/*.onnx*
+          find openpilot/third_party/ -name '*x86*' -exec rm -r {} +
+          find openpilot/third_party/ -name '*Darwin*' -exec rm -r {} +
+          cd -
 
       - name: 'Tar.gz files'
         run: |
-          tar czf prebuilt.tar.gz -C ${{ env.OUTPUT_DIR }} .
+          tar czf prebuilt.tar.gz -C ${{ env.BUILD_DIR }} .
           ls -la prebuilt.tar.gz
 
       - name: 'Upload Artifact'
@@ -242,6 +204,7 @@ jobs:
         with:
           name: prebuilt
           path: prebuilt.tar.gz
+          compression-level: 0
 
       - name: Re-enable powersave
         if: always()
@@ -249,22 +212,278 @@ jobs:
           source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
 
+  prepare_chestnut:
+    needs: [ prepare_strategy ]
+    runs-on: ubuntu-24.04
+    if: ${{ needs.prepare_strategy.outputs.include_big_model == 'true' }}
+    concurrency:
+      group: prepare-chestnut
+      cancel-in-progress: false
+    outputs:
+      onnx_sha256: ${{ steps.resolve.outputs.onnx_sha256 }}
+    env:
+      GH_REPO: ${{ github.repository }}
+      HF_REPO: sunnypilot/sunnypilot_models_v1
+      HF_DEFAULTS_PATH: models/defaults/big
+    steps:
+      - name: Resolve ONNX hash and tinygrad ref via API
+        id: resolve
+        run: |
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/big_driving_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 "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: $TINYGRAD_REF"
+
+          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
+
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            BUNDLE=$(echo "$DEFAULTS" | jq --arg hash "$ONNX_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+            [ -n "$BUNDLE" ] && [ "$BUNDLE" != "null" ]
+          }
+
+          if check_defaults; then
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
+            exit 0
+          fi
+
+          echo "No matching model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=big
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 90); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/90: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "Big model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
+          done
+
+          echo "::error::Build run did not complete within 45 minutes"
+          exit 1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Cancel run on failure
+        if: failure()
+        run: gh run cancel ${{ github.run_id }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  prepare_small_model:
+    needs: [ prepare_strategy ]
+    runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-small-model
+      cancel-in-progress: false
+    outputs:
+      driving_onnx_sha256: ${{ steps.resolve.outputs.driving_onnx_sha256 }}
+    env:
+      GH_REPO: ${{ github.repository }}
+      HF_REPO: sunnypilot/sunnypilot_models_v1
+      HF_DEFAULTS_PATH: models/defaults/small
+    steps:
+      - name: Resolve ONNX hash and tinygrad ref via API
+        id: resolve
+        run: |
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/driving_supercombo.onnx?ref=${REF}" --jq '.sha')
+          DRIVING_HASH=$(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: $TINYGRAD_REF"
+
+          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
+
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            DRIVING=$(echo "$DEFAULTS" | jq --arg hash "$DRIVING_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+            [ -n "$DRIVING" ] && [ "$DRIVING" != "null" ] || return 1
+          }
+
+          if check_defaults; then
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
+            exit 0
+          fi
+
+          echo "No matching model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=small
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 60); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/60: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "Small model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
+          done
+
+          echo "::error::Small model build did not complete within 30 minutes"
+          exit 1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Cancel run on failure
+        if: failure()
+        run: gh run cancel ${{ github.run_id }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  prepare_dm_model:
+    needs: [ prepare_strategy ]
+    runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-dm-model
+      cancel-in-progress: false
+    outputs:
+      dm_onnx_sha256: ${{ steps.resolve.outputs.dm_onnx_sha256 }}
+    env:
+      GH_REPO: ${{ github.repository }}
+      HF_REPO: sunnypilot/sunnypilot_models_v1
+      HF_DEFAULTS_PATH: models/defaults/dm
+    steps:
+      - name: Resolve ONNX hash and tinygrad ref via API
+        id: resolve
+        run: |
+          REF="${{ github.head_ref || github.ref_name }}"
+
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/dmonitoring_model.onnx?ref=${REF}" --jq '.sha')
+          DM_HASH=$(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: $TINYGRAD_REF"
+
+          JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
+
+          check_defaults() {
+            DEFAULTS=$(curl -fsSL "${JSON_URL}?t=$(date +%s)" 2>/dev/null) || return 1
+            TINYGRAD_MATCH=$(echo "$DEFAULTS" | jq -r --arg ref "$TINYGRAD_REF" '.tinygrad_ref == $ref' 2>/dev/null)
+            [ "$TINYGRAD_MATCH" = "true" ] || return 1
+            DM=$(echo "$DEFAULTS" | jq --arg hash "$DM_HASH" '.bundles[] | select(.onnx_sha256 == $hash)' 2>/dev/null)
+            [ -n "$DM" ] && [ "$DM" != "null" ] || return 1
+          }
+
+          if check_defaults; then
+            echo "HF defaults match DM ONNX hash and tinygrad ref"
+            exit 0
+          fi
+
+          echo "No matching DM model on HF — dispatching build"
+          gh workflow run build-default-models.yaml --ref "$REF" -f target=dm
+          sleep 10
+
+          BUILD_RUN_ID=$(gh run list --workflow build-default-models.yaml --branch "$REF" --limit 1 --json databaseId --jq '.[0].databaseId')
+          echo "Dispatched build run: $BUILD_RUN_ID"
+
+          echo "Waiting for build run to complete..."
+          for i in $(seq 1 60); do
+            sleep 30
+            STATUS=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.status')
+            CONCLUSION=$(gh api "repos/${GH_REPO}/actions/runs/${BUILD_RUN_ID}" --jq '.conclusion')
+            echo "Poll $i/60: status=$STATUS conclusion=$CONCLUSION"
+            if [ "$STATUS" = "completed" ]; then
+              if [ "$CONCLUSION" = "success" ]; then
+                echo "Build run succeeded, verifying HF..."
+                sleep 10
+                if check_defaults; then
+                  echo "DM model verified on HF"
+                  exit 0
+                fi
+                echo "::error::Build succeeded but DM model not found on HF"
+                exit 1
+              else
+                echo "::error::Build run failed with conclusion=$CONCLUSION"
+                exit 1
+              fi
+            fi
+          done
+
+          echo "::error::DM model build did not complete within 30 minutes"
+          exit 1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Cancel run on failure
+        if: failure()
+        run: gh run cancel ${{ github.run_id }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
   publish:
     concurrency:
-      # We do a bit of a hack here to avoid canceling the publishing job if a new commit comes in while we're publishing by adding the sha to the group name.
-      # This means that if multiple commits come in while we're publishing, they will be queued up and publish one after the other.
-      # Otherwise, if a job is waiting to be published due to environment wait time, it would be canceled by a new commit and restart the wait time.
       group: ${{ needs.prepare_strategy.outputs.publish_concurrency_group }}
       cancel-in-progress: ${{ needs.prepare_strategy.outputs.cancel_publish_in_progress == 'true' }}
-    if: ${{ (always() && !cancelled() && !failure()) && needs.build.result == 'success' && needs.prepare_strategy.result == 'success' && (!contains(github.event_name, 'pull_request') || (github.event.action == 'labeled' && github.event.label.name == 'prebuilt')) }}
-    needs: [ build, prepare_strategy ]
+    if: ${{
+      always() && !cancelled() &&
+      needs.build.result == 'success' &&
+      needs.prepare_strategy.result == 'success' &&
+      needs.prepare_small_model.result == 'success' &&
+      needs.prepare_dm_model.result == 'success' &&
+      (!contains(github.event_name, 'pull_request') || (github.event.action == 'labeled' && github.event.label.name == 'prebuilt')) &&
+      (needs.prepare_strategy.outputs.include_big_model != 'true' || needs.prepare_chestnut.result == 'success')
+      }}
+    needs: [ build, prepare_strategy, prepare_chestnut, prepare_small_model, prepare_dm_model ]
     runs-on: ubuntu-24.04
     environment: ${{ needs.prepare_strategy.outputs.environment }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
 
-      - name: Download build artifacts
+      - name: Download prebuilt artifact
         uses: actions/download-artifact@v4
         with:
           name: prebuilt
@@ -274,6 +493,17 @@ jobs:
           mkdir -p ${{ env.OUTPUT_DIR }}
           tar xzf prebuilt.tar.gz -C ${{ env.OUTPUT_DIR }}
 
+      - name: Download model chunks from HF
+        uses: ./.github/workflows/download-hf-model-chunks
+        with:
+          hf_repo: sunnypilot/sunnypilot_models_v1
+          dest_dir: ${{ env.OUTPUT_DIR }}/openpilot/selfdrive/modeld/models
+          models: |
+            [
+              {"hf_path": "models/defaults/small", "onnx_hash": "${{ needs.prepare_small_model.outputs.driving_onnx_sha256 }}", "canonical": "driving_tinygrad.pkl"},
+              {"hf_path": "models/defaults/dm", "onnx_hash": "${{ needs.prepare_dm_model.outputs.dm_onnx_sha256 }}", "canonical": "dmonitoring_model_tinygrad.pkl"}
+            ]
+
       - name: Configure Git
         run: |
           git config --global user.email "github-actions[bot]@users.noreply.github.com"
@@ -283,36 +513,95 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
-          echo '${{ toJSON(needs.build.outputs) }}'
+          echo '${{ toJSON(needs.prepare_strategy.outputs) }}'
           ls -la ${{ env.OUTPUT_DIR }}
 
           ${{ env.CI_DIR }}/publish.sh \
             "${{ github.workspace }}" \
             "${{ env.OUTPUT_DIR }}" \
-            "${{ needs.build.outputs.new_branch }}" \
-            "${{ needs.build.outputs.version }}" \
+            "${{ needs.prepare_strategy.outputs.new_branch }}" \
+            "${{ needs.prepare_strategy.outputs.version }}" \
             "https://x-access-token:${{github.token}}@github.com/sunnypilot/sunnypilot.git" \
-            "${{ needs.build.outputs.extra_version_identifier }}"
-
-          echo ""
-          echo "----   ℹ️ To update the list of branches that auto deploy prebuilts  -----"
-          echo ""
-          echo "1. Go to: ${{ github.server_url }}/${{ github.repository }}/settings/variables/actions/AUTO_DEPLOY_PREBUILT_BRANCHES"
-          echo "2. Current value: ${{ vars.AUTO_DEPLOY_PREBUILT_BRANCHES }}"
-          echo "3. Update as needed (JSON array with no spaces)"
+            "${{ needs.prepare_strategy.outputs.extra_version_identifier }}"
 
       - name: Tag ${{ needs.prepare_strategy.outputs.environment }}
         if: ${{ needs.prepare_strategy.outputs.is_stable_branch == 'true' && (github.event_name != 'push' || !startsWith(github.ref, 'refs/tags/')) }}
         run: |
             TAG="${{ needs.prepare_strategy.outputs.environment }}/${{ needs.prepare_strategy.outputs.version }}/${{ needs.prepare_strategy.outputs.build }}"
-            git tag -f -a ${TAG} -m "${{ needs.prepare_strategy.outputs.environment }} @ ${{ needs.prepare_strategy.outputs.version }} of build ${{ needs.build.outputs.build }}."
+            git tag -f -a ${TAG} -m "${{ needs.prepare_strategy.outputs.environment }} @ ${{ needs.prepare_strategy.outputs.version }} of build ${{ needs.prepare_strategy.outputs.build }}."
             git push -f origin ${TAG}
 
+  publish_chestnut:
+    concurrency:
+      group: ${{ needs.prepare_strategy.outputs.publish_concurrency_group }}-chestnut
+      cancel-in-progress: ${{ needs.prepare_strategy.outputs.cancel_publish_in_progress == 'true' }}
+    if: ${{
+      always() && !cancelled() &&
+      needs.build.result == 'success' &&
+      needs.prepare_strategy.result == 'success' &&
+      needs.prepare_small_model.result == 'success' &&
+      needs.prepare_dm_model.result == 'success' &&
+      needs.prepare_chestnut.result == 'success' &&
+      (!contains(github.event_name, 'pull_request') || (github.event.action == 'labeled' && github.event.label.name == 'prebuilt'))
+      }}
+    needs: [ build, prepare_strategy, prepare_chestnut, prepare_small_model, prepare_dm_model ]
+    runs-on: ubuntu-24.04
+    environment: ${{ needs.prepare_strategy.outputs.environment }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Download prebuilt artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: prebuilt
+
+      - name: Untar prebuilt
+        run: |
+          mkdir -p ${{ env.OUTPUT_DIR }}
+          tar xzf prebuilt.tar.gz -C ${{ env.OUTPUT_DIR }}
+
+      - name: Download model chunks from HF
+        uses: ./.github/workflows/download-hf-model-chunks
+        with:
+          hf_repo: sunnypilot/sunnypilot_models_v1
+          dest_dir: ${{ env.OUTPUT_DIR }}/openpilot/selfdrive/modeld/models
+          models: |
+            [
+              {"hf_path": "models/defaults/small", "onnx_hash": "${{ needs.prepare_small_model.outputs.driving_onnx_sha256 }}", "canonical": "driving_tinygrad.pkl"},
+              {"hf_path": "models/defaults/dm", "onnx_hash": "${{ needs.prepare_dm_model.outputs.dm_onnx_sha256 }}", "canonical": "dmonitoring_model_tinygrad.pkl"},
+              {"hf_path": "models/defaults/big", "onnx_hash": "${{ needs.prepare_chestnut.outputs.onnx_sha256 }}", "canonical": "big_driving_tinygrad.pkl"}
+            ]
+
+      - name: Configure Git
+        run: |
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git config --global user.name "github-actions[bot]"
+
+      - name: Publish chestnut branch
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          CHESTNUT_BRANCH="${{ needs.prepare_strategy.outputs.new_branch }}-chestnut"
+
+          ${{ env.CI_DIR }}/publish.sh \
+            "${{ github.workspace }}" \
+            "${{ env.OUTPUT_DIR }}" \
+            "$CHESTNUT_BRANCH" \
+            "${{ needs.prepare_strategy.outputs.version }}" \
+            "https://x-access-token:${{github.token}}@github.com/sunnypilot/sunnypilot.git" \
+            "${{ needs.prepare_strategy.outputs.extra_version_identifier }}"
+
   notify:
     needs:
     - prepare_strategy
     - build
     - publish
+    - publish_chestnut
+    - prepare_chestnut
+    - prepare_small_model
+    - prepare_dm_model
     runs-on: ubuntu-24.04
     if: ${{ (always() && !cancelled() && !failure())
       && needs.publish.result == 'success'
@@ -320,11 +609,12 @@ jobs:
       && (fromJSON(vars.DEV_FEEDBACK_NOTIFICATION_BRANCHES_V2)[github.head_ref || github.ref_name] != null) }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
 
       - name: Prepare notification message
         id: message
         run: |
-          TEMPLATE='${{ vars.DISCOURSE_GENERAL_UPDATE_NOTICE }}'
           export VERSION="${{ needs.prepare_strategy.outputs.version }}"
           export branch_name="${{ env.SOURCE_BRANCH }}"
           export new_branch="${{ needs.prepare_strategy.outputs.new_branch }}"
@@ -333,6 +623,7 @@ jobs:
           export commit_short_sha="${commit_short_sha:0:7}"
           export extra_version_identifier="${{ needs.prepare_strategy.outputs.extra_version_identifier || github.run_number }}"
           export PUBLIC_REPO_URL="${{ env.PUBLIC_REPO_URL }}"
+          export chestnut_branch="${{ needs.prepare_chestnut.result == 'success' && format('{0}-chestnut', needs.prepare_strategy.outputs.new_branch) || '' }}"
 
           MESSAGE=$(cat << 'EOF' | envsubst
           ${{ vars.DISCOURSE_GENERAL_UPDATE_NOTICE }}
@@ -373,7 +664,7 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo,
               issue_number: prNumber,
-              name: process.env.LABELf
+              name: process.env.LABEL
             });
 
             console.log(`Removed '${process.env.LABEL}' label from PR #${prNumber}`);

.github/workflows/test-discourse.yaml.yml

@@ -1,78 +0,0 @@
-name: Debug Discourse Posting
-
-on:
-  push:
-
-jobs:
-  test-discourse-post:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Post test message to Discourse
-        uses: ./.github/workflows/post-to-discourse
-        with:
-          discourse-url: ${{ vars.DISCOURSE_URL }}
-          api-key: ${{ secrets.DISCOURSE_API_KEY }}
-          api-username: ${{ secrets.DISCOURSE_API_USERNAME }}
-          topic-id: ${{ vars.DISCOURSE_UPDATES_TOPIC_ID }}
-          message: |
-            ## 🧪 Test Post from GitHub Actions
-            
-            **This is a test post to verify Discourse integration**
-            
-            - **Workflow**: ${{ github.workflow }}
-            - **Run Number**: #${{ github.run_number }}
-            - **Branch**: `${{ github.ref_name }}`
-            - **Commit**: ${{ github.sha }}
-            - **Actor**: @${{ github.actor }}
-            - **Timestamp**: ${{ github.event.head_commit.timestamp }}
-            
-            ---
-            
-            ### Fake Build Info (for testing)
-            - **Version**: 0.9.8-test
-            - **Build**: #42
-            - **Branch**: release-test
-            
-            [View workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }})
-            
-            *This is an automated test message. Drive safe! 🚗💨*
-      
-      
-      - name: Create topic on Discourse
-        uses: ./.github/workflows/post-to-discourse
-        with:
-          discourse-url: ${{ vars.DISCOURSE_URL }}
-          api-key: ${{ secrets.DISCOURSE_API_KEY }}
-          api-username: ${{ secrets.DISCOURSE_API_USERNAME }}
-          #topic-id: ${{ vars.DISCOURSE_UPDATES_TOPIC_ID }}
-          category-id: 4
-          title: "This is a test of a new topic instead of a reply"
-          message: |
-            ## 🧪 Test Post from GitHub Actions
-            
-            **This is a test post to verify Discourse integration**
-            
-            - **Workflow**: ${{ github.workflow }}
-            - **Run Number**: #${{ github.run_number }}
-            - **Branch**: `${{ github.ref_name }}`
-            - **Commit**: ${{ github.sha }}
-            - **Actor**: @${{ github.actor }}
-            - **Timestamp**: ${{ github.event.head_commit.timestamp }}
-            
-            ---
-            
-            ### Fake Build Info (for testing)
-            - **Version**: 0.9.8-test
-            - **Build**: #42
-            - **Branch**: release-test
-            
-            [View workflow run](${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }})
-            
-            *This is an automated test message. Drive safe! 🚗💨*
-      - name: Display results
-        if: always()
-        run: |
-          echo "::notice::Discourse post test completed"
-          echo "Check your Discourse topic to verify the post appeared correctly"

.github/workflows/test_models.yml

@@ -0,0 +1,79 @@
+name: Test Models Compatibility With Tinygrad Changes
+on:
+  pull_request:
+    paths:
+      - 'tinygrad_repo'
+  workflow_dispatch:
+
+jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      models: ${{ steps.set-matrix.outputs.models }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Fetch and Parse json
+        id: set-matrix
+        run: |
+          python3 -c '
+          import json, urllib.request, os, re
+
+          with open("openpilot/sunnypilot/models/fetcher.py", "r") as f:
+            urls = re.findall(r"MODEL_URL(?:_CHESTNUT)?\s*=\s*[\"'"'"']([^\"'"'"']+)[\"'"'"']", f.read())
+
+          artifacts = []
+          for url in urls:
+            data = json.loads(urllib.request.urlopen(url).read())
+            for bundle in data.get("bundles", []):
+              for model in bundle.get("models", []):
+                if "artifact" in model:
+                  artifacts.append(model["artifact"])
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+            f.write(f"models={json.dumps(artifacts)}\n")
+          '
+
+  test-model:
+    name: Test ${{ matrix.artifact.file_name }}
+    needs: generate-matrix
+    runs-on: ubuntu-latest
+    container: ghcr.io/commaai/openpilot-base:latest
+    strategy:
+      fail-fast: false
+      matrix:
+        artifact: ${{ fromJson(needs.generate-matrix.outputs.models) }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: true
+
+      - name: Download Model Chunks in Parallel
+        run: |
+          mkdir -p /tmp/model_chunks
+          echo '${{ toJson(matrix.artifact.chunks) }}' > chunks.json
+
+          BASE_URL="${{ matrix.artifact.download_uri.url }}"
+          export BASE_DIR=$(dirname "$BASE_URL")
+
+          python3 -c '
+          import json, os
+          with open("chunks.json") as f:
+            chunks = json.load(f)
+          manifest_path = f"/tmp/model_chunks/${{ matrix.artifact.file_name }}.chunkmanifest"
+          with open(manifest_path, "w") as f:
+            f.write(str(len(chunks)))
+          base_dir = os.environ["BASE_DIR"]
+          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")
+          '
+          curl -Z --parallel-immediate --parallel-max 16 -s -S -f -L -K /tmp/curl_config.txt
+
+      - name: Run Model Compatibility Test
+        env:
+          MODEL_BASE_NAME: ${{ matrix.artifact.file_name }}
+          MODEL_CHUNK_DIR: "/tmp/model_chunks"
+          PYTHONPATH: ".:./tinygrad_repo"
+        run: |
+          python3 -m pytest openpilot/sunnypilot/modeld_v2/tests/test_models.py

.github/workflows/tests.yaml

@@ -131,6 +131,7 @@ jobs:
 
   process_replay:
     name: process replay
+    if: false #process replay disabled for angle steering until we are ready to merge it
     runs-on: ${{
       (github.repository == 'commaai/openpilot') &&
       ((github.event_name != 'pull_request') ||

.github/workflows/ui_preview.yaml

@@ -25,7 +25,8 @@ env:
 
 jobs:
   preview:
-    if: github.repository == 'sunnypilot/sunnypilot'
+    if: false # tmp disable due to GH API rate limiting flakiness
+    #if: github.repository == 'sunnypilot/sunnypilot'
     name: preview
     runs-on: ubuntu-latest
     timeout-minutes: 20

.idea/customTargets.xml

@@ -21,5 +21,12 @@
         </clean>
       </configuration>
     </target>
+    <target id="f2590b2b-9b93-49f9-8510-da3f3724a2ae" name="replay" defaultType="TOOL">
+      <configuration id="d475264f-6f4c-4092-9b4e-6773309f38b7" name="replay" toolchainName="Default">
+        <build type="TOOL">
+          <tool actionId="Tool_External Tools_uv build tools replay" />
+        </build>
+      </configuration>
+    </target>
   </component>
 </project>

.idea/tools/External Tools.xml

@@ -20,4 +20,11 @@
       <option name="WORKING_DIRECTORY" value="$ProjectFileDir$" />
     </exec>
   </tool>
+  <tool name="uv build tools replay" showInMainMenu="false" showInEditor="false" showInProject="false" showInSearchPopup="false" disabled="false" useConsole="true" showConsoleOnStdOut="false" showConsoleOnStdErr="false" synchronizeAfterRun="true">
+    <exec>
+      <option name="COMMAND" value="bash" />
+      <option name="PARAMETERS" value="-c &quot;source .venv/bin/activate &amp;&amp; scons -u -j$(nproc) tools/replay/&quot;" />
+      <option name="WORKING_DIRECTORY" value="$ProjectFileDir$" />
+    </exec>
+  </tool>
 </toolSet>

.run/Build Debug.run.xml

@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Build Debug" type="CLionExternalRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="false" WORKING_DIR="file://$ProjectFileDir$/selfdrive/ui" PASS_PARENT_ENVS_2="true" PROJECT_NAME="sunnypilot" TARGET_NAME="uv Scons Build Debug" CONFIG_NAME="uv Scons Build Debug" RUN_PATH="ui">
+  <configuration default="false" name="Build Debug" type="CLionExternalRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="false" WORKING_DIR="file://$ProjectFileDir$/selfdrive/ui" PASS_PARENT_ENVS_2="true" PROJECT_NAME="openpilot-special" TARGET_NAME="uv Scons Build Debug" CONFIG_NAME="uv Scons Build Debug" RUN_PATH="ui">
     <envs>
       <env name="QT_DBL_CLICK_DIST" value="150" />
     </envs>

.run/Debug Route Controls.run.xml

@@ -0,0 +1,27 @@
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" name="Debug Route Controls" type="PythonConfigurationType" factoryName="Python">
+    <module name="openpilot-special" />
+    <option name="ENV_FILES" value="" />
+    <option name="INTERPRETER_OPTIONS" value="" />
+    <option name="PARENT_ENVS" value="true" />
+    <envs>
+      <env name="PYTHONUNBUFFERED" value="1" />
+      <env name="FINGERPRINT" value="KIA_EV9" />
+      <env name="SKIP_FW_QUERY" value="1" />
+    </envs>
+    <option name="SDK_HOME" value="" />
+    <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/selfdrive/car" />
+    <option name="IS_MODULE_SDK" value="true" />
+    <option name="ADD_CONTENT_ROOTS" value="true" />
+    <option name="ADD_SOURCE_ROOTS" value="true" />
+    <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/selfdrive/car/card.py" />
+    <option name="PARAMETERS" value="" />
+    <option name="SHOW_COMMAND_LINE" value="false" />
+    <option name="EMULATE_TERMINAL" value="true" />
+    <option name="MODULE_MODE" value="false" />
+    <option name="REDIRECT_INPUT" value="false" />
+    <option name="INPUT_FILE" value="" />
+    <method v="2" />
+  </configuration>
+</component>

.run/Replay for controls + ui.run.xml

@@ -0,0 +1,7 @@
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" name="Replay for controls + ui" type="Multirun" separateTabs="false" reuseTabsWithFailures="false" startOneByOne="true" markFailedProcess="true" hideSuccessProcess="false" delayTime="0.0">
+    <runConfiguration name="replay for controls" type="Native Application" />
+    <runConfiguration name="Build Debug" type="Custom Build Application" />
+    <method v="2" />
+  </configuration>
+</component>

.run/replay for controls.run.xml

@@ -0,0 +1,7 @@
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" name="replay for controls" type="CLionNativeAppRunConfigurationType" focusToolWindowBeforeRun="true" PROGRAM_PARAMS="&quot;$Prompt$&quot; --block &quot;sendcan,carState,carParams,carOutput,liveTracks,carParamsSP,carStateSP,bookmarkButton&quot;" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" EMULATE_TERMINAL="true" WORKING_DIR="file://$ProjectFileDir$/tools/replay" PASS_PARENT_ENVS_2="true" PROJECT_NAME="openpilot-special" TARGET_NAME="replay" CONFIG_NAME="replay" version="1" RUN_PATH="replay">
+    <method v="2">
+      <option name="CLION.COMPOUND.BUILD" enabled="true" />
+    </method>
+  </configuration>
+</component>

launch_chffrplus.sh

@@ -24,7 +24,9 @@ function agnos_init {
     if $AGNOS_PY --verify $MANIFEST; then
       sudo reboot
     fi
-    $DIR/openpilot/common/hardware/comma/updater $AGNOS_PY $MANIFEST
+    while true; do
+      $DIR/openpilot/common/hardware/comma/updater $AGNOS_PY $MANIFEST
+    done
   fi
 }
 

launch_env.sh

@@ -16,7 +16,7 @@ export VECLIB_MAXIMUM_THREADS=1
 export QCOM_PRIORITY=12
 
 if [ -z "$AGNOS_VERSION" ]; then
-  export AGNOS_VERSION="19.6"
+  export AGNOS_VERSION="19.7"
 fi
 
 export STAGING_ROOT="/data/safe_staging"

openpilot/cereal/custom.capnp

@@ -131,6 +131,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
     downloaded @2;
     cached @3;
     failed @4;
+    verifying @5;
   }
 
   struct DownloadProgress {
@@ -352,6 +353,7 @@ struct OnroadEventSP @0xda96579883444c35 {
     speedLimitPending @22;
     e2eChime @23;
     laneChangeRoadEdge @24;
+    bigModelReady @25;
   }
 }
 

openpilot/cereal/log.capnp

@@ -725,6 +725,7 @@ struct ChestnutState {
   pcieLtssm @7 :UInt8;
   supplyVoltage @8 :UInt16;  # mV
   supplyCurrent @9 :Int16;  # mA
+  supplyFault @10 :Bool;
 }
 
 struct RadarState @0x9a185389d6fdd05f {
@@ -1004,6 +1005,7 @@ struct DrivingModelData {
   frameIdExtra @1 :UInt32;
   frameDropPerc @6 :Float32;
   modelExecutionTime @7 :Float32;
+  big @8 :Bool;
 
   action @2 :ModelDataV2.Action;
 

openpilot/common/hardware/comma/agnos.json

@@ -56,29 +56,29 @@
   },
   {
     "name": "boot",
-    "url": "https://commadist.azureedge.net/agnosupdate/boot-b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd.img.xz",
-    "hash": "b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd",
-    "hash_raw": "b30f5eef65ec3878f3aa3dcaf2cc95c09e2c1e661cd3a38e94da37dee76f68bd",
+    "url": "https://commadist.azureedge.net/agnosupdate/boot-6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d.img.xz",
+    "hash": "6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d",
+    "hash_raw": "6ecf6f987cd11968104abcccabbe268485d329cdb73012dfd3c381a6b8deb27d",
     "size": 46897152,
     "sparse": false,
     "full_check": true,
     "has_ab": true,
-    "ondevice_hash": "6650e4c46df99ae6dfd6ee895a34b8a2a3cc490a8ce18e16cc3c451c3f822b6e"
+    "ondevice_hash": "d12e1e5b9455b62a1464558716493b33e470d7a7e88da1c4105a3b21d0961808"
   },
   {
     "name": "system",
-    "url": "https://commadist.azureedge.net/agnosupdate/system-5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3.img.xz",
-    "hash": "b134fd04e9da27fa1d359ea0f2742c216fa21a08b5c47e9be22ab3b0563d9b9b",
-    "hash_raw": "5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3",
+    "url": "https://commadist.azureedge.net/agnosupdate/system-3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f.img.xz",
+    "hash": "74ffc9c551e1f29cda897ace8a69080fe644f8039977c6885f2b48362e39b744",
+    "hash_raw": "3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f",
     "size": 4718592000,
     "sparse": true,
     "full_check": false,
     "has_ab": true,
-    "ondevice_hash": "91242772af771ae96fe2eebc105f2b80a7e1dbaaf6003c2574b62d51b806f468",
+    "ondevice_hash": "6a992680183685eea9db99d915219a37935f45989330d9b619e880450257f448",
     "alt": {
-      "hash": "5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3",
-      "url": "https://commadist.azureedge.net/agnosupdate/system-5b6ce7965904a157fd3a134ccfcb854f9ca5c1cc2a26b7cb80a4fa4e1cc4aaa3.img",
+      "hash": "3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f",
+      "url": "https://commadist.azureedge.net/agnosupdate/system-3c271e2b3d20d2f0a8bf6555a1319f3efb12845490967d6151195174a01e912f.img",
       "size": 4718592000
     }
   }
-]
+]

openpilot/common/hardware/comma/modem.py

@@ -5,6 +5,7 @@ import logging
 import os
 import select
 import signal
+import string
 import struct
 import subprocess
 import tempfile
@@ -354,7 +355,7 @@ class Modem:
       imei = ""
 
     iccid = (self._atv("AT+QCCID", "+QCCID:") or "").rstrip("F")
-    if not iccid.isdigit():
+    if not all(c in string.hexdigits for c in iccid):
       iccid = ""
 
     imsi = first_line("AT+CIMI")

openpilot/common/hardware/usb.py

@@ -4,11 +4,17 @@ from pathlib import Path
 CHESTNUT_FW_VERSION = "ed4e39b7"
 CHESTNUT_USB_IDS = ((0xADD1, 0x0001), (0x3801, 0x0001))
 CHESTNUT_ROM_USB_IDS = ((0x174C, 0x2464), (0x174C, 0x2463))
+CHESTNUT_USB_PRODUCT = f"custom {CHESTNUT_FW_VERSION}-CLEAN"
 USB_DEVICES_PATH = Path("/sys/bus/usb/devices")
 TYPEC_CC_ORIENTATION_PATH = Path("/sys/class/power_supply/usb/typec_cc_orientation")
 PRIMARY_USB_CONTROLLER = "a600000.ssusb"
 
 
+def is_chestnut_usb_id(vendor_id: int, product_id: int, include_bootloader: bool = False) -> bool:
+  ids = CHESTNUT_USB_IDS + CHESTNUT_ROM_USB_IDS if include_bootloader else CHESTNUT_USB_IDS
+  return (vendor_id, product_id) in ids
+
+
 def get_usb_topology() -> set[str]:
   try:
     return set(os.listdir(USB_DEVICES_PATH))
@@ -81,7 +87,7 @@ def set_usb_state(device_state, devices: list[dict]) -> None:
     entry.linkErrorCount = device["linkErrorCount"]
     entry.usb3Lane = device.get("usb3Lane", "unknown")
 
-    if (entry.vendorId, entry.productId) in CHESTNUT_USB_IDS:
+    if is_chestnut_usb_id(entry.vendorId, entry.productId):
       chestnut_present = True
 
   device_state.chestnutPresent = chestnut_present

openpilot/common/params_keys.h

@@ -59,7 +59,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"IsDriverViewEnabled", {CLEAR_ON_MANAGER_START, BOOL}},
     {"IsEngaged", {PERSISTENT, BOOL}},
     {"IsLdwEnabled", {PERSISTENT | BACKUP, BOOL}},
-    {"IsLiveStreaming", {CLEAR_ON_MANAGER_START, BOOL}},
+    {"IsLiveStreaming", {CLEAR_ON_MANAGER_START | CLEAR_ON_IGNITION_ON, BOOL}},
     {"IsMetric", {PERSISTENT | BACKUP, BOOL}},
     {"IsOffroad", {CLEAR_ON_MANAGER_START, BOOL}},
     {"IsRhdDetected", {PERSISTENT, BOOL}},
@@ -92,6 +92,12 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"ObdMultiplexingEnabled", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, BOOL}},
     {"Offroad_CarUnrecognized", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
     {"Offroad_ChestnutBranch", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutNotDetected", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutOverheated", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutPcieUnavailable", {CLEAR_ON_MANAGER_START, JSON}},
+    {"Offroad_ChestnutUncompiled", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutUpdateFailed", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
+    {"Offroad_ChestnutUsbSlow", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, JSON}},
     {"Offroad_ConnectivityNeeded", {CLEAR_ON_MANAGER_START, JSON}},
     {"Offroad_ConnectivityNeededPrompt", {CLEAR_ON_MANAGER_START, JSON}},
     {"Offroad_ExcessiveActuation", {PERSISTENT, JSON}},
@@ -130,8 +136,9 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"UpdaterLastFetchTime", {PERSISTENT, TIME}},
     {"UptimeOffroad", {PERSISTENT, FLOAT, "0.0"}},
     {"UptimeOnroad", {PERSISTENT, FLOAT, "0.0"}},
-    {"UsbGpuActive", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
-    {"UsbGpuLoading", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
+    {"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 --- //
@@ -195,14 +202,16 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
 
     // Model Manager params
     {"ModelManager_ActiveBundle", {PERSISTENT, JSON}},
-    {"ModelManager_ActiveJson", {CLEAR_ON_MANAGER_START, STRING}},
+    {"ModelManager_ActiveBundleUSBGPU", {PERSISTENT, JSON}}, //TODO-SP: kept for migration, remove on next sync?
+    {"ModelManager_ActiveBundleChestnut", {PERSISTENT, JSON}},
+    {"ModelManager_ActiveJson", {CLEAR_ON_MANAGER_START, JSON}},
     {"ModelManager_ClearCache", {CLEAR_ON_MANAGER_START, BOOL}},
-    {"ModelManager_DownloadIndex", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, INT}},
+    {"ModelManager_DownloadRef", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, STRING}},
     {"ModelManager_Favs", {PERSISTENT | BACKUP, STRING}},
     {"ModelManager_LastSyncTime", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
-    {"ModelManager_LastSyncTime_USBGPU", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
+    {"ModelManager_LastSyncTime_Chestnut", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION, INT, "0"}},
     {"ModelManager_ModelsCache", {PERSISTENT | BACKUP, JSON}},
-    {"ModelManager_ModelsCache_USBGPU", {PERSISTENT | BACKUP, JSON}},
+    {"ModelManager_ModelsCache_Chestnut", {PERSISTENT | BACKUP, JSON}},
 
     // Neural Network Lateral Control
     {"NeuralNetworkLateralControl", {PERSISTENT | BACKUP, BOOL, "0"}},
@@ -245,6 +254,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
 
     // mapd
     {"MapAdvisorySpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, FLOAT}},
+    {"Mapd_ClearCache", {CLEAR_ON_MANAGER_START, BOOL}},
     {"MapdVersion", {PERSISTENT, STRING}},
     {"MapSpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, FLOAT, "0.0"}},
     {"NextMapSpeedLimit", {CLEAR_ON_ONROAD_TRANSITION, JSON}},

openpilot/common/prefix.h

@@ -27,14 +27,14 @@ public:
     auto param_path = Params().getParamPath();
     if (util::file_exists(param_path)) {
       std::string real_path = util::readlink(param_path);
-      util::check_system(util::string_format("rm %s -rf", real_path.c_str()));
+      util::check_system(util::string_format("rm -rf %s", real_path.c_str()));
       unlink(param_path.c_str());
     }
     if (getenv("COMMA_CACHE") == nullptr) {
-      util::check_system(util::string_format("rm %s -rf", Path::download_cache_root().c_str()));
+      util::check_system(util::string_format("rm -rf %s", Path::download_cache_root().c_str()));
     }
-    util::check_system(util::string_format("rm %s -rf", Path::comma_home().c_str()));
-    util::check_system(util::string_format("rm %s -rf", msgq_path.c_str()));
+    util::check_system(util::string_format("rm -rf %s", Path::comma_home().c_str()));
+    util::check_system(util::string_format("rm -rf %s", msgq_path.c_str()));
     unsetenv("OPENPILOT_PREFIX");
   }
 

openpilot/common/version.py

@@ -16,6 +16,15 @@ MASTER_SP_BRANCHES = ['master']
 RELEASE_BRANCHES = ['release-tizi-staging', 'release-mici-staging', 'release-tizi', 'release-mici', 'nightly']
 TESTED_BRANCHES = RELEASE_BRANCHES + ['devel-staging', 'nightly-dev'] + RELEASE_SP_BRANCHES + TESTED_SP_BRANCHES
 
+CHESTNUT_BRANCHES = {
+  "staging": "staging-chestnut",
+  "dev": "dev-chestnut",
+  "release-mici": "release-chestnut",
+  "release-tizi": "release-chestnut",
+  "release-mici-staging": "release-chestnut-staging",
+  "release-tizi-staging": "release-chestnut-staging",
+}
+
 SP_BRANCH_MIGRATIONS = {
   ("tici", "staging-c3-new"): "staging-tici",
   ("tici", "dev-c3-new"): "staging-tici",
@@ -28,6 +37,8 @@ SP_BRANCH_MIGRATIONS = {
   ("tizi", "release3-staging"): "release-tizi-staging",
   ("mici", "release3"): "release-mici",
   ("mici", "release3-staging"): "release-mici-staging",
+  ("tici", "hkg-angle-steering-2025"): "hkg-angle-steering-2025-tici",
+  ("tici", "hkg-angle-steering-2025-prebuilt"): "hkg-angle-steering-2025-tici-prebuilt"
 }
 
 BUILD_METADATA_FILENAME = "build.json"

openpilot/selfdrive/assets/icons_mici/chestnut_orange.png

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:845c40ff0d37612e8f2f482a36845744b5ae91ce2fcfc8117990d7d278b59820
+size 13079

openpilot/selfdrive/assets/icons_mici/egpu_crossed.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a8c5fece2a1c7587feb41cbe04c6aee08e768ecd9b5d00da6af9832a4ccc842
-size 2034

openpilot/selfdrive/assets/icons_mici/egpu_gray.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7409c53d7c72681c24982fd83b56ce70f80797c9c0f936d9296a5c18557ac472
-size 7279

openpilot/selfdrive/assets/icons_mici/egpu_orange.png

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58bd6155433f623b1f75d134bd8ca4745d9aa71f6767eb807cdbcf7deb3089a1
-size 10876

openpilot/selfdrive/assets/icons_mici/usb.png

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07bda2fe5d6be0b2854044053c384fe002e96406da119863a443b9344258b500
+size 1544

openpilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -49,9 +49,8 @@ def get_cruise_accel(e2e, v_cruise, v_ego, a_cruise_prev, angle_steers, CP, dt,
       max_accel = min(max_accel, coast_limit)
 
   target_accel = np.clip(v_cruise - v_ego, A_CRUISE_MIN, max_accel)
-  if not e2e:
-    j_cruise = np.interp(v_ego, A_CRUISE_MAX_BP, J_CRUISE_VALS)
-    target_accel = float(np.clip(target_accel, a_cruise_prev - j_cruise * dt, a_cruise_prev + j_cruise * dt))
+  j_cruise = np.interp(v_ego, A_CRUISE_MAX_BP, J_CRUISE_VALS)
+  target_accel = float(np.clip(target_accel, a_cruise_prev - j_cruise * dt, a_cruise_prev + j_cruise * dt))
 
   return target_accel
 
@@ -65,10 +64,9 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     self.dt = dt
     self.allow_throttle = True
 
-    self.a_desired = init_a
     self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
-    self.a_cruise = 0.0
-    self.output_a_target = 0.0
+    self.a_cruise = init_a
+    self.output_a_target = init_a
     self.output_should_stop = False
 
     self.v_desired_trajectory = np.zeros(CONTROL_N)
@@ -105,7 +103,8 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
 
     if reset_state:
       self.v_desired_filter.x = v_ego
-      self.a_desired = np.clip(sm['carState'].aEgo, ACCEL_MIN, ACCEL_MAX)
+      self.output_a_target = np.clip(sm['carState'].aEgo, ACCEL_MIN, ACCEL_MAX)
+      self.a_cruise = self.output_a_target
 
     # Prevent divergence, smooth in current v_ego
     self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
@@ -113,11 +112,11 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     # No change cost when user is controlling the speed, or when standstill
     prev_accel_constraint = not (reset_state or sm['carState'].standstill)
 
-    # Get new v_cruise and a_desired from Smart Cruise Control and Speed Limit Assist
-    v_cruise, self.a_desired = LongitudinalPlannerSP.update_targets(self, sm, self.v_desired_filter.x, self.a_desired, v_cruise)
+    # Get new v_cruise and a_target from Smart Cruise Control and Speed Limit Assist
+    v_cruise, self.output_a_target = LongitudinalPlannerSP.update_targets(self, sm, self.v_desired_filter.x, self.output_a_target, v_cruise)
 
     self.mpc.set_weights(prev_accel_constraint, personality=sm['selfdriveState'].personality)
-    self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
+    self.mpc.set_cur_state(self.v_desired_filter.x, self.output_a_target)
     self.mpc.update(sm['radarState'], personality=sm['selfdriveState'].personality)
 
     self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
@@ -130,7 +129,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
       cloudlog.info("FCW triggered")
 
     # Save starting point for next iteration
-    a_prev = self.a_desired
+    a_prev = self.output_a_target
 
     action_t =  self.CP.longitudinalActuatorDelay + DT_MDL
     output_a_target_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
@@ -155,7 +154,6 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     self.output_should_stop = any(should_stop for _, _, should_stop in candidates)
     self.output_a_target = np.clip(output_a_target, ACCEL_MIN, ACCEL_MAX)
 
-    self.a_desired = float(self.output_a_target)
     self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.output_a_target + a_prev) / 2.0
 
   def publish(self, sm, pm):

openpilot/selfdrive/modeld/SConscript

@@ -7,14 +7,9 @@ from openpilot.common.file_chunker import chunk_file, get_chunk_targets, get_exi
 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.constants import ModelConstants
-from openpilot.selfdrive.modeld.helpers import TG_INPUT_DEVICES_PATH, usbgpu_present, modeld_pkl_path
+from openpilot.selfdrive.modeld.helpers import TG_INPUT_DEVICES_PATH, chestnut_present, modeld_pkl_path
 
 
-CAMERA_CONFIGS = [
-  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),  # tici: 1928x1208
-  (_os_fisheye.width, _os_fisheye.height),        # mici: 1344x760
-]
-
 Import('env', 'arch')
 chunker_file = File("#openpilot/common/file_chunker.py")
 lenv = env.Clone()
@@ -24,30 +19,32 @@ tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "
                   if 'pycache' not in x and os.path.isfile(os.path.join(tinygrad_root, x))]
 
 def estimate_pickle_max_size(onnx_size):
-  return 1.2 * onnx_size  + 10 * 1024 * 1024  # 20% + 10MB is plenty
+  # 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':
+  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:
+  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.modeld': {
-    'default': {'WARP_DEV': tg_backend, 'QUEUE_DEV': tg_backend},
-    'usbgpu': {'WARP_DEV': tg_backend, 'QUEUE_DEV': 'AMD'}
-  },
   'openpilot.selfdrive.modeld.dmonitoringmodeld': {
     'default': {'DEV': tg_backend}
   },
 }
 
-USBGPU = usbgpu_present()
-if USBGPU:
-  usbgpu_tg_flags = f'DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV={tg_backend} FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2'
+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_OCCUPANCY_OPT=1'
   # the USB+AMD GPU takes an exclusive flock; serialize all targets that touch it
-  usbgpu_lock = File("models/.usb_gpu.lock").abspath
+  chestnut_lock = File("models/.chestnut.lock").abspath
 
 def write_tg_devices(target, source, env):
   with open(str(target[0]), "w") as f:
@@ -73,44 +70,44 @@ compile_modeld_script = [
 model_w, model_h = MEDMODEL_INPUT_SIZE
 frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
 
-for usbgpu in [False, True] if USBGPU else [False]:
-  target_pkl_path = File(modeld_pkl_path(usbgpu)).abspath
-  # BIG_INTO_SMALL=1 builds the default target from the big model, e.g. to test it without a USB GPU
-  file_prefix, cmd_flags = ('big_', usbgpu_tg_flags) if usbgpu else ('big_' if os.getenv('BIG_INTO_SMALL') 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, "  [USBGPU] $TARGET") if usbgpu else [cmd, Action(do_chunk, "  [CHUNK] $TARGET")]
-  node = lenv.Command(
-    chunk_targets,
-    tinygrad_files + compile_modeld_script + driving_onnx_deps + [Value(chunk_targets), chunker_file],
-    actions,
-  )
-  if usbgpu:
-    lenv.SideEffect(usbgpu_lock, node)
+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
@@ -120,7 +117,7 @@ lenv.Command(fn + "_metadata.pkl", [fn + ".onnx"] + tinygrad_files + script_file
 
 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:
+for cam_w, cam_h in camera_configs:
   dm_pkl_path = File(f"models/dm_warp_{cam_w}x{cam_h}_tinygrad.pkl").abspath
   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} '

openpilot/selfdrive/modeld/compile_modeld.py

@@ -37,17 +37,12 @@ from tinygrad.engine.jit import TinyJit
 
 
 NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
-WARP_INPUTS = ['tfm', 'big_tfm']
-POLICY_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
-
-UV_SCALE_MATRIX = np.array([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 1]], dtype=np.float32)
-UV_SCALE_MATRIX_INV = np.linalg.inv(UV_SCALE_MATRIX)
-
-WARP_DEV = os.getenv('WARP_DEV')
+MODELD_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
 
 
-def make_random_images(keys, shape, device=None):
-  return {k: Tensor.randint(shape, low=0, high=256, dtype='uint8', device=device).realize() for k in keys}
+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):
@@ -99,7 +94,7 @@ def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
 
   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=WARP_DEV)
+    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):
@@ -118,49 +113,43 @@ def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
   return frame_prepare_tinygrad
 
 
-def make_warp_input_queues(vision_input_shapes, frame_skip, device):
-  img = vision_input_shapes['img']  # (1, 12, 128, 256)
-  n_frames = img[1] // 6
-  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
-
-  npy = {
-    'tfm': np.zeros((3, 3), dtype=np.float32),
-    'big_tfm': np.zeros((3, 3), dtype=np.float32),
-  }
-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
-  }
-  return input_queues, npy
-
-
 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, 24, 512)
+  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], 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):
-  input_queues, npy = make_warp_input_queues(input_shapes, frame_skip, device)
-
-  fb = input_shapes['features_buffer']  # (1, 24, 512), past features only; the model appends the current frame's feature
+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])
 
-  shapes, sizes = get_policy_npy_shapes(input_shapes)
-  packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)
+  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.update({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.update({
-    'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
+  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_npy_inputs, device='NPY').realize(),
-  })
-  return input_queues, npy
+    'packed_npy_inputs': Tensor(packed_input, device='NPY').realize(),
+  }
+  return input_queues, npy, frame_views
 
 
 def shift_and_sample(buf, new_val, sample_fn):
@@ -176,13 +165,15 @@ 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_skip):
+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(WARP_DEV)
-    big_tfm = big_tfm.to(WARP_DEV)
-    Tensor.realize(tfm, big_tfm)
+    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)
@@ -195,10 +186,10 @@ 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)
-    warped = warped.to(Device.DEFAULT)
     Tensor.realize(packed_npy_inputs, warped)
 
     img = shift_and_sample(img_q, warped[0:1], sample_skip_fn)
@@ -211,33 +202,50 @@ def make_run_policy(model_runner, model_metadata, frame_skip):
     inputs = {
       'img': img,
       'big_img': big_img,
-      'features_buffer': feat_buf,
+      '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 compile_jit(jit, make_random_inputs, input_keys, make_queues):
-  SEED = 42
-  def random_inputs_run(fn, seed, test_val=None, test_buffers=None, expect_match=True):
-    input_queues, npy = make_queues(Device.DEFAULT)
-    rng = np.random.default_rng(seed)
-    Tensor.manual_seed(seed)
+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
 
-    testing = test_val is not None or test_buffers is not None
-    n_runs = 1 if testing else 3
+  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()
-      random_inputs = make_random_inputs()
       st = time.perf_counter()
-      outs = fn(**{k: input_queues[k] for k in input_keys}, **random_inputs)
+      outs = fn(**{k: input_queues[k] for k in input_keys})
       mt = time.perf_counter()
       Device.default.synchronize()
       et = time.perf_counter()
@@ -256,14 +264,15 @@ def compile_jit(jit, make_random_inputs, input_keys, make_queues):
     return val, buffers
 
   print('capture + replay')
-  test_val, test_buffers = random_inputs_run(jit, SEED)
-  print('pickle round trip')
+  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)
-    jit = load_oob(f)
-  random_inputs_run(jit, SEED, test_val, test_buffers, expect_match=True)
-  random_inputs_run(jit, SEED+1, test_val, test_buffers, expect_match=False)
+    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
 
 
@@ -292,27 +301,31 @@ if __name__ == "__main__":
   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)}
+  out = {
+    'metadata': make_metadata_dict(model_path),
+    'input_devices': {'model': Device.DEFAULT},
+    'run_model': {},
+  }
 
-  run_policy_jit = TinyJit(make_run_policy(model_runner, out['metadata'], args.frame_skip), prune=True)
-
-  make_policy_queues = partial(make_input_queues, out['metadata']['input_shapes'], args.frame_skip)
-  make_random_model_inputs = partial(make_random_images, keys=['warped'], shape=(2, 6, *out['metadata']['input_shapes']['img'][2:]), device=WARP_DEV)
-  out['run_policy'] = compile_jit(run_policy_jit, make_random_model_inputs, POLICY_INPUTS,
-                                  make_policy_queues)
+  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))
-    make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=nv12.size, device=WARP_DEV)
-    warp = TinyJit(make_warp(nv12, model_w, model_h, args.frame_skip), prune=True)
-    make_warp_queues = partial(make_warp_input_queues, out['metadata']['input_shapes'], args.frame_skip)
-    out[(cam_w,cam_h)] = compile_jit(warp, make_random_warp_inputs, WARP_INPUTS, make_warp_queues)
+    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)

openpilot/selfdrive/modeld/dmonitoringmodeld.py

@@ -29,7 +29,7 @@ class ModelState:
   output: np.ndarray
 
   def __init__(self, cam_w: int, cam_h: int):
-    self.DEV = get_tg_input_devices(PROCESS_NAME, usbgpu=False)['DEV']
+    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']

openpilot/selfdrive/modeld/fill_model_msg.py

@@ -64,6 +64,7 @@ def fill_driving_model_data(msg: capnp._DynamicStructBuilder, modelv2_send: capn
   driving_model_data.frameIdExtra = modelV2.frameIdExtra
   driving_model_data.frameDropPerc = modelV2.frameDropPerc
   driving_model_data.modelExecutionTime = modelV2.modelExecutionTime
+  driving_model_data.big = modelV2.big
   driving_model_data.action = modelV2.action
   driving_model_data.meta.laneChangeState = modelV2.meta.laneChangeState
   driving_model_data.meta.laneChangeDirection = modelV2.meta.laneChangeDirection

openpilot/selfdrive/modeld/helpers.py

@@ -7,18 +7,20 @@ import tempfile
 from pathlib import Path
 
 from openpilot.common.file_chunker import get_manifest_path
-from openpilot.common.hardware.usb import CHESTNUT_FW_VERSION, CHESTNUT_USB_IDS, USB_DEVICES_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, usbgpu: bool):
+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 usbgpu else 'usbgpu']
+    return json.load(f)[process_name]['default' if not chestnut else 'chestnut']
 
-def modeld_pkl_path(usbgpu: bool):
-  prefix = 'big_' if usbgpu else ''
+def modeld_pkl_path(chestnut: bool):
+  prefix = 'big_' if chestnut else ''
   return MODELS_DIR / f'{prefix}driving_tinygrad.pkl'
 
 def dump_oob(obj, f):
@@ -45,16 +47,20 @@ def load_oob(f):
       yield pb
   return pickle.load(io.BytesIO(opcodes), buffers=buffers())
 
-def usbgpu_present() -> bool:
+def chestnut_present() -> bool:
   for d in USB_DEVICES_PATH.glob("*"):
     try:
       usb_id = (int((d / "idVendor").read_text(), 16), int((d / "idProduct").read_text(), 16))
       product = (d / "product").read_text().strip()
-      if usb_id in CHESTNUT_USB_IDS and product == f"custom {CHESTNUT_FW_VERSION}-CLEAN":
+      if is_chestnut_usb_id(*usb_id) and product == CHESTNUT_USB_PRODUCT:
         return True
     except Exception:
       pass
   return False
 
-def usbgpu_compiled() -> bool:
-  return Path(get_manifest_path(modeld_pkl_path(usbgpu=True))).is_file()
+def chestnut_compiled() -> bool:
+  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

@@ -1,9 +1,11 @@
 #!/usr/bin/env python3
+from collections.abc import Callable
+import ctypes
 from functools import cached_property
 import os
-os.environ['GMMU'] = '0' # for usbgpu fast loading, noop for qcom
-from tinygrad.tensor import Tensor
+os.environ['GMMU'] = '0' # for chestnut fast loading, noop for qcom
 from tinygrad.device import Device
+import usb1
 import struct
 import threading
 import time
@@ -26,17 +28,17 @@ 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, WARP_INPUTS, POLICY_INPUTS
+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 usbgpu_present, usbgpu_compiled, modeld_pkl_path, get_tg_input_devices, 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
 
-PROCESS_NAME = "openpilot.selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 
 LAT_SMOOTH_SECONDS = 0.0
@@ -81,6 +83,37 @@ class ChestnutState:
     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:
@@ -94,8 +127,10 @@ class ChestnutState:
     if self.big and "AMD" in Device._opened_devices and self.sends % 100 == 1:
       try:
         smu = Device["AMD"].iface.dev_impl.smu
+        metrics_t = smu.smu_mod.SmuMetricsExternal_t
         smu._send_msg(smu.smu_mod.PPSMC_MSG_TransferTableSmu2Dram, smu.smu_mod.TABLE_SMU_METRICS, timeout=100)
-        metrics = smu.read_table(smu.smu_mod.SmuMetricsExternal_t, smu.smu_mod.TABLE_SMU_METRICS).SmuMetrics
+        metrics_buf = bytearray(smu.adev.vram.view(smu.driver_table_paddr, ctypes.sizeof(metrics_t))[:])
+        metrics = metrics_t.from_buffer(metrics_buf).SmuMetrics
         self.metrics = {'tempC': metrics.AvgTemperature[smu.smu_mod.TEMP_HOTSPOT],
                         'memoryTempC': metrics.AvgTemperature[smu.smu_mod.TEMP_MEM],
                         'powerDrawW': metrics.AverageSocketPower,
@@ -114,13 +149,15 @@ class ChestnutState:
         setattr(state, k, v)
 
     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:
-        # ASM runs on USB-C power, these still read without a gpu
-        asm = Device["AMD"].iface.pci_dev.usb
-        state.pcieLtssm = asm.read(0xB450, 1)[0]
-        state.supplyVoltage, state.supplyCurrent = struct.unpack('<Hh', bytes(asm.usb.control_read(0xC0, 5))[:4])
-        asm_valid = True
+        state.pcieLtssm = Device["AMD"].iface.pci_dev.usb.read(0xB450, 1)[0]
       except Exception:
         pass
 
@@ -141,42 +178,34 @@ class FrameMeta:
 class ModelState(ModelStateBase):
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
-  def __init__(self, cam_w: int, cam_h: int, usbgpu: bool):
+  def __init__(self, cam_w: int, cam_h: int, chestnut: bool):
     ModelStateBase.__init__(self)
-    input_devices = get_tg_input_devices(PROCESS_NAME, usbgpu)
-    self.WARP_DEV, self.QUEUE_DEV = input_devices['WARP_DEV'], input_devices['QUEUE_DEV']
-    jits = load_oob(open_file_chunked(modeld_pkl_path(usbgpu)))
+    jits = load_oob(open_file_chunked(modeld_pkl_path(chestnut)))
+    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.usbgpu = usbgpu
+    self.chestnut = chestnut
 
     self.frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
-    self.input_queues, self.npy = make_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
-    self.full_frames: dict[str, Tensor] = {}
-    self._blob_cache: dict[tuple[str, int], Tensor] = {}
+    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()
-    self.frame_buf_params = {k: get_nv12_info(cam_w, cam_h) for k in ('img', 'big_img')}
-    self.run_policy = jits['run_policy']
-    self.warp = jits[(cam_w,cam_h)]
+    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]:
     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]) -> dict[str, np.ndarray] | None:
-    for key in bufs.keys():
-      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
-      yuv_size = self.frame_buf_params[key][3]
-      # There is a ringbuffer of imgs, just cache tensors pointing to all of them
-      cache_key = (key, ptr)
-      if cache_key not in self._blob_cache:
-        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
-      self.full_frames[key] = self._blob_cache[cache_key]
+          inputs: dict[str, np.ndarray], after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray]:
+    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
@@ -187,16 +216,12 @@ class ModelState(ModelStateBase):
     self.npy['tfm'][:,:] = transforms['img'][:,:]
     self.npy['big_tfm'][:,:] = transforms['big_img'][:,:]
 
-    warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames['img'], big_frame=self.full_frames['big_img'])
-
-    outs, = self.run_policy(
-      **{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped
-    )
+    outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
+    if after_enqueue is not None:
+      after_enqueue()
     model_output = outs.numpy()[0]
-    if self.usbgpu and not np.all(np.isfinite(model_output)):
-      # TODO remove with prev_feat
-      cloudlog.error("model output not finite, dropping frame")
-      return None
+    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']]
 
@@ -205,25 +230,37 @@ class ModelState(ModelStateBase):
     return outputs_dict
 
   def warmup(self) -> None:
-    dummy_frames = {k: np.zeros(self.frame_buf_params[k][3], dtype=np.uint8) for k in self.vision_input_names}
+    dummy_frames = {k: np.zeros(self.frame_copy_size, dtype=np.uint8) for k in self.vision_input_names}
     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.input_queues, self.npy = make_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
+    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
-    self.full_frames.clear()
-    self._blob_cache.clear()
 
 
 def main(demo=False):
   cloudlog.warning("modeld init")
 
-  USBGPU = usbgpu_present() and usbgpu_compiled()
-  if USBGPU:
+  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("UsbGpuLoading", USBGPU)
-  params.remove("UsbGpuActive")
+  params.put_bool("ChestnutLoading", CHESTNUT)
+  if chestnut_available and not CHESTNUT:
+    params.put_bool("ChestnutActive", False)
+  else:
+    params.remove("ChestnutActive")
 
   config_realtime_process(7, 54)
 
@@ -253,7 +290,7 @@ def main(demo=False):
   st = time.monotonic()
   cloudlog.warning("loading model")
   model = None
-  if USBGPU:
+  if CHESTNUT:
     big_model = None
     def load_big():
       nonlocal big_model
@@ -267,23 +304,27 @@ def main(demo=False):
     loader.start()
     loader.join(BIG_MODEL_TIMEOUT)
     model = big_model
-    params.put_bool("UsbGpuActive", model is not None)
+    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 USBGPU else None
+  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("UsbGpuLoading", False)
+  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", "modelDataV2SP"] + (["chestnutState"] if USBGPU 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 = ChestnutState(pm, model.usbgpu) if USBGPU 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)
@@ -393,13 +434,16 @@ def main(demo=False):
 
     mt1 = time.perf_counter()
     try:
-      model_output = model.run(bufs, transforms, inputs)
+      send_chestnut = (chestnut_state is not None and
+                       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("UsbGpuActive"):
+      if not params.get_bool("ChestnutActive"):
         raise
       # fallback to small model
       cloudlog.exception("big model failed, fall back to small")
-      params.put_bool("UsbGpuActive", False)
+      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:
@@ -419,18 +463,18 @@ def main(demo=False):
       fill_model_msg(modelv2_send, model_output, action,
                      publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
                      frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, extrinsics_calibration_seen)
-      modelv2_send.modelV2.big = model.usbgpu
+      modelv2_send.modelV2.big = model.chestnut
 
       desire_state = modelv2_send.modelV2.meta.desireState
       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)
-      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
-      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
-
       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)
@@ -441,10 +485,6 @@ def main(demo=False):
       pm.send('modelDataV2SP', mdv2sp_send)
     last_vipc_frame_id = meta_main.frame_id
 
-    if chestnut_state is not None and run_count % round(ModelConstants.MODEL_RUN_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0:
-      chestnut_state.send()
-
-
 if __name__ == "__main__":
   try:
     import argparse

openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a501760a9d1d5fef0eab2b8c5d122d06124fc26dc8e0782e0aa94b82a208f0ff
-size 1757355221
+oid sha256:1791d5940b2c048d0639813426dd2cf1d6f2a6727ed51e17c8bcea8bbe754123
+size 765950064

openpilot/selfdrive/pandad/pandad.cc

@@ -123,22 +123,22 @@ void fill_panda_state(cereal::PandaState::Builder &ps, cereal::PandaState::Panda
   ps.setUptime(health.uptime_pkt);
   ps.setSafetyTxBlocked(health.safety_tx_blocked_pkt);
   ps.setSafetyRxInvalid(health.safety_rx_invalid_pkt);
-  ps.setIgnitionLine(health.ignition_line_pkt);
-  ps.setIgnitionCan(health.ignition_can_pkt);
-  ps.setControlsAllowed(health.controls_allowed_pkt);
+  ps.setIgnitionLine((health.flags_pkt & HEALTH_FLAG_IGNITION_LINE) != 0U);
+  ps.setIgnitionCan((health.flags_pkt & HEALTH_FLAG_IGNITION_CAN) != 0U);
+  ps.setControlsAllowed((health.flags_pkt & HEALTH_FLAG_CONTROLS_ALLOWED) != 0U);
   ps.setTxBufferOverflow(health.tx_buffer_overflow_pkt);
   ps.setRxBufferOverflow(health.rx_buffer_overflow_pkt);
   ps.setPandaType(hw_type);
   ps.setSafetyModel(cereal::CarParams::SafetyModel(health.safety_mode_pkt));
   ps.setSafetyParam(health.safety_param_pkt);
   ps.setFaultStatus(cereal::PandaState::FaultStatus(health.fault_status_pkt));
-  ps.setPowerSaveEnabled((bool)(health.power_save_enabled_pkt));
-  ps.setHeartbeatLost((bool)(health.heartbeat_lost_pkt));
+  ps.setPowerSaveEnabled((health.flags_pkt & HEALTH_FLAG_POWER_SAVE_ENABLED) != 0U);
+  ps.setHeartbeatLost((health.flags_pkt & HEALTH_FLAG_HEARTBEAT_LOST) != 0U);
   ps.setAlternativeExperience(health.alternative_experience_pkt);
   ps.setHarnessStatus(cereal::PandaState::HarnessStatus(health.car_harness_status_pkt));
-  ps.setInterruptLoad(health.interrupt_load_pkt);
+  ps.setInterruptLoad(health.interrupt_load_pkt / 255.0f);
   ps.setFanPower(health.fan_power);
-  ps.setSafetyRxChecksInvalid((bool)(health.safety_rx_checks_invalid_pkt));
+  ps.setSafetyRxChecksInvalid((health.flags_pkt & HEALTH_FLAG_SAFETY_RX_CHECKS_INVALID) != 0U);
   ps.setSpiErrorCount(health.spi_error_count_pkt);
   ps.setSbu1Voltage(health.sbu1_voltage_mV / 1000.0f);
   ps.setSbu2Voltage(health.sbu2_voltage_mV / 1000.0f);
@@ -198,10 +198,10 @@ std::optional<bool> send_panda_states(PubMaster *pm, Panda *panda, bool is_onroa
   }
 
   if (spoofing_started) {
-    health.ignition_line_pkt = 1;
+    health.flags_pkt |= HEALTH_FLAG_IGNITION_LINE;
   }
 
-  bool ignition_local = ((health.ignition_line_pkt != 0) || (health.ignition_can_pkt != 0)) && !always_offroad;
+  bool ignition_local = ((health.flags_pkt & (HEALTH_FLAG_IGNITION_LINE | HEALTH_FLAG_IGNITION_CAN)) != 0U) && !always_offroad;
 
   // Make sure CAN buses are live: safety_setter_thread does not work if Panda CAN are silent and there is only one other CAN node
   if (health.safety_mode_pkt == (uint8_t)(cereal::CarParams::SafetyModel::SILENT)) {
@@ -209,7 +209,7 @@ std::optional<bool> send_panda_states(PubMaster *pm, Panda *panda, bool is_onroa
   }
 
   bool power_save_desired = !ignition_local;
-  if (health.power_save_enabled_pkt != power_save_desired) {
+  if (((health.flags_pkt & HEALTH_FLAG_POWER_SAVE_ENABLED) != 0U) != power_save_desired) {
     panda->set_power_saving(power_save_desired);
   }
 

openpilot/selfdrive/selfdrived/alerts_offroad.json

@@ -18,7 +18,31 @@
     "_comment": "Set extra field to the failed reason."
   },
   "Offroad_ChestnutBranch": {
-    "text": "Chestnut detected! Switch to the release-chestnut branch to use chestnut-class models.",
+    "text": "Chestnut detected! Switch to the %1 branch to use chestnut-class models.",
+    "severity": -1
+  },
+  "Offroad_ChestnutNotDetected": {
+    "text": "Chestnut not detected. Check USB and 12V connections.",
+    "severity": 0
+  },
+  "Offroad_ChestnutOverheated": {
+    "text": "Chestnut overheated. Ensure good airflow. Current GPU temperature is %1.",
+    "severity": 0
+  },
+  "Offroad_ChestnutPcieUnavailable": {
+    "text": "%1",
+    "severity": 0
+  },
+  "Offroad_ChestnutUncompiled": {
+    "text": "Chestnut model not compiled. Keep ignition on and reboot the comma.",
+    "severity": 0
+  },
+  "Offroad_ChestnutUpdateFailed": {
+    "text": "Chestnut update failed. Check the USB cable.",
+    "severity": 0
+  },
+  "Offroad_ChestnutUsbSlow": {
+    "text": "Chestnut USB link is slow. Check the USB cable. The current speed is %1.",
     "severity": 0
   },
   "Offroad_UnregisteredHardware": {

openpilot/selfdrive/selfdrived/selfdrived.py

@@ -195,17 +195,18 @@ class SelfdriveD(CruiseHelper):
       self.events.add(EventName.joystickDebug)
       self.startup_event = None
 
-    loading = self.params.get_bool("UsbGpuLoading")
+    loading = self.params.get_bool("ChestnutLoading")
     if self.big_model_loading and not loading:
       self.big_model_ready_t = time.monotonic()
+      self.events_sp.add(custom.OnroadEventSP.EventName.bigModelReady)
     self.big_model_loading = loading
     if self.big_model_loading:
       self.events.add(EventName.bigModelLoading)
 
-    big_active = self.params.get("UsbGpuActive")
-    usbgpu_present = self.sm['deviceState'].chestnutPresent
+    big_active = self.params.get("ChestnutActive")
+    chestnut_present = self.sm['deviceState'].chestnutPresent
     model_unavailable = big_active is True and self.sm.seen['modelV2'] and not self.sm.alive['modelV2']
-    big_failed = big_active is False or model_unavailable or (self.big_model_active and not usbgpu_present)
+    big_failed = big_active is False or model_unavailable or (self.big_model_active and not chestnut_present)
     if big_failed and not self.big_model_failed:
       self.events.add(EventName.bigModelFailed)
     self.big_model_failed = big_failed

openpilot/selfdrive/test/process_replay/migration.py

@@ -152,7 +152,7 @@ def migrate_drivingModelData(msgs):
   add_ops = []
   for _, msg in msgs:
     dmd = messaging.new_message('drivingModelData', valid=msg.valid, logMonoTime=msg.logMonoTime)
-    for field in ["frameId", "frameIdExtra", "frameDropPerc", "modelExecutionTime", "action"]:
+    for field in ["frameId", "frameIdExtra", "frameDropPerc", "modelExecutionTime", "big", "action"]:
       setattr(dmd.drivingModelData, field, getattr(msg.modelV2, field))
     for meta_field in ["laneChangeState", "laneChangeState"]:
       setattr(dmd.drivingModelData.meta, meta_field, getattr(msg.modelV2.meta, meta_field))

openpilot/selfdrive/test/process_replay/model_replay.py

@@ -33,9 +33,9 @@ MODEL_REPLAY_BUCKET="model_replay_master"
 GITHUB = GithubUtils(API_TOKEN, DATA_TOKEN)
 
 EXEC_TIMINGS = [
-  # model, instant max, average max
-  ("modelV2", 0.05, 0.028),
-  ("driverStateV2", 0.05, 0.018),
+  # model, instant max, average max, chestnut average max
+  ("modelV2", 0.05, 0.03, 0.05),
+  ("driverStateV2", 0.05, 0.018, 0.018),
 ]
 
 def get_log_fn(test_route, ref="master"):
@@ -169,11 +169,13 @@ def model_replay(lr, frs):
   dmonitoringmodeld_msgs = replay_process(dmonitoringmodeld, dmodeld_logs, frs)
 
   msgs = modeld_msgs + dmonitoringmodeld_msgs
+  chestnut = any(m.modelV2.big for m in modeld_msgs if m.which() == "modelV2")
 
   header = ['model', 'max instant', 'max instant allowed', 'average', 'max average allowed', 'test result']
   rows = []
   timings_ok = True
-  for (s, instant_max, avg_max) in EXEC_TIMINGS:
+  for (s, instant_max, avg_max, chestnut_avg_max) in EXEC_TIMINGS:
+    avg_max = chestnut_avg_max if chestnut else avg_max
     ts = [getattr(m, s).modelExecutionTime for m in msgs if m.which() == s]
     # TODO some init can happen in first iteration
     ts = ts[1:]

openpilot/selfdrive/ui/body/layouts/onroad.py

@@ -1,7 +1,7 @@
 import time
 import pyray as rl
 
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.selfdrive.ui.ui_state import ui_state
@@ -26,8 +26,8 @@ class BodyLayout(Widget):
     self._last_input_time = time.monotonic()
     self._was_active = False
     self._offroad_label = UnifiedLabel("turn on ignition to use", 95 if gui_app.big_ui() else 45, FontWeight.DISPLAY,
-                                       alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                       alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                       alignment=TextAlignment.CENTER,
+                                       alignment_vertical=TextAlignmentVertical.MIDDLE)
 
   def draw_dot_grid(self, rect: rl.Rectangle, dots: list[tuple[int, int]], color: rl.Color):
     spacing = min(rect.height / GRID_ROWS, rect.width / GRID_COLS)

openpilot/selfdrive/ui/layouts/home.py

@@ -8,7 +8,7 @@ from openpilot.selfdrive.ui.widgets.exp_mode_button import ExperimentalModeButto
 from openpilot.selfdrive.ui.widgets.prime import PrimeWidget
 from openpilot.selfdrive.ui.widgets.setup import SetupWidget
 from openpilot.system.ui.lib.text_measure import measure_text_cached
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignment
 from openpilot.system.ui.lib.multilang import tr, trn
 from openpilot.system.ui.widgets.label import gui_label
 from openpilot.system.ui.widgets import Widget
@@ -178,7 +178,7 @@ class HomeLayout(Widget):
 
     version_rect = rl.Rectangle(self.header_rect.x + self.header_rect.width - version_text_width, self.header_rect.y,
                                 version_text_width, self.header_rect.height)
-    gui_label(version_rect, self._version_text, 48, rl.WHITE, alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+    gui_label(version_rect, self._version_text, 48, rl.WHITE, alignment=TextAlignment.RIGHT)
 
   def _render_home_content(self):
     self._render_left_column()

openpilot/selfdrive/ui/layouts/onboarding.py

@@ -5,7 +5,7 @@ from enum import IntEnum
 
 import pyray as rl
 from openpilot.common.basedir import BASEDIR
-from openpilot.system.ui.lib.application import FontWeight, gui_app
+from openpilot.system.ui.lib.application import FontWeight, TextAlignment, gui_app
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import Button, ButtonStyle
@@ -115,9 +115,9 @@ class TermsPage(Widget):
     self._on_accept = on_accept
     self._on_decline = on_decline
 
-    self._title = Label(tr("Welcome to sunnypilot"), font_size=90, font_weight=FontWeight.BOLD, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+    self._title = Label(tr("Welcome to sunnypilot"), font_size=90, font_weight=FontWeight.BOLD, text_alignment=TextAlignment.LEFT)
     self._desc = Label(tr("You must accept the Terms of Service to use sunnypilot. Read the latest terms at https://sunnypilot.ai/terms before continuing."),
-                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT)
 
     self._decline_btn = Button(tr("Decline"), click_callback=on_decline)
     self._accept_btn = Button(tr("Agree"), button_style=ButtonStyle.PRIMARY, click_callback=on_accept)
@@ -150,7 +150,7 @@ class DeclinePage(Widget):
   def __init__(self, back_callback=None):
     super().__init__()
     self._text = Label(tr("You must accept the Terms of Service in order to use sunnypilot."),
-                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT)
+                       font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT)
     self._back_btn = Button(tr("Back"), click_callback=back_callback)
     self._uninstall_btn = Button(tr("Decline, uninstall sunnypilot"), button_style=ButtonStyle.DANGER,
                                  click_callback=self._on_uninstall_clicked)

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

@@ -199,6 +199,9 @@ class SoftwareLayout(Widget):
         selection = self._branch_dialog.selection
         ui_state.params.put("UpdaterTargetBranch", selection, block=True)
         self._branch_btn.action_item.set_value(selection)
+        self._download_btn.action_item.set_enabled(False)
+        self._waiting_for_updater = True
+        self._waiting_start_ts = time.monotonic()
         subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
       self._branch_dialog = None
 

openpilot/selfdrive/ui/layouts/sidebar.py

@@ -168,9 +168,16 @@ class Sidebar(Widget, SidebarSP):
     # Home/Flag button
     flag_pressed = mouse_down and rl.check_collision_point_rec(mouse_pos, HOME_BTN)
     button_img = self._flag_img if ui_state.started else self._home_img
+    button_pos = rl.Vector2(HOME_BTN.x, HOME_BTN.y)
+    icon_opacity = 1.0
+
+    if gui_app.sunnypilot_ui():
+      button_img, button_pos, icon_opacity = SidebarSP._get_home_icon(self, button_img)
 
     tint = Colors.BUTTON_PRESSED if (ui_state.started and flag_pressed) else Colors.BUTTON_NORMAL
-    rl.draw_texture_ex(button_img, rl.Vector2(HOME_BTN.x, HOME_BTN.y), 0.0, 1.0, tint)
+    if icon_opacity < 1.0:
+      tint = rl.Color(tint[0], tint[1], tint[2], int(255 * icon_opacity))
+    rl.draw_texture_ex(button_img, button_pos, 0.0, 1.0, tint)
 
     # Microphone button
     if self._recording_audio:

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

@@ -1,4 +1,5 @@
 import datetime
+import math
 import time
 
 from openpilot.cereal import log
@@ -8,8 +9,8 @@ from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.layouts import HBoxLayout
 from openpilot.system.ui.widgets.icon_widget import IconWidget
 from openpilot.system.ui.widgets.label import UnifiedLabel, gui_label
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
-from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignment, TextAlignmentVertical
+from openpilot.selfdrive.ui.ui_state import ui_state, ChestnutState
 from openpilot.common.version import RELEASE_BRANCHES
 
 HEAD_BUTTON_FONT_SIZE = 40
@@ -69,8 +70,8 @@ class AlertsPill(Widget):
 
       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,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                alignment=TextAlignment.CENTER,
+                alignment_vertical=TextAlignmentVertical.MIDDLE)
 
 
 class NetworkIcon(Widget):
@@ -139,8 +140,10 @@ class MiciHomeLayout(Widget):
     self._version_text = self._get_version_text()
 
     self._experimental_icon = IconWidget("icons_mici/experimental_mode.png", (48, 48))
-    self._egpu_icon = IconWidget("icons_mici/egpu_green.png", (50, 37))
-    self._egpu_icon_gray = IconWidget("icons_mici/egpu_gray.png", (50, 37))
+    self._usb_icon = IconWidget("icons_mici/usb.png", (62, 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))
     self._body_icon = IconWidget("icons_mici/body.png", (54, 37))
 
@@ -150,13 +153,15 @@ class MiciHomeLayout(Widget):
       IconWidget("icons_mici/settings.png", (48, 48), opacity=0.9),
       NetworkIcon(),
       self._experimental_icon,
-      self._egpu_icon,
-      self._egpu_icon_gray,
+      self._usb_icon,
+      self._chestnut_icon,
+      self._chestnut_loading_icon,
+      self._chestnut_failed_icon,
       self._body_icon,
       self._mic_icon,
     ], spacing=18)
 
-    self._openpilot_label = UnifiedLabel("sunnypilot", font_size=96, font_weight=FontWeight.DISPLAY, max_width=480, wrap_text=False)
+    self._openpilot_label = UnifiedLabel("openpilot", font_size=96, font_weight=FontWeight.DISPLAY, max_width=480, wrap_text=False)
     self._version_label = UnifiedLabel("", font_size=36, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
     self._large_version_label = UnifiedLabel("", font_size=64, text_color=rl.GRAY, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
     self._date_label = UnifiedLabel("", font_size=36, text_color=rl.GRAY, font_weight=FontWeight.ROMAN, max_width=480, wrap_text=False)
@@ -247,9 +252,20 @@ class MiciHomeLayout(Widget):
         self._version_commit_label.render()
 
     # ***** Center-aligned bottom section icons *****
+    usb_connected = ui_state.usb_connected
+    usb_unknown = ui_state.usb_unknown
+    chestnut_state = ui_state.chestnut_state
     self._experimental_icon.set_visible(ui_state.experimental_mode)
-    self._egpu_icon.set_visible(ui_state.sm["deviceState"].chestnutPresent and ui_state.usbgpu_compiled)
-    self._egpu_icon_gray.set_visible(ui_state.sm["deviceState"].chestnutPresent and not ui_state.usbgpu_compiled)
+    if gui_app.sunnypilot_ui():
+      self._set_chestnut_visibility()
+    else:
+      self._usb_icon.set_visible(usb_connected and usb_unknown)
+      self._chestnut_icon.set_visible(not usb_unknown and chestnut_state not in
+                                      (ChestnutState.LOADING, ChestnutState.UNCOMPILED, ChestnutState.FAILED) and
+                                      (usb_connected or chestnut_state in (ChestnutState.READY, ChestnutState.ACTIVE)))
+      self._chestnut_loading_icon.set_visible(not usb_unknown and chestnut_state == ChestnutState.LOADING)
+      self._chestnut_loading_icon.set_opacity(0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0)))
+      self._chestnut_failed_icon.set_visible(not usb_unknown and chestnut_state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED))
     self._mic_icon.set_visible(ui_state.recording_audio)
     self._body_icon.set_visible(bool(ui_state.is_body))
 

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

@@ -11,7 +11,7 @@ from openpilot.common.hardware import HARDWARE
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.scroller import Scroller
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 
 REFRESH_INTERVAL = 5.0  # seconds
@@ -62,12 +62,12 @@ class AlertItem(Widget):
     self._icon_green = gui_app.texture("icons_mici/offroad_alerts/green_wheel.png", self.ICON_SIZE, self.ICON_SIZE)
 
     self._title_label = UnifiedLabel(text="", font_size=32, font_weight=FontWeight.SEMI_BOLD, text_color=self.TEXT_COLOR,
-                                     alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT,
-                                     alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP, line_height=0.95)
+                                     alignment=TextAlignment.LEFT,
+                                     alignment_vertical=TextAlignmentVertical.TOP, line_height=0.95)
 
     self._body_label = UnifiedLabel(text="", font_size=28, font_weight=FontWeight.ROMAN, text_color=self.TEXT_COLOR,
-                                    alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT,
-                                    alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM, line_height=0.95)
+                                    alignment=TextAlignment.LEFT,
+                                    alignment_vertical=TextAlignmentVertical.BOTTOM, line_height=0.95)
 
     self._title_text = ""
     self._body_text = ""
@@ -200,8 +200,8 @@ class MiciOffroadAlerts(Scroller):
 
     # Create empty state label
     self._empty_label = UnifiedLabel(tr("no alerts"), 65, FontWeight.DISPLAY, rl.WHITE,
-                                     alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                     alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                     alignment=TextAlignment.CENTER,
+                                     alignment_vertical=TextAlignmentVertical.MIDDLE)
 
     # Build initial alert list
     self._build_alerts()

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

@@ -4,7 +4,7 @@ import pyray as rl
 from collections.abc import Callable
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.qrcode import make_texture
-from openpilot.system.ui.lib.application import FontWeight, gui_app
+from openpilot.system.ui.lib.application import FontWeight, gui_app, TextAlignment
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import SmallCircleIconButton
 from openpilot.system.ui.widgets.scroller import NavScroller, Scroller
@@ -35,7 +35,7 @@ class DriverCameraSetupDialog(BaseCabinCameraDialog):
 
     if not self._camera_view.frame:
       gui_label(rect, tr("camera starting"), font_size=64, font_weight=FontWeight.BOLD,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER)
+                alignment=TextAlignment.CENTER)
       rl.end_scissor_mode()
       return
 

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

@@ -74,6 +74,10 @@ class SoftwareInfoLayoutMici(Widget):
 
 
 class CheckUpdateButton(BigButton):
+  UPDATER_PROC = "openpilot.system.updated.updated"
+  CHECK_FOR_UPDATE = "SIGUSR1"
+  DOWNLOAD_UPDATE = "SIGHUP"
+
   def __init__(self):
     self._txt_update_icon = gui_app.texture("icons_mici/settings/device/update.png", 64, 75)
     self._txt_up_to_date_icon = gui_app.texture("icons_mici/settings/device/up_to_date.png", 64, 64)
@@ -97,15 +101,20 @@ class CheckUpdateButton(BigButton):
       gui_app.push_widget(dlg)
       return
 
+    self._signal_updater(self.DOWNLOAD_UPDATE if self.get_value() == "download update" else self.CHECK_FOR_UPDATE)
+
+  def check_for_update(self):
+    self._signal_updater(self.CHECK_FOR_UPDATE)
+
+  def _signal_updater(self, sig: str):
     self.set_enabled(False)
     self._state = UpdaterState.WAITING_FOR_UPDATER
+    self._hide_value_t = None
+    self.set_value("")
     self.set_icon(self._txt_update_icon)
 
     def run():
-      if self.get_value() == "download update":
-        subprocess.run("pkill -SIGHUP -f openpilot.system.updated.updated", shell=True)
-      else:
-        subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
+      subprocess.run(f"pkill -{sig} -f {self.UPDATER_PROC}", shell=True)
 
     threading.Thread(target=run, daemon=True).start()
 
@@ -184,7 +193,7 @@ class CheckUpdateButton(BigButton):
 
 class InstallUpdateButton(BigButton):
   def __init__(self):
-    super().__init__("install update", "", gui_app.texture("icons_mici/settings/device/reboot.png", 64, 70))
+    super().__init__("install now", "", gui_app.texture("icons_mici/settings/device/reboot.png", 64, 70))
     self.set_visible(lambda: ui_state.is_offroad() and ui_state.params.get_bool("UpdateAvailable"))
 
   def _update_state(self):
@@ -232,8 +241,9 @@ class BranchSelectPage(NavScroller):
 
 
 class TargetBranchButton(BigButton):
-  def __init__(self):
+  def __init__(self, check_update_btn: CheckUpdateButton):
     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"))
     self.set_enabled(lambda: ui_state.is_offroad())
@@ -246,12 +256,15 @@ class TargetBranchButton(BigButton):
       self.set_value(target)
 
   def _on_click(self):
+    if not ui_state.params.get("UpdaterAvailableBranches"):
+      gui_app.push_widget(BigDialog("", tr("Failed to get available branches. Ensure you're connected to the internet and try again.")))
+      return
     gui_app.push_widget(BranchSelectPage(self._on_select))
 
   def _on_select(self, branch: str):
     ui_state.params.put("UpdaterTargetBranch", branch, block=True)
     self.set_value(branch)
-    subprocess.run("pkill -SIGUSR1 -f openpilot.system.updated.updated", shell=True)
+    self._check_update_btn.check_for_update()
 
 
 class SoftwareLayoutMici(NavScroller):
@@ -265,10 +278,11 @@ class SoftwareLayoutMici(NavScroller):
                                                         gui_app.texture("icons_mici/settings/device/uninstall.png", 64, 64),
                                                         uninstall_openpilot_callback, exit_on_confirm=False)
 
+    check_update_btn = CheckUpdateButton()
     self._scroller.add_widgets([
       SoftwareInfoLayoutMici(),
-      CheckUpdateButton(),
+      check_update_btn,
       InstallUpdateButton(),
-      TargetBranchButton(),
+      TargetBranchButton(check_update_btn),
       uninstall_openpilot_btn,
     ])

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

@@ -10,7 +10,7 @@ from opendbc.car.structs import car
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.common.filter_simple import BounceFilter, FirstOrderFilter
 from openpilot.common.hardware import COMMA_HARDWARE
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 
@@ -333,7 +333,7 @@ class AlertRenderer(Widget, SpeedLimitAlertRenderer):
     self._alert_text1_label.set_text(alert_text1)
     self._alert_text1_label.set_text_color(color)
     self._alert_text1_label.set_font_size(font_size)
-    self._alert_text1_label.set_alignment(rl.GuiTextAlignment.TEXT_ALIGN_LEFT if icon_side != 'left' else rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+    self._alert_text1_label.set_alignment(TextAlignment.LEFT if icon_side != 'left' else TextAlignment.RIGHT)
     self._alert_text1_label.render(text_rect1)
 
     alert_text2 = alert.text2.lower()
@@ -365,5 +365,5 @@ class AlertRenderer(Widget, SpeedLimitAlertRenderer):
       self._alert_text2_label.set_text(alert_text2)
       self._alert_text2_label.set_text_color(color)
       self._alert_text2_label.set_font_size(small_font_size)
-      self._alert_text2_label.set_alignment(rl.GuiTextAlignment.TEXT_ALIGN_LEFT if icon_side != 'left' else rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+      self._alert_text2_label.set_alignment(TextAlignment.LEFT if icon_side != 'left' else TextAlignment.RIGHT)
       self._alert_text2_label.render(text_rect2)

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

@@ -11,7 +11,7 @@ from openpilot.selfdrive.ui.mici.onroad.hud_renderer import HudRenderer
 from openpilot.selfdrive.ui.mici.onroad.model_renderer import ModelRenderer
 from openpilot.selfdrive.ui.mici.onroad.confidence_ball import ConfidenceBall
 from openpilot.selfdrive.ui.mici.onroad.cameraview import CameraView
-from openpilot.system.ui.lib.application import FontWeight, gui_app, MousePos, MouseEvent
+from openpilot.system.ui.lib.application import FontWeight, gui_app, MousePos, MouseEvent, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets import Widget
 from openpilot.common.filter_simple import BounceFilter
@@ -158,8 +158,8 @@ class AugmentedRoadView(CameraView):
     self._confidence_ball = ConfidenceBall()
     self._offroad_label = UnifiedLabel("start the car to\nuse sunnypilot", 54, FontWeight.DISPLAY,
                                        text_color=rl.Color(255, 255, 255, int(255 * 0.9)),
-                                       alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                       alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE)
+                                       alignment=TextAlignment.CENTER,
+                                       alignment_vertical=TextAlignmentVertical.MIDDLE)
 
     self._fade_texture = gui_app.texture("icons_mici/onroad/onroad_fade.png")
 

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

@@ -4,7 +4,7 @@ from openpilot.cereal.visionipc import VisionStreamType
 from openpilot.selfdrive.ui.mici.onroad.cameraview import CameraView
 from openpilot.selfdrive.ui.mici.onroad.driver_state import DriverStateRenderer
 from openpilot.selfdrive.ui.ui_state import ui_state, device
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.nav_widget import NavWidget
@@ -76,7 +76,7 @@ class BaseCabinCameraDialog(Widget):
 
     if not self._camera_view.frame:
       gui_label(rect, tr("camera starting"), font_size=54, font_weight=FontWeight.BOLD,
-                alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER)
+                alignment=TextAlignment.CENTER)
       rl.end_scissor_mode()
       self._publish_alert_sound(None)
       return
@@ -124,12 +124,12 @@ class BaseCabinCameraDialog(Widget):
     awareness_pct = dm_state.visionPolicyState.awarenessPercent if is_vision else dm_state.wheeltouchPolicyState.awarenessPercent
     gui_label(rl.Rectangle(rect.x + 2, rect.y + 2, rect.width, rect.height),
               f"Awareness: {awareness_pct:.0f}%", font_size=44, font_weight=FontWeight.MEDIUM,
-              alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+              alignment=TextAlignment.RIGHT,
+              alignment_vertical=TextAlignmentVertical.TOP,
               color=rl.Color(0, 0, 0, 180))
     gui_label(rect, f"Awareness: {awareness_pct:.0f}%", font_size=44, font_weight=FontWeight.MEDIUM,
-              alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+              alignment=TextAlignment.RIGHT,
+              alignment_vertical=TextAlignmentVertical.TOP,
               color=rl.Color(255, 255, 255, int(255 * 0.9)))
 
     if dm_state.alertLevel == log.DriverMonitoringState.AlertLevel.none:
@@ -137,16 +137,16 @@ class BaseCabinCameraDialog(Widget):
 
     # Show alert level
     alert_level_str = f"{'Pay Attention' if is_vision else 'Touch Wheel'} - level {dm_state.alertLevel}"
-    alignment = rl.GuiTextAlignment.TEXT_ALIGN_RIGHT if self.driver_state_renderer.is_rhd else rl.GuiTextAlignment.TEXT_ALIGN_LEFT
+    alignment = TextAlignment.RIGHT if self.driver_state_renderer.is_rhd else TextAlignment.LEFT
 
     shadow_rect = rl.Rectangle(rect.x + 2, rect.y + 2, rect.width, rect.height)
     gui_label(shadow_rect, alert_level_str, font_size=40, font_weight=FontWeight.BOLD,
               alignment=alignment,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM,
+              alignment_vertical=TextAlignmentVertical.BOTTOM,
               color=rl.Color(0, 0, 0, 180))
     gui_label(rect, alert_level_str, font_size=40, font_weight=FontWeight.BOLD,
               alignment=alignment,
-              alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM,
+              alignment_vertical=TextAlignmentVertical.BOTTOM,
               color=rl.Color(255, 255, 255, int(255 * 0.9)))
 
   def _load_eye_textures(self):

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

@@ -3,7 +3,7 @@ import pyray as rl
 from dataclasses import dataclass
 from openpilot.common.constants import CV
 from openpilot.selfdrive.ui.mici.onroad.torque_bar import TorqueBar
-from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
+from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus, ChestnutState
 from openpilot.system.ui.lib.application import gui_app, FontWeight
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.text_measure import measure_text_cached
@@ -107,8 +107,7 @@ class HudRenderer(Widget):
     self.speed: float = 0.0
     self.v_ego_cluster_seen: bool = False
     self._engaged: bool = False
-    self._small_model_engaged: bool = False
-    self._egpu_fade_time: float = 0
+    self._chestnut_fade_time: float = 0
 
     self._can_draw_top_icons = True
     self._show_wheel_critical = False
@@ -124,17 +123,15 @@ class HudRenderer(Widget):
     self._txt_wheel: rl.Texture = gui_app.texture('icons_mici/wheel.png', 50, 50)
     self._txt_wheel_critical: rl.Texture = gui_app.texture('icons_mici/wheel_critical.png', 50, 50)
     self._txt_exclamation_point: rl.Texture = gui_app.texture('icons_mici/exclamation_point.png', 9, 44)
-    self._txt_egpu: rl.Texture = gui_app.texture('icons_mici/egpu.png', 60, 44)
-    self._txt_egpu_green: rl.Texture = gui_app.texture('icons_mici/egpu_green.png', 60, 44)
-    self._txt_egpu_orange: rl.Texture = gui_app.texture('icons_mici/egpu_orange.png', 60, 44)
-    self._txt_egpu_crossed: rl.Texture = gui_app.texture('icons_mici/egpu_crossed.png', 60, 52)
-    self._egpu_icon: rl.Texture | None = None
-
+    self._txt_chestnut: rl.Texture = gui_app.texture('icons_mici/chestnut.png', 60, 44)
+    self._txt_chestnut_green: rl.Texture = gui_app.texture('icons_mici/chestnut_green.png', 60, 44)
+    self._txt_chestnut_orange: rl.Texture = gui_app.texture('icons_mici/chestnut_orange.png', 75, 44)
+    self._chestnut_icon: rl.Texture | None = None
     self._wheel_alpha_filter = FirstOrderFilter(0, 0.05, 1 / gui_app.target_fps)
     self._wheel_y_filter = FirstOrderFilter(0, 0.1, 1 / gui_app.target_fps)
 
     self._set_speed_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
-    self._egpu_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
+    self._chestnut_alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
 
   def set_wheel_critical_icon(self, critical: bool):
     """Set the wheel icon to critical or normal state."""
@@ -165,13 +162,10 @@ class HudRenderer(Widget):
       controls_state.deprecated.vCruise if v_cruise_cluster == 0.0 else v_cruise_cluster
     )
     engaged = sm['selfdriveState'].enabled
-    if (engaged and not self._engaged and not ui_state.usbgpu_loading and ui_state.usbgpu_active is not True and
-        ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame):
-      self._small_model_engaged = True
-    if engaged != self._engaged:
-      self._egpu_fade_time = rl.get_time() if engaged else 0
     if (set_speed != self.set_speed and engaged) or (engaged and not self._engaged):
       self._set_speed_changed_time = rl.get_time()
+    if engaged != self._engaged:
+      self._chestnut_fade_time = rl.get_time() if engaged else 0
     self._engaged = engaged
     self.set_speed = set_speed
     self.is_cruise_set = 0 < self.set_speed < SET_SPEED_NA
@@ -191,8 +185,7 @@ class HudRenderer(Widget):
     if self.is_cruise_set:
       self._draw_set_speed(rect)
 
-    if ui_state.usbgpu and ui_state.usbgpu_compiled:
-      self._draw_model_source(rect)
+    self._draw_model_source(rect)
 
     self._draw_steering_wheel(rect)
 
@@ -200,30 +193,24 @@ class HudRenderer(Widget):
     if ui_state.sm.recv_frame['selfdriveState'] < ui_state.started_frame:
       return
 
-    big_failed = (ui_state.usbgpu_active is False or not ui_state.sm['deviceState'].chestnutPresent or
-                  (ui_state.usbgpu_active is True and ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame and
-                   not ui_state.sm.alive['modelV2']) or
-                  (ui_state.usbgpu_active is None and ui_state.sm.recv_frame['modelV2'] > ui_state.started_frame))
-    self._small_model_engaged &= big_failed
-    loading = ui_state.usbgpu_loading or (ui_state.usbgpu_active is None and not big_failed)
+    loading = ui_state.chestnut_state == ChestnutState.LOADING
     if loading:
-      pulse = 0.5 - 0.5 * math.cos(rl.get_time() * 6.0)
-      icon = self._txt_egpu
-      opacity = 0.35 + 0.65 * pulse
-    elif self._small_model_engaged:
-      icon = self._txt_egpu_crossed
-      opacity = 0.65
-    elif big_failed:
-      icon = self._txt_egpu_orange
+      icon = self._txt_chestnut
+      opacity = 0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0))
+    elif ui_state.chestnut_state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED):
+      icon = self._txt_chestnut_orange
+      opacity = 1.0
+    elif ui_state.chestnut_state == ChestnutState.ACTIVE:
+      icon = self._txt_chestnut_green
       opacity = 1.0
     else:
-      icon = self._txt_egpu_green
-      opacity = 1.0
+      return
 
-    if icon is not self._egpu_icon:
-      self._egpu_fade_time = rl.get_time()
-      self._egpu_icon = icon
-    alpha = self._egpu_alpha_filter.update(loading or 0 < rl.get_time() - self._egpu_fade_time < SET_SPEED_PERSISTENCE)
+    if icon is not self._chestnut_icon:
+      self._chestnut_fade_time = rl.get_time()
+      self._chestnut_icon = icon
+    visible = loading or rl.get_time() - self._chestnut_fade_time < SET_SPEED_PERSISTENCE
+    alpha = self._chestnut_alpha_filter.update(visible)
     if alpha < 1e-2:
       return
 

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

@@ -6,7 +6,7 @@ from collections.abc import Callable
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.scroller import DO_ZOOM
-from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos
+from openpilot.system.ui.lib.application import gui_app, FontWeight, MousePos, TextAlignmentVertical
 from openpilot.common.filter_simple import BounceFilter
 
 if TYPE_CHECKING:
@@ -125,10 +125,10 @@ class BigButton(Widget):
     self._rotate_icon_t: float | None = None
 
     self._label = UnifiedLabel(text, font_size=self._get_label_font_size(), font_weight=FontWeight.BOLD,
-                               text_color=LABEL_COLOR, alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM, scroll=scroll,
+                               text_color=LABEL_COLOR, alignment_vertical=TextAlignmentVertical.BOTTOM, scroll=scroll,
                                line_height=0.9)
     self._sub_label = UnifiedLabel(value, font_size=COMPLICATION_SIZE, font_weight=FontWeight.ROMAN,
-                                   text_color=COMPLICATION_GREY, alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+                                   text_color=COMPLICATION_GREY, alignment_vertical=TextAlignmentVertical.BOTTOM)
     self._update_label_layout()
 
     self._load_images()
@@ -149,11 +149,15 @@ class BigButton(Widget):
   def set_touch_valid_callback(self, touch_callback: Callable[[], bool]) -> None:
     super().set_touch_valid_callback(lambda: touch_callback() and self._grow_animation_until is None)
 
-  def _width_hint(self) -> int:
-    # A value moves the title to the top, where it shares space with the icon.
+  def _title_width_hint(self) -> int:
+    # A value moves the title to the top, where it shares space with the icon
     icon_size = self._txt_icon.width if self._txt_icon and self.value else 0
     return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2 - icon_size)
 
+  def _subtitle_width_hint(self) -> int:
+    # Bottom aligned, so it sits below the icon
+    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2)
+
   def _get_label_font_size(self):
     if len(self.text) <= 18:
       return 48
@@ -163,9 +167,9 @@ class BigButton(Widget):
   def _update_label_layout(self):
     self._label.set_font_size(self._get_label_font_size())
     if self.value:
-      self._label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
+      self._label.set_alignment_vertical(TextAlignmentVertical.TOP)
     else:
-      self._label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+      self._label.set_alignment_vertical(TextAlignmentVertical.BOTTOM)
 
   def set_text(self, text: str):
     self.text = text
@@ -228,14 +232,14 @@ class BigButton(Widget):
 
     label_color = LABEL_COLOR if self.enabled else rl.Color(255, 255, 255, int(255 * 0.35))
     self._label.set_color(label_color)
-    label_rect = rl.Rectangle(label_x, btn_y + self.LABEL_VERTICAL_PADDING, self._width_hint(),
+    label_rect = rl.Rectangle(label_x, btn_y + self.LABEL_VERTICAL_PADDING, self._title_width_hint(),
                               self._rect.height - self.LABEL_VERTICAL_PADDING * 2)
     self._label.render(label_rect)
 
     if self.value:
-      label_y = btn_y + self.LABEL_VERTICAL_PADDING + self._label.get_content_height(self._width_hint())
+      label_y = label_rect.y + self._label.get_content_height(int(label_rect.width))
       sub_label_height = btn_y + self._rect.height - self.LABEL_VERTICAL_PADDING - label_y
-      sub_label_rect = rl.Rectangle(label_x, label_y, self._width_hint(), sub_label_height)
+      sub_label_rect = rl.Rectangle(label_x, label_y, self._subtitle_width_hint(), sub_label_height)
       self._sub_label.render(sub_label_rect)
 
     # ICON -------------------------------------------------------------------
@@ -312,9 +316,6 @@ class BigMultiToggle(BigToggle):
 
     self.set_value(self._options[0])
 
-  def _width_hint(self) -> int:
-    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2 - self._txt_enabled_toggle.width)
-
   def _handle_mouse_release(self, mouse_pos: MousePos):
     super()._handle_mouse_release(mouse_pos)
     cur_idx = self._options.index(self.value)
@@ -355,17 +356,14 @@ class GreyBigButton(BigButton):
     self._sub_label.set_font_size(36)
     self._sub_label.set_text_color(rl.Color(255, 255, 255, int(255 * 0.9)))
     self._sub_label.set_font_weight(FontWeight.DISPLAY_REGULAR)
-    self._sub_label.set_alignment_vertical(rl.GuiTextAlignmentVertical.TEXT_ALIGN_MIDDLE if not self._label.text else
-                                           rl.GuiTextAlignmentVertical.TEXT_ALIGN_BOTTOM)
+    self._sub_label.set_alignment_vertical(TextAlignmentVertical.MIDDLE if not self._label.text else
+                                           TextAlignmentVertical.BOTTOM)
     self._sub_label.set_line_height(0.95)
 
   @property
   def LABEL_VERTICAL_PADDING(self):
     return BigButton.LABEL_VERTICAL_PADDING if self._label.text else 18
 
-  def _width_hint(self) -> int:
-    return int(self._rect.width - self.LABEL_HORIZONTAL_PADDING * 2)
-
   def _get_label_font_size(self):
     return 36
 

openpilot/selfdrive/ui/onroad/alert_renderer.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 from openpilot.cereal import messaging, log
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.common.hardware import COMMA_HARDWARE
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.text_measure import measure_text_cached
 from openpilot.system.ui.widgets import Widget
@@ -76,10 +76,10 @@ class AlertRenderer(Widget):
     self.font_bold: rl.Font = gui_app.font(FontWeight.BOLD)
 
     # font size is set dynamically
-    self._full_text1_label = Label("", font_size=0, font_weight=FontWeight.BOLD, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                   text_alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
-    self._full_text2_label = Label("", font_size=ALERT_FONT_BIG, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-                                   text_alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP)
+    self._full_text1_label = Label("", font_size=0, font_weight=FontWeight.BOLD, text_alignment=TextAlignment.CENTER,
+                                   text_alignment_vertical=TextAlignmentVertical.TOP)
+    self._full_text2_label = Label("", font_size=ALERT_FONT_BIG, text_alignment=TextAlignment.CENTER,
+                                   text_alignment_vertical=TextAlignmentVertical.TOP)
 
   def get_alert(self, sm: messaging.SubMaster) -> Alert | None:
     """Generate the current alert based on selfdrive state."""

openpilot/selfdrive/ui/onroad/cabin_camera_dialog.py

@@ -4,7 +4,7 @@ from openpilot.cereal.visionipc import VisionStreamType
 from openpilot.selfdrive.ui.onroad.cameraview import CameraView
 from openpilot.selfdrive.ui.onroad.driver_state import DriverStateRenderer
 from openpilot.selfdrive.ui.ui_state import ui_state, device
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets.label import gui_label
 
@@ -38,7 +38,7 @@ class CabinCameraDialog(CameraView):
         tr("camera starting"),
         font_size=100,
         font_weight=FontWeight.BOLD,
-        alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
+        alignment=TextAlignment.CENTER,
       )
       return -1
 

openpilot/selfdrive/ui/onroad/model_renderer.py

@@ -192,7 +192,7 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
 
     max_idx = self._get_path_length_idx(path_x_array, max_distance)
     self._path.projected_points = self._map_line_to_polygon(
-      self._path.raw_points, 0.9, self._path_offset_z, max_idx, max_distance, allow_invert=False
+      self._path.raw_points, self._get_path_half_width(), self._path_offset_z, max_idx, max_distance, allow_invert=False
     )
 
     self._update_experimental_gradient()
@@ -292,7 +292,7 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     allow_throttle = sm['longitudinalPlan'].allowThrottle or not self._longitudinal_control
     self._blend_filter.update(int(allow_throttle))
 
-    if ui_state.rainbow_path:
+    if ui_state.rainbow_path and self._lateral_active:
       self.rainbow_path.draw_rainbow_path(self._rect, self._path)
       return
 

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

@@ -6,7 +6,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 from openpilot.selfdrive.ui.layouts.home import HomeLayout, HomeLayoutState, HEAD_BUTTON_FONT_SIZE, SPACING
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment
 from openpilot.system.ui.lib.text_measure import measure_text_cached
 from openpilot.system.ui.lib.multilang import tr, trn
 from openpilot.system.ui.widgets.label import gui_label
@@ -59,7 +59,7 @@ class HomeLayoutSP(HomeLayout):
       desc_size = measure_text_cached(gui_app.font(FontWeight.NORMAL), description, BRAND_FONT_SIZE)
       desc_width = desc_size.x
       desc_rect = rl.Rectangle(version_right - desc_width, self.header_rect.y, desc_width, self.header_rect.height)
-      gui_label(desc_rect, description, BRAND_FONT_SIZE, rl.WHITE, alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+      gui_label(desc_rect, description, BRAND_FONT_SIZE, rl.WHITE, alignment=TextAlignment.RIGHT)
 
     brand_size = measure_text_cached(gui_app.font(FontWeight.AUDIOWIDE), brand, BRAND_FONT_SIZE)
     spacing = BRAND_DESC_SPACING if description else 0

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

@@ -6,7 +6,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.system.ui.lib.application import FontWeight
+from openpilot.system.ui.lib.application import FontWeight, TextAlignment
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.button import Button, ButtonStyle
@@ -20,7 +20,7 @@ class SunnylinkConsentPage(Widget):
     self._done_callback = done_callback
     self._step = 0
 
-    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.AUDIOWIDE, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
+    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.AUDIOWIDE, text_alignment=TextAlignment.LEFT))
 
     self._content = [
       {
@@ -43,7 +43,7 @@ class SunnylinkConsentPage(Widget):
     self._primary_btn = self._child(Button("", button_style=ButtonStyle.PRIMARY, click_callback=lambda: self._handle_choice("enable")))
     self._secondary_btn = self._child(Button("", button_style=ButtonStyle.NORMAL, click_callback=lambda: self._handle_choice("secondary")))
     self._danger_btn = self._child(Button("", button_style=ButtonStyle.DANGER, click_callback=lambda: self._handle_choice("disable")))
-    self._desc = self._child(Label("", font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
+    self._desc = self._child(Label("", font_size=90, font_weight=FontWeight.MEDIUM, text_alignment=TextAlignment.LEFT))
 
   def _handle_choice(self, choice):
     if choice == "enable":

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

@@ -4,15 +4,16 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
-import os
 import re
 import time
 import pyray as rl
 
 from openpilot.cereal import custom
-from openpilot.sunnypilot.models.default_model import DEFAULT_MODEL
+from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_selected_bundle, resolve_bundle_by_ref
 from openpilot.common.constants import CV
 from openpilot.selfdrive.ui.ui_state import device, ui_state
+from openpilot.selfdrive.ui.sunnypilot.model_info import (big_model_state, bundles_for_source, carrying_model, default_model_name,
+                                                           model_cache_size_mb, queued_name, refresh_in_progress, refresh_model_list)
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.lib.application import gui_app
 from openpilot.system.ui.widgets import DialogResult, Widget
@@ -20,11 +21,10 @@ from openpilot.system.ui.widgets.confirm_dialog import alert_dialog, ConfirmDial
 from openpilot.system.ui.widgets.scroller_tici import Scroller
 from openpilot.system.ui.widgets.toggle import ON_COLOR
 
-from openpilot.sunnypilot.models.runners.constants import CUSTOM_MODEL_PATH
 from openpilot.system.ui.sunnypilot.lib.styles import style
-from openpilot.system.ui.sunnypilot.lib.utils import NoElideButtonAction
+from openpilot.system.ui.sunnypilot.lib.utils import NoElideButtonAction, ScrollingButtonAction
 from openpilot.system.ui.sunnypilot.widgets.list_view import ListItemSP, toggle_item_sp, option_item_sp
-from openpilot.system.ui.sunnypilot.widgets.progress_bar import progress_item
+from openpilot.system.ui.sunnypilot.widgets.download_status import download_status_item
 from openpilot.system.ui.sunnypilot.widgets.tree_dialog import TreeOptionDialog, TreeNode, TreeFolder
 
 if gui_app.sunnypilot_ui():
@@ -35,9 +35,14 @@ class ModelsLayout(Widget):
   def __init__(self):
     super().__init__()
     self.model_manager = None
-    self.download_status = None
-    self.prev_download_status = None
     self.model_dialog = None
+    self._selection_source = None
+    self._downloading = False
+    self._verifying = False
+    self._clearing = False
+    self._refreshing = False
+    self._refresh_start: float | None = None
+    self._last_note = None
     self.last_cache_calc_time = 0
 
     self._initialize_items()
@@ -49,31 +54,35 @@ class ModelsLayout(Widget):
     self._scroller = Scroller(self.items, line_separator=True, spacing=0)
 
   def _initialize_items(self):
-    self.current_model_item = ListItemSP(
-      title=tr("Current Model"),
+    self.small_model_item = ListItemSP(
+      title=tr("Small Model"),
       description="",
-      action_item=NoElideButtonAction(tr("SELECT")),
-      callback=self._handle_current_model_clicked
+      action_item=ScrollingButtonAction(tr("SELECT")),
+      callback=lambda: self._open_source_dialog("qcom")
     )
 
-    self.supercombo_label = progress_item(tr("Driving Model"))
-    self.vision_label = progress_item(tr("Vision Model"))
-    self.policy_label = progress_item(tr("Policy Model"))
-    self.off_policy_label = progress_item(tr("Off-Policy Model"))
-    self.on_policy_label = progress_item(tr("On-Policy Model"))
+    self.big_model_item = ListItemSP(
+      title=tr("Big Model"),
+      action_item=ScrollingButtonAction(tr("SELECT")),
+      callback=lambda: self._open_source_dialog("chestnut")
+    )
 
-    self.refresh_item = button_item(tr("Refresh Model List"), tr("REFRESH"), "",
-                                    lambda: (ui_state.params.put("ModelManager_LastSyncTime", 0),
-                                             gui_app.push_widget(alert_dialog(tr("Fetching Latest Models")))))
+    self.download_item = download_status_item(lambda: tr("Download") if self._downloading else tr("Model Status"))
+
+    self.refresh_item = button_item(tr("Refresh Model List"),
+                                    lambda: tr("FETCHING...") if self._refreshing else tr("REFRESH"), "",
+                                    self._refresh_models)
 
     self.clear_cache_item = ListItemSP(
       title=tr("Clear Model Cache"),
       description="",
-      action_item=NoElideButtonAction(tr("CLEAR")),
+      action_item=NoElideButtonAction(lambda: tr("CLEARING...") if self._clearing else tr("CLEAR")),
       callback=self._clear_cache
     )
 
-    self.cancel_download_item = button_item(tr("Cancel Download"), tr("Cancel"), "", lambda: ui_state.params.remove("ModelManager_DownloadIndex"))
+    self.cancel_download_item = button_item(lambda: tr("Cancel Verification") if self._verifying else tr("Cancel Download"),
+                                            tr("Cancel"), "",
+                                            lambda: ui_state.params.remove("ModelManager_DownloadRef"))
 
     self.lane_turn_value_control = option_item_sp(tr("Adjust Lane Turn Speed"), "LaneTurnValue", 500, 2000,
                                                   tr("Set the maximum speed for lane turn desires. Default is 19 mph."),
@@ -98,8 +107,7 @@ class ModelsLayout(Widget):
                                         1, None, True, "", style.BUTTON_ACTION_WIDTH, None, True,
                                         lambda v: f"{v / 100:.2f} m")
 
-    self.items = [self.current_model_item, self.cancel_download_item, self.supercombo_label, self.vision_label,
-                  self.policy_label, self.off_policy_label, self.on_policy_label, self.refresh_item, self.clear_cache_item,
+    self.items = [self.small_model_item, self.big_model_item, self.cancel_download_item, self.download_item, self.refresh_item, self.clear_cache_item,
                   self.lane_turn_desire_toggle, self.lane_turn_value_control, self.lagd_toggle, self.delay_control, self.camera_offset]
 
   def _update_lagd_description(self, lagd_toggle: bool):
@@ -108,136 +116,204 @@ class ModelsLayout(Widget):
     if lagd_toggle:
       desc += f"<br>{tr('Live Steer Delay:')} {ui_state.sm['lateralDelay'].lateralDelay:.3f} s"
     elif ui_state.CP is not None:
-      sw = float(ui_state.params.get("LagdToggleDelay", "0.2"))
+      sw = float(ui_state.params.get("LagdToggleDelay", return_default=True))
       cp = ui_state.CP.steerActuatorDelay
       desc += f"<br>{tr('Actuator Delay:')} {cp:.2f} s + {tr('Software Delay:')} {sw:.2f} s = {tr('Total Delay:')} {cp + sw:.2f} s"
     self.lagd_toggle.set_description(desc)
 
-  def _is_downloading(self):
-    return (self.model_manager and self.model_manager.selectedBundle and
-            self.model_manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.downloading)
-
   @staticmethod
   def calculate_cache_size():
-    cache_size = 0.0
-    if os.path.exists(CUSTOM_MODEL_PATH):
-      cache_size = sum(os.path.getsize(os.path.join(CUSTOM_MODEL_PATH, file)) for file in os.listdir(CUSTOM_MODEL_PATH)) / (1024**2)
-    return cache_size
+    return model_cache_size_mb()
 
   def _clear_cache(self):
     def _callback(response):
       if response == DialogResult.CONFIRM:
         ui_state.params.put_bool("ModelManager_ClearCache", True)
-        self.clear_cache_item.action_item.set_value(f"{self.calculate_cache_size():.2f} MB")
 
     dialog = ConfirmDialog(tr("This will delete ALL downloaded models from the cache except the currently active model. Are you sure?"),
                            tr("Clear Cache"), callback=_callback)
     gui_app.push_widget(dialog)
 
+  def _refresh_models(self):
+    refresh_model_list()
+    self._refresh_start = time.monotonic()
+
   def _handle_bundle_download_progress(self):
-    labels = {custom.ModelManagerSP.Model.Type.supercombo: self.supercombo_label,
-              custom.ModelManagerSP.Model.Type.vision: self.vision_label,
-              custom.ModelManagerSP.Model.Type.policy: self.policy_label,
-              custom.ModelManagerSP.Model.Type.offPolicy: self.off_policy_label,
-              custom.ModelManagerSP.Model.Type.onPolicy: self.on_policy_label}
-    for label in labels.values():
-      label.set_visible(False)
     self.cancel_download_item.set_visible(False)
+    self._downloading = False
+    self._verifying = False
+    self.download_item.set_visible(True)
 
-    if not self.model_manager or (not self.model_manager.selectedBundle and not self.model_manager.activeBundle):
-      return
-
-    bundle = self.model_manager.selectedBundle if self._is_downloading() or (
-      self.model_manager.selectedBundle and self.model_manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.failed
-    ) else self.model_manager.activeBundle
-    if not bundle:
-      return
-
-    self.download_status = bundle.status
-    status_changed = self.prev_download_status != self.download_status
-    self.prev_download_status = self.download_status
-
-    self.cancel_download_item.set_visible(bool(self.model_manager.selectedBundle) and ui_state.params.get("ModelManager_DownloadIndex") is not None)
-
-    if (current_time := time.monotonic()) - self.last_cache_calc_time > 0.5:
+    self._clearing = ui_state.params.get_bool("ModelManager_ClearCache")
+    if self._clearing:
+      self.last_cache_calc_time = 0.0  # refresh the size as soon as clearing finishes
+    elif (current_time := time.monotonic()) - self.last_cache_calc_time > 0.5:
       self.last_cache_calc_time = current_time
       self.clear_cache_item.action_item.set_value(f"{self.calculate_cache_size():.2f} MB")
 
-    if self.download_status == custom.ModelManagerSP.DownloadStatus.downloading:
+    bundle = self.model_manager.selectedBundle if self.model_manager else None
+    progresses = [model.artifact.downloadProgress for model in bundle.models if model.artifact.fileName] if bundle else []
+    if not progresses or bundle.status not in (custom.ModelManagerSP.DownloadStatus.downloading,
+                                               custom.ModelManagerSP.DownloadStatus.failed):
+      self.download_item.action_item.update(name="", segments=self._slot_segments())
+      return
+
+    self.cancel_download_item.set_visible(ui_state.params.get("ModelManager_DownloadRef") is not None)
+    if bundle.status == custom.ModelManagerSP.DownloadStatus.downloading:
       device._reset_interactive_timeout()
 
-    for model in bundle.models:
-      if label := labels.get(getattr(model.type, 'raw', model.type)):
-        label.set_visible(True)
-        p = model.artifact.downloadProgress
-        text, show, color = f"pending - {bundle.displayName}", False, rl.GRAY
-        if p.status == custom.ModelManagerSP.DownloadStatus.downloading:
-          text, show = f"{int(p.progress)}% - {bundle.displayName}", True
-        elif p.status in (custom.ModelManagerSP.DownloadStatus.downloaded, custom.ModelManagerSP.DownloadStatus.cached):
-          status_text = tr("from cache" if p.status == custom.ModelManagerSP.DownloadStatus.cached else "downloaded")
-          text, color = f"{bundle.displayName} - {status_text if status_changed else tr('ready')}", ON_COLOR
-        elif p.status == custom.ModelManagerSP.DownloadStatus.failed:
-          text, color = f"download failed - {bundle.displayName}", rl.RED
-        label.action_item.update(p.progress, text, show, color)
+    state = self._download_row_state(progresses, bundle.internalName)
+    if queued := queued_name(bundle.ref):
+      state["name"] += f"  |  {queued} {tr('queued')}"
+    self.download_item.action_item.update(**state)
+    self._downloading = self.download_item.action_item.downloading
+    ds = custom.ModelManagerSP.DownloadStatus
+    self._verifying = any(getattr(p.status, 'raw', p.status) == ds.verifying for p in progresses)
+
+  def _slot_segments(self):
+    """small and big slots side by side; green marks the slot whose pick is actually
+    driving (runner-matched, so a failed Default big greens neither slot), an empty
+    slot shows its default."""
+    big_state = big_model_state()
+    carry_source, carry_internal, _ = carrying_model()
+    segments = []
+    for source, label in (("qcom", tr("small")), ("chestnut", tr("big"))):
+      if segments:
+        segments.append(("|", rl.GRAY, None, None))
+      bundle = get_selected_bundle(ui_state.params, source)
+      name = bundle.internalName if bundle else default_model_name(source)
+      color = ON_COLOR if (source == carry_source and name == carry_internal) else rl.LIGHTGRAY
+      name = "● " + name
+      if source == "chestnut":
+        if big_state == 'failed':
+          color = rl.RED
+        elif big_state == 'loading':
+          color = rl.GOLD
+      segments.append((label, rl.GRAY, None, None))
+      segments.append((name, color, None, None))
+    return segments
 
   @staticmethod
-  def _show_reset_params_dialog():
-    def _callback(response):
-      if response == DialogResult.CONFIRM:
-        ui_state.params.remove("CalibrationParams")
-        ui_state.params.remove("LiveTorqueParameters")
-    msg = tr("Model download has started in the background. We suggest resetting calibration. Would you like to do that now?")
-    dialog = ConfirmDialog(msg, tr("Reset Calibration"), callback=_callback)
-    gui_app.push_widget(dialog)
+  def _set_item_note(item, text):
+    # a description renders only while shown; hide before clearing or the
+    # empty description keeps its visible state
+    if text:
+      item.set_description(text)
+      item.show_description(True)
+    else:
+      item.show_description(False)
+      item.set_description("")
+
+  def _status_note(self) -> str:
+    """The failover story for the Model Status row. One-way big -> small, and the
+    fallback is runner-matched: a Default big can only fall back to the Default
+    small (stock modeld), a custom big has no automatic fallback yet."""
+    if not ui_state.chestnut_present:
+      return ""
+    big_bundle = get_selected_bundle(ui_state.params, "chestnut")
+    big_name = big_bundle.internalName if big_bundle else default_model_name("chestnut")
+    big_is_default = big_bundle is None
+    fallback_name = default_model_name("qcom")
+    state = big_model_state()
+    if state == 'failed':
+      if big_is_default:
+        return tr("Big model unavailable, {} is driving until the next drive.").format(fallback_name)
+      return tr("Big model unavailable until the next drive.")
+    if state == 'loading':
+      if big_is_default:
+        return tr("{} drives until the big model is ready.").format(fallback_name)
+      return tr("Getting the big model ready.")
+    if big_is_default:
+      return tr("{} will drive. If it fails during a drive, {} takes over until the next drive.").format(big_name, fallback_name)
+    return tr("{} will drive when the chestnut is ready.").format(big_name)
+
+  @staticmethod
+  def _download_row_state(progresses, name: str) -> dict:
+    """Maps a bundle's artifact progress to DownloadStatusAction.update kwargs."""
+    # .raw: _DynamicEnum equals its int but does not hash like it
+    statuses = {getattr(p.status, 'raw', p.status) for p in progresses}
+    progress = sum(p.progress for p in progresses) / len(progresses)
+    ds = custom.ModelManagerSP.DownloadStatus
+
+    if ds.failed in statuses:
+      # close.png is authored black and a tint cannot lift it, hence close2
+      return {"name": name, "status_text": tr("download failed"), "text_color": rl.RED, "icon": "icons/close2.png"}
+    if ds.verifying in statuses:
+      return {"name": name, "downloading": True, "progress": progress, "status_text": tr("verifying")}
+    if ds.downloading in statuses:
+      return {"name": name, "downloading": True, "progress": progress}
+    if statuses <= {ds.downloaded, ds.cached}:
+      return {"name": name, "text_color": ON_COLOR, "icon": "icons/checkmark.png"}
+    # circled_slash is authored grey; tinting it again only darkens it
+    return {"name": name, "text_color": rl.GRAY, "icon": "icons/circled_slash.png", "icon_color": rl.WHITE}
 
   def _on_model_selected(self, result):
     if result != DialogResult.CONFIRM:
+      self.model_dialog = None
       return
     selected_ref = self.model_dialog.selection_ref
-    if selected_ref == "Default":
-      ui_state.params.remove("ModelManager_ActiveBundle")
-      self._show_reset_params_dialog()
-    elif selected_bundle := next((bundle for bundle in self.model_manager.availableBundles if bundle.ref == selected_ref), None):
-      ui_state.params.put("ModelManager_DownloadIndex", selected_bundle.index)
-      if self.model_manager.activeBundle and selected_bundle.generation != self.model_manager.activeBundle.generation:
-        self._show_reset_params_dialog()
     self.model_dialog = None
+    if selected_ref == "Default":
+      if self._selection_source in ACTIVE_BUNDLE_KEYS:
+        ui_state.params.remove(ACTIVE_BUNDLE_KEYS[self._selection_source])
+      return
+    if selected_bundle := self._resolve_selected_bundle(selected_ref):
+      ui_state.params.put("ModelManager_DownloadRef", selected_bundle.ref)
+
+  def _resolve_selected_bundle(self, ref):
+    source_bundles = {source: bundles_for_source(source) for source in ("qcom", "chestnut")}
+    resolved = resolve_bundle_by_ref(ref, source_bundles)
+    return resolved[0] if resolved else None
 
   @staticmethod
   def _bundle_to_node(bundle):
     return TreeNode(bundle.ref, {'display_name': bundle.displayName, 'short_name': bundle.internalName})
 
-  def _get_folders(self, favorites):
-    bundles = self.model_manager.availableBundles
+  def _get_folders(self, favorites, bundles):
     folders = {}
     for bundle in bundles:
       folders.setdefault(next((ov_ride.value for ov_ride in bundle.overrides if ov_ride.key == "folder"), ""), []).append(bundle)
 
-    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': f"{DEFAULT_MODEL} (Default)", 'short_name': "Default"})])]
+    folders_list = []
     for folder, folder_bundles in sorted(folders.items(), key=lambda x: max((bundle.index for bundle in x[1]), default=-1), reverse=True):
       folder_bundles.sort(key=lambda bundle: bundle.index, reverse=True)
       name = folder + (f" - (Updated: {m.group(1)})" if folder_bundles and (m := re.search(r'\(([^)]*)\)[^(]*$', folder_bundles[0].displayName)) else "")
       folders_list.append(TreeFolder(name, [self._bundle_to_node(bundle) for bundle in folder_bundles]))
 
     if favorites and (fav_bundles := [bundle for bundle in bundles if bundle.ref in favorites]):
-      folders_list.insert(1, TreeFolder("Favorites", [self._bundle_to_node(bundle) for bundle in fav_bundles]))
+      folders_list.insert(0, TreeFolder("Favorites", [self._bundle_to_node(bundle) for bundle in fav_bundles]))
     return folders_list
 
-  def _handle_current_model_clicked(self):
+  def _open_source_dialog(self, source):
+    self._selection_source = source
     favs = ui_state.params.get("ModelManager_Favs")
     favorites = set(favs.split(';')) if favs else set()
-    folders_list = self._get_folders(favorites)
-
-    active_ref = self.model_manager.activeBundle.ref if self.model_manager.activeBundle else "Default"
-    self.model_dialog = TreeOptionDialog(tr("Select a Model"), folders_list, active_ref, "ModelManager_Favs",
-                                         get_folders_fn=self._get_folders, on_exit=self._on_model_selected)
+    folders_list = self._source_folders(favorites, source)
+    if not folders_list:
+      gui_app.push_widget(alert_dialog(tr("No models are available for this hardware yet. Connect to the internet and refresh the model list.")))
+      return
+    self.model_dialog = TreeOptionDialog(tr("Select a Model"), folders_list, self._slot_active_ref(source), "ModelManager_Favs",
+                                         get_folders_fn=lambda favs: self._source_folders(favs, source), on_exit=self._on_model_selected)
     gui_app.push_widget(self.model_dialog)
 
+  def _source_folders(self, favorites, source):
+    bundles = bundles_for_source(source)
+    if not bundles:
+      return []
+    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': default_model_name(source)})])]
+    folders_list.extend(self._get_folders(favorites, bundles))
+    return folders_list
+
+  @staticmethod
+  def _slot_active_ref(source: str) -> str:
+    bundle = get_selected_bundle(ui_state.params, source)
+    return bundle.ref if bundle else "Default"
+
   def _update_state(self):
     advanced_controls: bool = ui_state.params.get_bool("ShowAdvancedControls")
     turn_desire: bool = ui_state.params.get_bool("LaneTurnDesire")
     live_delay: bool = ui_state.params.get_bool("LagdToggle")
-    camera_offset: bool = ui_state.params.get("ModelManager_ActiveBundle") is not None
+    camera_offset: bool = ui_state.active_bundle is not None
 
     self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
     self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
@@ -251,18 +327,34 @@ class ModelsLayout(Widget):
     self._update_lagd_description(live_delay)
     self.model_manager = ui_state.sm["modelManagerSP"]
     self._handle_bundle_download_progress()
-    active_name = self.model_manager.activeBundle.internalName if self.model_manager and self.model_manager.activeBundle.ref else f"{DEFAULT_MODEL} (Default)"
-    self.current_model_item.action_item.set_value(active_name)
 
-    if not ui_state.is_offroad():
-      self.current_model_item.action_item.set_enabled(False)
-      self.current_model_item.set_description(tr("Only available when vehicle is off, or always offroad mode is on"))
-    else:
-      self.current_model_item.action_item.set_enabled(True)
-      self.current_model_item.set_description("")
+    carry_source, _, carry_display = carrying_model()
+    for item, item_source in ((self.small_model_item, "qcom"), (self.big_model_item, "chestnut")):
+      bundle = get_selected_bundle(ui_state.params, item_source)
+      name = bundle.displayName if bundle else default_model_name(item_source)
+      color = ON_COLOR if (item_source == carry_source and name == carry_display) else style.ITEM_TEXT_VALUE_COLOR
+      item.action_item.set_value(name, color)
+
+    note = self._status_note()
+    if note != self._last_note:
+      self._last_note = note
+      self._set_item_note(self.download_item, note)
+
+    offroad = ui_state.is_offroad()
+    self.small_model_item.action_item.set_enabled(offroad)
+    self.big_model_item.action_item.set_enabled(offroad)
+    self.small_model_item.set_description("" if offroad else tr("Only available when vehicle is off, or always offroad mode is on"))
+
+    # manager is offroad-only, so an onroad clear would never be serviced
+    self.clear_cache_item.action_item.set_enabled(offroad and not self._downloading and not self._clearing)
+
+    # manager is offroad-only, so a refresh queued onroad would never be serviced
+    self._refreshing = refresh_in_progress(self._refresh_start)
+    self.refresh_item.action_item.set_enabled(offroad and not self._downloading and not self._refreshing)
 
   def _render(self, rect):
     self._scroller.render(rect)
 
   def show_event(self):
     self._scroller.show_event()
+    self._last_note = None  # re-expand the failover note every time the page opens

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

@@ -8,7 +8,6 @@ import datetime
 import os
 import platform
 import requests
-import shutil
 import threading
 from pathlib import Path
 from time import monotonic
@@ -75,22 +74,12 @@ class OSMLayout(Widget):
   def _update_map_size(self):
     threading.Thread(target=self.calculate_size, daemon=True).start()
 
-  def _do_delete_maps(self):
-    if MAP_PATH.exists():
-      shutil.rmtree(MAP_PATH)
-
-    for param in ("OsmDownloadedDate", "OsmLocal", "OsmLocationName", "OsmLocationTitle", "OsmStateName", "OsmStateTitle"):
-      ui_state.params.remove(param)
-
+  def _on_confirm_delete_maps(self):
+    ui_state.params.put_bool("Mapd_ClearCache", True)
     self._delete_maps_btn.action_item.set_enabled(True)
     self._delete_maps_btn.action_item.set_text(tr("DELETE"))
     self._update_map_size()
 
-  def _on_confirm_delete_maps(self):
-    self._delete_maps_btn.action_item.set_enabled(False)
-    self._delete_maps_btn.action_item.set_text("DELETING...")
-    threading.Thread(target=self._do_delete_maps).start()
-
   def _delete_maps(self):
     self._show_confirm(tr("This will delete ALL downloaded maps\n\nAre you sure you want to delete all maps?"),
                        tr("Yes, delete all maps"), self._on_confirm_delete_maps)

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

@@ -9,7 +9,7 @@ from openpilot.cereal import custom
 from openpilot.selfdrive.ui.sunnypilot.layouts.onboarding import SunnylinkConsentPage
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.sunnypilot.sunnylink.api import UNREGISTERED_SUNNYLINK_DONGLE_ID
-from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.application import gui_app, FontWeight, TextAlignment, TextAlignmentVertical
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.sunnypilot.widgets.list_view import button_item_sp
 from openpilot.system.ui.sunnypilot.widgets.list_view import toggle_item_sp
@@ -32,8 +32,8 @@ class SunnylinkHeader(Widget):
       font_size=90,
       font_weight=FontWeight.AUDIOWIDE,
       text_color=rl.WHITE,
-      alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-      alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+      alignment=TextAlignment.CENTER,
+      alignment_vertical=TextAlignmentVertical.TOP,
       wrap_text=False,
       elide=False
     )
@@ -43,8 +43,8 @@ class SunnylinkHeader(Widget):
       font_size=40,
       font_weight=FontWeight.NORMAL,
       text_color=rl.Color(0, 255, 0, 255),  # Green
-      alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-      alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+      alignment=TextAlignment.CENTER,
+      alignment_vertical=TextAlignmentVertical.TOP,
       wrap_text=True,
       elide=False
     )
@@ -55,8 +55,8 @@ class SunnylinkHeader(Widget):
       font_size=35,
       font_weight=FontWeight.NORMAL,
       text_color=rl.Color(255, 165, 0, 255),  # Orange
-      alignment=rl.GuiTextAlignment.TEXT_ALIGN_CENTER,
-      alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+      alignment=TextAlignment.CENTER,
+      alignment_vertical=TextAlignmentVertical.TOP,
       wrap_text=True,
       elide=False
     )
@@ -109,8 +109,8 @@ class SunnylinkDescriptionItem(Widget):
       font_size=40,
       font_weight=FontWeight.NORMAL,
       text_color=rl.WHITE,
-      alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT,
-      alignment_vertical=rl.GuiTextAlignmentVertical.TEXT_ALIGN_TOP,
+      alignment=TextAlignment.LEFT,
+      alignment_vertical=TextAlignmentVertical.TOP,
       wrap_text=True,
       elide=False,
     )

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

@@ -4,11 +4,14 @@ 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 pyray as rl
 import time
 from dataclasses import dataclass
-from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.selfdrive.ui.ui_state import ui_state, ChestnutState
 from openpilot.sunnypilot.sunnylink.api import UNREGISTERED_SUNNYLINK_DONGLE_ID
+from openpilot.system.ui.lib.application import gui_app
 from openpilot.system.ui.lib.multilang import tr_noop
 
 
@@ -18,6 +21,9 @@ METRIC_MARGIN = 30
 METRIC_START_Y = 300
 HOME_BTN = rl.Rectangle(60, 860, 180, 180)
 
+CHESTNUT_ICON_WIDTH = 180
+CHESTNUT_ICON_HEIGHT = 133
+
 
 # Color scheme
 class Colors:
@@ -53,6 +59,9 @@ class MetricData:
 class SidebarSP:
   def __init__(self):
     self._sunnylink_status = MetricData(tr_noop("SUNNYLINK"), tr_noop("OFFLINE"), Colors.WARNING)
+    self._chestnut_green_img = gui_app.texture("icons_mici/chestnut_green.png", CHESTNUT_ICON_WIDTH, CHESTNUT_ICON_HEIGHT)
+    self._chestnut_default_img = gui_app.texture("icons_mici/chestnut.png", CHESTNUT_ICON_WIDTH, CHESTNUT_ICON_HEIGHT)
+    self._chestnut_orange_img = gui_app.texture("icons_mici/chestnut_orange.png", CHESTNUT_ICON_WIDTH, CHESTNUT_ICON_HEIGHT)
 
   def _update_sunnylink_status(self):
     if not ui_state.params.get_bool("SunnylinkEnabled"):
@@ -78,6 +87,24 @@ class SidebarSP:
 
     self._sunnylink_status.update(tr_noop("SUNNYLINK"), status, color)
 
+  def _get_home_icon(self, default_img: rl.Texture) -> tuple[rl.Texture, rl.Vector2, float]:
+    default_pos = rl.Vector2(HOME_BTN.x, HOME_BTN.y)
+    state = ui_state.chestnut_state
+    if state == ChestnutState.DISCONNECTED:
+      return default_img, default_pos, 1.0
+
+    if state == ChestnutState.LOADING:
+      icon = self._chestnut_default_img
+      opacity = 0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0))
+    elif state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED):
+      icon, opacity = self._chestnut_orange_img, 1.0
+    else:
+      icon, opacity = self._chestnut_green_img, 1.0
+
+    x = HOME_BTN.x + (HOME_BTN.width - icon.width) / 2
+    y = HOME_BTN.y + (HOME_BTN.height - icon.height) / 2
+    return icon, rl.Vector2(x, y), opacity
+
   def _draw_metrics_w_sunnylink(self, rect: rl.Rectangle, _temp, _panda, _connect):
     metrics = [_temp, _panda, _connect, self._sunnylink_status]
     start_y = int(rect.y) + METRIC_START_Y

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

@@ -4,7 +4,12 @@ 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 pyray as rl
+
 from openpilot.selfdrive.ui.mici.layouts.home import MiciHomeLayout
+from openpilot.selfdrive.ui.ui_state import ui_state, ChestnutState
 from openpilot.system.ui.lib.application import FontWeight
 from openpilot.system.ui.widgets.label import UnifiedLabel
 
@@ -13,3 +18,16 @@ class MiciHomeLayoutSP(MiciHomeLayout):
   def __init__(self):
     super().__init__()
     self._openpilot_label = UnifiedLabel("sunnypilot", font_size=88, font_weight=FontWeight.AUDIOWIDE, max_width=480, wrap_text=False)
+
+  def _set_chestnut_visibility(self):
+    usb_connected = ui_state.usb_connected
+    usb_unknown = ui_state.usb_unknown
+    chestnut_state = ui_state.chestnut_state
+    loading = chestnut_state == ChestnutState.LOADING
+
+    self._usb_icon.set_visible(usb_connected and usb_unknown)
+    self._chestnut_loading_icon.set_opacity(0.35 + 0.65 * (0.5 - 0.5 * math.cos(rl.get_time() * 6.0)))
+    self._chestnut_loading_icon.set_visible(not usb_unknown and loading)
+    self._chestnut_icon.set_visible(not usb_unknown and not loading and
+                                    chestnut_state in (ChestnutState.READY, ChestnutState.ACTIVE))
+    self._chestnut_failed_icon.set_visible(not usb_unknown and chestnut_state in (ChestnutState.UNCOMPILED, ChestnutState.FAILED))

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

@@ -4,19 +4,43 @@ 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 time
+
 import pyray as rl
 
 from openpilot.cereal import custom
-from openpilot.sunnypilot.models.default_model import DEFAULT_MODEL
+from openpilot.selfdrive.ui.mici.widgets.dialog import BigConfirmationDialog, BigDialog
+from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_selected_bundle
 from openpilot.selfdrive.ui.mici.widgets.button import BigButton
-from openpilot.selfdrive.ui.sunnypilot.layouts.settings.models import ModelsLayout
 from openpilot.selfdrive.ui.ui_state import ui_state, device
+from openpilot.selfdrive.ui.sunnypilot.model_info import (active_source, big_model_state, bundles_for_source, carrying_model,
+                                                           default_model_name, model_cache_size_mb, model_info, queued_name,
+                                                           refresh_in_progress, refresh_model_list)
 from openpilot.system.ui.lib.application import FontWeight, gui_app
 from openpilot.system.ui.lib.multilang import tr
 from openpilot.system.ui.widgets import Widget
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.scroller import NavScroller
 
+def _model_info() -> tuple[str, str, str]:
+  """(active model, info header, info text) for the panel. Runner-matched: the
+  active line names what actually drives, and a notable big-model state takes
+  the info pair."""
+  source, active_name, other_name = model_info()
+  state = big_model_state()
+  _, _, carry_display = carrying_model()
+  if carry_display is None:
+    big = get_selected_bundle(ui_state.params, "chestnut")
+    carry_display = big.displayName if big else default_model_name("chestnut")
+  active_text = (carry_display or active_name).lower()
+  if state == 'failed':
+    return active_text, tr("big model"), tr("unavailable")
+  if state == 'loading':
+    return active_text, tr("big model"), tr("getting ready")
+  header = tr("small model") if source == "chestnut" else tr("big model")
+  return active_text, header, other_name.lower()
+
+
 class CurrentModelInfo(Widget):
   def __init__(self):
     super().__init__()
@@ -26,12 +50,12 @@ class CurrentModelInfo(Widget):
     header_color = rl.Color(255, 255, 255, int(255 * 0.9))
     subheader_color = rl.Color(255, 255, 255, int(255 * 0.9 * 0.65))
     max_width = int(self._rect.width - 20)
+    active_text, info_header, info_text = _model_info()
     self.current_model_header = UnifiedLabel(tr("active model"), 48, max_width=max_width, text_color=header_color, font_weight=FontWeight.DISPLAY)
-    default_text = f"{DEFAULT_MODEL} (Default)".lower()
-    self.current_model_text = UnifiedLabel(default_text, 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN, scroll=True)
+    self.current_model_text = UnifiedLabel(active_text, 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN, scroll=True)
 
-    self.info_header = UnifiedLabel("cache size", 48, max_width=max_width, text_color=header_color, font_weight=FontWeight.DISPLAY)
-    self.info_text = UnifiedLabel("0 mb", 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN)
+    self.info_header = UnifiedLabel(info_header, 48, max_width=max_width, text_color=header_color, font_weight=FontWeight.DISPLAY)
+    self.info_text = UnifiedLabel(info_text, 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN, scroll=True)
 
   def _render(self, _):
     self.current_model_header.set_position(self._rect.x + 20, self._rect.y - 10)
@@ -55,22 +79,30 @@ class ModelsLayoutMici(NavScroller):
     self._download_progress = "."
     self._download_frame = 0
     self._was_downloading = False
+    self._selection_source: str | None = None
 
     self.select_model_btn = BigButton(tr("select model"))
     self.select_model_btn.set_click_callback(self._show_folders)
 
-    self.cancel_download_btn = BigButton(tr("cancel download"))
-    self.cancel_download_btn.set_click_callback(lambda: ui_state.params.remove("ModelManager_DownloadIndex"))
+    self.refresh_btn = BigButton(tr("refresh models"))
+    self.refresh_btn.set_click_callback(self._refresh_models)
+    self._refresh_start: float | None = None
 
-    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn]
+    self.cancel_download_btn = BigButton(tr("cancel download"))
+    self.cancel_download_btn.set_click_callback(lambda: ui_state.params.remove("ModelManager_DownloadRef"))
+
+    self.clear_cache_btn = BigButton(tr("clear cache"), value=f"{model_cache_size_mb():.1f} MB")
+    self.clear_cache_btn.set_click_callback(self._confirm_clear_cache)
+    self._cache_size_time = 0.0
+
+    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn, self.refresh_btn, self.clear_cache_btn]
     self._scroller.add_widgets(self.main_items)
 
   @property
   def model_manager(self):
     return ui_state.sm["modelManagerSP"]
 
-  def _get_grouped_bundles(self, favorites = None):
-    bundles = self.model_manager.availableBundles
+  def _get_grouped_bundles(self, bundles, favorites = None):
     folders = {}
     for bundle in bundles:
       folder = next((override.value for override in bundle.overrides if override.key == "folder"), "")
@@ -90,47 +122,79 @@ class ModelsLayoutMici(NavScroller):
   def _show_folders(self):
     self.focused_widget = self.select_model_btn
 
+    hardware_btns = []
+    active = active_source()
+    for source, label in (("qcom", tr("small models")), ("chestnut", tr("big models"))):
+      bundle = get_selected_bundle(ui_state.params, source)
+      value = (bundle.internalName if bundle else default_model_name(source)).lower()
+      if source == active:
+        value += f" ({tr('active')})"
+      btn = BigButton(label.lower(), value=value)
+      btn.set_click_callback(lambda s=source: self._select_hardware(s))
+      hardware_btns.append(btn)
+    self._push_selection_view(hardware_btns)
+
+  def _select_hardware(self, source):
+    self._selection_source = source
+
     favs = ui_state.params.get("ModelManager_Favs")
     favorites = set(favs.split(';')) if favs else set()
 
-    folders = self._get_grouped_bundles(favorites)
+    bundles = bundles_for_source(source)
+    if not bundles:
+      gui_app.push_widget(BigDialog(title=tr("No models available"),
+                                    description=tr("No models are available for this hardware yet. Connect to the internet and refresh the model list.")))
+      return
+    folders = self._get_grouped_bundles(bundles, favorites)
+
     folder_buttons = []
-    default_btn = BigButton(f"{DEFAULT_MODEL} (Default)".lower())
-    default_btn.set_click_callback(self._select_default)
+    default_btn = BigButton(default_model_name(source).lower())
+    default_btn.set_click_callback(lambda s=source: self._select_default(s))
     folder_buttons.append(default_btn)
 
     for folder in sorted(folders.keys(), key=lambda f: max((bundle.index for bundle in folders[f]), default=-1), reverse=True):
-      if folder.lower() in ["release models", "master models", "favorites"]:
-        btn = BigButton(folder.lower())
-        btn.set_click_callback(lambda f=folder: self._select_folder(f))
-        if folder.lower() == "favorites":
-          folder_buttons.insert(0, btn)
-        else:
-          folder_buttons.append(btn)
+      btn = BigButton(folder.lower())
+      btn.set_click_callback(lambda f=folder: self._select_folder(f))
+      if folder.lower() == "favorites":
+        folder_buttons.insert(0, btn)
+      else:
+        folder_buttons.append(btn)
     self._push_selection_view(folder_buttons)
 
   def _pop_to_main(self):
     gui_app.pop_widgets_to(self)
+    self._scroller.scroll_panel.set_offset(0.0)
 
   def _select_model(self, bundle):
-    ui_state.params.put("ModelManager_DownloadIndex", bundle.index)
+    ui_state.params.put("ModelManager_DownloadRef", bundle.ref)
     self._pop_to_main()
 
-  def _select_default(self):
-    ui_state.params.remove("ModelManager_ActiveBundle")
+  def _select_default(self, source):
+    ui_state.params.remove(ACTIVE_BUNDLE_KEYS[source])
     self._pop_to_main()
 
+  def _confirm_clear_cache(self):
+    icon = gui_app.texture("icons_mici/settings/network/new/trash.png", 54, 64)
+    gui_app.push_widget(BigConfirmationDialog(f"{tr('slide to')}\n{tr('clear cache')}", icon,
+                                              lambda: ui_state.params.put_bool("ModelManager_ClearCache", True), red=True))
+
+  def _refresh_models(self):
+    refresh_model_list()
+    self._refresh_start = time.monotonic()
+
   def _select_folder(self, folder_name):
+    source = self._selection_source
+    if source is None:  # folders are only reachable after picking a hardware
+      return
     favs = ui_state.params.get("ModelManager_Favs")
     favorites = set(favs.split(';')) if favs else set()
 
-    folders = self._get_grouped_bundles(favorites)
+    folders = self._get_grouped_bundles(bundles_for_source(source), favorites)
     bundles = sorted(folders.get(folder_name, []), key=lambda b: b.index, reverse=True)
 
     btns = []
     for bundle in bundles:
-      txt = bundle.displayName.lower()
-      btn = BigButton(txt)
+      btn = BigButton(bundle.displayName.lower())
       btn.set_click_callback(lambda b=bundle: self._select_model(b))
       btns.append(btn)
     self._push_selection_view(btns)
@@ -161,11 +225,26 @@ class ModelsLayoutMici(NavScroller):
       device.set_override_interactive_timeout(None)
     self._was_downloading = is_downloading
 
+    # manager is offroad-only, so an onroad clear would never be serviced
+    clearing = ui_state.params.get_bool("ModelManager_ClearCache")
+    self.clear_cache_btn.set_enabled(ui_state.is_offroad() and not is_downloading and not clearing)
+    if clearing:
+      self.clear_cache_btn.set_value(tr("clearing..."))
+      self._cache_size_time = 0.0  # refresh the size as soon as clearing finishes
+    elif (now := time.monotonic()) - self._cache_size_time > 0.5:
+      self._cache_size_time = now
+      self.clear_cache_btn.set_value(f"{model_cache_size_mb():.1f} MB")
+
+    # manager is offroad-only, so a refresh queued onroad would never be serviced
+    refreshing = refresh_in_progress(self._refresh_start)
+    self.refresh_btn.set_enabled(ui_state.is_offroad() and not is_downloading and not refreshing)
+    self.refresh_btn.set_value(tr("fetching...") if refreshing else "")
+
     self.current_model_info.current_model_header.set_text(tr("active model"))
-    model_text = manager.activeBundle.displayName.lower() if manager.activeBundle.ref else f"{DEFAULT_MODEL} (Default)".lower()
-    self.current_model_info.current_model_text.set_text(model_text)
-    self.current_model_info.info_header.set_text(tr("cache size"))
-    self.current_model_info.info_text.set_text(f"{ModelsLayout.calculate_cache_size():.2f} MB")
+    active_text, info_header, info_text = _model_info()
+    self.current_model_info.current_model_text.set_text(active_text)
+    self.current_model_info.info_header.set_text(info_header)
+    self.current_model_info.info_text.set_text(info_text)
 
     if manager.selectedBundle and manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.failed:
       self.current_model_info.info_header.set_text(tr("error") + self._download_progress)
@@ -176,19 +255,29 @@ class ModelsLayoutMici(NavScroller):
       device.set_override_interactive_timeout(5)
       progress = 0.0
       count = 0
+      verifying = False
       for model in manager.selectedBundle.models:
         count += 1
         p = model.artifact.downloadProgress
-        if p.status == custom.ModelManagerSP.DownloadStatus.downloading:
+        if p.status in (custom.ModelManagerSP.DownloadStatus.downloading,
+                        custom.ModelManagerSP.DownloadStatus.verifying):
           progress += p.progress
+          verifying = verifying or p.status == custom.ModelManagerSP.DownloadStatus.verifying
         elif p.status in (custom.ModelManagerSP.DownloadStatus.downloaded,
                           custom.ModelManagerSP.DownloadStatus.cached):
           progress += 100.0
 
-      self.current_model_info.current_model_header.set_text(tr("downloading"))
+      self.current_model_info.current_model_header.set_text(tr("verifying") if verifying else tr("downloading"))
+      self.cancel_download_btn.set_text(tr("cancel verification") if verifying else tr("cancel download"))
       self.current_model_info.current_model_header._shimmer = True
-      self.current_model_info.current_model_text.set_text(f"{manager.selectedBundle.internalName.lower()}")
+      name_text = manager.selectedBundle.internalName.lower()
+      if queued := queued_name(manager.selectedBundle.ref):
+        name_text += f"  |  {queued.lower()} {tr('queued')}"
+      self.current_model_info.current_model_text.set_text(name_text)
       self.current_model_info.info_header.set_text(tr("progress") + self._download_progress)
       self.current_model_info.info_header._shimmer = True
       self.current_model_info.info_text.set_text(f"{progress/count:.2f}%")
 
+    elif manager.selectedBundle and manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.downloaded:
+      self.current_model_info.info_header.set_text(tr("downloaded"))
+      self.current_model_info.info_text.set_text(tr("downloaded"))

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

@@ -12,13 +12,23 @@ from openpilot.selfdrive.ui.mici.widgets.dialog import BigConfirmationDialog, Bi
 from openpilot.selfdrive.ui.sunnypilot.mici.layouts.sunnylink import SunnylinkLayoutMici
 from openpilot.selfdrive.ui.sunnypilot.mici.layouts.models import ModelsLayoutMici
 from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.system.ui.lib.application import gui_app
+from openpilot.system.ui.lib.application import gui_app, FontWeight
 from openpilot.system.ui.lib.multilang import tr
 
 ICON_SIZE = 70
 BIG_ICON_SIZE = 110
 
 
+class SunnylinkBigButton(SettingsBigButton):
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+    self._label.set_font_weight(FontWeight.AUDIOWIDE)
+
+  def _get_label_font_size(self):
+    # Audiowide runs wider than Inter: "sunnylink" wraps to two lines at 64
+    return 56
+
+
 class SettingsLayoutSP(OP.SettingsLayout):
   def __init__(self):
     OP.SettingsLayout.__init__(self)
@@ -33,7 +43,7 @@ class SettingsLayoutSP(OP.SettingsLayout):
     self.icon_offroad_slider = gui_app.texture("icons_mici/settings/device/lkas.png", BIG_ICON_SIZE, BIG_ICON_SIZE)
 
     sunnylink_panel = SunnylinkLayoutMici()
-    sunnylink_btn = SettingsBigButton(tr("sunnylink"), "", gui_app.texture("icons_mici/settings/developer/ssh.png", 55, 55))
+    sunnylink_btn = SunnylinkBigButton(tr("sunnylink"), "", gui_app.texture("../../sunnypilot/selfdrive/assets/icons_mici/sunnylink.png", 76, 44))
     sunnylink_btn.set_click_callback(lambda: gui_app.push_widget(sunnylink_panel))
 
     models_panel = ModelsLayoutMici()
@@ -56,8 +66,8 @@ class SettingsLayoutSP(OP.SettingsLayout):
 
     items = self._scroller._items.copy()
 
-    items.insert(1, sunnylink_btn)
-    items.insert(2, models_btn)
+    items.insert(1, models_btn)
+    items.insert(5, sunnylink_btn)
 
     # front slots (only one ever visible at a time): exit-always-offroad, then enable-onroad
     items.insert(0, self._enable_offroad_btn_onroad)

openpilot/selfdrive/ui/sunnypilot/model_info.py

@@ -0,0 +1,120 @@
+"""
+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 contextlib
+import os
+import time
+
+from openpilot.common.hardware.hw import Paths
+from openpilot.selfdrive.ui.ui_state import ui_state, ChestnutState
+from openpilot.sunnypilot.models.fetcher import get_cached_bundles
+from openpilot.sunnypilot.models.helpers import get_active_source, get_selected_bundle, resolve_bundle_by_ref
+from openpilot.sunnypilot.models.model_name import DEFAULT_BIG_MODEL, DEFAULT_MODEL
+
+
+def model_cache_size_mb() -> float:
+  """Bytes on disk under the model cache directory, in MB."""
+  model_root = Paths.model_root()
+  total = 0
+  if os.path.isdir(model_root):
+    for name in os.listdir(model_root):
+      with contextlib.suppress(OSError):
+        total += os.path.getsize(os.path.join(model_root, name))
+  return total / (1024 ** 2)
+
+
+def active_source() -> str:
+  return get_active_source(chestnut=ui_state.chestnut_present,
+                           chestnut_active=ui_state.chestnut_active, chestnut_loading=ui_state.chestnut_loading,
+                           offroad=ui_state.is_offroad())
+
+
+def bundles_for_source(source: str):
+  if source == active_source():
+    return ui_state.sm["modelManagerSP"].availableBundles
+  return get_cached_bundles(ui_state.params, source)
+
+
+def default_model(source: str) -> str:
+  return DEFAULT_BIG_MODEL if source == 'chestnut' else DEFAULT_MODEL
+
+
+def default_model_name(source: str) -> str:
+  return f"{default_model(source)} (Default)"
+
+
+def big_model_state() -> str | None:
+  """'failed' | 'loading' | None, from the same state the icons render."""
+  return {ChestnutState.UNCOMPILED: 'failed',
+          ChestnutState.FAILED: 'failed',
+          ChestnutState.LOADING: 'loading'}.get(ui_state.chestnut_state)
+
+
+def carrying_model() -> tuple[str | None, str | None, str | None]:
+  """(source, internal name, display name) of what actually drives. Runner-matched:
+  when a Default big cannot carry, stock modeld runs the Default small, never the
+  small slot's pick; a custom big has no automatic fallback yet -> (None, None, None)."""
+  source = active_source()
+  if source == "chestnut":
+    bundle = get_selected_bundle(ui_state.params, "chestnut")
+    if bundle:
+      return "chestnut", bundle.internalName, bundle.displayName
+    name = default_model_name("chestnut")
+    return "chestnut", name, name
+  if ui_state.chestnut_present:
+    if get_selected_bundle(ui_state.params, "chestnut") is None:
+      name = default_model_name("qcom")
+      return "qcom", name, name
+    return None, None, None
+  bundle = get_selected_bundle(ui_state.params, "qcom")
+  if bundle:
+    return "qcom", bundle.internalName, bundle.displayName
+  name = default_model_name("qcom")
+  return "qcom", name, name
+
+
+def queued_name(current_ref) -> str | None:
+  ref = ui_state.params.get("ModelManager_DownloadRef")
+  if ref and ref != current_ref:
+    source_bundles = {source: bundles_for_source(source) for source in ("qcom", "chestnut")}
+    if resolved := resolve_bundle_by_ref(ref, source_bundles):
+      return resolved[0].internalName
+  return None
+
+
+def model_info() -> tuple[str, str, str]:
+  """returns (active source, active model name, other model name)
+
+  Names come from the params slots, never modelManagerSP.activeBundle — the
+  manager republishes a tick after a chestnut change, so the stale bundle
+  would flash the wrong model."""
+  source = active_source()
+  other = "qcom" if source == "chestnut" else "chestnut"
+  active_bundle = get_selected_bundle(ui_state.params, source)
+  other_bundle = get_selected_bundle(ui_state.params, other)
+
+  active_name = active_bundle.displayName if active_bundle else default_model_name(source)
+  other_name = other_bundle.displayName if other_bundle else default_model_name(other)
+  return source, active_name, other_name
+
+
+# mirrors the manager's ModelCache keys; the manager restamps them on a successful fetch
+MODEL_SYNC_KEYS = ("ModelManager_LastSyncTime", "ModelManager_LastSyncTime_Chestnut")
+MODEL_SYNC_TIMEOUT = 20.0
+
+
+def refresh_model_list() -> None:
+  # zeroing the sync keys makes the manager refetch each manifest on its next tick
+  for key in MODEL_SYNC_KEYS:
+    ui_state.params.put(key, 0)
+
+
+def refresh_in_progress(started_at: float | None) -> bool:
+  """Whether a user refresh is still outstanding. A failed fetch never restamps the
+  sync keys, so the spinner is bounded by MODEL_SYNC_TIMEOUT rather than sticking."""
+  if started_at is None or time.monotonic() - started_at > MODEL_SYNC_TIMEOUT:
+    return False
+  return not all(ui_state.params.get(key) for key in MODEL_SYNC_KEYS)

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

@@ -4,11 +4,29 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
 from openpilot.selfdrive.ui.sunnypilot.onroad.chevron_metrics import ChevronMetrics
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
+from openpilot.selfdrive.ui.sunnypilot.ui_state import MADSState
+from openpilot.system.ui.lib.application import gui_app
 
 
 class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
     self.chevron_metrics = ChevronMetrics()
+    self._width_filter = FirstOrderFilter(0.9, 0.1, 1 / gui_app.target_fps)
+
+  @property
+  def _lateral_active(self) -> bool:
+    sm = ui_state.sm
+    if sm.valid["selfdriveStateSP"]:
+      mads = sm["selfdriveStateSP"].mads
+      if mads.available:
+        return mads.enabled and mads.state != MADSState.paused
+    return ui_state.status in (UIStatus.ENGAGED, UIStatus.LAT_ONLY)
+
+  def _get_path_half_width(self) -> float:
+    target = 0.9 if self._lateral_active else 0.40
+    return self._width_filter.update(target)

openpilot/selfdrive/ui/sunnypilot/ui_state.py

@@ -10,6 +10,7 @@ from openpilot.cereal import messaging, log, custom
 from opendbc.car.structs import car
 from openpilot.common.params import Params
 from openpilot.selfdrive.ui.sunnypilot.layouts.settings.display import OnroadBrightness
+from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_active_source
 from openpilot.sunnypilot.sunnylink.sunnylink_state import SunnylinkState
 from openpilot.system.ui.lib.application import gui_app
 from openpilot.system.ui.sunnypilot.widgets.screen_saver import ScreenSaverSP
@@ -43,6 +44,7 @@ class UIStateSP:
     self.screensaver_enabled: bool = False
 
     self.active_bundle = None
+    self.model_runner_tinygrad: bool = False
     self.blindspot: bool = False
     self.chevron_metrics = None
     self.custom_interactive_timeout: int = 0
@@ -150,7 +152,13 @@ class UIStateSP:
       self.has_icbm = self.CP_SP.intelligentCruiseButtonManagementAvailable and self.params.get_bool("IntelligentCruiseButtonManagement")
 
     self._enforce_constraints()
-    self.active_bundle = self.params.get("ModelManager_ActiveBundle")
+    source = get_active_source(chestnut=self.chestnut_present, chestnut_active=self.chestnut_active,
+                               chestnut_loading=self.chestnut_loading, offroad=self.is_offroad())
+    self.active_bundle = self.params.get(ACTIVE_BUNDLE_KEYS[source])
+    self.model_runner_tinygrad = self.active_bundle is not None and self.active_bundle.get("runner") == "tinygrad"
+    # stock only counts the default big model's compiled pkl. a downloaded big bundle runs on the
+    # chestnut just the same, so ChestnutState has to see it as available too.
+    self.chestnut_compiled = self.chestnut_compiled or self.model_runner_tinygrad
     self.blindspot = self.params.get_bool("BlindSpot")
     self.chevron_metrics = self.params.get("ChevronInfo")
     self.custom_interactive_timeout = self.params.get("InteractivityTimeout", return_default=True)

openpilot/selfdrive/ui/ui_state.py

@@ -12,7 +12,8 @@ from openpilot.common.swaglog import cloudlog
 from openpilot.selfdrive.ui.lib.prime_state import PrimeState
 from openpilot.system.ui.lib.application import gui_app
 from openpilot.common.hardware import HARDWARE, PC
-from openpilot.selfdrive.modeld.helpers import usbgpu_compiled
+from openpilot.common.hardware.usb import TYPEC_CC_ORIENTATION_PATH, get_usb_state, is_chestnut_usb_id, read_int
+from openpilot.selfdrive.modeld.helpers import chestnut_compiled
 
 from openpilot.selfdrive.ui.sunnypilot.ui_state import UIStateSP, DeviceSP
 
@@ -28,6 +29,15 @@ class UIStatus(Enum):
   LONG_ONLY = "long_only"
 
 
+class ChestnutState(Enum):
+  DISCONNECTED = "disconnected"
+  UNCOMPILED = "uncompiled"
+  READY = "ready"
+  LOADING = "loading"
+  ACTIVE = "active"
+  FAILED = "failed"
+
+
 class UIState(UIStateSP):
   _instance: 'UIState | None' = None
 
@@ -82,10 +92,15 @@ class UIState(UIStateSP):
     self.always_on_dm: bool = self.params.get_bool("AlwaysOnDM")
     self.experimental_mode: bool = self.params.get_bool("ExperimentalMode")
     self.experimental_mode_confirmed: bool = self.params.get_bool("ExperimentalModeConfirmed")
-    self.usbgpu: bool = False
-    self.usbgpu_compiled: bool = usbgpu_compiled()
-    self.usbgpu_active: bool | None = self.params.get("UsbGpuActive")
-    self.usbgpu_loading: bool = self.params.get_bool("UsbGpuLoading")
+    self.chestnut_present: bool = False
+    self.chestnut_compiled: bool = chestnut_compiled()
+    self.chestnut_active: bool | None = None
+    self.chestnut_loading: bool = False
+    self.usb_connected: bool = False
+    self.usb_connected_ts: float | None = None
+    self.usb_disconnected_ts: float | None = None
+    self.usb_unknown: bool = False
+    self.chestnut_state = ChestnutState.DISCONNECTED
     self.started: bool = False
     self.ignition: bool = False
     self.recording_audio: bool = False
@@ -131,6 +146,7 @@ class UIState(UIStateSP):
     self.sm.update(0)
     self._update_state()
     self._update_status()
+    self._update_chestnut_state()
     device.update()
     UIStateSP.update(self)
 
@@ -194,12 +210,35 @@ class UIState(UIStateSP):
         self.status = UIStatus.DISENGAGED
         self.started_frame = self.sm.frame
         self.started_time = time.monotonic()
+        self.chestnut_present = self.sm["deviceState"].chestnutPresent
 
       for callback in self._offroad_transition_callbacks:
         callback()
 
       self._started_prev = self.started
 
+  def _update_chestnut_state(self) -> None:
+    detected = self.sm["deviceState"].chestnutPresent
+    if not self.started:
+      self.chestnut_present = detected
+      self.chestnut_state = (ChestnutState.READY if detected and self.chestnut_compiled else
+                             ChestnutState.UNCOMPILED if detected else ChestnutState.DISCONNECTED)
+      return
+
+    model_seen = self.sm.recv_frame["modelV2"] > self.started_frame
+    if not self.chestnut_present:
+      self.chestnut_state = ChestnutState.DISCONNECTED
+    elif not self.chestnut_compiled:
+      self.chestnut_state = ChestnutState.UNCOMPILED
+    elif self.chestnut_state == ChestnutState.FAILED or not detected or (model_seen and (not self.sm.alive["modelV2"] or not self.sm["modelV2"].big)):
+      self.chestnut_state = ChestnutState.FAILED
+    elif self.chestnut_loading or not model_seen:
+      self.chestnut_state = ChestnutState.LOADING
+    elif self.chestnut_active is False:
+      self.chestnut_state = ChestnutState.FAILED
+    else:
+      self.chestnut_state = ChestnutState.ACTIVE
+
   def update_params(self) -> None:
     # For slower operations
     # Update longitudinal control state
@@ -216,12 +255,27 @@ class UIState(UIStateSP):
     self.always_on_dm = self.params.get_bool("AlwaysOnDM")
     self.experimental_mode = self.params.get_bool("ExperimentalMode")
     self.experimental_mode_confirmed = self.params.get_bool("ExperimentalModeConfirmed")
-    # keep usbgpu UI active until offroad transition when gpu disappears
-    self.usbgpu = self.sm["deviceState"].chestnutPresent or (self.usbgpu and self.started)
-    if not self.usbgpu_compiled:
-      self.usbgpu_compiled = usbgpu_compiled()
-    self.usbgpu_active = self.params.get("UsbGpuActive")
-    self.usbgpu_loading = self.params.get_bool("UsbGpuLoading")
+    if not self.chestnut_compiled:
+      self.chestnut_compiled = chestnut_compiled()
+    self.chestnut_active = self.params.get("ChestnutActive")
+    self.chestnut_loading = self.params.get_bool("ChestnutLoading")
+    now = time.monotonic()
+    if read_int(TYPEC_CC_ORIENTATION_PATH) != 0:
+      self.usb_disconnected_ts = None
+      if not self.usb_connected:
+        self.usb_connected = True
+        self.usb_connected_ts = now
+        self.usb_unknown = False
+      elif self.usb_connected_ts is not None and now - self.usb_connected_ts > 10.:
+        self.usb_unknown = not any(is_chestnut_usb_id(d["vendorId"], d["productId"], True) for d in get_usb_state())
+        self.usb_connected_ts = None
+    elif self.usb_connected:
+      if self.usb_disconnected_ts is None:
+        self.usb_disconnected_ts = now
+      elif now - self.usb_disconnected_ts > PARAM_UPDATE_TIME:
+        self.usb_connected = False
+        self.usb_connected_ts = None
+        self.usb_unknown = False
 
     UIStateSP.update_params(self)
 

openpilot/sunnypilot/livedelay/helpers.py

@@ -8,7 +8,10 @@ from openpilot.common.params import Params
 
 
 def get_lat_delay(params: Params, stock_lat_delay: float) -> float:
-  if params.get_bool("LagdToggle"):
-    return float(params.get("LagdValueCache", return_default=True))
+# live learning on: use what lagd publishes.
+# off: use the fixed steerActuatorDelay + software delay sum that LagdToggle caches.
 
-  return stock_lat_delay
+  if params.get_bool("LagdToggle"):
+    return stock_lat_delay
+
+  return float(params.get("LagdValueCache", return_default=True))

openpilot/sunnypilot/mapd/mapd_manager.py

@@ -55,6 +55,19 @@ def cleanup_old_osm_data(files_to_remove: list[str]) -> None:
       shutil.rmtree(file, ignore_errors=False)
 
 
+def clear_downloaded_maps() -> None:
+  """Deletes downloaded OSM map data and resets params."""
+  path = f"{Paths.mapd_root()}/offline"
+  if os.path.exists(path):
+    shutil.rmtree(path, ignore_errors=True)
+
+  for param in ("OsmDownloadedDate", "OsmLocal", "OsmLocationName", "OsmLocationTitle",
+                "OsmStateName", "OsmStateTitle"):
+    params.remove(param)
+
+  cloudlog.info("mapd: downloaded maps cleared")
+
+
 def request_refresh_osm_location_data(nations: list[str], states: list[str] | None = None) -> None:
   params.put("OsmDownloadedDate", str(datetime.now().timestamp()), block=True)
   params.put_bool("OsmDbUpdatesCheck", False, block=True)
@@ -131,6 +144,10 @@ def main_thread():
     show_alert = bool(get_files_for_cleanup() and params.get_bool("OsmLocal"))
     set_offroad_alert("Offroad_OSMUpdateRequired", show_alert, "This alert will be cleared when new maps are downloaded.")
 
+    if params.get("Mapd_ClearCache"):
+      clear_downloaded_maps()
+      params.remove("Mapd_ClearCache")
+
     update_osm_db()
     live_map_sp.tick()
     rk.keep_time()

openpilot/sunnypilot/modeld_v2/__init__.py

@@ -1,5 +0,0 @@
-from pathlib import Path
-
-MODEL_PATH = Path(__file__).parent / 'models/supercombo.onnx'
-MODEL_PKL_PATH = Path(__file__).parent / 'models/supercombo_tinygrad.pkl'
-METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'

openpilot/sunnypilot/modeld_v2/camera_offset_helper.py

@@ -15,11 +15,19 @@ class CameraOffsetHelper:
     self.actual_camera_offset = 0.0
 
   @staticmethod
-  def apply_camera_offset(model_transform, intrinsics, height, offset_param):
+  def get_v_horizon(intrinsics, rpy_calib):
     cy = intrinsics[1, 2]
+    if len(rpy_calib) == 3 and np.isfinite(rpy_calib).all():
+      fy = intrinsics[1, 1]
+      pitch = rpy_calib[1]
+      return float(cy - fy * np.tan(pitch))
+    return float(cy)
+
+  @staticmethod
+  def apply_camera_offset(model_transform, height, offset_param, v_horizon):
     shear = np.eye(3, dtype=np.float32)
     shear[0, 1] = offset_param / height
-    shear[0, 2] = -offset_param / height * cy
+    shear[0, 2] = -offset_param / height * v_horizon
     model_transform = (shear @ model_transform).astype(np.float32)
     return model_transform
 
@@ -30,10 +38,13 @@ class CameraOffsetHelper:
     self.actual_camera_offset = (0.9 * self.actual_camera_offset) + (0.1 * self.camera_offset)
     dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['narrowRoadCameraState'].sensor))]
     height = sm["extrinsicsCalibration"].height[0] if sm['extrinsicsCalibration'].height else 1.22
+    rpy_calib = sm['extrinsicsCalibration'].rpyCalib
 
     intrinsics_main = dc.wide_road.intrinsics if main_wide_camera else dc.narrow_road.intrinsics
-    model_transform_main = self.apply_camera_offset(model_transform_main, intrinsics_main, height, self.actual_camera_offset)
+    v_horizon_main = self.get_v_horizon(intrinsics_main, rpy_calib)
+    model_transform_main = self.apply_camera_offset(model_transform_main, height, self.actual_camera_offset, v_horizon_main)
 
     intrinsics_extra = dc.wide_road.intrinsics
-    model_transform_extra = self.apply_camera_offset(model_transform_extra, intrinsics_extra, height, self.actual_camera_offset)
+    v_horizon_extra = self.get_v_horizon(intrinsics_extra, rpy_calib)
+    model_transform_extra = self.apply_camera_offset(model_transform_extra, height, self.actual_camera_offset, v_horizon_extra)
     return model_transform_main, model_transform_extra

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -7,6 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 import argparse
+import math
 import os
 import tempfile
 import time
@@ -31,7 +32,7 @@ def _patch_tinygrad_fetch_fw():
   helpers.fetch_fw = fetch_fw
 _patch_tinygrad_fetch_fw()
 
-from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, make_frame_prepare, sample_desire, sample_skip, shift_and_sample
+import openpilot.selfdrive.modeld.compile_modeld as stock
 from tinygrad import dtypes
 from tinygrad.device import Device
 from tinygrad.engine.jit import TinyJit
@@ -40,8 +41,7 @@ from tinygrad.tensor import Tensor
 MODEL_TYPES = ('vision_policy', 'supercombo', 'vision_multi_policy')
 WARP_INPUTS = ['tfm', 'big_tfm']
 POLICY_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
-WARP_DEV = os.getenv('WARP_DEV')
-
+nv12_copy_size = stock.nv12_copy_size
 
 def _detect_desire_key(shapes: dict) -> str | None:
   return next((key for key in shapes if key.startswith('desire')), None)
@@ -66,14 +66,15 @@ def get_policy_npy_shapes(input_shapes: dict, is_supercombo: bool = False) -> tu
   if desire_key:
     shapes['desire'] = (input_shapes[desire_key][2],)
 
-  if is_supercombo and 'features_buffer' in input_shapes:
-    fb = input_shapes['features_buffer']
-    shapes['prev_feat'] = (fb[0], fb[2])
-
   for key, shape in input_shapes.items():
     if key not in (desire_key, 'features_buffer') and 'img' not in key:
       shapes[key] = tuple(shape)
 
+  if is_supercombo and 'features_buffer' in input_shapes:
+    fb = input_shapes['features_buffer']
+    feat_dim = math.prod(fb[2:])
+    shapes['prev_feat'] = (fb[0], feat_dim)
+
   sizes = [int(np.prod(size)) for size in shapes.values()]
   return shapes, sizes
 
@@ -117,7 +118,9 @@ def generate_queues_and_npy(input_shapes: dict, frame_skip: int, device: str = D
   }
 
   if features_buffer:
-    queues['feat_q'] = Tensor(np.zeros((frame_skip * (features_buffer[1] - 1) + 1, features_buffer[0], features_buffer[2]),
+    feat_dim = math.prod(features_buffer[2:])
+    feat_q_len = frame_skip * features_buffer[1] if is_supercombo else frame_skip * (features_buffer[1] - 1) + 1
+    queues['feat_q'] = Tensor(np.zeros((feat_q_len, features_buffer[0], feat_dim),
                        dtype=np.float32), device=device).contiguous().realize()
 
   queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')})
@@ -135,7 +138,7 @@ def make_supercombo_input_queues(input_shapes: dict, frame_skip: int,
   return generate_queues_and_npy(input_shapes, frame_skip, device, is_supercombo=True)
 
 
-def make_random_images(keys, shape, device):
+def make_random_images(keys, shape, device, rng=None):
   return {k: Tensor.randint(shape, low=0, high=256, dtype=dtypes.uint8, device=device).realize() for k in keys}
 
 
@@ -148,24 +151,9 @@ def make_warp_queues(device=Device.DEFAULT):
   return queues, npy
 
 
-def make_warp(nv12: NV12Frame, model_w: int, model_h: int):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  WARP_DEV = os.getenv('WARP_DEV', Device.DEFAULT)
-
-  def warp(tfm, big_tfm, frame, big_frame):
-    tfm = tfm.to(WARP_DEV)
-    big_tfm = big_tfm.to(WARP_DEV)
-    Tensor.realize(tfm, big_tfm)
-
-    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(vision_runner, policy_runners: list, features_slice: slice, frame_skip: int, input_shapes: dict):
-  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
-  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+  sample_skip_fn = partial(stock.sample_skip, frame_skip=frame_skip)
+  sample_desire_fn = partial(stock.sample_desire, frame_skip=frame_skip)
 
   desire_key = _detect_desire_key(input_shapes)
   road_key, wide_key = _detect_vision_keys(input_shapes)
@@ -182,14 +170,14 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
     warped_dev = warped.to(Device.DEFAULT)
     Tensor.realize(packed_npy_inputs_dev, warped_dev)
 
-    img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn).realize()
-    big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn).realize()
+    img = stock.shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn)
+    big_img = stock.shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn)
 
     unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)]
     unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True))
 
     desire_dev = unpacked_dict['desire']
-    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()
+    desire_buf = stock.shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn)
 
     inputs = {desire_key: desire_buf}
     for key, tensor_val in unpacked_dict.items():
@@ -198,44 +186,45 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
 
     if 'prev_feat' in unpacked_dict:
       prev_feat_dev = unpacked_dict['prev_feat']
-      inputs['features_buffer'] = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()
+      inputs['features_buffer'] = stock.shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).reshape(input_shapes['features_buffer'])
 
     if vision_runner:
       vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize()
       if 'features_buffer' not in inputs:
         new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0)
-        inputs['features_buffer'] = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+        inputs['features_buffer'] = stock.shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
       policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
       return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])
 
     inputs.update({road_key: img, wide_key: big_img})
     if 'features_buffer' not in inputs:
-      inputs['features_buffer'] = sample_skip_fn(feat_q)
+      inputs['features_buffer'] = sample_skip_fn(feat_q).reshape(input_shapes['features_buffer'])
 
     policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
     if 'features_buffer' not in inputs and features_slice is not None:
       new_feat = policy_out[:, features_slice].reshape(1, -1).unsqueeze(0)
-      shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+      stock.shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
     return policy_out
 
   return run_policy
 
 
-def compile_jit(jit, make_random_inputs, input_keys, make_queues):
+def compile_jit(jit, input_keys, make_queues, make_random_inputs=None, benchmark_runs: int = 1):
   SEED = 42
-  def random_inputs_run(fn, seed, test_val=None, test_buffers=None, expect_match=True):
-    input_queues, npy = make_queues(Device.DEFAULT)
+  def random_inputs_run(fn, seed, n_runs, test_val=None, test_buffers=None, expect_match=True):
+    queues_res = make_queues(Device.DEFAULT)
+    input_queues, npy = queues_res[0], queues_res[1]
+    frame_views = queues_res[2] if len(queues_res) > 2 else {}
     rng = np.random.default_rng(seed)
     Tensor.manual_seed(seed)
 
-    testing = test_val is not None or test_buffers is not None
-    n_runs = 1 if testing else 3
-
     for i in range(n_runs):
       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()
-      random_inputs = make_random_inputs()
+      random_inputs = make_random_inputs(rng=rng) if make_random_inputs is not None else {}
       st = time.perf_counter()
       outs = fn(**{k: input_queues[k] for k in input_keys if k in input_queues}, **random_inputs)
       mt = time.perf_counter()
@@ -256,14 +245,15 @@ def compile_jit(jit, make_random_inputs, input_keys, make_queues):
     return val, buffers
 
   print('capture + replay')
-  test_val, test_buffers = random_inputs_run(jit, SEED)
-  print('pickle round trip')
+  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)
-    deserialized_jit = load_oob(f)
-  random_inputs_run(deserialized_jit, SEED, test_val=test_val, test_buffers=test_buffers)
-  return deserialized_jit
+    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)
+  return jit
 
 
 def _parse_size(size_str: str) -> tuple[int, int]:
@@ -271,18 +261,17 @@ def _parse_size(size_str: str) -> tuple[int, int]:
   return int(width), int(height)
 
 
-def read_file_chunked_to_shm(path):
+def read_file_chunked_to_disk(path):
   if not path:
     return None
   import atexit
   import shutil
   from openpilot.common.file_chunker import open_file_chunked
-  from openpilot.common.hardware.hw import Paths
-  shm_path = os.path.join(Paths.shm_path(), os.path.basename(path))
-  atexit.register(lambda: os.path.exists(shm_path) and os.remove(shm_path))
-  with open(shm_path, 'wb') as dst, open_file_chunked(path) as src:
-    shutil.copyfileobj(src, dst)
-  return shm_path
+  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
 
 
 def _load_policy_runners(args: argparse.Namespace) -> tuple[list, list]:
@@ -295,7 +284,7 @@ def _load_policy_runners(args: argparse.Namespace) -> tuple[list, list]:
 
 
 if __name__ == "__main__":
-  if 'USB' in os.getenv('DEV', '') or os.getenv('USBGPU'):
+  if 'USB' in os.getenv('DEV', '') or os.getenv('CHESTNUT'):
     from openpilot.system.hardware.chestnut.flash import link_up
     for _ in range(10):
       if link_up():
@@ -314,6 +303,7 @@ if __name__ == "__main__":
   parser.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
   parser.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True)
   parser.add_argument('--frame-skip', type=int, default=None, help='frame skip value (auto-derived if not provided)')
+  parser.add_argument('--benchmark-runs', type=int, default=1, help='benchmark runs')
   parser.add_argument('--output', required=True)
 
   parser.add_argument('--vision-onnx', help='vision ONNX (for split models)')
@@ -326,54 +316,70 @@ if __name__ == "__main__":
   model_w, model_h = args.model_size
   output_data = {}
 
-  args.vision_onnx = read_file_chunked_to_shm(args.vision_onnx)
-  args.policy_onnx = read_file_chunked_to_shm(args.policy_onnx)
-  args.off_policy_onnx = read_file_chunked_to_shm(args.off_policy_onnx)
-  args.on_policy_onnx = read_file_chunked_to_shm(args.on_policy_onnx)
-  args.supercombo_onnx = read_file_chunked_to_shm(args.supercombo_onnx)
+  args.vision_onnx = read_file_chunked_to_disk(args.vision_onnx)
+  args.policy_onnx = read_file_chunked_to_disk(args.policy_onnx)
+  args.off_policy_onnx = read_file_chunked_to_disk(args.off_policy_onnx)
+  args.on_policy_onnx = read_file_chunked_to_disk(args.on_policy_onnx)
+  args.supercombo_onnx = read_file_chunked_to_disk(args.supercombo_onnx)
 
-  vision_runner = OnnxRunner(args.vision_onnx) if args.vision_onnx else None
-
-  if args.model_type == 'vision_policy':
-    assert vision_runner and args.policy_onnx
-    policy_runners = [OnnxRunner(args.policy_onnx)]
-    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx), 'policy': make_metadata_dict(args.policy_onnx)}
-  elif args.model_type == 'supercombo':
+  if args.model_type == 'supercombo':
     assert args.supercombo_onnx
-    policy_runners = [OnnxRunner(args.supercombo_onnx)]
-    output_data['metadata'] = {'model': make_metadata_dict(args.supercombo_onnx)}
-  elif args.model_type == 'vision_multi_policy':
-    assert vision_runner
-    policy_runners, policy_names = _load_policy_runners(args)
-    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx)}
-    for name in policy_names:
-      runner_arg = getattr(args, f"{name}_onnx")
-      output_data['metadata'][name] = make_metadata_dict(runner_arg)
+    model_metadata = make_metadata_dict(args.supercombo_onnx)
+    output_data['metadata'] = {'model': model_metadata, **model_metadata}
+    output_data['input_devices'] = {'model': Device.DEFAULT}
+    output_data['run_model'] = {}
+    derived_frame_skip = args.frame_skip or derive_frame_skip({}, model_metadata['input_shapes'])
+    model_runner = OnnxRunner(args.supercombo_onnx)
+    run_policy = stock.make_run_policy(model_runner, model_metadata, derived_frame_skip)
+    for cam_w, cam_h in args.camera_resolutions:
+      print(f"Compiling unified run_model JIT for {cam_w}x{cam_h}...")
+      nv12 = stock.NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+      frame_copy_size = stock.nv12_copy_size(nv12.stride, nv12.y_height, nv12.uv_height)
+      make_model_queues = partial(stock.make_input_queues, model_metadata['input_shapes'], derived_frame_skip,
+                                  frame_copy_size=frame_copy_size)
+      warp = stock.make_warp(nv12, model_w, model_h)
+      run_model_jit = TinyJit(stock.make_run_model(warp, run_policy, model_metadata, frame_copy_size), prune=True)
+      output_data['run_model'][(cam_w, cam_h)] = compile_jit(run_model_jit, stock.MODELD_INPUTS, make_model_queues, benchmark_runs=args.benchmark_runs)
+  else:
+    vision_runner = OnnxRunner(args.vision_onnx) if args.vision_onnx else None
+    if args.model_type == 'vision_policy':
+      assert vision_runner and args.policy_onnx
+      policy_runners = [OnnxRunner(args.policy_onnx)]
+      output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx), 'policy': make_metadata_dict(args.policy_onnx)}
+    elif args.model_type == 'vision_multi_policy':
+      assert vision_runner
+      policy_runners, policy_names = _load_policy_runners(args)
+      output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx)}
+      for name in policy_names:
+        runner_arg = getattr(args, f"{name}_onnx")
+        output_data['metadata'][name] = make_metadata_dict(runner_arg)
 
-  policy_keys = [key for key in output_data['metadata'].keys() if key != 'vision']
-  first_policy_meta = output_data['metadata'][policy_keys[0]] if policy_keys else {}
-  vision_meta = output_data['metadata'].get('vision', {})
+    policy_keys = [key for key in output_data['metadata'].keys() if key != 'vision']
+    first_policy_meta = output_data['metadata'][policy_keys[0]] if policy_keys else {}
+    vision_meta = output_data['metadata'].get('vision', {})
 
-  derived_frame_skip = args.frame_skip or derive_frame_skip(vision_meta.get('input_shapes', {}), first_policy_meta.get('input_shapes', {}))
-  all_shapes = {key: value for meta in output_data['metadata'].values() for key, value in meta['input_shapes'].items()}
-  feat_meta = output_data['metadata'].get('vision') or output_data['metadata'].get('model') or output_data['metadata'].get('policy')
-  assert feat_meta is not None
-  features_slice = feat_meta['output_slices']['hidden_state']
-  is_supercombo = vision_runner is None
+    derived_frame_skip = args.frame_skip or derive_frame_skip(vision_meta.get('input_shapes', {}), first_policy_meta.get('input_shapes', {}))
+    all_shapes = {key: value for meta in output_data['metadata'].values() for key, value in meta['input_shapes'].items()}
+    feat_meta = output_data['metadata'].get('vision') or output_data['metadata'].get('policy')
+    assert feat_meta is not None
+    features_slice = feat_meta['output_slices']['hidden_state']
 
-  print(f"Compiling run_policy JIT (model_size={model_w}x{model_h}, frame_skip={derived_frame_skip})...")
-  run_policy_func = make_run_policy(vision_runner, policy_runners, features_slice, derived_frame_skip, all_shapes)
-  run_policy_jit = TinyJit(run_policy_func, prune=True)
-  make_policy_queues = partial(generate_queues_and_npy, all_shapes, derived_frame_skip, is_supercombo=is_supercombo)
-  make_random_model_inputs = partial(make_random_images, keys=['warped'], shape=(2, 6, model_h // 2, model_w // 2), device=WARP_DEV)
-  output_data['run_policy'] = compile_jit(run_policy_jit, make_random_model_inputs, POLICY_INPUTS, make_policy_queues)
+    print(f"Compiling run_policy JIT (model_size={model_w}x{model_h}, frame_skip={derived_frame_skip})...")
+    run_policy_func = make_run_policy(vision_runner, policy_runners, features_slice, derived_frame_skip, all_shapes)
+    run_policy_jit = TinyJit(run_policy_func, prune=True)
+    make_policy_queues = partial(generate_queues_and_npy, all_shapes, derived_frame_skip, is_supercombo=False)
+    make_random_model_inputs = partial(make_random_images, keys=['warped'], shape=(2, 6, model_h // 2, model_w // 2), device=Device.DEFAULT)
+    output_data['run_policy'] = compile_jit(run_policy_jit, POLICY_INPUTS, make_policy_queues, make_random_inputs=make_random_model_inputs)
 
-  for cam_w, cam_h in args.camera_resolutions:
-    print(f"Compiling warp JIT for {cam_w}x{cam_h}...")
-    nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-    make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=nv12.size, device=WARP_DEV)
-    warp = TinyJit(make_warp(nv12, model_w, model_h), prune=True)
-    output_data[(cam_w, cam_h)] = compile_jit(warp, make_random_warp_inputs, WARP_INPUTS, make_warp_queues)
+    for cam_w, cam_h in args.camera_resolutions:
+      print(f"Compiling warp JIT for {cam_w}x{cam_h}...")
+      nv12 = stock.NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+      frame_copy_size = stock.nv12_copy_size(nv12.stride, nv12.y_height, nv12.uv_height)
+      make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=frame_copy_size, device=Device.DEFAULT)
+      warp = TinyJit(stock.make_warp(nv12, model_w, model_h), prune=True)
+      output_data[(cam_w, cam_h)] = compile_jit(warp, WARP_INPUTS, make_warp_queues, make_random_inputs=make_random_warp_inputs)
+
+    output_data['metadata']['warp_dev'] = Device.DEFAULT
 
   with open(args.output, "wb") as file:
     dump_oob(output_data, file)

openpilot/sunnypilot/modeld_v2/constants.py

@@ -14,6 +14,8 @@ class ModelConstants:
 
   # model inputs constants
   MODEL_FREQ = 20
+  MODEL_RUN_FREQ = 20
+  MODEL_CONTEXT_FREQ = 5
   FEATURE_LEN = 512
   FULL_HISTORY_BUFFER_LEN = 99
   DESIRE_LEN = 8
@@ -35,6 +37,7 @@ class ModelConstants:
   LANE_LINES_WIDTH = 2
   ROAD_EDGES_WIDTH = 2
   PLAN_WIDTH = 15
+  ACTION_WIDTH = 2
   DESIRE_PRED_WIDTH = 8
   LAT_PLANNER_SOLUTION_WIDTH = 4
   DESIRED_CURV_WIDTH = 1

openpilot/sunnypilot/modeld_v2/helpers.py

@@ -0,0 +1,100 @@
+"""
+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 io
+import struct
+import pickle
+import inspect
+import importlib
+import enum
+
+
+def _pad_args(func, args, kwargs):
+  try:
+    sig = inspect.signature(func)
+  except Exception:
+    return args, kwargs
+  params = list(sig.parameters.values())
+  if inspect.isfunction(func) and params and params[0].name in ('cls', 'self'):
+    params = params[1:]
+
+  new_args = list(args)
+  has_varargs = any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params)
+  if len(new_args) > len(params) and not has_varargs:
+    new_args = new_args[:len(params)]
+
+  for i in range(len(new_args), len(params)):
+    param = params[i]
+    if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+      continue
+    val = param.default if param.default is not inspect.Parameter.empty else None
+    new_args.append(val)
+  return new_args, kwargs
+
+
+def _enum_factory(enum_class):
+  def factory(*args, **kwargs):
+    try:
+      return enum_class(*args, **kwargs)
+    # OptOps and UOp objects in the .pkl are left over from the compilation phase,
+    # reassignment does nothing because they aren't tied to the execution graph
+    # It never executes or evaluates the UOp nodes again.
+    except ValueError:
+      return list(enum_class)[0]
+  factory.__name__ = enum_class.__name__
+  factory.__module__ = enum_class.__module__
+  return factory
+
+
+def _dynamic_factory(real_class):
+  if isinstance(real_class, type) and issubclass(real_class, enum.Enum):
+    return _enum_factory(real_class)
+
+  def factory(*args, **kwargs):
+    try:
+      return real_class(*args, **kwargs)
+    except TypeError:
+      new_args, new_kwargs = _pad_args(real_class, args, kwargs)
+      return real_class(*new_args, **new_kwargs)
+
+  class DynamicMeta(type(real_class)):
+    def __call__(cls, *args, **kwargs):
+      return factory(*args, **kwargs)
+
+  class DynamicProxy(real_class, metaclass=DynamicMeta):
+    __slots__ = ()
+
+    def __new__(cls, *args, **kwargs):
+      return factory(*args, **kwargs)
+
+  DynamicProxy.__name__ = real_class.__name__
+  DynamicProxy.__module__ = real_class.__module__
+  return DynamicProxy
+
+
+class DynamicTinygradUnpickler(pickle.Unpickler):
+  def find_class(self, module, name):
+    if module == "tinygrad.ops":
+      try:
+        importlib.import_module("tinygrad.uops")
+        module = "tinygrad.uops"
+      except ImportError:
+        pass
+    real_class = getattr(importlib.import_module(module), name)
+    if module.startswith("tinygrad"):
+      return _dynamic_factory(real_class)
+    return real_class
+
+
+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]))
+      f.readinto(pb)
+      yield pb
+  return DynamicTinygradUnpickler(io.BytesIO(opcodes), buffers=buffers()).load()

openpilot/sunnypilot/modeld_v2/meta_helper.py

@@ -1,26 +1,9 @@
 from openpilot.sunnypilot.modeld_v2.constants import Meta
-from openpilot.cereal import custom
 from openpilot.sunnypilot.modeld_v2.meta_20hz import Meta20hz
 from openpilot.sunnypilot.models.helpers import get_active_bundle
 
-ModelBundle = custom.ModelManagerSP.ModelBundle
-
 
 def load_meta_constants():
-  """
-  Determines and loads the appropriate meta model class based on the metadata provided. The function checks
-  specific keys and conditions within the provided metadata dictionary to identify the corresponding meta
-  model class to return.
-
-  :param model_metadata: Dictionary containing metadata about the model. It includes
-      details such as input shapes, output slices, and other configurations for identifying
-      metadata-dependent meta model classes.
-  :type model_metadata: dict
-  :return: The appropriate meta model class (Meta, MetaSimPose, or MetaTombRaider)
-      based on the conditions and metadata provided.
-  :rtype: type
-  """
   if (bundle := get_active_bundle()) and bundle.is20hz:
     return Meta20hz
-
-  return Meta  # Default
+  return Meta

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -6,24 +6,25 @@ This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
 
+from collections.abc import Callable
 import os
 os.environ['GMMU'] = '0'
-from openpilot.common.hardware import COMMA_HARDWARE
-from openpilot.selfdrive.modeld.helpers import usbgpu_present, load_oob
-import time
 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
+from openpilot.selfdrive.modeld.helpers import chestnut_present
 from openpilot.cereal import log
 from opendbc.car.structs import car
 from openpilot.cereal.services import SERVICE_LIST
-from setproctitle import setproctitle
 from openpilot.cereal.messaging import PubMaster, SubMaster
 from openpilot.cereal.visionipc import VisionStreamType
 from msgq.visionipc import VisionIpcClient, VisionBuf
 from opendbc.car.car_helpers import get_demo_car_params
-
-from tinygrad.tensor import Tensor
-
 from openpilot.common.file_chunker import open_file_chunked
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
@@ -37,18 +38,26 @@ 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 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.constants import Plan
+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, WARP_INPUTS, POLICY_INPUTS
-
+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.models.helpers import get_active_bundle
 from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController
 
 PROCESS_NAME = "openpilot.selfdrive.modeld.modeld_tinygrad"
+BIG_MODEL_TIMEOUT = 60
 
 
 def _pkl_exists(path):
@@ -68,6 +77,7 @@ def _find_driving_pkl(bundle):
   pkl_path = os.path.join(model_root, pkl_name)
   if _pkl_exists(pkl_path):
     return pkl_path
+  return None
 
 
 class FrameMeta:
@@ -84,14 +94,14 @@ class ModelState(ModelStateBase):
   inputs: dict[str, np.ndarray]
   prev_desire: np.ndarray
 
-  def __init__(self, cam_w: int, cam_h: int, usbgpu: bool = False):
+  def __init__(self, cam_w: int, cam_h: int, chestnut: bool = False):
     ModelStateBase.__init__(self)
 
     env_pkl = os.environ.get('COMBINED_MODEL_PKL')
     if env_pkl and os.path.exists(env_pkl):
       model_bundle = None
     else:
-      model_bundle = get_active_bundle()
+      model_bundle = get_active_bundle(chestnut=chestnut)
     self.generation = model_bundle.generation if model_bundle is not None else None
     overrides = {override.key: override.value for override in model_bundle.overrides} if model_bundle else {}
 
@@ -99,46 +109,50 @@ class ModelState(ModelStateBase):
     self.LONG_SMOOTH_SECONDS = float(overrides.get('long', ".0"))
     self.MIN_LAT_CONTROL_SPEED = 0.3
     self.PLANPLUS_CONTROL: float = 1.0
-    self.usbgpu = usbgpu
+    self.chestnut = chestnut
 
     pkl_path = _find_driving_pkl(model_bundle)
-    assert pkl_path is not None, "No driving pkl found — all models must be compiled with compile_modeld.py"
+    assert pkl_path is not None, f"No driving pkl found for {'chestnut' if chestnut else 'small model'} — all models must be compiled with compile_modeld.py"
     self._init_combined(pkl_path, cam_w, cam_h, model_bundle)
 
   def _init_combined(self, pkl_path, cam_w, cam_h, bundle):
     cloudlog.warning(f"loading combined pkl: {pkl_path}")
     jits = load_oob(open_file_chunked(pkl_path))
 
-    self.WARP_DEV = 'QCOM' if COMMA_HARDWARE else 'CPU'
-    self.DEV = 'AMD' if self.usbgpu else self.WARP_DEV
-    self.QUEUE_DEV = self.DEV
     metadata = jits['metadata']
+    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.is_run_model = 'run_model' in jits
 
-    self.is_legacy_model = 'run_policy' not in jits  # remove after next recompile
-    if self.is_legacy_model:
-      self.warp = jits[(cam_w, cam_h)]['warp_enqueue']
-      self.run_policy = jits[(cam_w, cam_h)]['run_policy']
-    else:
-      self.run_policy = jits['run_policy']
-      self.warp = jits[(cam_w, cam_h)]
+    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 'model' in metadata:
-      model_metadata = metadata['model']
+    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 model_metadata['input_shapes'] if 'img' in key]
-      frame_skip = derive_frame_skip({}, model_metadata['input_shapes'])
-      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'],
-                                                                          frame_skip, device=self.QUEUE_DEV)
-    else:
-      vision_metadata = metadata['vision']
-      policy_keys = [k for k in metadata if k != 'vision']
-      if policy_keys == ['policy']:
-        self._combined_model_type = 'split'
+      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._combined_model_type = 'multi_policy'
+        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]
@@ -154,57 +168,39 @@ class ModelState(ModelStateBase):
     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:
       from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
       self.constants = SplitModelConstants()
     else:
-      from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
       self.constants = ModelConstants()
 
-    if self._combined_model_type != 'supercombo':
-      from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
-      self.parser = SplitParser()
-    else:
-      from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
-      self.parser = CombinedParser()
-
+    self.parser = Parser()
     self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
-    self.full_frames: dict = {}
-    self._blob_cache: dict = {}
-    nv12_info = get_nv12_info(cam_w, cam_h)
-    self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)
 
-    yuv_size = self.frame_buf_params[self._road_key][3]
-    frame_tensor = Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize()
-    big_frame_tensor = Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize()
-
-    if self.is_legacy_model: # Remove this conditional hack after recompile
-      self.warp(**self.input_queues, frame=frame_tensor, big_frame=big_frame_tensor)
-    else:
-      self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=frame_tensor, big_frame=big_frame_tensor)
-
-    if self.usbgpu:
-      self.warmup()
+    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 = {k: np.zeros(self.frame_buf_params[k][3], dtype=np.uint8) for k in self._vision_input_names}
+    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 = {}
-    for k, v in self.numpy_inputs.items():
-      if k not in ['tfm', 'big_tfm', 'prev_feat']:
-        dummy_inputs[k] = np.zeros(v.shape, dtype=v.dtype)
-
-    self.run(dummy_frames, transforms, dummy_inputs, prepare_only=False)
-
-    for v in self.numpy_inputs.values():
-      v[:] = 0
+    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)
+    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
-    self.full_frames.clear()
-    self._blob_cache.clear()
-
 
   @property
   def mlsim(self) -> bool:
@@ -219,45 +215,50 @@ class ModelState(ModelStateBase):
     return self._desire_key
 
   def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
-                inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
-    for key in bufs.keys():
-      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
-      yuv_size = self.frame_buf_params[key][3]
-      cache_key = (key, ptr)
-      if cache_key not in self._blob_cache:
-        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
-      self.full_frames[key] = self._blob_cache[cache_key]
+          inputs: dict[str, np.ndarray],
+          after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray] | None:
+    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][:] = 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'):
       if key in self.numpy_inputs and key in inputs:
         self.numpy_inputs[key][:] = inputs[key]
 
-    road_key = self._road_key
-    wide_key = self._wide_key
-    self.numpy_inputs['tfm'][:, :] = transforms[road_key].reshape(3, 3)
-    self.numpy_inputs['big_tfm'][:, :] = transforms[wide_key].reshape(3, 3)
+    self.numpy_inputs['tfm'][:, :] = transforms[self._road_key].reshape(3, 3)
+    self.numpy_inputs['big_tfm'][:, :] = transforms[self._wide_key].reshape(3, 3)
 
-    if self.is_legacy_model:  # remove after next recompile
-      if prepare_only:
-        self.warp(**self.input_queues, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])
-        return None
-      raw_outputs = self.run_policy(**self.input_queues, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])
+    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:
-      if prepare_only:
-        self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])
-        return None
-      warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[road_key], big_frame=self.full_frames[wide_key])
+      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()
+
     if self._combined_model_type == 'supercombo':
       model_output = raw_outputs.numpy().flatten()
+      if self.chestnut and not np.all(np.isfinite(model_output)):
+        raise RuntimeError("model output not finite")
       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:
+      if 'prev_feat' in self.numpy_inputs and 'hidden_state' in self.vision_output_slices:
         self.numpy_inputs['prev_feat'][:] = model_output[self.vision_output_slices['hidden_state']]
     else:
       vision_output = raw_outputs[0].numpy().flatten()
@@ -287,10 +288,6 @@ class ModelState(ModelStateBase):
       buf[0, :-1] = buf[0, 1:]
       buf[0, -1, :] = outputs['desired_curvature'][0, :] if not self.mlsim else 0
 
-    if self.usbgpu and not np.all(np.isfinite(outputs.get('plan', np.array([0.])))):
-      cloudlog.error("model output not finite, dropping frame")
-      return None
-
     return outputs
 
   def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
@@ -327,13 +324,13 @@ def main(demo=False):
   setproctitle(PROCESS_NAME)
   config_realtime_process(7, 54)
 
-  USBGPU = usbgpu_present()
-  if USBGPU:
+  CHESTNUT = chestnut_present()
+  if CHESTNUT:
     os.environ['HCQDEV_WAIT_TIMEOUT_MS'] = '3000'
 
   params = Params()
-  params.put_bool("UsbGpuLoading", USBGPU)
-  params.remove("UsbGpuActive")
+  params.put_bool("ChestnutLoading", CHESTNUT)
+  params.remove("ChestnutActive")
 
   # visionipc clients
   while True:
@@ -362,31 +359,40 @@ def main(demo=False):
   st = time.monotonic()
 
   model = None
-  if USBGPU:
-    import threading
-    def load():
-      nonlocal model
-      model = ModelState(cam_w=vipc_client_main.width, cam_h=vipc_client_main.height, usbgpu=True)
-    t = threading.Thread(target=load, daemon=True)
-    t.start()
-    t.join(60)
+  if CHESTNUT:
+    big_model = None
+    def load_big():
+      nonlocal big_model
+      try:
+        m = ModelState(cam_w=vipc_client_main.width, cam_h=vipc_client_main.height, chestnut=True)
+        m.warmup()
+        big_model = m
+      except Exception:
+        cloudlog.exception("chestnut load failed")
+    loader = threading.Thread(target=load_big, daemon=True)
+    loader.start()
+    loader.join(BIG_MODEL_TIMEOUT)
+    model = big_model
     if model is None:
-      params.put_bool("UsbGpuActive", False)
-      raise RuntimeError("eGPU model load failed or timed out (60s)")
-    params.put_bool("UsbGpuActive", True)
-  else:
-    model = ModelState(cam_w=vipc_client_main.width, cam_h=vipc_client_main.height, usbgpu=False)
+      params.put_bool("ChestnutModelError", True)
+    params.put_bool("ChestnutActive", model is not None)
+    if model is not None:
+      params.remove("ChestnutModelError")
 
-  params.put_bool("UsbGpuLoading", False)
+  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:
+    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", "modelDataV2SP"] + (["chestnutState"] if USBGPU 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 = ChestnutState(pm, USBGPU) if USBGPU 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)
@@ -485,9 +491,6 @@ def main(demo=False):
     run_count = run_count + 1
 
     frame_drop_ratio = frames_dropped / (1 + frames_dropped)
-    prepare_only = vipc_dropped_frames > 0
-    if prepare_only:
-      cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")
 
     bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
     transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}
@@ -509,7 +512,22 @@ def main(demo=False):
       inputs['action_t'] = np.array([lat_action_t, long_action_t], dtype=np.float32)
 
     mt1 = time.perf_counter()
-    model_output = model.run(bufs, transforms, inputs, prepare_only)
+    try:
+      send_chestnut = (chestnut_state is not None and
+                       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("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
+      if chestnut_state is not None:
+        chestnut_state.big = False
+      run_count = 0
+      model_output = None
     mt2 = time.perf_counter()
     model_execution_time = mt2 - mt1
 
@@ -524,7 +542,7 @@ def main(demo=False):
       fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                      publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
                      frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen, meta_constants)
-      modelv2_send.modelV2.big = model.usbgpu
+      modelv2_send.modelV2.big = model.chestnut
 
       desire_state = modelv2_send.modelV2.meta.desireState
       l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
@@ -534,6 +552,7 @@ def main(demo=False):
       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
       drivingdata_send.drivingModelData.meta.laneChangeState = DH.lane_change_state
       drivingdata_send.drivingModelData.meta.laneChangeDirection = DH.lane_change_direction
@@ -545,9 +564,6 @@ def main(demo=False):
       pm.send('modelDataV2SP', mdv2sp_send)
     last_vipc_frame_id = meta_main.frame_id
 
-    if chestnut_state is not None and run_count % round(model.constants.MODEL_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0:
-      chestnut_state.send()
-
 if __name__ == "__main__":
   try:
     import argparse

openpilot/sunnypilot/modeld_v2/parse_model_outputs.py

@@ -115,22 +115,41 @@ class Parser:
     outs[name + '_stds'] = pred_std_final.reshape(final_shape)
 
   def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    # supercombo (4955 / 102) and newer variants (e.g. 990 / 144).
-    self.parse_mdn('plan',   outs, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
-    self.parse_mdn('lane_lines', outs, out_shape=(ModelConstants.NUM_LANE_LINES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('road_edges', outs, out_shape=(ModelConstants.NUM_ROAD_EDGES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('pose',         outs, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('road_transform', outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    if 'plan' in outs:
+      self.parse_mdn('plan', outs, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
+    if 'planplus' in outs:
+      self.parse_mdn('planplus', outs, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
+    if 'lane_lines' in outs:
+      self.parse_mdn('lane_lines', outs, out_shape=(ModelConstants.NUM_LANE_LINES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    if 'road_edges' in outs:
+      self.parse_mdn('road_edges', outs, out_shape=(ModelConstants.NUM_ROAD_EDGES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    if 'pose' in outs:
+      self.parse_mdn('pose', outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    if 'road_transform' in outs:
+      self.parse_mdn('road_transform', outs, out_shape=(ModelConstants.POSE_WIDTH,))
     if 'sim_pose' in outs:
       self.parse_mdn('sim_pose', outs, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('wide_from_device_euler', outs, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
-    self.parse_mdn('lead', outs, out_shape=(ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH))
+    if 'wide_from_device_euler' in outs:
+      self.parse_mdn('wide_from_device_euler', outs, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
+    if 'lead' in outs:
+      self.parse_mdn('lead', outs, out_shape=(ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH))
     if 'lat_planner_solution' in outs:
       self.parse_mdn('lat_planner_solution', outs, out_shape=(ModelConstants.IDX_N, ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
     if 'desired_curvature' in outs:
       self.parse_mdn('desired_curvature', outs, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
+    if 'action' in outs:
+      self.parse_mdn('action', outs, out_shape=(ModelConstants.ACTION_WIDTH,))
     for k in ['lead_prob', 'lane_lines_prob', 'meta']:
-      self.parse_binary_crossentropy(k, outs)
-    self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
-    self.parse_categorical_crossentropy('desire_pred',  outs, out_shape=(ModelConstants.DESIRE_PRED_LEN, ModelConstants.DESIRE_PRED_WIDTH))
+      if k in outs:
+        self.parse_binary_crossentropy(k, outs)
+    if 'desire_state' in outs:
+      self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
+    if 'desire_pred' in outs:
+      self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN, ModelConstants.DESIRE_PRED_WIDTH))
     return outs
+
+  def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    return self.parse_outputs(outs)
+
+  def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    return self.parse_outputs(outs)

openpilot/sunnypilot/modeld_v2/parse_model_outputs_split.py

@@ -1,159 +0,0 @@
-import numpy as np
-from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
-
-
-def safe_exp(x, out=None):
-  # -11 is around 10**14, more causes float16 overflow
-  return np.exp(np.clip(x, -np.inf, 11), out=out)
-
-
-def sigmoid(x):
-  return 1. / (1. + safe_exp(-x))
-
-
-def softmax(x, axis=-1):
-  x -= np.max(x, axis=axis, keepdims=True)
-  if x.dtype == np.float32 or x.dtype == np.float64:
-    safe_exp(x, out=x)
-  else:
-    x = safe_exp(x)
-  x /= np.sum(x, axis=axis, keepdims=True)
-  return x
-
-
-class Parser:
-  def __init__(self, ignore_missing=False):
-    self.ignore_missing = ignore_missing
-
-  def check_missing(self, outs, name):
-    if name not in outs and not self.ignore_missing:
-      raise ValueError(f"Missing output {name}")
-    return name not in outs
-
-  def parse_categorical_crossentropy(self, name, outs, out_shape=None):
-    if self.check_missing(outs, name):
-      return
-    raw = outs[name]
-    if out_shape is not None:
-      raw = raw.reshape((raw.shape[0],) + out_shape)
-    outs[name] = softmax(raw, axis=-1)
-
-  def parse_binary_crossentropy(self, name, outs):
-    if self.check_missing(outs, name):
-      return
-    raw = outs[name]
-    outs[name] = sigmoid(raw)
-
-  def parse_mdn(self, name, outs, in_N=0, out_N=1, out_shape=None):
-    if self.check_missing(outs, name):
-      return
-    raw = outs[name]
-    raw = raw.reshape((raw.shape[0], max(in_N, 1), -1))
-
-    n_values = (raw.shape[2] - out_N)//2
-    pred_mu = raw[:,:,:n_values]
-    pred_std = safe_exp(raw[:,:,n_values: 2*n_values])
-
-    if in_N > 1:
-      weights = np.zeros((raw.shape[0], in_N, out_N), dtype=raw.dtype)
-      for i in range(out_N):
-        weights[:,:,i - out_N] = softmax(raw[:,:,i - out_N], axis=-1)
-
-      if out_N == 1:
-        for fidx in range(weights.shape[0]):
-          idxs = np.argsort(weights[fidx][:,0])[::-1]
-          weights[fidx] = weights[fidx][idxs]
-          pred_mu[fidx] = pred_mu[fidx][idxs]
-          pred_std[fidx] = pred_std[fidx][idxs]
-      assert out_shape is not None
-      full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
-      outs[name + '_weights'] = weights
-      outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
-      outs[name + '_stds_hypotheses'] = pred_std.reshape(full_shape)
-
-      pred_mu_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
-      pred_std_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
-      for fidx in range(weights.shape[0]):
-        for hidx in range(out_N):
-          idxs = np.argsort(weights[fidx,:,hidx])[::-1]
-          pred_mu_final[fidx, hidx] = pred_mu[fidx, idxs[0]]
-          pred_std_final[fidx, hidx] = pred_std[fidx, idxs[0]]
-    else:
-      pred_mu_final = pred_mu
-      pred_std_final = pred_std
-
-    if out_N > 1:
-      assert out_shape is not None
-      final_shape = tuple([raw.shape[0], out_N] + list(out_shape))
-    else:
-      assert out_shape is not None
-      final_shape = tuple([raw.shape[0],] + list(out_shape))
-    outs[name] = pred_mu_final.reshape(final_shape)
-    outs[name + '_stds'] = pred_std_final.reshape(final_shape)
-
-  def is_mhp(self, outs, name, shape):
-    if self.check_missing(outs, name):
-      return False
-    if outs[name].shape[1] == 2 * shape:
-      return False
-    return True
-
-  def parse_dynamic_outputs(self, outs: dict[str, np.ndarray]) -> None:
-    if 'lead' in outs:
-      lead_mhp = self.is_mhp(outs, 'lead',
-                             SplitModelConstants.LEAD_MHP_SELECTION * SplitModelConstants.LEAD_TRAJ_LEN * SplitModelConstants.LEAD_WIDTH)
-      lead_in_N, lead_out_N = (SplitModelConstants.LEAD_MHP_N, SplitModelConstants.LEAD_MHP_SELECTION) if lead_mhp else (0, 0)
-      lead_out_shape = (SplitModelConstants.LEAD_TRAJ_LEN, SplitModelConstants.LEAD_WIDTH) if lead_mhp else \
-        (SplitModelConstants.LEAD_MHP_SELECTION, SplitModelConstants.LEAD_TRAJ_LEN, SplitModelConstants.LEAD_WIDTH)
-      self.parse_mdn('lead', outs, in_N=lead_in_N, out_N=lead_out_N, out_shape=lead_out_shape)
-    if 'plan' in outs:
-      plan_mhp = self.is_mhp(outs, 'plan', SplitModelConstants.IDX_N * SplitModelConstants.PLAN_WIDTH)
-      plan_in_N, plan_out_N = (SplitModelConstants.PLAN_MHP_N, SplitModelConstants.PLAN_MHP_SELECTION) if plan_mhp else (0, 0)
-      self.parse_mdn('plan', outs, in_N=plan_in_N, out_N=plan_out_N,
-                     out_shape=(SplitModelConstants.IDX_N, SplitModelConstants.PLAN_WIDTH))
-    if 'planplus' in outs:
-      self.parse_mdn('planplus', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.IDX_N, SplitModelConstants.PLAN_WIDTH))
-
-  def split_outputs(self, outs: dict[str, np.ndarray]) -> None:
-    if 'desired_curvature' in outs:
-      self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.DESIRED_CURV_WIDTH,))
-    if 'desire_pred' in outs:
-      self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(SplitModelConstants.DESIRE_PRED_LEN,SplitModelConstants.DESIRE_PRED_WIDTH))
-    if 'desire_state' in outs:
-      self.parse_categorical_crossentropy('desire_state', outs, out_shape=(SplitModelConstants.DESIRE_PRED_WIDTH,))
-    if 'lane_lines' in outs:
-      self.parse_mdn('lane_lines', outs, in_N=0, out_N=0,
-                     out_shape=(SplitModelConstants.NUM_LANE_LINES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
-    if 'lane_lines_prob' in outs:
-      self.parse_binary_crossentropy('lane_lines_prob', outs)
-    if 'lead_prob' in outs:
-      self.parse_binary_crossentropy('lead_prob', outs)
-    if 'lat_planner_solution' in outs:
-      self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.IDX_N,SplitModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
-    if 'meta' in outs:
-      self.parse_binary_crossentropy('meta', outs)
-    if 'road_edges' in outs:
-      self.parse_mdn('road_edges', outs, in_N=0, out_N=0,
-                     out_shape=(SplitModelConstants.NUM_ROAD_EDGES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
-    if 'sim_pose' in outs:
-      self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
-    if 'action' in outs:
-      self.parse_mdn('action', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.ACTION_WIDTH,))
-
-  def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
-    self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.WIDE_FROM_DEVICE_WIDTH,))
-    self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
-    self.parse_dynamic_outputs(outs)
-    self.split_outputs(outs)
-    return outs
-
-  def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    self.parse_dynamic_outputs(outs)
-    self.split_outputs(outs)
-    return outs
-
-  def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    outs = self.parse_vision_outputs(outs)
-    outs = self.parse_policy_outputs(outs)
-    return outs

openpilot/sunnypilot/modeld_v2/tests/helpers.py

@@ -117,7 +117,7 @@ ARCHETYPES = {
     is_20hz=True,
     expected_model_type='split',
     expected_constants_class=SplitModelConstants,
-    expected_parser_module='parse_model_outputs_split',
+    expected_parser_module='parse_model_outputs',
     expected_desire_key='desire',
   ),
   'vision_multi_policy': Archetype(
@@ -130,7 +130,7 @@ ARCHETYPES = {
     is_20hz=True,
     expected_model_type='multi_policy',
     expected_constants_class=SplitModelConstants,
-    expected_parser_module='parse_model_outputs_split',
+    expected_parser_module='parse_model_outputs',
     expected_desire_key='desire',
   ),
   'tri_policy': Archetype(
@@ -144,7 +144,7 @@ ARCHETYPES = {
     is_20hz=True,
     expected_model_type='multi_policy',
     expected_constants_class=SplitModelConstants,
-    expected_parser_module='parse_model_outputs_split',
+    expected_parser_module='parse_model_outputs',
     expected_desire_key='desire',
   ),
   'supercombo_non20hz': Archetype(
@@ -190,8 +190,8 @@ def tmp_path():
 
 def patch_modeld(monkeypatch):
   def _patch(bundle):
-    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None: bundle)
-    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None: bundle)
+    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None, *, chestnut=None: bundle)
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None, *, chestnut=None: bundle)
 
   return _patch
 

openpilot/sunnypilot/modeld_v2/tests/test_camera_offset_helper.py

@@ -6,8 +6,9 @@ See the LICENSE.md file in the root directory for more details.
 """
 import numpy as np
 
-from openpilot.common.transformations.camera import DEVICE_CAMERAS
+from openpilot.common.transformations.camera import DEVICE_CAMERAS, view_frame_from_device_frame
 from openpilot.common.transformations.model import get_warp_matrix
+from openpilot.common.transformations.orientation import rot_from_euler
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
 from openpilot.common.test import OpenpilotTestCase
 
@@ -46,29 +47,50 @@ class TestCameraOffset(OpenpilotTestCase):
     self.camera_offset.update(main_transform, extra_transform, sm, False)
     np.testing.assert_almost_equal(self.camera_offset.actual_camera_offset, 0.038)
 
-  def test_camera_offset_(self):
+  def test_apply_camera_offset(self):
     intrinsics = self.dc.narrow_road.intrinsics
+    v_horizon = CameraOffsetHelper.get_v_horizon(intrinsics, [])  # pitch = 0 fallback: v_horizon == cy
     transform = np.eye(3, dtype=np.float32)
     height = 1.22
     offset = 0.1
 
-    cy = intrinsics[1, 2]
     expected_shear = np.eye(3, dtype=np.float32)
     expected_shear[0, 1] = offset / height
-    expected_shear[0, 2] = -offset / height * cy
+    expected_shear[0, 2] = -offset / height * v_horizon
 
-    result = CameraOffsetHelper.apply_camera_offset(transform, intrinsics, height, offset)
+    result = CameraOffsetHelper.apply_camera_offset(transform, height, offset, v_horizon)
     np.testing.assert_array_almost_equal(result, expected_shear)
 
+  def test_v_horizon_empty_rpy(self):
+    intrinsics = self.dc.narrow_road.intrinsics
+    v_horizon = CameraOffsetHelper.get_v_horizon(intrinsics, [])
+    np.testing.assert_almost_equal(v_horizon, intrinsics[1, 2])
+
+  def test_v_horizon_projection(self):
+    intrinsics = self.dc.narrow_road.intrinsics
+    f, cy = intrinsics[1, 1], intrinsics[1, 2]
+
+    for pitch_deg in [6.0, -6.0, 0.0]:
+      rpy = [0.0, np.radians(pitch_deg), 0.0]
+      d_dev = rot_from_euler(rpy) @ np.array([1.0, 0.0, 0.0])
+      view = view_frame_from_device_frame @ d_dev
+      expected = cy + f * view[1] / view[2]
+
+      v_horizon = CameraOffsetHelper.get_v_horizon(intrinsics, rpy)
+      np.testing.assert_almost_equal(v_horizon, expected, decimal=4)
+
   def test_update(self):
+    height = 1.2
+    pitch = np.radians(-8.0)
+
     sm = MockStruct(
       deviceState=MockStruct(deviceType='mici'),
       narrowRoadCameraState=MockStruct(sensor='os04c10'),
-      extrinsicsCalibration=MockStruct(rpyCalib=[0.0, 0.0, 0.0], height=[1.22])
+      extrinsicsCalibration=MockStruct(rpyCalib=[0.0, pitch, 0.0], height=[height])
     )
     intrinsics_main = self.dc.narrow_road.intrinsics
     intrinsics_extra = self.dc.wide_road.intrinsics
-    device_from_calib_euler = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+    device_from_calib_euler = np.array(sm['extrinsicsCalibration'].rpyCalib, dtype=np.float32)
     main_transform = get_warp_matrix(device_from_calib_euler, intrinsics_main, False).astype(np.float32)
     extra_transform = get_warp_matrix(device_from_calib_euler, intrinsics_extra, True).astype(np.float32)
 
@@ -81,5 +103,13 @@ class TestCameraOffset(OpenpilotTestCase):
     main_out, extra_out = self.camera_offset.update(main_transform, extra_transform, sm, False)
     assert not np.array_equal(main_out, main_transform)
     assert not np.array_equal(extra_out, extra_transform)
-    assert main_out[0, 1] != 0.0
-    assert main_out[0, 2] != 0.0
+
+    # settle the low-pass filter
+    for _ in range(100):
+      main_out, extra_out = self.camera_offset.update(main_transform, extra_transform, sm, False)
+
+    # undo main_transform dot product to get shear matrix
+    shear = main_out @ np.linalg.inv(main_transform)
+    expected_v_horizon = intrinsics_main[1, 2] - intrinsics_main[1, 1] * np.tan(pitch)
+    np.testing.assert_almost_equal(shear[0, 1], self.camera_offset.actual_camera_offset / height, decimal=4)
+    np.testing.assert_almost_equal(shear[0, 2], -self.camera_offset.actual_camera_offset / height * expected_v_horizon, decimal=4)

openpilot/sunnypilot/modeld_v2/tests/test_combined_pkl_loader.py

@@ -59,8 +59,8 @@ class TestFindDrivingPkl(OpenpilotTestCase):
 class TestModelStateCombinedInit(OpenpilotTestCase):
   def test_asserts_when_no_pkl(self, monkeypatch):
     bundle = DummyBundle(models=[], is_20hz=True)
-    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None: bundle)
-    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None: bundle)
+    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None, *, chestnut=None: bundle)
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None, *, chestnut=None: bundle)
     with self.assertRaisesRegex(AssertionError, "No driving pkl found"):
       ModelState(cam_w=CAM_W, cam_h=CAM_H)
 
@@ -75,11 +75,11 @@ class TestStockEquivalence(OpenpilotTestCase):
 
     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 = make_input_queues(stock_shapes, frame_skip, device='NPY')
+    stock_queues, stock_npy, _frame_views = make_input_queues(stock_shapes, frame_skip, device='NPY', frame_copy_size=49152)
 
-    assert set(state.input_queues.keys()) == set(stock_queues.keys())
+    # 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())
-    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - {'action_t', 'prev_feat'}
 
   def test_split_queue_keys_work_with_desire_key(self, model_state_factory):
     from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
@@ -103,6 +103,23 @@ class TestStockEquivalence(OpenpilotTestCase):
     assert state.vision_output_slices == arch.metadata_structure['vision']['output_slices']
     assert state.policy_output_slices == arch.metadata_structure['policy']['output_slices']
 
+  def test_unified_run_model(self, tmp_path, monkeypatch, patch_modeld):
+    from openpilot.common.hardware import hw
+    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)}
+    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.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())
 

openpilot/sunnypilot/modeld_v2/tests/test_compile_modeld.py

@@ -5,10 +5,16 @@ This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
 
+import os
+import tempfile
+import unittest
+from pathlib import Path
+
 import numpy as np
 from openpilot.common.parameterized import parameterized
 
-from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, _detect_desire_key
+from openpilot.common.file_chunker import chunk_file, get_chunk_targets
+from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, _detect_desire_key, read_file_chunked_to_disk
 from openpilot.common.test import OpenpilotTestCase
 
 
@@ -160,3 +166,119 @@ class TestOutputSlicePreservation(OpenpilotTestCase):
     policy_slices = {'plan': slice(0, 495), 'meta': slice(495, 550)}
     assert set(vision_slices.keys()) & set(policy_slices.keys()) == set(), \
       "vision and policy slices should not overlap in keys"
+
+
+class TestReadFileChunkedToDisk(OpenpilotTestCase):
+  def test_none_passthrough(self):
+    assert read_file_chunked_to_disk(None) is None
+
+  def test_unchunked_source_staged_on_disk(self):
+    with tempfile.TemporaryDirectory() as d:
+      src = Path(d) / "driving_supercombo.onnx"
+      payload = os.urandom(1024)
+      src.write_bytes(payload)
+
+      out = Path(read_file_chunked_to_disk(str(src)))
+
+      assert out.parent == Path(d)
+      assert out.name == "driving_supercombo.onnx.unchunked"
+      assert out.read_bytes() == payload
+
+  def test_chunked_source_reassembled_on_disk(self):
+    with tempfile.TemporaryDirectory() as d:
+      src = Path(d) / "driving_supercombo.onnx"
+      payload = os.urandom(4096)
+      src.write_bytes(payload)
+      chunk_file(str(src), get_chunk_targets(str(src), len(payload)))
+      assert not src.exists()
+
+      out = Path(read_file_chunked_to_disk(str(src)))
+
+      assert out.parent == Path(d)
+      assert out.read_bytes() == payload
+
+
+class Test4DFeaturesBuffer(OpenpilotTestCase):
+  def test_get_policy_npy_shapes_4d(self):
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes
+    input_shapes = {
+      'desire_pulse': (1, 25, 8),
+      'features_buffer': (1, 24, 32, 512),  # compare 4d to 3d for regression
+      'traffic_convention': (1, 2),
+      'action_t': (1, 2)
+    }
+    shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=True)
+    assert shapes['prev_feat'] == (1, 16384)
+    assert sizes == [8, 2, 2, 16384]
+
+  def test_get_policy_npy_shapes_3d(self):
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes
+    input_shapes = {
+      'desire_pulse': (1, 25, 8),
+      'features_buffer': (1, 24, 512),
+      'traffic_convention': (1, 2),
+      'action_t': (1, 2)
+    }
+    shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=True)
+    assert shapes['prev_feat'] == (1, 512)
+    assert sizes == [8, 2, 2, 512]
+
+
+class TestStockCompileModeldEquivalence(OpenpilotTestCase):
+  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
+
+    stock_input_shapes = {
+      'desire_pulse': (1, 25, 8),
+      'features_buffer': (1, 24, 512), # see below comment
+      'traffic_convention': (1, 2),
+      'action_t': (1, 2),
+    }
+
+    stock_shapes, stock_sizes = stock_get_policy_npy_shapes(stock_input_shapes)
+    sunny_shapes, sunny_sizes = sunny_get_policy_npy_shapes(stock_input_shapes, is_supercombo=True)
+
+    assert sunny_shapes == stock_shapes
+    assert sunny_sizes == stock_sizes
+    assert sunny_shapes['prev_feat'] == (1, 512)
+
+  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
+    input_shapes = {
+      'img': (1, 12, 128, 256),
+      'desire_pulse': (1, 25, 8),
+      'features_buffer': (1, 24, 32, 512),
+      'traffic_convention': (1, 2),
+      'action_t': (1, 2),
+    }
+    frame_skip = 4
+
+    stock_queues, stock_npy, _frame_views = stock_make_input_queues(input_shapes, frame_skip, device='NPY', frame_copy_size=49152)
+    sunny_queues, sunny_npy = sunny_make_supercombo_input_queues(input_shapes, frame_skip, device='NPY')
+    # sunnypilot split pipeline has tfm/big_tfm as queues; packed_npy_inputs size differs (different frame packing)
+    assert set(stock_queues.keys()) <= set(sunny_queues.keys())
+    for key in stock_queues:
+      if key == 'packed_npy_inputs':
+        continue
+      assert sunny_queues[key].shape == stock_queues[key].shape, \
+        f"Queue shape mismatch for {key}: sunny {sunny_queues[key].shape} != stock {stock_queues[key].shape}"
+    assert set(stock_npy.keys()) <= set(sunny_npy.keys())
+    for key in stock_npy:
+      assert sunny_npy[key].shape == stock_npy[key].shape, \
+        f"Numpy array shape mismatch for {key}: sunny {sunny_npy[key].shape} != stock {stock_npy[key].shape}"
+
+  @unittest.skip("upstream removed make_warp_input_queues — warp merged into run_model")
+  def test_make_warp_queues_stock_equivalence(self):
+    from openpilot.selfdrive.modeld.compile_modeld import make_warp_input_queues as stock_make_warp_queues
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import make_warp_queues as sunny_make_warp_queues
+    stock_vision_shapes = {'img': (1, 12, 128, 256)}  # for now?
+    stock_queues, stock_npy = stock_make_warp_queues(stock_vision_shapes, frame_skip=4, device='NPY')
+    sunny_queues, sunny_npy = sunny_make_warp_queues(device='NPY')
+
+    assert set(sunny_npy.keys()) == set(stock_npy.keys()) == {'tfm', 'big_tfm'}
+    for key in sunny_npy:
+      assert sunny_npy[key].shape == stock_npy[key].shape == (3, 3)
+
+

openpilot/sunnypilot/modeld_v2/tests/test_fallback.py

@@ -0,0 +1,62 @@
+"""
+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 io
+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.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
+from openpilot.sunnypilot.models.fetcher import ModelParser, ModelFetcher
+
+tmp_path = tests_helpers.tmp_path
+
+
+class TestFallback(OpenpilotTestCase):
+  def test_find_dual_model_in_bundle(self, tmp_path, monkeypatch):
+    lebowski_file = 'driving_lebowski.pkl'
+    tsfdo_file = 'driving_tsfdo.pkl'
+    (tmp_path / lebowski_file).write_bytes(b'fkasdjfkljf')
+    (tmp_path / tsfdo_file).write_bytes(b'dskfajklsdjlsfka')
+
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+    big_bundle = DummyBundle(models=[DummyModel('supercombo', lebowski_file)])
+    small_bundle = DummyBundle(models=[DummyModel('supercombo', tsfdo_file)])
+    big_pkl = modeld_module._find_driving_pkl(big_bundle)
+    small_pkl = modeld_module._find_driving_pkl(small_bundle)
+
+    assert big_pkl is not None and lebowski_file in big_pkl
+    assert small_pkl is not None and tsfdo_file in small_pkl
+
+  def test_download_models_and_init_modelstate_fallback(self, tmp_path, monkeypatch):
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+    big_json = requests.get(ModelFetcher.MODEL_URL_CHESTNUT).json()
+    big_bundle = ModelParser.parse_models(big_json)[-1]
+    small_json = requests.get(ModelFetcher.MODEL_URL).json()
+    small_bundle = ModelParser.parse_models(small_json)[-1]
+
+    buf = io.BytesIO()
+    dump_oob(tests_helpers.make_pkl_data(tests_helpers.ARCHETYPES['supercombo_non20hz']), buf)
+    oob_bytes = buf.getvalue()
+
+    for bundle in (big_bundle, small_bundle):
+      artifact = bundle.models[0].artifact
+      for i in range(len(artifact.chunks)):
+        (tmp_path / get_chunk_name(artifact.fileName, i, len(artifact.chunks))).write_bytes(oob_bytes if i == 0 else b"")
+
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None, *, chestnut=None: small_bundle)
+    assert modeld_module.ModelState(CAM_W, CAM_H, chestnut=False).chestnut is False
+
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None, *, chestnut=None: big_bundle)
+    try:
+      assert modeld_module.ModelState(CAM_W, CAM_H, chestnut=True).chestnut is True
+    except Exception as e:
+      assert "AMD" in str(e) or "device" in str(e).lower()

openpilot/sunnypilot/modeld_v2/tests/test_models.py

@@ -0,0 +1,43 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import os
+import unittest
+from unittest.mock import patch
+from openpilot.common.file_chunker import open_file_chunked
+from openpilot.sunnypilot.modeld_v2.helpers import load_oob
+from tinygrad.device import Device
+
+
+class TestLegacyModels(unittest.TestCase):
+  def test_legacy_model_load(self):
+    base_name = os.environ.get("MODEL_BASE_NAME")
+    if not base_name:
+      raise unittest.SkipTest("MODEL_BASE_NAME env var not set, skipping integration test.")
+    chunk_dir = os.environ.get("MODEL_CHUNK_DIR", "/tmp/model_chunks")
+    base_path = os.path.join(chunk_dir, base_name)
+
+    try:
+      f = open_file_chunked(base_path)
+    except Exception as error:
+      self.fail(f"Failed to open chunked file {base_path}: {error}")
+    self.addCleanup(f.close)
+
+    real_getitem = Device.__class__.__getitem__
+
+    def safe_getitem(device_self, ix):
+      if ix == "QCOM" and not os.path.exists("/dev/kgsl-3d0"):
+        return real_getitem(device_self, "CPU")
+      if ix == "AMD" and not os.path.exists("/dev/kfd"):
+        return real_getitem(device_self, "CPU")
+      return real_getitem(device_self, ix)
+
+    with patch.object(Device.__class__, "__getitem__", safe_getitem):
+      obj = load_oob(f)
+
+    assert isinstance(obj, dict), "Parsed object is not a dictionary"
+    assert "metadata" in obj, "Metadata key is missing"

openpilot/sunnypilot/modeld_v2/tests/test_parse_model_outputs.py

@@ -0,0 +1,81 @@
+import numpy as np
+from openpilot.common.test import OpenpilotTestCase
+from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
+from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser, _infer_mhp, sigmoid, softmax
+
+
+class TestParseModelOutputs(OpenpilotTestCase):
+  def test_infer_mhp_lead(self):
+    in_hypotheses, out_selections = _infer_mhp(102, 24)
+    assert in_hypotheses == 2
+    assert out_selections == 3
+
+  def test_infer_mhp_plan(self):
+    in_hypotheses, out_selections = _infer_mhp(4955, 495)
+    assert in_hypotheses == 5
+    assert out_selections == 1
+
+  def test_infer_mhp_non_mdn(self):
+    in_hypotheses, out_selections = _infer_mhp(48, 24)
+    assert in_hypotheses == 1
+    assert out_selections == 0
+
+  def test_check_missing_raises(self):
+    parser = Parser(ignore_missing=False)
+    with self.assertRaises(ValueError):
+      parser.check_missing({}, "missing_key")
+
+  def test_check_missing_ignored(self):
+    parser = Parser(ignore_missing=True)
+    assert parser.check_missing({}, "missing_key") is True
+
+  def test_binary_crossentropy(self):
+    parser = Parser()
+    raw_logits = np.array([[-10.0, 0.0, 10.0]], dtype=np.float32)
+    outs = {"meta": raw_logits.copy()}
+    parser.parse_binary_crossentropy("meta", outs)
+    expected_probabilities = sigmoid(raw_logits)
+    np.testing.assert_allclose(outs["meta"], expected_probabilities, rtol=1e-5, atol=1e-6)
+
+  def test_categorical_crossentropy(self):
+    parser = Parser()
+    raw_logits = np.array([[1.0, 2.0, 3.0]], dtype=np.float32)
+    outs = {"desire_state": raw_logits.copy()}
+    parser.parse_categorical_crossentropy("desire_state", outs)
+    expected_probabilities = softmax(raw_logits)
+    np.testing.assert_allclose(outs["desire_state"], expected_probabilities, rtol=1e-5, atol=1e-6)
+
+  def test_parse_vision_outputs(self):
+    parser = Parser()
+    pose_raw = np.zeros((1, ModelConstants.POSE_WIDTH * 2), dtype=np.float32)
+    road_transform_raw = np.zeros((1, ModelConstants.POSE_WIDTH * 2), dtype=np.float32)
+    lead_raw = np.zeros((1, 102), dtype=np.float32)
+    meta_raw = np.zeros((1, 55), dtype=np.float32)
+    vision_outputs = {"pose": pose_raw, "road_transform": road_transform_raw, "lead": lead_raw, "meta": meta_raw}
+    parsed = parser.parse_vision_outputs(vision_outputs)
+    assert "pose" in parsed
+    assert "road_transform" in parsed
+    assert "lead" in parsed
+    assert "meta" in parsed
+    assert parsed["pose"].shape == (1, ModelConstants.POSE_WIDTH)
+    assert parsed["lead"].shape == (1, ModelConstants.LEAD_MHP_SELECTION, ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH)
+
+  def test_parse_policy_outputs(self):
+    parser = Parser()
+    plan_raw = np.zeros((1, 4955), dtype=np.float32)
+    desire_state_raw = np.zeros((1, ModelConstants.DESIRE_PRED_WIDTH), dtype=np.float32)
+    action_raw = np.zeros((1, ModelConstants.ACTION_WIDTH * 2), dtype=np.float32)
+    policy_outputs = {"plan": plan_raw, "desire_state": desire_state_raw, "action": action_raw}
+    parsed = parser.parse_policy_outputs(policy_outputs)
+    assert parsed["plan"].shape == (1, ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH)
+    assert parsed["action"].shape == (1, ModelConstants.ACTION_WIDTH)
+    assert parsed["desire_state"].shape == (1, ModelConstants.DESIRE_PRED_WIDTH)
+
+  def test_parse_outputs_combined(self):
+    parser = Parser()
+    outputs = {"plan": np.zeros((1, 4955), dtype=np.float32), "pose": np.zeros((1, ModelConstants.POSE_WIDTH * 2),
+               dtype=np.float32), "meta": np.zeros((1, 55), dtype=np.float32)}
+    parsed = parser.parse_outputs(outputs)
+    assert parsed["plan"].shape == (1, ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH)
+    assert parsed["pose"].shape == (1, ModelConstants.POSE_WIDTH)
+    assert parsed["meta"].shape == (1, 55)

openpilot/sunnypilot/models/constants.py

@@ -1,121 +0,0 @@
-import numpy as np
-
-def index_function(idx, max_val=192, max_idx=32):
-  return max_val * ((idx/max_idx)**2)
-
-
-class ModelConstants:
-  # time and distance indices
-  IDX_N = 33
-  T_IDXS = [index_function(idx, max_val=10.0) for idx in range(IDX_N)]
-  X_IDXS = [index_function(idx, max_val=192.0) for idx in range(IDX_N)]
-  LEAD_T_IDXS = [0., 2., 4., 6., 8., 10.]
-  LEAD_T_OFFSETS = [0., 2., 4.]
-  META_T_IDXS = [2., 4., 6., 8., 10.]
-
-  # model inputs constants
-  MODEL_FREQ = 20
-  FEATURE_LEN = 512
-  HISTORY_BUFFER_LEN = 99
-  DESIRE_LEN = 8
-  TRAFFIC_CONVENTION_LEN = 2
-  NAV_FEATURE_LEN = 256
-  NAV_INSTRUCTION_LEN = 150
-  LAT_PLANNER_STATE_LEN = 4
-  LATERAL_CONTROL_PARAMS_LEN = 2
-  PREV_DESIRED_CURV_LEN = 1
-
-  # model outputs constants
-  FCW_THRESHOLDS_5MS2 = np.array([.05, .05, .15, .15, .15], dtype=np.float32)
-  FCW_THRESHOLDS_3MS2 = np.array([.7, .7], dtype=np.float32)
-  FCW_5MS2_PROBS_WIDTH = 5
-  FCW_3MS2_PROBS_WIDTH = 2
-
-  DISENGAGE_WIDTH = 5
-  POSE_WIDTH = 6
-  WIDE_FROM_DEVICE_WIDTH = 3
-  SIM_POSE_WIDTH = 6
-  LEAD_WIDTH = 4
-  LANE_LINES_WIDTH = 2
-  ROAD_EDGES_WIDTH = 2
-  PLAN_WIDTH = 15
-  DESIRE_PRED_WIDTH = 8
-  LAT_PLANNER_SOLUTION_WIDTH = 4
-  DESIRED_CURV_WIDTH = 1
-
-  NUM_LANE_LINES = 4
-  NUM_ROAD_EDGES = 2
-
-  LEAD_TRAJ_LEN = 6
-  DESIRE_PRED_LEN = 4
-
-  PLAN_MHP_N = 5
-  LEAD_MHP_N = 2
-  PLAN_MHP_SELECTION = 1
-  LEAD_MHP_SELECTION = 3
-
-  FCW_THRESHOLD_5MS2_HIGH = 0.15
-  FCW_THRESHOLD_5MS2_LOW = 0.05
-  FCW_THRESHOLD_3MS2 = 0.7
-
-  CONFIDENCE_BUFFER_LEN = 5
-  RYG_GREEN = 0.01165
-  RYG_YELLOW = 0.06157
-
-  POLY_PATH_DEGREE = 4
-
-
-# model outputs slices
-class Plan:
-  POSITION = slice(0, 3)
-  VELOCITY = slice(3, 6)
-  ACCELERATION = slice(6, 9)
-  T_FROM_CURRENT_EULER = slice(9, 12)
-  ORIENTATION_RATE = slice(12, 15)
-
-
-class Meta:
-  ENGAGED = slice(0, 1)
-  # next 2, 4, 6, 8, 10 seconds
-  GAS_DISENGAGE = slice(1, 31, 6)
-  BRAKE_DISENGAGE = slice(2, 31, 6)
-  STEER_OVERRIDE = slice(3, 31, 6)
-  HARD_BRAKE_3 = slice(4, 31, 6)
-  HARD_BRAKE_4 = slice(5, 31, 6)
-  HARD_BRAKE_5 = slice(6, 31, 6)
-  # next 0, 2, 4, 6, 8, 10 seconds
-  GAS_PRESS = slice(31, 55, 4)
-  BRAKE_PRESS = slice(32, 55, 4)
-  LEFT_BLINKER = slice(33, 55, 4)
-  RIGHT_BLINKER = slice(34, 55, 4)
-
-
-class MetaTombRaider:
-  ENGAGED = slice(0, 1)
-  # next 2, 4, 6, 8, 10 seconds
-  GAS_DISENGAGE = slice(1, 41, 8)
-  BRAKE_DISENGAGE = slice(2, 41, 8)
-  STEER_OVERRIDE = slice(3, 41, 8)
-  HARD_BRAKE_3 = slice(4, 41, 8)
-  HARD_BRAKE_4 = slice(5, 41, 8)
-  HARD_BRAKE_5 = slice(6, 41, 8)
-  GAS_PRESS = slice(7, 41, 8)
-  BRAKE_PRESS = slice(8, 41, 8)
-  # next 0, 2, 4, 6, 8, 10 seconds
-  LEFT_BLINKER = slice(41, 53, 2)
-  RIGHT_BLINKER = slice(42, 53, 2)
-
-
-class MetaSimPose:
-  ENGAGED = slice(0, 1)
-  # next 2, 4, 6, 8, 10 seconds
-  GAS_DISENGAGE = slice(1, 36, 7)
-  BRAKE_DISENGAGE = slice(2, 36, 7)
-  STEER_OVERRIDE = slice(3, 36, 7)
-  HARD_BRAKE_3 = slice(4, 36, 7)
-  HARD_BRAKE_4 = slice(5, 36, 7)
-  HARD_BRAKE_5 = slice(6, 36, 7)
-  GAS_PRESS = slice(7, 36, 7)
-  # next 0, 2, 4, 6, 8, 10 seconds
-  LEFT_BLINKER = slice(36, 48, 2)
-  RIGHT_BLINKER = slice(37, 48, 2)