v24

replace oauth with token

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

@@ -78,6 +78,7 @@ 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

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

@@ -341,7 +341,7 @@ jobs:
       - name: Upload model to HF
         if: ${{ inputs.target == 'small' || inputs.target == 'big' }}
         env:
-          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
           ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
         run: |
           rm -f output/artifact_name.txt
@@ -367,7 +367,7 @@ jobs:
       - name: Generate DM metadata and upload to HF
         if: ${{ inputs.target == 'dm' }}
         env:
-          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
           export PYTHONPATH=$(pwd)
           python3 -c "
@@ -484,11 +484,29 @@ jobs:
           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

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

@@ -146,7 +146,7 @@ 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 $JSON_FILE does not exist!"
@@ -155,13 +155,8 @@ jobs:
           python3 -c "
           import sys
           from huggingface_hub import HfApi
-          try:
-              api = HfApi()
-              api.repo_info(repo_id=sys.argv[1], repo_type='dataset')
-              print(f'Success: Repo {sys.argv[1]} exists.')
-          except Exception as e:
-              print('HF validation failed:', e)
-              sys.exit(1)
+          HfApi().repo_info(repo_id=sys.argv[1], repo_type='dataset')
+          print(f'Success: Repo {sys.argv[1]} exists.')
           " "${{ inputs.hf_repo }}"
 
       - name: Download artifact name file
@@ -192,7 +187,7 @@ 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

@@ -46,6 +46,13 @@ runs:
             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

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

@@ -188,7 +188,7 @@ jobs:
           if [ "${{ inputs.target_hardware }}" == "chestnut" ]; then
             echo "CHESTNUT build"
             export CHESTNUT=1
-            TG_FLAGS="DEBUG=1 DEV=USB+AMD:LLVM WARP_DEV=QCOM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2"
+            TG_FLAGS="DEBUG=1 DEV=USB+AMD:LLVM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_OCCUPANCY_OPT=1"
             OUTPUT_PKL="${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl"
           else
             echo "QCOM build"

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

@@ -216,6 +216,9 @@ jobs:
     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: ${{ steps.resolve.outputs.onnx_sha256 }}
     env:
@@ -228,8 +231,10 @@ jobs:
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
 
-          ONNX_HASH=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx?ref=${REF}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          BLOB_SHA=$(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=${REF}" --jq '.sha')
@@ -238,7 +243,7 @@ jobs:
           JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
 
           check_defaults() {
-            DEFAULTS=$(curl -fsSL "$JSON_URL" 2>/dev/null) || return 1
+            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)
@@ -252,18 +257,35 @@ jobs:
 
           echo "No matching model on HF — dispatching build"
           gh workflow run build-default-models.yaml --ref "$REF" -f target=big
+          sleep 10
 
-          echo "Polling HF for big model availability..."
+          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
-            if check_defaults; then
-              echo "Big model available on HF after $((i * 30))s"
-              exit 0
+            STATUS=$(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
-            echo "Poll $i/90: not yet available"
           done
 
-          echo "::error::Big model not available on HF after 45 minutes"
+          echo "::error::Build run did not complete within 45 minutes"
           exit 1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -277,6 +299,9 @@ jobs:
   prepare_small_model:
     needs: [ prepare_strategy ]
     runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-small-model
+      cancel-in-progress: false
     outputs:
       driving_onnx_sha256: ${{ steps.resolve.outputs.driving_onnx_sha256 }}
     env:
@@ -289,8 +314,10 @@ jobs:
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
 
-          DRIVING_HASH=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/driving_supercombo.onnx?ref=${REF}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          BLOB_SHA=$(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=${REF}" --jq '.sha')
@@ -299,7 +326,7 @@ jobs:
           JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
 
           check_defaults() {
-            DEFAULTS=$(curl -fsSL "$JSON_URL" 2>/dev/null) || return 1
+            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)
@@ -313,18 +340,35 @@ jobs:
 
           echo "No matching model on HF — dispatching build"
           gh workflow run build-default-models.yaml --ref "$REF" -f target=small
+          sleep 10
 
-          echo "Polling HF for model availability..."
+          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
-            if check_defaults; then
-              echo "Model available on HF after $((i * 30))s"
-              exit 0
+            STATUS=$(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
-            echo "Poll $i/60: not yet available"
           done
 
-          echo "::error::Small driving model not available on HF after 30 minutes"
+          echo "::error::Small model build did not complete within 30 minutes"
           exit 1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -338,6 +382,9 @@ jobs:
   prepare_dm_model:
     needs: [ prepare_strategy ]
     runs-on: ubuntu-24.04
+    concurrency:
+      group: prepare-dm-model
+      cancel-in-progress: false
     outputs:
       dm_onnx_sha256: ${{ steps.resolve.outputs.dm_onnx_sha256 }}
     env:
@@ -350,8 +397,10 @@ jobs:
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
 
-          DM_HASH=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/dmonitoring_model.onnx?ref=${REF}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
+          BLOB_SHA=$(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=${REF}" --jq '.sha')
@@ -360,7 +409,7 @@ jobs:
           JSON_URL="https://huggingface.co/datasets/${HF_REPO}/resolve/main/${HF_DEFAULTS_PATH}/default_models.json"
 
           check_defaults() {
-            DEFAULTS=$(curl -fsSL "$JSON_URL" 2>/dev/null) || return 1
+            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)
@@ -374,18 +423,35 @@ jobs:
 
           echo "No matching DM model on HF — dispatching build"
           gh workflow run build-default-models.yaml --ref "$REF" -f target=dm
+          sleep 10
 
-          echo "Polling HF for DM model availability..."
+          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
-            if check_defaults; then
-              echo "DM model available on HF after $((i * 30))s"
-              exit 0
+            STATUS=$(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
-            echo "Poll $i/60: not yet available"
           done
 
-          echo "::error::DM model not available on HF after 30 minutes"
+          echo "::error::DM model build did not complete within 30 minutes"
           exit 1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

openpilot/selfdrive/selfdrived/selfdrived.py

@@ -576,7 +576,7 @@ class SelfdriveD(CruiseHelper):
     clear_event_types = set()
     if ET.WARNING not in self.state_machine.current_alert_types:
       clear_event_types.add(ET.WARNING)
-    if self.enabled or self.mads.clear_no_entry:
+    if self.enabled:
       clear_event_types.add(ET.NO_ENTRY)
 
     pers = LONGITUDINAL_PERSONALITY_MAP[self.personality]

openpilot/sunnypilot/mads/mads.py

@@ -10,7 +10,6 @@ from openpilot.cereal import log, custom
 from opendbc.car import structs
 from opendbc.car.hyundai.values import HyundaiFlags
 from openpilot.common.params import Params
-from openpilot.selfdrive.selfdrived.events import ET
 from openpilot.sunnypilot.mads.helpers import MadsSteeringModeOnBrake, read_steering_mode_param, MADS_NO_ACC_MAIN_BUTTON
 from openpilot.sunnypilot.mads.state import StateMachine, GEARS_ALLOW_PAUSED_SILENT
 
@@ -34,7 +33,6 @@ class ModularAssistiveDrivingSystem:
     self.enabled = False
     self.active = False
     self.available = False
-    self.clear_no_entry = False
     self.lateral_mismatch_counter = 0
     self.allow_always = False
     self.no_main_cruise = False
@@ -220,8 +218,5 @@ class ModularAssistiveDrivingSystem:
     if not self.CP.passive and self.selfdrive.initialized:
       self.enabled, self.active = self.state_machine.update()
 
-    cat = self.selfdrive.state_machine.current_alert_types
-    self.clear_no_entry = (ET.ENABLE in cat or ET.USER_DISABLE in cat) and ET.NO_ENTRY not in cat
-
     # Copy of previous SelfdriveD states for MADS events handling
     self.selfdrive.enabled_prev = self.selfdrive.enabled

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -41,7 +41,6 @@ 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')
 
 
 def _detect_desire_key(shapes: dict) -> str | None:
@@ -154,12 +153,13 @@ def make_warp_queues(device=Device.DEFAULT):
 
 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)
+    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)
@@ -368,16 +368,18 @@ if __name__ == "__main__":
   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)
+  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, make_random_model_inputs, POLICY_INPUTS, make_policy_queues)
 
   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)
+    make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=nv12.size, device=Device.DEFAULT)
     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)
 
+  output_data['metadata']['warp_dev'] = Device.DEFAULT
+
   with open(args.output, "wb") as file:
     dump_oob(output_data, file)
 

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -6,6 +6,7 @@ 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'
 import numpy as np
@@ -110,18 +111,12 @@ class ModelState(ModelStateBase):
     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.chestnut else self.WARP_DEV
-    self.QUEUE_DEV = self.DEV
     metadata = jits['metadata']
-
-    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)]
+    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.run_policy = jits['run_policy']
+    self.warp = jits[(cam_w, cam_h)]
 
     if 'model' in metadata:
       model_metadata = metadata['model']
@@ -180,11 +175,7 @@ class ModelState(ModelStateBase):
     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)
+    self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=frame_tensor, big_frame=big_frame_tensor)
 
   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}
@@ -217,7 +208,8 @@ 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:
+          inputs: dict[str, np.ndarray], prepare_only: bool,
+          after_enqueue: Callable[[], None] | None = None) -> 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]
@@ -239,20 +231,18 @@ class ModelState(ModelStateBase):
     self.numpy_inputs['tfm'][:, :] = transforms[road_key].reshape(3, 3)
     self.numpy_inputs['big_tfm'][:, :] = transforms[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])
-    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])
-      raw_outputs = self.run_policy(**{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped)
+    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])
+    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:
@@ -285,9 +275,6 @@ class ModelState(ModelStateBase):
       buf[0, :-1] = buf[0, 1:]
       buf[0, -1, :] = outputs['desired_curvature'][0, :] if not self.mlsim else 0
 
-    if self.chestnut and not np.all(np.isfinite(outputs.get('plan', np.array([0.])))):
-      raise RuntimeError("model output not finite")
-
     return outputs
 
   def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
@@ -512,7 +499,9 @@ def main(demo=False):
 
     mt1 = time.perf_counter()
     try:
-      model_output = model.run(bufs, transforms, inputs, prepare_only)
+      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, prepare_only, chestnut_state.send if send_chestnut else None)
     except Exception:
       if not params.get_bool("ChestnutActive"):
         raise
@@ -559,9 +548,6 @@ 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/models/fetcher.py

@@ -139,7 +139,7 @@ class ModelCache:
 class ModelFetcher:
   """Handles fetching and caching of model data from remote source"""
   MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v22.json"
-  MODEL_URL_CHESTNUT = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_chestnut_v23.json"
+  MODEL_URL_CHESTNUT = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_chestnut_v24.json"
 
   MODEL_SOURCES = {
     "qcom": (MODEL_URL, ""),