models: prepare for upstream grand slam

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

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

.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

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -32,7 +32,7 @@ def _patch_tinygrad_fetch_fw():
   helpers.fetch_fw = fetch_fw
 _patch_tinygrad_fetch_fw()
 
-import openpilot.selfdrive.modeld.compile_modeld as stock
+import openpilot.sunnypilot.modeld_v2.stock_dependencies as stock
 from tinygrad import dtypes
 from tinygrad.device import Device
 from tinygrad.engine.jit import TinyJit

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/model_adapters.py

@@ -0,0 +1,177 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import numpy as np
+from tinygrad.tensor import Tensor
+
+from openpilot.sunnypilot.modeld_v2.stock_dependencies import MODELD_INPUTS, make_input_queues as make_stock_input_queues
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.sunnypilot.modeld_v2.compile_modeld import (derive_frame_skip, make_split_input_queues, make_supercombo_input_queues,
+                                                           WARP_INPUTS, POLICY_INPUTS, nv12_copy_size)
+
+
+class BaseModelAdapter:
+  def __init__(self, jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=False):
+    self.jits = jits
+    self.DEV = model_device
+    self.QUEUE_DEV = queue_device
+    self.WARP_DEV = warp_device
+    self.chestnut = chestnut
+    self.cam_w = cam_w
+    self.cam_h = cam_h
+    self._combined_model_type = 'supercombo'
+    self._policy_slices_list = []
+    self._has_on_policy = False
+    self.policy_output_slices = {}
+    self._policy_keys = []
+    self.full_frames = {}
+    self._blob_cache = {}
+    self.frame_buffers = {}
+    self.frame_views = {}
+    self.nv12_info = get_nv12_info(cam_w, cam_h)
+
+  def _init_common(self):
+    self._desire_key = next((key for key in getattr(self, 'numpy_inputs', {}) if key.startswith('desire')), 'desire')
+    self._road_key = next((key for key in getattr(self, '_vision_input_names', []) if 'big' not in key), 'img')
+    self._wide_key = next((key for key in getattr(self, '_vision_input_names', []) if 'big' in key), 'big_img')
+    self.frame_buf_params = dict.fromkeys(getattr(self, '_vision_input_names', ['img', 'big_img']), self.nv12_info)
+
+  def get_dummy_inputs(self):
+    dummy_size = getattr(self, 'frame_copy_size', self.frame_buf_params[self._road_key][3])
+    if getattr(self, 'is_run_model', True) is False:
+      dummy_size = self.frame_buf_params[self._road_key][3]
+
+    dummy_frames = {k: np.zeros(dummy_size, dtype=np.uint8) for k in self._vision_input_names}
+    transforms = {k: np.eye(3, dtype=np.float32) for k in [self._road_key, self._wide_key] if k}
+    dummy_inputs = {k: np.zeros(v.shape, dtype=v.dtype) for k, v in self.numpy_inputs.items() if k not in ['tfm', 'big_tfm', 'prev_feat']}
+    return dummy_frames, transforms, dummy_inputs
+
+
+class LegacyModelAdapter(BaseModelAdapter):
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+
+    metadata = self.jits['metadata']
+    self.is_run_model = 'run_model' in self.jits
+    self.frame_copy_size = nv12_copy_size(*self.nv12_info[:3])
+
+    if self.is_run_model or 'model' in metadata:
+      model_metadata = metadata.get('model', metadata)
+      self.input_shapes = model_metadata['input_shapes']
+      self.vision_output_slices = model_metadata['output_slices']
+      self._vision_input_names = [key for key in self.input_shapes if 'img' in key]
+      self.frame_skip = derive_frame_skip({}, self.input_shapes)
+
+      if self.is_run_model:
+        self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
+          self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
+        self.frame_views, self.npy = self.frame_buffers, self.numpy_inputs
+        self.run_model, self.run_policy, self.warp = self.jits['run_model'][(self.cam_w, self.cam_h)], None, None
+      else:
+        self.input_queues, self.numpy_inputs = make_supercombo_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
+        self.run_model, self.run_policy, self.warp = None, self.jits['run_policy'], self.jits[(self.cam_w, self.cam_h)]
+    else:
+      self.run_model, self.run_policy, self.warp = None, self.jits['run_policy'], self.jits[(self.cam_w, self.cam_h)]
+      vision_metadata = metadata['vision']
+      policy_keys = [k for k in metadata if k not in ('vision', 'warp_dev')]
+      self._combined_model_type = 'split' if policy_keys == ['policy'] else 'multi_policy'
+      self.vision_output_slices = vision_metadata['output_slices']
+      self._policy_keys = policy_keys
+      self._policy_slices_list = [metadata[k]['output_slices'] for k in policy_keys]
+      self.policy_output_slices = self._policy_slices_list[0]
+      self._has_on_policy = any('on' in k.lower() for k in policy_keys)
+      self._vision_input_names = [key for key in vision_metadata['input_shapes'] if 'img' in key]
+      first_policy_meta = metadata[policy_keys[0]]
+      self.frame_skip = derive_frame_skip(vision_metadata['input_shapes'], first_policy_meta['input_shapes'])
+      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_metadata['input_shapes'],
+                                                                     first_policy_meta['input_shapes'],
+                                                                     self.frame_skip, device=self.QUEUE_DEV)
+
+    self._init_common()
+    if self.warp is not None:
+      self.full_frames = {k: Tensor(np.zeros(self.nv12_info[3], dtype=np.uint8), device=self.WARP_DEV).contiguous().realize() for k in self._vision_input_names}
+      self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
+
+  def copy_frames(self, bufs):
+    if getattr(self, 'is_run_model', True):
+      for key, buf in bufs.items():
+        data = buf.data if hasattr(buf, 'data') else buf
+        np.copyto(self.frame_buffers[key], np.frombuffer(data, dtype=np.uint8, count=self.frame_copy_size))
+    else:
+      for key, buf in bufs.items():
+        ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
+        cache_key = (key, ptr)
+        if cache_key not in self._blob_cache:
+          self._blob_cache[cache_key] = Tensor.from_blob(ptr, (self.frame_buf_params[key][3],), dtype='uint8', device=self.WARP_DEV)
+        self.full_frames[key] = self._blob_cache[cache_key]
+
+  def reset_warmup_buffers(self):
+    if getattr(self, 'is_run_model', True):
+      self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
+        self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
+      self.frame_views = self.frame_buffers
+      self.npy = self.numpy_inputs
+    else:
+      for v in self.numpy_inputs.values():
+        v[:] = 0
+      self.full_frames.clear()
+      self._blob_cache.clear()
+
+  def run(self):
+    if self.run_model is not None:
+      outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
+      return outs
+    else:
+      warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
+      raw_outputs = self.run_policy(**{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped)
+      return raw_outputs
+
+
+class NativeTinygradAdapter(BaseModelAdapter):
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+
+    self.input_specs = self.jits['input_specs'][(self.cam_w, self.cam_h)]
+    self.npy_shapes = self.jits['npy_shapes']
+    self.run_model = self.jits['run_model'][(self.cam_w, self.cam_h)]
+    self.vision_output_slices = self.jits['metadata']['model']['output_slices']
+
+    self._vision_input_names = ['img', 'big_img']
+
+    self.reset_warmup_buffers()
+    self._init_common()
+
+  def copy_frames(self, bufs):
+    for key, buf in bufs.items():
+      data = buf.data if hasattr(buf, 'data') else buf
+      if key in self.frame_views:
+        np.copyto(self.frame_views[key], np.frombuffer(data, dtype=np.uint8, count=self.frame_copy_size))
+
+  def reset_warmup_buffers(self) -> None:
+    buffers = {name: np.zeros(shape, dtype=dtype) for name, (shape, dtype, _) in self.input_specs.items()}
+    self.input_queues = {name: Tensor(buffers[name], device=device).realize() for name, (_, _, device) in self.input_specs.items()}
+
+    sizes = [int(np.prod(shape)) for shape in self.npy_shapes.values()]
+    packed = buffers['packed_npy_inputs']
+    npy_size = sum(sizes) * np.dtype(np.float32).itemsize
+
+    self.numpy_inputs = {name: v.reshape(shape) for (name, shape), v in
+                zip(self.npy_shapes.items(), np.split(packed[:npy_size].view(np.float32), np.cumsum(sizes[:-1])), strict=True)}
+
+    self.frame_copy_size = (packed.size - npy_size) // 2
+    self.frame_views = {'img': packed[npy_size:npy_size+self.frame_copy_size],
+                        'big_img': packed[npy_size+self.frame_copy_size:]}
+
+  def run(self):
+    outs, = self.run_model(**self.input_queues)
+    return outs
+
+
+def get_model_adapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=False):
+  if 'input_specs' in jits:
+    return NativeTinygradAdapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=chestnut)
+  else:
+    return LegacyModelAdapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=chestnut)

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -17,7 +17,7 @@ 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, load_oob
+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
@@ -38,20 +38,15 @@ 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.parse_model_outputs import Parser
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants, Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
-from openpilot.sunnypilot.modeld_v2.compile_modeld import (derive_frame_skip, make_split_input_queues,
-                                                           make_supercombo_input_queues, nv12_copy_size,
-                                                           WARP_INPUTS, POLICY_INPUTS)
 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
+from openpilot.sunnypilot.modeld_v2.helpers import load_oob
+from openpilot.sunnypilot.modeld_v2.model_adapters import get_model_adapter
 from openpilot.sunnypilot.models.helpers import get_active_bundle
 from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController
 
@@ -122,52 +117,19 @@ class ModelState(ModelStateBase):
     self.WARP_DEV = metadata.get('warp_dev', 'QCOM') if COMMA_HARDWARE else 'CPU'
     self.DEV = ('AMD' if self.chestnut else 'QCOM') if COMMA_HARDWARE else 'CPU'
     self.QUEUE_DEV = self.DEV
-    self.is_run_model = 'run_model' in jits
-
-    nv12_info = get_nv12_info(cam_w, cam_h)
-    self.frame_copy_size = nv12_copy_size(*nv12_info[:3])
-    self.full_frames: dict = {}
-    self._blob_cache: dict = {}
-    self.frame_buffers: dict = {}
-
-    if self.is_run_model or 'model' in metadata:
-      model_metadata = metadata.get('model', metadata)
-      self.input_shapes = model_metadata['input_shapes']
-      self.vision_output_slices = model_metadata['output_slices']
-      self.policy_output_slices = {}
-      self._policy_slices_list = []
-      self._combined_model_type = 'supercombo'
-      self._vision_input_names = [key for key in self.input_shapes if 'img' in key]
-      self.frame_skip = derive_frame_skip({}, self.input_shapes)
-      if self.is_run_model:
-        self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
-          self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
-        self.frame_views, self.npy = self.frame_buffers, self.numpy_inputs
-        self.run_model, self.run_policy, self.warp = jits['run_model'][(cam_w, cam_h)], None, None
-      else:
-        self.input_queues, self.numpy_inputs = make_supercombo_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
-        self.run_model, self.run_policy, self.warp = None, jits['run_policy'], jits[(cam_w, cam_h)]
-    else:
-      self.run_model, self.run_policy, self.warp = None, jits['run_policy'], jits[(cam_w, cam_h)]
-      vision_metadata = metadata['vision']
-      policy_keys = [k for k in metadata if k not in ('vision', 'warp_dev')]
-      self._combined_model_type = 'split' if policy_keys == ['policy'] else 'multi_policy'
-      self.vision_output_slices = vision_metadata['output_slices']
-      self._policy_keys = policy_keys
-      self._policy_slices_list = [metadata[k]['output_slices'] for k in policy_keys]
-      self.policy_output_slices = self._policy_slices_list[0]
-      self._has_on_policy = any('on' in k.lower() for k in policy_keys)
-      self._vision_input_names = [key for key in vision_metadata['input_shapes'] if 'img' in key]
-      first_policy_meta = metadata[policy_keys[0]]
-      frame_skip = derive_frame_skip(vision_metadata['input_shapes'], first_policy_meta['input_shapes'])
-      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_metadata['input_shapes'],
-                                                                     first_policy_meta['input_shapes'],
-                                                                     frame_skip, device=self.QUEUE_DEV)
-
-    self._desire_key = next(key for key in self.numpy_inputs if key.startswith('desire'))
-    self._road_key = next(key for key in self._vision_input_names if 'big' not in key)
-    self._wide_key = next(key for key in self._vision_input_names if 'big' in key)
-    self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)
+    self.adapter = get_model_adapter(jits, cam_w, cam_h, self.DEV, self.QUEUE_DEV, self.WARP_DEV, self.chestnut)
+    self.vision_output_slices = self.adapter.vision_output_slices
+    self.policy_output_slices = self.adapter.policy_output_slices
+    self._policy_slices_list = self.adapter._policy_slices_list
+    self._combined_model_type = self.adapter._combined_model_type
+    self._vision_input_names = self.adapter._vision_input_names
+    self.numpy_inputs = self.adapter.numpy_inputs
+    self._policy_keys = self.adapter._policy_keys
+    self._has_on_policy = self.adapter._has_on_policy
+    self._desire_key = self.adapter._desire_key
+    self._road_key = self.adapter._road_key
+    self._wide_key = self.adapter._wide_key
+    self.frame_buf_params = self.adapter.frame_buf_params
 
     is_20hz = bundle.is20hz if bundle else self._combined_model_type in ('split', 'multi_policy')
     if is_20hz:
@@ -179,26 +141,10 @@ class ModelState(ModelStateBase):
     self.parser = Parser()
     self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
 
-    if self.warp is not None:
-      self.full_frames = {k: Tensor(np.zeros(nv12_info[3], dtype=np.uint8), device=self.WARP_DEV).contiguous().realize() for k in self._vision_input_names}
-      self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
-
   def warmup(self) -> None:
-    dummy_size = self.frame_copy_size if self.is_run_model else self.frame_buf_params[self._road_key][3]
-    dummy_frames = {k: np.zeros(dummy_size, dtype=np.uint8) for k in self._vision_input_names}
-    transforms = {k: np.eye(3, dtype=np.float32) for k in [self._road_key, self._wide_key] if k}
-    dummy_inputs = {k: np.zeros(v.shape, dtype=v.dtype) for k, v in self.numpy_inputs.items() if k not in ['tfm', 'big_tfm', 'prev_feat']}
+    dummy_frames, transforms, dummy_inputs = self.adapter.get_dummy_inputs()
     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.adapter.reset_warmup_buffers()
     self.prev_desire[:] = 0
 
   @property
@@ -216,17 +162,7 @@ class ModelState(ModelStateBase):
   def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
           inputs: dict[str, np.ndarray],
           after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray] | None:
-    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]
+    self.adapter.copy_frames(bufs)
 
     desire_key = self.desire_key
     inputs[desire_key][0] = 0
@@ -240,13 +176,7 @@ class ModelState(ModelStateBase):
     self.numpy_inputs['tfm'][:, :] = transforms[self._road_key].reshape(3, 3)
     self.numpy_inputs['big_tfm'][:, :] = transforms[self._wide_key].reshape(3, 3)
 
-    if self.run_model is not None:
-      outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
-      raw_outputs = outs
-    else:
-      assert self.warp is not None and self.run_policy is not None
-      warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
-      raw_outputs = self.run_policy(**{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped)
+    raw_outputs = self.adapter.run()
 
     if after_enqueue is not None:
       after_enqueue()

openpilot/sunnypilot/modeld_v2/stock_dependencies.py

@@ -0,0 +1,167 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import math
+import numpy as np
+from collections import namedtuple
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import Context
+from tinygrad.device import Device
+
+"""
+Frozen in time compile_modeld dependencies to support all models prior to transition to tinygrad compilation.
+This file is not meant to be modified.
+"""
+NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
+MODELD_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
+
+
+def nv12_copy_size(stride: int, y_height: int, uv_height: int) -> int:
+  return stride * (y_height + uv_height)
+
+
+def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad, border_fill_val=None):
+  w_dst, h_dst = dst_shape
+  h_src, w_src = src_shape
+
+  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
+  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
+
+  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
+  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
+  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
+
+  src_x = src_x / src_w
+  src_y = src_y / src_w
+
+  x_round = Tensor.round(src_x)
+  y_round = Tensor.round(src_y)
+  x_nn_clipped = x_round.clip(0, w_src - 1).cast('int')
+  y_nn_clipped = y_round.clip(0, h_src - 1).cast('int')
+  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
+  sampled = src_flat[idx]
+
+  if border_fill_val is None:
+    return sampled
+
+  in_bounds = ((x_round >= 0) & (x_round <= w_src - 1) &
+               (y_round >= 0) & (y_round <= h_src - 1)).cast(sampled.dtype)
+  return sampled * in_bounds + Tensor(border_fill_val, dtype=sampled.dtype) * (1 - in_bounds)
+
+
+def frames_to_tensor(frames):
+  H = (frames.shape[0] * 2) // 3
+  W = frames.shape[1]
+  in_img1 = Tensor.cat(frames[0:H:2, 0::2],
+                       frames[1:H:2, 0::2],
+                       frames[0:H:2, 1::2],
+                       frames[1:H:2, 1::2],
+                       frames[H:H+H//4].reshape((H//2, W//2)),
+                       frames[H+H//4:H+H//2].reshape((H//2, W//2)), dim=0).reshape((6, H//2, W//2))
+  return in_img1
+
+
+def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
+  cam_w, cam_h, stride, y_height, uv_height, _ = nv12
+  uv_offset = stride * y_height
+  stride_pad = stride - cam_w
+
+  def frame_prepare_tinygrad(input_frame, M_inv):
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=Device.DEFAULT)
+    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
+    with Context(SPLIT_REDUCEOP=0):
+      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
+                                    M_inv, (model_w, model_h),
+                                    (cam_h, cam_w), stride_pad).realize()
+      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
+    tensor = frames_to_tensor(yuv)
+    return tensor
+  return frame_prepare_tinygrad
+
+
+def get_policy_npy_shapes(input_shapes):
+  dp = input_shapes['desire_pulse']
+  tc = input_shapes['traffic_convention']
+  at = input_shapes['action_t']
+  fb = input_shapes['features_buffer']
+  feat_dim = math.prod(fb[2:])
+  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], feat_dim)}
+  return shapes, [math.prod(s) for s in shapes.values()]
+
+
+def make_input_queues(input_shapes, frame_skip, device, frame_copy_size):
+  img = input_shapes['img']
+  fb = input_shapes['features_buffer']
+  feat_dim = math.prod(fb[2:])
+  dp = input_shapes['desire_pulse']
+  n_frames = img[1] // 6
+  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
+
+  policy_shapes, _ = get_policy_npy_shapes(input_shapes)
+  shapes = {'tfm': (3, 3), 'big_tfm': (3, 3)} | policy_shapes
+  sizes = [math.prod(s) for s in shapes.values()]
+  packed_npy_size = sum(sizes) * np.dtype(np.float32).itemsize
+  packed_input = np.zeros(packed_npy_size + 2 * frame_copy_size, dtype=np.uint8)
+  packed_npy_inputs = packed_input[:packed_npy_size].view(np.float32)
+  frames = packed_input[packed_npy_size:]
+  frame_views = {'img': frames[:frame_copy_size], 'big_img': frames[frame_copy_size:]}
+  npy = {k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)}
+  input_queues = {
+    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], feat_dim), dtype=np.float32), device=device).contiguous().realize(),
+    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
+    'packed_npy_inputs': Tensor(packed_input, device='NPY').realize(),
+  }
+  return input_queues, npy, frame_views
+
+
+def shift_and_sample(buf, new_val, sample_fn):
+  buf.assign(buf[1:].cat(new_val, dim=0).contiguous())
+  return sample_fn(buf)
+
+
+def sample_skip(buf, frame_skip):
+  return buf[::frame_skip].contiguous().flatten(0, 1).unsqueeze(0)
+
+
+def sample_desire(buf, frame_skip):
+  return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)
+
+
+def make_warp(nv12, model_w, model_h):
+  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+  def warp(tfm, big_tfm, frame, big_frame):
+    tfm = tfm.to(Device.DEFAULT)
+    big_tfm = big_tfm.to(Device.DEFAULT)
+    frame = frame.to(Device.DEFAULT)
+    big_frame = big_frame.to(Device.DEFAULT)
+    Tensor.realize(tfm, big_tfm, frame, big_frame)
+    warped_frame = frame_prepare(frame, tfm).unsqueeze(0)
+    warped_big_frame = frame_prepare(big_frame, big_tfm).unsqueeze(0)
+    return Tensor.cat(warped_frame, warped_big_frame)
+  return warp
+
+
+def make_run_model(warp, run_policy, model_metadata, frame_copy_size):
+  _, policy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+  packed_npy_size = (18 + sum(policy_sizes)) * np.dtype(np.float32).itemsize
+  def run_model(img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_input = packed_npy_inputs.to(Device.DEFAULT)
+    Tensor.realize(packed_input)
+    packed_npy_inputs = packed_input[:packed_npy_size].bitcast('float32')
+    frame = packed_input[packed_npy_size:packed_npy_size + frame_copy_size]
+    big_frame = packed_input[packed_npy_size + frame_copy_size:]
+    tfm, big_tfm, policy_inputs = packed_npy_inputs.split([9, 9, sum(policy_sizes)])
+    warped = warp(tfm.reshape(3, 3), big_tfm.reshape(3, 3), frame, big_frame)
+    return run_policy(warped, img_q, big_img_q, feat_q, desire_q, policy_inputs)
+  return run_model

openpilot/sunnypilot/modeld_v2/tests/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/models/tests/test_tinygrad_ref.py

@@ -1,24 +0,0 @@
-import requests
-
-from openpilot.sunnypilot.models.tinygrad_ref import get_tinygrad_ref
-from openpilot.sunnypilot.models.fetcher import ModelFetcher
-from openpilot.common.test import OpenpilotTestCase
-
-def fetch_tinygrad_ref():
-  response = requests.get(ModelFetcher.MODEL_URL, timeout=10)
-  response.raise_for_status()
-  json_data = response.json()
-  return json_data.get("tinygrad_ref")
-
-
-class TestTinygradRef(OpenpilotTestCase):
-  def test_tinygrad_ref(self):
-    current_ref = get_tinygrad_ref()
-    remote_ref = fetch_tinygrad_ref()
-    assert remote_ref == current_ref, (
-      f"""tinygrad_repo ref does not match remote tinygrad_ref of current compiled driving models json.
-    Current: {current_ref}
-    Remote: {remote_ref}
-    Please run build-all workflow to update models."""
-    )
-    print("tinygrad_repo ref matches current compiled driving models json ref.")