Merge branch 'master' into ccnc-port

This reverts commit 15e5d2efb9.

.github/workflows/build-default-big-model.yaml

@@ -1,78 +0,0 @@
-name: Build default big model
-
-on:
-  workflow_dispatch:
-
-env:
-  HF_REPO: sunnypilot/sunnypilot_models_v1
-  HF_DEFAULTS_PATH: models/defaults/big
-
-jobs:
-  build_model:
-    uses: ./.github/workflows/sunnypilot-build-model.yaml
-    with:
-      upstream_branch: ${{ github.sha }}
-      custom_name: default-big-model
-      target_hardware: usbgpu
-    secrets: inherit
-
-  upload_defaults:
-    needs: build_model
-    runs-on: ubuntu-24.04
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - run: git lfs pull -I "openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
-
-      - name: Install huggingface_hub
-        run: pip install --upgrade "huggingface_hub>=0.22.0"
-
-      - name: Download artifact name
-        uses: actions/download-artifact@v4
-        with:
-          name: artifact-name-default-big-model
-          path: artifact_name
-
-      - name: Read artifact name
-        id: artifact
-        run: |
-          ARTIFACT_NAME=$(cat artifact_name/artifact_name.txt)
-          echo "artifact_name=$ARTIFACT_NAME" >> $GITHUB_OUTPUT
-
-      - name: Download model artifact
-        uses: actions/download-artifact@v4
-        with:
-          name: ${{ steps.artifact.outputs.artifact_name }}
-          path: output
-
-      - name: Upload model to HF defaults
-        env:
-          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
-          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
-        run: |
-          rm -f output/artifact_name.txt
-          hf upload ${{ env.HF_REPO }} \
-            output/ \
-            "${HF_DEFAULTS_PATH}/${ARTIFACT_NAME}/" \
-            --repo-type=dataset
-
-      - name: Get tinygrad ref and ONNX hash
-        id: meta
-        run: |
-          export PYTHONPATH=$(pwd)
-          echo "tinygrad_ref=$(python3 openpilot/sunnypilot/models/tinygrad_ref.py)" >> $GITHUB_OUTPUT
-          echo "onnx_sha256=$(sha256sum openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx | cut -d' ' -f1)" >> $GITHUB_OUTPUT
-
-      - name: Update default_models.json on HF
-        env:
-          HF_OIDC_RESOURCE: datasets/${{ env.HF_REPO }}
-          ARTIFACT_NAME: ${{ steps.artifact.outputs.artifact_name }}
-        run: |
-          python3 release/ci/upload_default_model.py \
-            --hf-repo "${{ env.HF_REPO }}" \
-            --hf-defaults-path "${{ env.HF_DEFAULTS_PATH }}" \
-            --artifact-name "$ARTIFACT_NAME" \
-            --metadata-path "output/metadata.json" \
-            --onnx-sha256 "${{ steps.meta.outputs.onnx_sha256 }}" \
-            --tinygrad-ref "${{ steps.meta.outputs.tinygrad_ref }}"

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, self._get_path_half_width(), self._path_offset_z, max_idx, max_distance, allow_invert=False
+      self._path.raw_points, 0.9, 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 and self._lateral_active:
+    if ui_state.rainbow_path:
       self.rainbow_path.draw_rainbow_path(self._rect, self._path)
       return
 

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

@@ -4,23 +4,11 @@ Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
 This file is part of sunnypilot and is licensed under the MIT License.
 See the LICENSE.md file in the root directory for more details.
 """
-from 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.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:
-    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/sunnypilot/modeld_v2/compile_modeld.py

@@ -7,7 +7,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 import argparse
-import math
 import os
 import tempfile
 import time
@@ -67,15 +66,14 @@ 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
 
@@ -119,9 +117,8 @@ def generate_queues_and_npy(input_shapes: dict, frame_skip: int, device: str = D
   }
 
   if features_buffer:
-    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),
+    queues['feat_q'] = Tensor(np.zeros((feat_q_len, features_buffer[0], features_buffer[2]),
                        dtype=np.float32), device=device).contiguous().realize()
 
   queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')})
@@ -186,14 +183,14 @@ 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)
-    big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn)
+    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()
 
     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)
+    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()
 
     inputs = {desire_key: desire_buf}
     for key, tensor_val in unpacked_dict.items():
@@ -202,22 +199,19 @@ 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']
-      feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn)
-      inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
+      inputs['features_buffer'] = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()
 
     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)
-        feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
-        inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
+        inputs['features_buffer'] = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
       policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
       return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])
 
     inputs.update({road_key: img, wide_key: big_img})
     if 'features_buffer' not in inputs:
-      feat_buf = sample_skip_fn(feat_q)
-      inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
+      inputs['features_buffer'] = sample_skip_fn(feat_q)
 
     policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
     if 'features_buffer' not in inputs and features_slice is not None:

openpilot/sunnypilot/modeld_v2/tests/test_compile_modeld.py

@@ -195,85 +195,3 @@ class TestReadFileChunkedToDisk(OpenpilotTestCase):
 
       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, 512),  # when https://github.com/commaai/openpilot/pull/38681 merges, update to 1,24,32,512
-      'traffic_convention': (1, 2),
-      'action_t': (1, 2),
-    }
-    frame_skip = 4
-
-    stock_queues, stock_npy = stock_make_input_queues(input_shapes, frame_skip, device='NPY')
-    sunny_queues, sunny_npy = sunny_make_supercombo_input_queues(input_shapes, frame_skip, device='NPY')
-    assert set(sunny_queues.keys()) == set(stock_queues.keys())
-    for key in stock_queues:
-      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(sunny_npy.keys()) == set(stock_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}"
-
-  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/models/fetcher.py

@@ -141,7 +141,7 @@ 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_v20.json"
-  MODEL_URL_USBGPU = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_usbgpu_v22.json"
+  MODEL_URL_USBGPU = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_usbgpu_v21.json"
 
   def __init__(self, params: Params):
     self.params = params

release/ci/upload_default_model.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-"""
-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 argparse
-import json
-import sys
-import tempfile
-
-from huggingface_hub import HfApi, hf_hub_download
-
-
-def main():
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--hf-repo", required=True)
-  parser.add_argument("--hf-defaults-path", required=True)
-  parser.add_argument("--artifact-name", required=True)
-  parser.add_argument("--metadata-path", required=True)
-  parser.add_argument("--onnx-sha256", required=True)
-  parser.add_argument("--tinygrad-ref", required=True)
-  args = parser.parse_args()
-
-  with open(args.metadata_path) as f:
-    metadata = json.load(f)
-
-  bundle = metadata['bundles'][0]
-  bundle['onnx_sha256'] = args.onnx_sha256
-
-  artifact = bundle['models'][0]['artifact']
-  hf_base = f"https://huggingface.co/datasets/{args.hf_repo}/resolve/main/{args.hf_defaults_path}/{args.artifact_name}"
-  artifact['download_uri']['url'] = f"{hf_base}/{artifact['file_name']}"
-  for chunk in artifact.get('chunks', []):
-    chunk['url'] = f"{hf_base}/{chunk['file_name']}"
-
-  json_filename = f"{args.hf_defaults_path}/default_models.json"
-  try:
-    local_path = hf_hub_download(repo_id=args.hf_repo, repo_type='dataset', filename=json_filename)
-    with open(local_path) as f:
-      defaults_json = json.load(f)
-  except Exception:
-    defaults_json = {"tinygrad_ref": args.tinygrad_ref, "bundles": []}
-
-  defaults_json['tinygrad_ref'] = args.tinygrad_ref
-
-  existing_idx = next((i for i, b in enumerate(defaults_json['bundles'])
-                       if b.get('display_name') == bundle.get('display_name')), None)
-  if existing_idx is not None:
-    defaults_json['bundles'][existing_idx] = bundle
-  else:
-    defaults_json['bundles'].append(bundle)
-
-  print(json.dumps(defaults_json, indent=2))
-
-  api = HfApi()
-  with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
-    json.dump(defaults_json, f, indent=2)
-    tmp_path = f.name
-
-  api.upload_file(
-    path_or_fileobj=tmp_path,
-    path_in_repo=json_filename,
-    repo_id=args.hf_repo,
-    repo_type="dataset",
-  )
-
-  print(f"Updated {json_filename}")
-
-
-if __name__ == "__main__":
-  main()