Merge branch 'master' into tinygrad-sync-4/25

* sunnylink SDUI: tweak DisableUpdate param for clarity

* sync

.github/workflows/cereal_validation.yaml

@@ -23,56 +23,43 @@ env:
   CI: 1
 
 jobs:
-  generate_cereal_artifact:
-    name: Generate cereal validation artifacts
-    runs-on: ubuntu-24.04
-    steps:
-      - uses: actions/checkout@v6
-        with:
-          submodules: true
-      - run: ./tools/op.sh setup
-      - name: Build openpilot
-        run: scons -j$(nproc) cereal
-      - name: Dump sunnypilot schema
-        run: |
-          export PYTHONPATH=${{ github.workspace }}
-          python3 cereal/messaging/tests/validate_sp_cereal_upstream.py -g -f schema.json
-      - name: 'Prepare artifact'
-        run: |
-          mkdir -p "cereal/messaging/tests/cereal_validations"
-          cp cereal/messaging/tests/validate_sp_cereal_upstream.py "cereal/messaging/tests/cereal_validations/validate_sp_cereal_upstream.py"
-          cp schema.json "cereal/messaging/tests/cereal_validations/schema.json"
-      - name: 'Upload Artifact'
-        uses: actions/upload-artifact@v4
-        with:
-          name: cereal_validations
-          path: cereal/messaging/tests/cereal_validations
-
   validate_cereal_with_upstream:
     name: Validate cereal with Upstream
     runs-on: ubuntu-24.04
-    needs: generate_cereal_artifact
     steps:
-      - name: Checkout sunnypilot
+      - name: Checkout sunnypilot cereal
         uses: actions/checkout@v6
-      - name: Checkout upstream openpilot
+        with:
+          sparse-checkout: cereal
+
+      - name: Init sunnypilot opendbc submodule
+        run: git submodule update --init --depth 1 opendbc_repo
+
+      - name: Checkout upstream openpilot cereal
         uses: actions/checkout@v6
         with:
           repository: 'commaai/openpilot'
-          path: openpilot
-          submodules: true
+          path: upstream_openpilot
+          sparse-checkout: cereal
           ref: "refs/heads/master"
-      - run: ./tools/op.sh setup
-      - name: Build openpilot
-        working-directory: openpilot
-        run: scons -j$(nproc) cereal
-      - name: Download build artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: cereal_validations
-          path: openpilot/cereal/messaging/tests/cereal_validations
-      - name: 'Validate sunnypilot schema against upstream'
+
+      - name: Init upstream opendbc submodule
+        working-directory: upstream_openpilot
+        run: git submodule update --init --depth 1 opendbc_repo
+
+      - name: Install uv
+        run: pip install uv
+
+      - name: Generate sunnypilot schema
         run: |
-          export PYTHONPATH=${{ github.workspace }}/openpilot
-          chmod +x openpilot/cereal/messaging/tests/cereal_validations/validate_sp_cereal_upstream.py
-          python3 openpilot/cereal/messaging/tests/cereal_validations/validate_sp_cereal_upstream.py -r -f openpilot/cereal/messaging/tests/cereal_validations/schema.json
+          PYCAPNP_VER=$(python3 -c "import re; m=re.search(r'name = \"pycapnp\"\nversion = \"([^\"]+)\"', open('uv.lock').read()); print(m.group(1))")
+          uv run --isolated --with "pycapnp==${PYCAPNP_VER}" \
+            python3 cereal/messaging/tests/validate_sp_cereal_upstream.py \
+            -g -f /tmp/sp_schema.json --cereal-dir cereal
+
+      - name: Validate against upstream
+        run: |
+          PYCAPNP_VER=$(python3 -c "import re; m=re.search(r'name = \"pycapnp\"\nversion = \"([^\"]+)\"', open('uv.lock').read()); print(m.group(1))")
+          uv run --isolated --with "pycapnp==${PYCAPNP_VER}" \
+            python3 cereal/messaging/tests/validate_sp_cereal_upstream.py \
+            -r -f /tmp/sp_schema.json --cereal-dir upstream_openpilot/cereal

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

@@ -172,7 +172,7 @@ jobs:
             output_file="${{ env.MODELS_DIR }}/${base_name}_tinygrad.pkl"
 
             echo "Compiling: $onnx_file -> $output_file"
-            QCOM=1 python3 "${{ env.TINYGRAD_PATH }}/examples/openpilot/compile3.py" "$onnx_file" "$output_file"
+            DEV=QCOM FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 IMAGE=2 python3 "${{ env.TINYGRAD_PATH }}/examples/openpilot/compile3.py" "$onnx_file" "$output_file"
             DEV=QCOM FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 python3 "${{ env.MODELS_DIR }}/../get_model_metadata.py" "$onnx_file" || true
           done
 

CHANGELOG.md

@@ -170,6 +170,20 @@ sunnypilot Version 2026.001.000 (2026-05-06)
   * @royjr made their first contribution in "HKG: add KIA_FORTE_2019_NON_SCC fingerprint"
   * @ssysm made their first contribution in "Tesla: remove `TESLA_MODEL_X` from `dashcamOnly`"
 * Full Changelog: https://github.com/sunnypilot/sunnypilot/compare/v2025.002.000...v2026.001.000
+************************
+* Synced with commaai's openpilot (v0.11.1)
+  * master commit c001f3c9b490a80e69539f0af6022f6e07ceb721 (April 16, 2026)
+* New driver monitoring model
+* Improved image processing pipeline for driver camera
+* Rivian R1S and R1T 2025 support thanks to lukasloetkolben!
+* New driving model #36798
+  * Fully trained using a learned simulator
+  * Improved longitudinal performance in Experimental mode
+* Reduce comma four standby power usage by 77% to 52 mW
+* Kia K7 2017 support thanks to royjr!
+* Lexus LS 2018 support thanks to Hacheoy!
+* Improved inter-process communication memory efficiency
+* comma four support
 
 sunnypilot Version 2025.002.000 (2025-11-06)
 ========================

cereal/messaging/tests/validate_sp_cereal_upstream.py

@@ -13,6 +13,7 @@ from __future__ import annotations
 
 import argparse
 import json
+import os
 import sys
 from typing import Any
 
@@ -104,8 +105,15 @@ def collect_schema(root: Any) -> dict[str, dict]:
   return structs
 
 
-def dump_schema(path: str) -> None:
-  from cereal import log
+def load_log(cereal_dir: str) -> Any:
+  import capnp
+  cereal_dir = os.path.abspath(cereal_dir)
+  capnp.remove_import_hook()
+  return capnp.load(os.path.join(cereal_dir, "log.capnp"), imports=[cereal_dir])
+
+
+def dump_schema(cereal_dir: str, path: str) -> None:
+  log = load_log(cereal_dir)
   payload = {
     "root": hex_id(log.Event.schema.node.id),
     "structs": collect_schema(log.Event.schema),
@@ -206,8 +214,8 @@ def load_peer(path: str) -> dict:
     return json.load(handle)
 
 
-def run_read(peer_path: str) -> int:
-  from cereal import log
+def run_read(cereal_dir: str, peer_path: str) -> int:
+  log = load_log(cereal_dir)
   peer_dump = load_peer(peer_path)
   local_dump = {
     "root": hex_id(log.Event.schema.node.id),
@@ -235,16 +243,13 @@ def main() -> int:
   mode.add_argument("-g", "--generate", action="store_true", help="dump local schema to JSON")
   mode.add_argument("-r", "--read", action="store_true", help="load peer JSON and diff against local")
   parser.add_argument("-f", "--file", default="schema.json", help="JSON file path (default: schema.json)")
+  parser.add_argument("--cereal-dir", required=True, help="path to cereal directory containing log.capnp")
   args = parser.parse_args()
 
-  try:
-    if args.generate:
-      dump_schema(args.file)
-      return 0
-    return run_read(args.file)
-  except ImportError as exc:
-    print(f"error: cannot import cereal ({exc}). did scons build cereal?")
-    return 2
+  if args.generate:
+    dump_schema(args.cereal_dir, args.file)
+    return 0
+  return run_read(args.cereal_dir, args.file)
 
 
 if __name__ == "__main__":

common/model.h

@@ -1 +0,0 @@
-#define DEFAULT_MODEL "POP model (Default)"

selfdrive/car/card.py

@@ -86,7 +86,7 @@ class Car:
 
     self.can_callbacks = can_comm_callbacks(self.can_sock, self.pm.sock['sendcan'])
 
-    is_release = self.params.get_bool("IsReleaseBranch")
+    is_release = False  # self.params.get_bool("IsReleaseBranch")
     is_release_sp = self.params.get_bool("IsReleaseSpBranch")
 
     if CI is None:

selfdrive/modeld/SConscript

@@ -1,10 +1,23 @@
 import glob
 import json
 import os
+from itertools import product
 from SCons.Script import Value
 from openpilot.common.file_chunker import chunk_file, get_chunk_paths
+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 CompileConfig
 from tinygrad import Device
 
+CAMERA_CONFIGS = [
+  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),  # tici: 1928x1208
+  (_os_fisheye.width, _os_fisheye.height),        # mici: 1344x760
+]
+MODELD_CONFIGS = [CompileConfig(cam_w, cam_h, prepare_only, 'driving_')
+                  for (cam_w, cam_h), prepare_only in product(CAMERA_CONFIGS, [True, False])]
+DM_WARP_CONFIGS = [CompileConfig(cam_w, cam_h, True, 'dm_') for cam_w, cam_h in CAMERA_CONFIGS]
+
 Import('env', 'arch')
 chunker_file = File("#common/file_chunker.py")
 lenv = env.Clone()
@@ -16,18 +29,17 @@ tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "
 def estimate_pickle_max_size(onnx_size):
   return 1.2 * onnx_size  + 10 * 1024 * 1024  # 20% + 10MB is plenty
 
-# THREADS=0 is need to prevent bug: https://github.com/tinygrad/tinygrad/issues/14689
 # get fastest TG config
 available = set(Device.get_available_devices())
-# FIXME-SP: reset when we bump tg
-if False:  # 'CUDA' in available:
+if 'CUDA' in available:
   tg_backend = 'CUDA'
   tg_flags = f'DEV={tg_backend}'
 elif 'QCOM' in available:
   tg_backend = 'QCOM'
-  tg_flags = f'DEV={tg_backend} FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0'
+  tg_flags = f'DEV={tg_backend} FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1'
 else:
-  tg_backend = 'CPU' if arch == 'Darwin' else 'CPU CPU_LLVM=1'  # FIXME-SP: reset when we bump tg
+  tg_backend = 'CPU' if arch == 'Darwin' else 'CPU:LLVM'
+  # THREADS=0 is need to prevent bug: https://github.com/tinygrad/tinygrad/issues/14689
   tg_flags = f'DEV={tg_backend} THREADS=0'
 
 def write_tg_compiled_flags(target, source, env):
@@ -54,14 +66,35 @@ for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
 image_flag = {
      'larch64': 'IMAGE=2',
 }.get(arch, 'IMAGE=0')
-script_files = [File(Dir("#selfdrive/modeld").File("compile_warp.py").abspath)]
-compile_warp_cmd = f'{tg_flags} {mac_brew_string} python3 {Dir("#selfdrive/modeld").abspath}/compile_warp.py '
-from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
-warp_targets = []
-for cam in [_ar_ox_fisheye, _os_fisheye]:
-  w, h = cam.width, cam.height
-  warp_targets += [File(f"models/warp_{w}x{h}_tinygrad.pkl").abspath, File(f"models/dm_warp_{w}x{h}_tinygrad.pkl").abspath]
-lenv.Command(warp_targets, tinygrad_files + script_files + [compiled_flags_node], compile_warp_cmd)
+modeld_dir = Dir("#selfdrive/modeld").abspath
+compile_modeld_script = [File(f"{modeld_dir}/compile_modeld.py")]
+compile_dm_warp_script = [File(f"{modeld_dir}/compile_dm_warp.py")]
+driving_onnx_deps = [File(f"models/{m}.onnx").abspath for m in ['driving_vision', 'driving_policy']]
+driving_metadata_deps = [File(f"models/{m}_metadata.pkl").abspath for m in ['driving_vision', 'driving_policy']]
+
+model_w, model_h = MEDMODEL_INPUT_SIZE
+frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
+for cfg in MODELD_CONFIGS:
+  cmd = (f'{tg_flags} {mac_brew_string} {image_flag} python3 {modeld_dir}/compile_modeld.py '
+         f'--model-size {model_w}x{model_h} '
+         f'--nv12 {",".join(str(x) for x in cfg.nv12)} '
+         f'--vision-onnx {File("models/driving_vision.onnx").abspath} '
+         f'--policy-onnx {File("models/driving_policy.onnx").abspath} '
+         f'--output {cfg.pkl_path} --frame-skip {frame_skip}'
+         + (' --prepare-only' if cfg.prepare_only else ''))
+  node = lenv.Command(cfg.pkl_path, tinygrad_files + compile_modeld_script + driving_onnx_deps + driving_metadata_deps + [chunker_file, compiled_flags_node], cmd)
+  onnx_sizes_sum = sum(os.path.getsize(f) for f in driving_onnx_deps)
+  chunk_targets = get_chunk_paths(cfg.pkl_path, estimate_pickle_max_size(onnx_sizes_sum))
+  def do_chunk(target, source, env, pkl=cfg.pkl_path, chunks=chunk_targets):
+    chunk_file(pkl, chunks)
+  lenv.Command(chunk_targets, node, do_chunk)
+
+dm_w, dm_h = DM_INPUT_SIZE
+for cfg in DM_WARP_CONFIGS:
+  cmd = (f'{tg_flags} {mac_brew_string} {image_flag} python3 {modeld_dir}/compile_dm_warp.py '
+         f'--nv12 {",".join(str(x) for x in cfg.nv12)} --warp-to {dm_w}x{dm_h} '
+         f'--output {cfg.pkl_path}')
+  lenv.Command(cfg.pkl_path, tinygrad_files + compile_dm_warp_script + compile_modeld_script + [compiled_flags_node], cmd)
 
 def tg_compile(flags, model_name):
   pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
@@ -82,7 +115,4 @@ def tg_compile(flags, model_name):
     do_chunk,
   )
 
-# Compile small models
-for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
-  tg_compile(tg_flags, model_name)
-
+tg_compile(tg_flags, 'dmonitoring_model')

selfdrive/modeld/compile_dm_warp.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+import argparse
+import pickle
+import time
+
+from tinygrad.tensor import Tensor
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+
+from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, warp_perspective_tinygrad, _parse_size, _parse_nv12
+
+
+def make_warp_dm(nv12: NV12Frame, dm_w, dm_h):
+  cam_w, cam_h, stride, _, _, _ = nv12
+  stride_pad = stride - cam_w
+
+  def warp_dm(input_frame, M_inv):
+    M_inv = M_inv.to(Device.DEFAULT).realize()
+    return warp_perspective_tinygrad(input_frame[:cam_h*stride], M_inv,
+                                     (dm_w, dm_h), (cam_h, cam_w), stride_pad).reshape(-1, dm_h * dm_w)
+  return warp_dm
+
+
+def compile_dm_warp(nv12: NV12Frame, dm_w, dm_h, pkl_path):
+  print(f"Compiling DM warp for {nv12.width}x{nv12.height} -> {dm_w}x{dm_h}...")
+
+  warp_dm_jit = TinyJit(make_warp_dm(nv12, dm_w, dm_h), prune=True)
+
+  for i in range(10):
+    frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
+    M_inv = Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')
+    Device.default.synchronize()
+    st = time.perf_counter()
+    warp_dm_jit(frame, M_inv).realize()
+    mt = time.perf_counter()
+    Device.default.synchronize()
+    et = time.perf_counter()
+    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
+
+  with open(pkl_path, "wb") as f:
+    pickle.dump(warp_dm_jit, f)
+  print(f"  Saved to {pkl_path}")
+
+
+if __name__ == "__main__":
+  p = argparse.ArgumentParser()
+  p.add_argument('--nv12', type=_parse_nv12, required=True,
+                 help=f'NV12 frame layout: {",".join(NV12Frame._fields)}')
+  p.add_argument('--warp-to', type=_parse_size, required=True, help='DM input WxH')
+  p.add_argument('--output', required=True)
+  args = p.parse_args()
+
+  dm_w, dm_h = args.warp_to
+  compile_dm_warp(args.nv12, dm_w, dm_h, args.output)

selfdrive/modeld/compile_modeld.py

@@ -0,0 +1,253 @@
+#!/usr/bin/env python3
+import argparse
+import pickle
+import time
+from functools import partial
+from collections import namedtuple
+
+import numpy as np
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import Context
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+from tinygrad.nn.onnx import OnnxRunner
+
+# https://github.com/tinygrad/tinygrad/issues/15682
+from tinygrad.uop.ops import UOp, Ops
+_orig = UOp.__reduce__
+UOp.__reduce__ = lambda self: (UOp.unique, ()) if self.op is Ops.UNIQUE else _orig(self)
+
+
+NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
+
+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)
+
+
+def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad):
+  w_dst, h_dst = dst_shape
+  h_src, w_src = src_shape
+
+  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
+  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
+
+  # inline 3x3 matmul as elementwise to avoid reduce op (enables fusion with gather)
+  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
+  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
+  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
+
+  src_x = src_x / src_w
+  src_y = src_y / src_w
+
+  x_nn_clipped = Tensor.round(src_x).clip(0, w_src - 1).cast('int')
+  y_nn_clipped = Tensor.round(src_y).clip(0, h_src - 1).cast('int')
+  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
+
+  return src_flat[idx]
+
+
+def frames_to_tensor(frames):
+  H = (frames.shape[0] * 2) // 3
+  W = frames.shape[1]
+  in_img1 = Tensor.cat(frames[0:H:2, 0::2],
+                       frames[1:H:2, 0::2],
+                       frames[0:H:2, 1::2],
+                       frames[1:H:2, 1::2],
+                       frames[H:H+H//4].reshape((H//2, W//2)),
+                       frames[H+H//4:H+H//2].reshape((H//2, W//2)), dim=0).reshape((6, H//2, W//2))
+  return in_img1
+
+
+def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
+  cam_w, cam_h, stride, y_height, uv_height, _ = nv12
+  uv_offset = stride * y_height
+  stride_pad = stride - cam_w
+
+  def frame_prepare_tinygrad(input_frame, M_inv):
+    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]])
+    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
+    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
+    with Context(SPLIT_REDUCEOP=0):
+      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
+                                    M_inv, (model_w, model_h),
+                                    (cam_h, cam_w), stride_pad).realize()
+      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
+    tensor = frames_to_tensor(yuv)
+    return tensor
+  return frame_prepare_tinygrad
+
+
+def make_input_queues(vision_input_shapes, policy_input_shapes, frame_skip):
+  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])
+
+  fb = policy_input_shapes['features_buffer']  # (1, 25, 512)
+  dp = policy_input_shapes['desire_pulse']  # (1, 25, 8)
+  tc = policy_input_shapes['traffic_convention']  # (1, 2)
+
+  npy = {
+    'desire': np.zeros(dp[2], dtype=np.float32),
+    'traffic_convention': np.zeros(tc, dtype=np.float32),
+    'tfm': np.zeros((3, 3), dtype=np.float32),
+    'big_tfm': np.zeros((3, 3), dtype=np.float32),
+  }
+  input_queues = {
+    'img_q': Tensor.zeros(img_buf_shape, dtype='uint8').contiguous().realize(),
+    'big_img_q': Tensor.zeros(img_buf_shape, dtype='uint8').contiguous().realize(),
+    'feat_q': Tensor.zeros(frame_skip * (fb[1] - 1) + 1, fb[0], fb[2]).contiguous().realize(),
+    'desire_q': Tensor.zeros(frame_skip * dp[1], dp[0], dp[2]).contiguous().realize(),
+    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
+  }
+  return input_queues, npy
+
+
+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_run_policy(vision_runner, policy_runner, nv12: NV12Frame, model_w, model_h,
+                    vision_features_slice, frame_skip, prepare_only=False):
+  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
+  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+
+  def run_policy(img_q, big_img_q, feat_q, desire_q, desire, traffic_convention, tfm, big_tfm, frame, big_frame):
+    tfm = tfm.to(Device.DEFAULT)
+    big_tfm = big_tfm.to(Device.DEFAULT)
+    desire = desire.to(Device.DEFAULT)
+    traffic_convention = traffic_convention.to(Device.DEFAULT)
+    Tensor.realize(tfm, big_tfm, desire, traffic_convention)
+
+    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
+    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+
+    if prepare_only:
+      return img, big_img
+
+    vision_out = next(iter(vision_runner({'img': img, 'big_img': big_img}).values())).cast('float32')
+
+    new_feat = vision_out[:, vision_features_slice].reshape(1, -1).unsqueeze(0)
+    feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn)
+    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
+
+    inputs = {'features_buffer': feat_buf, 'desire_pulse': desire_buf, 'traffic_convention': traffic_convention}
+    policy_out = next(iter(policy_runner(inputs).values())).cast('float32')
+
+    return vision_out, policy_out
+  return run_policy
+
+
+def compile_modeld(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
+                   vision_onnx, policy_onnx, pkl_path):
+  from get_model_metadata import metadata_path_for
+
+  print(f"Compiling combined policy JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
+
+  vision_runner = OnnxRunner(vision_onnx)
+  policy_runner = OnnxRunner(policy_onnx)
+
+  with open(metadata_path_for(vision_onnx), 'rb') as f:
+    vision_metadata = pickle.load(f)
+    vision_features_slice = vision_metadata['output_slices']['hidden_state']
+    vision_input_shapes = vision_metadata['input_shapes']
+  with open(metadata_path_for(policy_onnx), 'rb') as f:
+    policy_input_shapes = pickle.load(f)['input_shapes']
+
+  _run = make_run_policy(vision_runner, policy_runner, nv12, model_w, model_h,
+                         vision_features_slice, frame_skip, prepare_only)
+  run_policy_jit = TinyJit(_run, prune=True)
+
+  N_RUNS = 3
+  SEED = 42
+
+  def random_inputs_run_fn(fn, seed, test_val=None, test_buffers=None, expect_match=True):
+    input_queues, npy = make_input_queues(vision_input_shapes, policy_input_shapes, frame_skip)
+    np.random.seed(seed)
+    Tensor.manual_seed(seed)
+
+    for i in range(N_RUNS):
+      frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
+      big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
+      for v in npy.values():
+        v[:] = np.random.randn(*v.shape).astype(v.dtype)
+      Device.default.synchronize()
+      st = time.perf_counter()
+      outs = fn(**input_queues, frame=frame, big_frame=big_frame)
+      mt = time.perf_counter()
+      for o in outs:
+        # .realize() not needed once jitted, but needed for unjitted fn
+        o.realize()
+      Device.default.synchronize()
+      et = time.perf_counter()
+      print(f"  [{i+1}/{N_RUNS}] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
+
+    val = [np.copy(v.numpy()) for v in outs]
+    buffers = [np.copy(v.numpy().copy()) for v in input_queues.values()]
+
+    if test_val is not None:
+      match = all(np.array_equal(a, b) for a, b in zip(val, test_val, strict=True))
+      assert match == expect_match, f"outputs {'differ from' if expect_match else 'match'} baseline (seed={seed})"
+    if test_buffers is not None:
+      match = all(np.array_equal(a, b) for a, b in zip(buffers, test_buffers, strict=True))
+      assert match == expect_match, f"buffers {'differ from' if expect_match else 'match'} baseline (seed={seed})"
+    return fn, val, buffers
+
+  print('run unjitted')
+  _, test_val, test_buffers = random_inputs_run_fn(_run, seed=SEED)
+  print('capture + replay')
+  run_policy_jit, _, _ = random_inputs_run_fn(run_policy_jit, SEED, test_val, test_buffers)
+
+  print('pickle round trip')
+  with open(pkl_path, "wb") as f:
+    pickle.dump(run_policy_jit, f)
+    print(f"  Saved to {pkl_path}")
+  with open(pkl_path, "rb") as f:
+    run_policy_jit = pickle.load(f)
+  random_inputs_run_fn(run_policy_jit, SEED, test_val, test_buffers, expect_match=True)
+  random_inputs_run_fn(run_policy_jit, SEED+1, test_val, test_buffers, expect_match=False)
+
+
+def _parse_size(s):
+  w, h = s.lower().split('x')
+  return int(w), int(h)
+
+
+def _parse_nv12(s):
+  parts = s.split(',')
+  assert len(parts) == len(NV12Frame._fields), \
+    f"--nv12 expects {','.join(NV12Frame._fields)} (got {s!r})"
+  return NV12Frame(*(int(x) for x in parts))
+
+
+if __name__ == "__main__":
+  p = argparse.ArgumentParser()
+  p.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
+  p.add_argument('--nv12', type=_parse_nv12, required=True,
+                 help=f'NV12 frame layout: {",".join(NV12Frame._fields)}')
+  p.add_argument('--vision-onnx', required=True)
+  p.add_argument('--policy-onnx', required=True)
+  p.add_argument('--output', required=True)
+  p.add_argument('--prepare-only', action='store_true')
+  p.add_argument('--frame-skip', type=int, required=True)
+  args = p.parse_args()
+
+  model_w, model_h = args.model_size
+  compile_modeld(args.nv12, model_w, model_h, args.prepare_only, args.frame_skip,
+                 args.vision_onnx, args.policy_onnx, args.output)

selfdrive/modeld/compile_warp.py

@@ -1,201 +0,0 @@
-#!/usr/bin/env python3
-import time
-import pickle
-import numpy as np
-from pathlib import Path
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import Context
-from tinygrad.device import Device
-from tinygrad.engine.jit import TinyJit
-
-from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE, DM_INPUT_SIZE
-from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
-
-MODELS_DIR = Path(__file__).parent / 'models'
-
-CAMERA_CONFIGS = [
-  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),  # tici: 1928x1208
-  (_os_fisheye.width, _os_fisheye.height),        # mici: 1344x760
-]
-
-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)
-
-IMG_BUFFER_SHAPE = (30, MEDMODEL_INPUT_SIZE[1] // 2, MEDMODEL_INPUT_SIZE[0] // 2)
-
-
-def warp_pkl_path(w, h):
-  return MODELS_DIR / f'warp_{w}x{h}_tinygrad.pkl'
-
-
-def dm_warp_pkl_path(w, h):
-  return MODELS_DIR / f'dm_warp_{w}x{h}_tinygrad.pkl'
-
-
-def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad):
-  w_dst, h_dst = dst_shape
-  h_src, w_src = src_shape
-
-  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
-  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
-
-  # inline 3x3 matmul as elementwise to avoid reduce op (enables fusion with gather)
-  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
-  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
-  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
-
-  src_x = src_x / src_w
-  src_y = src_y / src_w
-
-  x_nn_clipped = Tensor.round(src_x).clip(0, w_src - 1).cast('int')
-  y_nn_clipped = Tensor.round(src_y).clip(0, h_src - 1).cast('int')
-  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
-
-  return src_flat[idx]
-
-
-def frames_to_tensor(frames, model_w, model_h):
-  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(cam_w, cam_h, model_w, model_h):
-  stride, y_height, uv_height, _ = get_nv12_info(cam_w, cam_h)
-  uv_offset = stride * y_height
-  stride_pad = stride - cam_w
-
-  def frame_prepare_tinygrad(input_frame, M_inv):
-    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
-    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]])
-    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
-    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
-    with Context(SPLIT_REDUCEOP=0):
-      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
-                                    M_inv, (model_w, model_h),
-                                    (cam_h, cam_w), stride_pad).realize()
-      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
-                                    M_inv_uv, (model_w//2, model_h//2),
-                                    (cam_h//2, cam_w//2), 0).realize()
-      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
-                                    M_inv_uv, (model_w//2, model_h//2),
-                                    (cam_h//2, cam_w//2), 0).realize()
-    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
-    tensor = frames_to_tensor(yuv, model_w, model_h)
-    return tensor
-  return frame_prepare_tinygrad
-
-
-def make_update_img_input(frame_prepare, model_w, model_h):
-  def update_img_input_tinygrad(tensor, frame, M_inv):
-    M_inv = M_inv.to(Device.DEFAULT)
-    new_img = frame_prepare(frame, M_inv)
-    tensor.assign(tensor[6:].cat(new_img, dim=0).contiguous())
-    return Tensor.cat(tensor[:6], tensor[-6:], dim=0).contiguous().reshape(1, 12, model_h//2, model_w//2)
-  return update_img_input_tinygrad
-
-
-def make_update_both_imgs(frame_prepare, model_w, model_h):
-  update_img = make_update_img_input(frame_prepare, model_w, model_h)
-
-  def update_both_imgs_tinygrad(calib_img_buffer, new_img, M_inv,
-                                calib_big_img_buffer, new_big_img, M_inv_big):
-    calib_img_pair = update_img(calib_img_buffer, new_img, M_inv)
-    calib_big_img_pair = update_img(calib_big_img_buffer, new_big_img, M_inv_big)
-    return calib_img_pair, calib_big_img_pair
-  return update_both_imgs_tinygrad
-
-
-def make_warp_dm(cam_w, cam_h, dm_w, dm_h):
-  stride, y_height, _, _ = get_nv12_info(cam_w, cam_h)
-  stride_pad = stride - cam_w
-
-  def warp_dm(input_frame, M_inv):
-    M_inv = M_inv.to(Device.DEFAULT)
-    result = warp_perspective_tinygrad(input_frame[:cam_h*stride], M_inv, (dm_w, dm_h), (cam_h, cam_w), stride_pad).reshape(-1, dm_h * dm_w)
-    return result
-  return warp_dm
-
-
-def compile_modeld_warp(cam_w, cam_h):
-  model_w, model_h = MEDMODEL_INPUT_SIZE
-  _, _, _, yuv_size = get_nv12_info(cam_w, cam_h)
-
-  print(f"Compiling modeld warp for {cam_w}x{cam_h}...")
-
-  frame_prepare = make_frame_prepare(cam_w, cam_h, model_w, model_h)
-  update_both_imgs = make_update_both_imgs(frame_prepare, model_w, model_h)
-  update_img_jit = TinyJit(update_both_imgs, prune=True)
-
-  full_buffer = Tensor.zeros(IMG_BUFFER_SHAPE, dtype='uint8').contiguous().realize()
-  big_full_buffer = Tensor.zeros(IMG_BUFFER_SHAPE, dtype='uint8').contiguous().realize()
-  new_frame_np = np.random.randint(0, 256, yuv_size, dtype=np.uint8)
-  new_big_frame_np = np.random.randint(0, 256, yuv_size, dtype=np.uint8)
-  for i in range(10):
-    img_inputs = [full_buffer,
-                  Tensor.from_blob(new_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                  Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    big_img_inputs = [big_full_buffer,
-                      Tensor.from_blob(new_big_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                      Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    inputs = img_inputs + big_img_inputs
-    Device.default.synchronize()
-
-    st = time.perf_counter()
-    _ = update_img_jit(*inputs)
-    mt = time.perf_counter()
-    Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-  pkl_path = warp_pkl_path(cam_w, cam_h)
-  with open(pkl_path, "wb") as f:
-    pickle.dump(update_img_jit, f)
-  print(f"  Saved to {pkl_path}")
-
-  jit = pickle.load(open(pkl_path, "rb"))
-  jit(*inputs)
-
-
-def compile_dm_warp(cam_w, cam_h):
-  dm_w, dm_h = DM_INPUT_SIZE
-  _, _, _, yuv_size = get_nv12_info(cam_w, cam_h)
-
-  print(f"Compiling DM warp for {cam_w}x{cam_h}...")
-
-  warp_dm = make_warp_dm(cam_w, cam_h, dm_w, dm_h)
-  warp_dm_jit = TinyJit(warp_dm, prune=True)
-
-  new_frame_np = np.random.randint(0, 256, yuv_size, dtype=np.uint8)
-  for i in range(10):
-    inputs = [Tensor.from_blob(new_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-              Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    Device.default.synchronize()
-    st = time.perf_counter()
-    warp_dm_jit(*inputs)
-    mt = time.perf_counter()
-    Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-  pkl_path = dm_warp_pkl_path(cam_w, cam_h)
-  with open(pkl_path, "wb") as f:
-    pickle.dump(warp_dm_jit, f)
-  print(f"  Saved to {pkl_path}")
-
-
-def run_and_save_pickle():
-  for cam_w, cam_h in CAMERA_CONFIGS:
-    compile_modeld_warp(cam_w, cam_h)
-    compile_dm_warp(cam_w, cam_h)
-
-
-if __name__ == "__main__":
-  run_and_save_pickle()

selfdrive/modeld/dmonitoringmodeld.py

@@ -1,12 +1,8 @@
 #!/usr/bin/env python3
 import os
-from openpilot.selfdrive.modeld.tinygrad_helpers import MODELS_DIR, set_tinygrad_backend_from_compiled_flags
+from openpilot.selfdrive.modeld.helpers import MODELS_DIR, CompileConfig, set_tinygrad_backend_from_compiled_flags
 set_tinygrad_backend_from_compiled_flags()
 
-# FIXME-SP: remove once we bump tg
-from openpilot.system.hardware import TICI
-os.environ['DEV'] = 'QCOM' if TICI else 'CPU'
-
 from tinygrad.tensor import Tensor
 import time
 import pickle
@@ -32,7 +28,7 @@ class ModelState:
   inputs: dict[str, np.ndarray]
   output: np.ndarray
 
-  def __init__(self):
+  def __init__(self, cam_w: int, cam_h: int):
     with open(METADATA_PATH, 'rb') as f:
       model_metadata = pickle.load(f)
       self.input_shapes = model_metadata['input_shapes']
@@ -44,22 +40,18 @@ class ModelState:
 
     self.warp_inputs_np = {'transform': np.zeros((3,3), dtype=np.float32)}
     self.warp_inputs = {k: Tensor(v, device='NPY') for k,v in self.warp_inputs_np.items()}
-    self.frame_buf_params = None
+    self.frame_buf_params = get_nv12_info(cam_w, cam_h)
     self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
     self._blob_cache : dict[int, Tensor] = {}
-    self.image_warp = None
     self.model_run = pickle.loads(read_file_chunked(str(MODEL_PKL_PATH)))
+    with open(CompileConfig(cam_w, cam_h, prefix='dm_', prepare_only=True).pkl_path, "rb") as f:
+      self.image_warp = pickle.load(f)
 
   def run(self, buf: VisionBuf, calib: np.ndarray, transform: np.ndarray) -> tuple[np.ndarray, float]:
     self.numpy_inputs['calib'][0,:] = calib
 
     t1 = time.perf_counter()
 
-    if self.image_warp is None:
-      self.frame_buf_params = get_nv12_info(buf.width, buf.height)
-      warp_path = MODELS_DIR / f'dm_warp_{buf.width}x{buf.height}_tinygrad.pkl'
-      with open(warp_path, "rb") as f:
-        self.image_warp = pickle.load(f)
     ptr = buf.data.ctypes.data
     # There is a ringbuffer of imgs, just cache tensors pointing to all of them
     if ptr not in self._blob_cache:
@@ -113,9 +105,6 @@ def get_driverstate_packet(model_output, frame_id: int, location_ts: int, exec_t
 def main():
   config_realtime_process(7, 5)
 
-  model = ModelState()
-  cloudlog.warning("models loaded, dmonitoringmodeld starting")
-
   cloudlog.warning("connecting to driver stream")
   vipc_client = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_DRIVER, True)
   while not vipc_client.connect(False):
@@ -123,6 +112,9 @@ def main():
   assert vipc_client.is_connected()
   cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
 
+  model = ModelState(vipc_client.width, vipc_client.height)
+  cloudlog.warning("models loaded, dmonitoringmodeld starting")
+
   sm = SubMaster(["liveCalibration"])
   pm = PubMaster(["driverStateV2"])
 

selfdrive/modeld/get_model_metadata.py

@@ -7,6 +7,10 @@ from typing import Any
 
 from tinygrad.nn.onnx import OnnxPBParser
 
+def metadata_path_for(onnx_path) -> pathlib.Path:
+  p = pathlib.Path(onnx_path)
+  return p.parent / (p.stem + '_metadata.pkl')
+
 
 class MetadataOnnxPBParser(OnnxPBParser):
   def _parse_ModelProto(self) -> dict:
@@ -48,7 +52,7 @@ if __name__ == "__main__":
     'output_shapes': dict(get_name_and_shape(x) for x in model["graph"]["output"]),
   }
 
-  metadata_path = model_path.parent / (model_path.stem + '_metadata.pkl')
+  metadata_path = metadata_path_for(model_path)
   with open(metadata_path, 'wb') as f:
     pickle.dump(metadata, f)
 

selfdrive/modeld/helpers.py

@@ -0,0 +1,31 @@
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+
+MODELS_DIR = Path(__file__).resolve().parent / 'models'
+COMPILED_FLAGS_PATH = MODELS_DIR / 'tg_compiled_flags.json'
+
+
+def set_tinygrad_backend_from_compiled_flags() -> None:
+  if os.path.isfile(COMPILED_FLAGS_PATH):
+    with open(COMPILED_FLAGS_PATH) as f:
+      os.environ['DEV'] = str(json.load(f)['DEV'])
+
+
+@dataclass
+class CompileConfig:
+  cam_w: int
+  cam_h: int
+  prepare_only: bool
+  prefix: str
+
+  @property
+  def pkl_path(self):
+    return str(MODELS_DIR / f'{self.prefix}{"warp_" if self.prepare_only else ""}{self.cam_w}x{self.cam_h}_tinygrad.pkl')
+
+  @property
+  def nv12(self):
+    return (self.cam_w, self.cam_h, *get_nv12_info(self.cam_w, self.cam_h))

selfdrive/modeld/modeld.py

@@ -1,12 +1,8 @@
 #!/usr/bin/env python3
 import os
-from openpilot.selfdrive.modeld.tinygrad_helpers import MODELS_DIR, set_tinygrad_backend_from_compiled_flags
+from openpilot.selfdrive.modeld.helpers import MODELS_DIR, CompileConfig, set_tinygrad_backend_from_compiled_flags
 set_tinygrad_backend_from_compiled_flags()
 
-# FIXME-SP: remove once we bump tg
-from openpilot.system.hardware import TICI
-os.environ['DEV'] = 'QCOM' if TICI else 'CPU'
-
 USBGPU = "USBGPU" in os.environ
 if USBGPU:
   os.environ['DEV'] = 'AMD'
@@ -30,6 +26,7 @@ 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, 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
 from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
 from openpilot.common.file_chunker import read_file_chunked
 from openpilot.selfdrive.modeld.constants import ModelConstants, Plan
@@ -41,17 +38,13 @@ from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
 PROCESS_NAME = "selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 
-VISION_PKL_PATH = MODELS_DIR / 'driving_vision_tinygrad.pkl'
 VISION_METADATA_PATH = MODELS_DIR / 'driving_vision_metadata.pkl'
-POLICY_PKL_PATH = MODELS_DIR / 'driving_policy_tinygrad.pkl'
 POLICY_METADATA_PATH = MODELS_DIR / 'driving_policy_metadata.pkl'
 
 LAT_SMOOTH_SECONDS = 0.0
 LONG_SMOOTH_SECONDS = 0.3
 MIN_LAT_CONTROL_SPEED = 0.3
 
-IMG_QUEUE_SHAPE = (6*(ModelConstants.MODEL_RUN_FREQ//ModelConstants.MODEL_CONTEXT_FREQ + 1), 128, 256)
-assert IMG_QUEUE_SHAPE[0] == 30
 
 
 def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
@@ -86,108 +79,39 @@ class FrameMeta:
     if vipc is not None:
       self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof
 
-class InputQueues:
-  def __init__ (self, model_fps, env_fps, n_frames_input):
-    assert env_fps % model_fps == 0
-    assert env_fps >= model_fps
-    self.model_fps = model_fps
-    self.env_fps = env_fps
-    self.n_frames_input = n_frames_input
-
-    self.dtypes = {}
-    self.shapes = {}
-    self.q = {}
-
-  def update_dtypes_and_shapes(self, input_dtypes, input_shapes) -> None:
-    self.dtypes.update(input_dtypes)
-    if self.env_fps == self.model_fps:
-      self.shapes.update(input_shapes)
-    else:
-      for k in input_shapes:
-        shape = list(input_shapes[k])
-        if 'img' in k:
-          n_channels = shape[1] // self.n_frames_input
-          shape[1] = (self.env_fps // self.model_fps + (self.n_frames_input - 1)) * n_channels
-        else:
-          shape[1] = (self.env_fps // self.model_fps) * shape[1]
-        self.shapes[k] = tuple(shape)
-
-  def reset(self) -> None:
-    self.q = {k: np.zeros(self.shapes[k], dtype=self.dtypes[k]) for k in self.dtypes.keys()}
-
-  def enqueue(self, inputs:dict[str, np.ndarray]) -> None:
-    for k in inputs.keys():
-      if inputs[k].dtype != self.dtypes[k]:
-        raise ValueError(f'supplied input <{k}({inputs[k].dtype})> has wrong dtype, expected {self.dtypes[k]}')
-      input_shape = list(self.shapes[k])
-      input_shape[1] = -1
-      single_input = inputs[k].reshape(tuple(input_shape))
-      sz = single_input.shape[1]
-      self.q[k][:,:-sz] = self.q[k][:,sz:]
-      self.q[k][:,-sz:] = single_input
-
-  def get(self, *names) -> dict[str, np.ndarray]:
-    if self.env_fps == self.model_fps:
-      return {k: self.q[k] for k in names}
-    else:
-      out = {}
-      for k in names:
-        shape = self.shapes[k]
-        if 'img' in k:
-          n_channels = shape[1] // (self.env_fps // self.model_fps + (self.n_frames_input - 1))
-          out[k] = np.concatenate([self.q[k][:, s:s+n_channels] for s in np.linspace(0, shape[1] - n_channels, self.n_frames_input, dtype=int)], axis=1)
-        elif 'pulse' in k:
-          # any pulse within interval counts
-          out[k] = self.q[k].reshape((shape[0], shape[1] * self.model_fps // self.env_fps, self.env_fps // self.model_fps, -1)).max(axis=2)
-        else:
-          idxs = np.arange(-1, -shape[1], -self.env_fps // self.model_fps)[::-1]
-          out[k] = self.q[k][:, idxs]
-      return out
 
 class ModelState(ModelStateBase):
-  inputs: dict[str, np.ndarray]
-  output: np.ndarray
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
-  def __init__(self):
+  def __init__(self, cam_w: int, cam_h: int):
     ModelStateBase.__init__(self)
     self.LAT_SMOOTH_SECONDS = LAT_SMOOTH_SECONDS
+
     with open(VISION_METADATA_PATH, 'rb') as f:
       vision_metadata = pickle.load(f)
       self.vision_input_shapes =  vision_metadata['input_shapes']
       self.vision_input_names = list(self.vision_input_shapes.keys())
       self.vision_output_slices = vision_metadata['output_slices']
-      vision_output_size = vision_metadata['output_shapes']['outputs'][1]
 
     with open(POLICY_METADATA_PATH, 'rb') as f:
       policy_metadata = pickle.load(f)
       self.policy_input_shapes =  policy_metadata['input_shapes']
       self.policy_output_slices = policy_metadata['output_slices']
-      policy_output_size = policy_metadata['output_shapes']['outputs'][1]
 
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
 
-    # policy inputs
-    self.numpy_inputs = {k: np.zeros(self.policy_input_shapes[k], dtype=np.float32) for k in self.policy_input_shapes}
-    self.full_input_queues = InputQueues(ModelConstants.MODEL_CONTEXT_FREQ, ModelConstants.MODEL_RUN_FREQ, ModelConstants.N_FRAMES)
-    for k in ['desire_pulse', 'features_buffer']:
-      self.full_input_queues.update_dtypes_and_shapes({k: self.numpy_inputs[k].dtype}, {k: self.numpy_inputs[k].shape})
-    self.full_input_queues.reset()
-
-    self.img_queues = {'img': Tensor.zeros(IMG_QUEUE_SHAPE, dtype='uint8').contiguous().realize(),
-                       'big_img': Tensor.zeros(IMG_QUEUE_SHAPE, dtype='uint8').contiguous().realize()}
+    self.frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
+    self.input_queues, self.npy = make_input_queues(self.vision_input_shapes, self.policy_input_shapes, self.frame_skip)
     self.full_frames : dict[str, Tensor] = {}
     self._blob_cache : dict[int, Tensor] = {}
-    self.transforms_np = {k: np.zeros((3,3), dtype=np.float32) for k in self.img_queues}
-    self.transforms = {k: Tensor(v, device='NPY').realize() for k, v in self.transforms_np.items()}
-    self.vision_output = np.zeros(vision_output_size, dtype=np.float32)
-    self.policy_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
-    self.policy_output = np.zeros(policy_output_size, dtype=np.float32)
     self.parser = Parser()
-    self.frame_buf_params : dict[str, tuple[int, int, int, int]] = {}
-    self.update_imgs = None
-    self.vision_run = pickle.loads(read_file_chunked(str(VISION_PKL_PATH)))
-    self.policy_run = pickle.loads(read_file_chunked(str(POLICY_PKL_PATH)))
+    self.frame_buf_params = {k: get_nv12_info(cam_w, cam_h) for k in ('img', 'big_img')}
+    self.run_policy = pickle.loads(read_file_chunked(CompileConfig(cam_w, cam_h, prefix='driving_', prepare_only=False).pkl_path))
+    self.warp_enqueue = pickle.loads(read_file_chunked(CompileConfig(cam_w, cam_h, prefix='driving_', prepare_only=True).pkl_path))
+    self.warp_enqueue(
+      **self.input_queues,
+      frame=Tensor.zeros(self.frame_buf_params['img'][3], dtype='uint8').contiguous().realize(),
+      big_frame=Tensor.zeros(self.frame_buf_params['big_img'][3], dtype='uint8').contiguous().realize())
 
   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()}
@@ -195,18 +119,6 @@ class ModelState(ModelStateBase):
 
   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:
-    # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
-    inputs['desire_pulse'][0] = 0
-    new_desire = np.where(inputs['desire_pulse'] - self.prev_desire > .99, inputs['desire_pulse'], 0)
-    self.prev_desire[:] = inputs['desire_pulse']
-    if self.update_imgs is None:
-      for key in bufs.keys():
-        w, h = bufs[key].width, bufs[key].height
-        self.frame_buf_params[key] = get_nv12_info(w, h)
-      warp_path = MODELS_DIR / f'warp_{w}x{h}_tinygrad.pkl'
-      with open(warp_path, "rb") as f:
-        self.update_imgs = pickle.load(f)
-
     for key in bufs.keys():
       ptr = bufs[key].data.ctypes.data
       yuv_size = self.frame_buf_params[key][3]
@@ -215,30 +127,31 @@ class ModelState(ModelStateBase):
       if cache_key not in self._blob_cache:
         self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8')
       self.full_frames[key] = self._blob_cache[cache_key]
-    for key in bufs.keys():
-      self.transforms_np[key][:,:] = transforms[key][:,:]
 
-    out = self.update_imgs(self.img_queues['img'], self.full_frames['img'], self.transforms['img'],
-                           self.img_queues['big_img'], self.full_frames['big_img'], self.transforms['big_img'])
-    vision_inputs = {'img': out[0], 'big_img': out[1]}
+    # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
+    inputs['desire_pulse'][0] = 0
+    self.npy['desire'][:] = np.where(inputs['desire_pulse'] - self.prev_desire > .99, inputs['desire_pulse'], 0)
+    self.prev_desire[:] = inputs['desire_pulse']
+    self.npy['traffic_convention'][:] = inputs['traffic_convention']
+    self.npy['tfm'][:,:] = transforms['img'][:,:]
+    self.npy['big_tfm'][:,:] = transforms['big_img'][:,:]
 
     if prepare_only:
+      self.warp_enqueue(**self.input_queues, frame=self.full_frames['img'], big_frame=self.full_frames['big_img'])
       return None
 
-    self.vision_output = self.vision_run(**vision_inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
-    vision_outputs_dict = self.parser.parse_vision_outputs(self.slice_outputs(self.vision_output, self.vision_output_slices))
+    vision_output, policy_output = self.run_policy(
+      **self.input_queues, frame=self.full_frames['img'], big_frame=self.full_frames['big_img']
+    )
 
-    self.full_input_queues.enqueue({'features_buffer': vision_outputs_dict['hidden_state'], 'desire_pulse': new_desire})
-    for k in ['desire_pulse', 'features_buffer']:
-      self.numpy_inputs[k][:] = self.full_input_queues.get(k)[k]
-    self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
-
-    self.policy_output = self.policy_run(**self.policy_inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
-    policy_outputs_dict = self.parser.parse_policy_outputs(self.slice_outputs(self.policy_output, self.policy_output_slices))
+    vision_output = vision_output.numpy().flatten()
+    policy_output = policy_output.numpy().flatten()
+    vision_outputs_dict = self.parser.parse_vision_outputs(self.slice_outputs(vision_output, self.vision_output_slices))
+    policy_outputs_dict = self.parser.parse_policy_outputs(self.slice_outputs(policy_output, self.policy_output_slices))
     combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
-    if SEND_RAW_PRED:
-      combined_outputs_dict['raw_pred'] = np.concatenate([self.vision_output.copy(), self.policy_output.copy()])
 
+    if SEND_RAW_PRED:
+      combined_outputs_dict['raw_pred'] = np.concatenate([vision_output.copy(), policy_output.copy()])
     return combined_outputs_dict
 
 
@@ -250,11 +163,6 @@ def main(demo=False):
     # also need to move the aux USB interrupts for good timings
     config_realtime_process(7, 54)
 
-  st = time.monotonic()
-  cloudlog.warning("loading model")
-  model = ModelState()
-  cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")
-
   # visionipc clients
   while True:
     available_streams = VisionIpcClient.available_streams("camerad", block=False)
@@ -278,6 +186,11 @@ def main(demo=False):
   if use_extra_client:
     cloudlog.warning(f"connected extra cam with buffer size: {vipc_client_extra.buffer_len} ({vipc_client_extra.width} x {vipc_client_extra.height})")
 
+  st = time.monotonic()
+  cloudlog.warning("loading model")
+  model = ModelState(vipc_client_main.width, vipc_client_main.height)
+  cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")
+
   # messaging
   pm = PubMaster(["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"])
   sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "carControl", "liveDelay"])

selfdrive/modeld/tinygrad_helpers.py

@@ -1,12 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-MODELS_DIR = Path(__file__).parent / 'models'
-COMPILED_FLAGS_PATH = MODELS_DIR / 'tg_compiled_flags.json'
-
-
-def set_tinygrad_backend_from_compiled_flags() -> None:
-  if os.path.isfile(COMPILED_FLAGS_PATH):
-    with open(COMPILED_FLAGS_PATH) as f:
-      os.environ['DEV'] = str(json.load(f)['DEV'])

selfdrive/ui/layouts/settings/developer.py

@@ -36,7 +36,7 @@ class DeveloperLayout(Widget):
   def __init__(self):
     super().__init__()
     self._params = Params()
-    self._is_release = self._params.get_bool("IsReleaseBranch")
+    self._is_release = False  # self._params.get_bool("IsReleaseBranch")
 
     # Build items and keep references for callbacks/state updates
     self._adb_toggle = toggle_item(

selfdrive/ui/layouts/settings/toggles.py

@@ -42,7 +42,7 @@ class TogglesLayout(Widget):
   def __init__(self):
     super().__init__()
     self._params = Params()
-    self._is_release = self._params.get_bool("IsReleaseBranch")
+    self._is_release = False  # self._params.get_bool("IsReleaseBranch")
 
     # param, title, desc, icon, needs_restart
     self._toggle_defs = {

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

@@ -10,6 +10,7 @@ import time
 import pyray as rl
 
 from cereal import custom
+from openpilot.sunnypilot.models.default_model import DEFAULT_MODEL
 from openpilot.common.constants import CV
 from openpilot.selfdrive.ui.ui_state import device, ui_state
 from openpilot.system.ui.lib.multilang import tr
@@ -207,7 +208,7 @@ class ModelsLayout(Widget):
     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': tr("Default Model"), 'short_name': "Default"})])]
+    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': f"{DEFAULT_MODEL} (Default)", 'short_name': "Default"})])]
     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 "")
@@ -243,7 +244,7 @@ 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 tr("Default Model")
+    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():

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

@@ -8,6 +8,7 @@ from collections.abc import Callable
 import pyray as rl
 
 from cereal import custom
+from openpilot.sunnypilot.models.default_model import DEFAULT_MODEL
 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
@@ -27,7 +28,8 @@ class CurrentModelInfo(Widget):
     subheader_color = rl.Color(255, 255, 255, int(255 * 0.9 * 0.65))
     max_width = int(self._rect.width - 20)
     self.current_model_header = UnifiedLabel(tr("active model"), 48, max_width=max_width, text_color=header_color, font_weight=FontWeight.DISPLAY)
-    self.current_model_text = UnifiedLabel(tr("default model"), 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN, scroll=True)
+    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.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)
@@ -98,7 +100,7 @@ class ModelsLayoutMici(NavScroller):
 
     folders = self._get_grouped_bundles(favorites)
     folder_buttons = []
-    default_btn = BigButton(tr("default model"))
+    default_btn = BigButton(f"{DEFAULT_MODEL} (Default)".lower())
     default_btn.set_click_callback(self._select_default)
     folder_buttons.append(default_btn)
 
@@ -168,7 +170,8 @@ class ModelsLayoutMici(NavScroller):
     self._was_downloading = is_downloading
 
     self.current_model_info.current_model_header.set_text(tr("active model"))
-    self.current_model_info.current_model_text.set_text(manager.activeBundle.displayName.lower() if manager.activeBundle.index > 0 else tr("default model"))
+    model_text = manager.activeBundle.displayName.lower() if manager.activeBundle.index > 0 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")
 

selfdrive/ui/sunnypilot/ui_state.py

@@ -159,7 +159,6 @@ class UIStateSP:
 
   def _enforce_constraints(self) -> None:
     has_long = self.has_longitudinal_control
-    has_icbm = self.has_icbm
     CP = self.CP
 
     if CP is not None:
@@ -168,8 +167,8 @@ class UIStateSP:
         self.params.remove("EnforceTorqueControl")
         self.params.remove("NeuralNetworkLateralControl")
 
-      # Alpha longitudinal: clear if not available or on release branch
-      if not CP.alphaLongitudinalAvailable or self.params.get_bool("IsReleaseBranch"):
+      # Alpha longitudinal: clear if not available
+      if not CP.alphaLongitudinalAvailable:
         self.params.remove("AlphaLongitudinalEnabled")
 
       # BSM not available: clear BSM-dependent settings
@@ -181,21 +180,23 @@ class UIStateSP:
       self.params.remove("NeuralNetworkLateralControl")
       self.params.remove("AlphaLongitudinalEnabled")
 
-    # No longitudinal control: no experimental mode
+    # No longitudinal control: no experimental mode or DEC
     if not has_long:
       self.params.remove("ExperimentalMode")
+      self.params.remove("DynamicExperimentalControl")
 
     # ICBM: clear if not available or if full longitudinal control is active
     if self.CP_SP is not None:
       if not self.CP_SP.intelligentCruiseButtonManagementAvailable or has_long:
         self.params.remove("IntelligentCruiseButtonManagement")
+        self.has_icbm = False
     else:
       self.params.remove("IntelligentCruiseButtonManagement")
+      self.has_icbm = False
 
     # Cruise features requiring longitudinal or ICBM
-    if not (has_long or has_icbm):
+    if not (has_long or self.has_icbm):
       self.params.remove("CustomAccIncrementsEnabled")
-      self.params.remove("DynamicExperimentalControl")
       self.params.remove("SmartCruiseControlVision")
       self.params.remove("SmartCruiseControlMap")
 

selfdrive/ui/ui_state.py

@@ -74,7 +74,7 @@ class UIState(UIStateSP):
 
     # Core state variables
     self.is_metric: bool = self.params.get_bool("IsMetric")
-    self.is_release = self.params.get_bool("IsReleaseBranch")
+    self.is_release = False  # self.params.get_bool("IsReleaseBranch")
     self.always_on_dm: bool = self.params.get_bool("AlwaysOnDM")
     self.started: bool = False
     self.ignition: bool = False

sunnypilot/modeld_v2/SConscript

@@ -1,5 +1,6 @@
 import os
 import glob
+from tinygrad import Device
 
 Import('env', 'arch')
 lenv = env.Clone()
@@ -21,10 +22,19 @@ if PC:
   if outputs:
     lenv.Command(outputs, inputs, cmd)
 
-tg_flags = {
-  'larch64': 'DEV=QCOM FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0',
-  'Darwin': f'DEV=CPU THREADS=0 HOME={os.path.expanduser("~")}',
-}.get(arch, 'DEV=CPU CPU_LLVM=1 THREADS=0')
+available = set(Device.get_available_devices())
+if 'CUDA' in available:
+  tg_backend = 'CUDA'
+  tg_flags = f'DEV={tg_backend}'
+elif 'QCOM' in available:
+  tg_backend = 'QCOM'
+  tg_flags = f'DEV={tg_backend} FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0'
+else:
+  tg_backend = 'CPU' if arch == 'Darwin' else 'CPU:LLVM'
+  # THREADS=0 is need to prevent bug: https://github.com/tinygrad/tinygrad/issues/14689
+  tg_flags = f'DEV={tg_backend} THREADS=0'
+
+mac_brew_string = f'HOME={os.path.expanduser("~")}' if arch == 'Darwin' else ''
 
 image_flag = {
   'larch64': 'IMAGE=2',
@@ -38,7 +48,7 @@ def tg_compile(flags, model_name):
   return lenv.Command(
     out,
     [fn + ".onnx"] + tinygrad_files,
-    f'{pythonpath_string} {flags} {image_flag} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {out}'
+    f'{pythonpath_string} {tg_flags} {mac_brew_string} {image_flag} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {out}'
   )
 
 # Compile models
@@ -46,9 +56,9 @@ for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'drivin
   if File(f"models/{model_name}.onnx").exists():
     tg_compile(tg_flags, model_name)
 
-script_files = [File("warp.py"), File(Dir("#selfdrive/modeld").File("compile_warp.py").abspath)]
+script_files = [File("warp.py")]
 pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + ':' + env.Dir("#").abspath + '"'
-compile_warp_cmd = f'{pythonpath_string} {tg_flags} python3 -m sunnypilot.modeld_v2.warp'
+compile_warp_cmd = f'{pythonpath_string} {tg_flags} {mac_brew_string} {image_flag} python3 -m sunnypilot.modeld_v2.warp'
 
 from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
 warp_targets = []

sunnypilot/modeld_v2/modeld.py

@@ -129,8 +129,7 @@ class ModelState(ModelStateBase):
         self.numpy_inputs[key][:] = inputs[key]
 
     imgs_tensors = self.warp.process(bufs, transforms)
-    for name, tensor in imgs_tensors.items():
-      self.model_runner.inputs[name] = tensor
+    self.model_runner.update_vision_inputs(imgs_tensors)
     self.model_runner.prepare_inputs(self.numpy_inputs)
 
     if prepare_only:

sunnypilot/modeld_v2/tests/test_warp.py

@@ -2,8 +2,11 @@ import os
 os.environ['DEV'] = 'CPU'
 import pytest
 import numpy as np
-from openpilot.selfdrive.modeld.compile_warp import get_nv12_info, CAMERA_CONFIGS
-from openpilot.sunnypilot.modeld_v2.warp import Warp, MODEL_W, MODEL_H
+from openpilot.sunnypilot.modeld_v2.warp import CAMERA_CONFIGS
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.sunnypilot.modeld_v2.warp import Warp
+from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE
+MODEL_W, MODEL_H = MEDMODEL_INPUT_SIZE
 
 VISION_NAME_PAIRS = [  # needed to account for supercombos input_imgs
   ('img', 'big_img'),

sunnypilot/modeld_v2/warp.py

@@ -6,29 +6,128 @@ from tinygrad.tensor import Tensor
 from tinygrad.engine.jit import TinyJit
 from tinygrad.device import Device
 
+from typing import NamedTuple
+# https://github.com/tinygrad/tinygrad/issues/15682
+from tinygrad.uop.ops import UOp, Ops
+_orig = UOp.__reduce__
+UOp.__reduce__ = lambda self: (UOp.unique, ()) if self.op is Ops.UNIQUE else _orig(self)
+
+from tinygrad.helpers import Context
 from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-from openpilot.selfdrive.modeld.compile_warp import (
-  CAMERA_CONFIGS, MEDMODEL_INPUT_SIZE, make_frame_prepare, make_update_both_imgs,
-  warp_pkl_path,
-)
+from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
+
+class NV12Frame(NamedTuple):
+  cam_w: int
+  cam_h: int
+  stride: int
+  y_height: int
+  uv_height: int
+  size: int
+
+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)
+
+CAMERA_CONFIGS = [
+  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),  # tici: 1928x1208
+  (_os_fisheye.width, _os_fisheye.height),        # mici: 1344x760
+]
+from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE
 
 MODELS_DIR = Path(__file__).parent / 'models'
-MODEL_W, MODEL_H = MEDMODEL_INPUT_SIZE
+
 UPSTREAM_BUFFER_LENGTH = 5
 
+def warp_pkl_path(cam_w, cam_h):
+  return MODELS_DIR / f'warp_{cam_w}x{cam_h}_tinygrad.pkl'
+
+def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad):
+  w_dst, h_dst = dst_shape
+  h_src, w_src = src_shape
+
+  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
+  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
+
+  # inline 3x3 matmul as elementwise to avoid reduce op (enables fusion with gather)
+  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
+  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
+  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
+
+  src_x = src_x / src_w
+  src_y = src_y / src_w
+
+  x_nn_clipped = Tensor.round(src_x).clip(0, w_src - 1).cast('int')
+  y_nn_clipped = Tensor.round(src_y).clip(0, h_src - 1).cast('int')
+  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
+
+  return src_flat[idx]
+
+def frames_to_tensor(frames, model_w, model_h):
+  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(cam_w, cam_h, model_w, model_h):
+  stride, y_height, uv_height, _ = get_nv12_info(cam_w, cam_h)
+  uv_offset = stride * y_height
+  stride_pad = stride - cam_w
+
+  def frame_prepare_tinygrad(input_frame, M_inv):
+    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]])
+    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
+    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
+    with Context(SPLIT_REDUCEOP=0):
+      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
+                                    M_inv, (model_w, model_h),
+                                    (cam_h, cam_w), stride_pad).realize()
+      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
+    tensor = frames_to_tensor(yuv, model_w, model_h)
+    return tensor
+  return frame_prepare_tinygrad
+
+def make_update_img_input(frame_prepare, model_w, model_h):
+  def update_img_input_tinygrad(tensor, frame, M_inv):
+    M_inv = M_inv.to(Device.DEFAULT)
+    new_img = frame_prepare(frame, M_inv)
+    tensor.assign(tensor[6:].cat(new_img, dim=0).contiguous())
+    return tensor, Tensor.cat(tensor[:6], tensor[-6:], dim=0).contiguous().reshape(1, 12, model_h//2, model_w//2)
+  return update_img_input_tinygrad
+
+def make_update_both_imgs(frame_prepare, model_w, model_h):
+  update_img = make_update_img_input(frame_prepare, model_w, model_h)
+
+  def update_both_imgs_tinygrad(calib_img_buffer, new_img, M_inv,
+                                calib_big_img_buffer, new_big_img, M_inv_big):
+    r1, r2 = update_img(calib_img_buffer, new_img, M_inv)
+    w1, w2 = update_img(calib_big_img_buffer, new_big_img, M_inv_big)
+    return r1, r2, w1, w2
+  return update_both_imgs_tinygrad
+
 
 def v2_warp_pkl_path(cam_w, cam_h, buffer_length):
   return MODELS_DIR / f'warp_{cam_w}x{cam_h}_b{buffer_length}_tinygrad.pkl'
 
 
-def compile_v2_warp(cam_w, cam_h, buffer_length):
+def compile_v2_warp(cam_w, cam_h, buffer_length, model_w=MEDMODEL_INPUT_SIZE[0], model_h=MEDMODEL_INPUT_SIZE[1], pkl_path=None):
   _, _, _, yuv_size = get_nv12_info(cam_w, cam_h)
-  img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
+  img_buffer_shape = (buffer_length * 6, model_h // 2, model_w // 2)
 
   print(f"Compiling v2 warp for {cam_w}x{cam_h} buffer_length={buffer_length}...")
 
-  frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-  update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
+  frame_prepare = make_frame_prepare(cam_w, cam_h, model_w, model_h)
+  update_both_imgs = make_update_both_imgs(frame_prepare, model_w, model_h)
   update_img_jit = TinyJit(update_both_imgs, prune=True)
 
   full_buffer = Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize()
@@ -46,25 +145,25 @@ def compile_v2_warp(cam_w, cam_h, buffer_length):
     Device.default.synchronize()
 
     st = time.perf_counter()
-    _ = update_img_jit(*inputs)
+    update_img_jit(*inputs)
     mt = time.perf_counter()
     Device.default.synchronize()
     et = time.perf_counter()
     print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
 
-  pkl_path = v2_warp_pkl_path(cam_w, cam_h, buffer_length)
+  if pkl_path is None:
+    pkl_path = v2_warp_pkl_path(cam_w, cam_h, buffer_length)
   with open(pkl_path, "wb") as f:
     pickle.dump(update_img_jit, f)
   print(f"  Saved to {pkl_path}")
 
-  jit = pickle.load(open(pkl_path, "rb"))
-  jit(*inputs)
-
 
 class Warp:
-  def __init__(self, buffer_length=2):
+  def __init__(self, buffer_length=2, model_w=MEDMODEL_INPUT_SIZE[0], model_h=MEDMODEL_INPUT_SIZE[1]):
     self.buffer_length = buffer_length
-    self.img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
+    self.model_w = model_w
+    self.model_h = model_h
+    self.img_buffer_shape = (buffer_length * 6, model_h // 2, model_w // 2)
 
     self.jit_cache = {}
     self.full_buffers = {k: Tensor.zeros(self.img_buffer_shape, dtype='uint8').contiguous().realize() for k in ['img', 'big_img']}
@@ -92,8 +191,8 @@ class Warp:
           with open(upstream_pkl, 'rb') as f:
             self.jit_cache[key] = pickle.load(f)
       if key not in self.jit_cache:
-        frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-        update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
+        frame_prepare = make_frame_prepare(cam_w, cam_h, self.model_w, self.model_h)
+        update_both_imgs = make_update_both_imgs(frame_prepare, self.model_w, self.model_h)
         self.jit_cache[key] = TinyJit(update_both_imgs, prune=True)
 
     if key not in self._nv12_cache:
@@ -107,7 +206,7 @@ class Warp:
     if wide_ptr not in self._blob_cache:
       self._blob_cache[wide_ptr] = Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
     road_blob = self._blob_cache[road_ptr]
-    wide_blob = self._blob_cache[wide_ptr] if wide_ptr != road_ptr else Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
+    wide_blob = self._blob_cache[wide_ptr]
     np.copyto(self.transforms_np['img'], transforms[road].reshape(3, 3))
     np.copyto(self.transforms_np['big_img'], transforms[wide].reshape(3, 3))
 
@@ -116,13 +215,11 @@ class Warp:
       self.full_buffers['img'], road_blob, self.transforms['img'],
       self.full_buffers['big_img'], wide_blob, self.transforms['big_img'],
     )
-    out_road = res[0].realize()
-    out_wide = res[1].realize()
-
-    return {road: out_road, wide: out_wide}
+    return {road: res[1].realize(), wide: res[3].realize()}
 
 
 if __name__ == "__main__":
   for cam_w, cam_h in CAMERA_CONFIGS:
+    compile_v2_warp(cam_w, cam_h, 5, pkl_path=warp_pkl_path(cam_w, cam_h))
     for bl in [2, 5]:
       compile_v2_warp(cam_w, cam_h, bl)

sunnypilot/models/default_model.py

@@ -4,8 +4,9 @@ import hashlib
 
 from openpilot.common.basedir import BASEDIR
 from openpilot.sunnypilot import get_file_hash
+from openpilot.sunnypilot.models.model_name import DEFAULT_MODEL
 
-DEFAULT_MODEL_NAME_PATH = os.path.join(BASEDIR, "common", "model.h")
+DEFAULT_MODEL_NAME_PATH = os.path.join(BASEDIR, "sunnypilot", "models", "model_name.py")
 MODEL_HASH_PATH = os.path.join(BASEDIR, "sunnypilot", "models", "tests", "model_hash")
 VISION_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_vision.onnx")
 POLICY_ONNX_PATH = os.path.join(BASEDIR, "selfdrive", "modeld", "models", "driving_policy.onnx")
@@ -25,8 +26,7 @@ def update_model_hash():
 
 def get_current_default_model_name():
   print("[GET DEFAULT MODEL NAME]")
-  with open(DEFAULT_MODEL_NAME_PATH) as f:
-    name = f.read().split('"')[1]
+  name = DEFAULT_MODEL
   print(f'Current default model name: "{name}"')
 
   return name
@@ -35,7 +35,7 @@ def get_current_default_model_name():
 def update_default_model_name(name: str):
   print("[CHANGE DEFAULT MODEL NAME]")
   with open(DEFAULT_MODEL_NAME_PATH, "w") as f:
-    f.write(f'#define DEFAULT_MODEL "{name}"\n')
+    f.write(f'DEFAULT_MODEL = "{name}"\n')
   print(f'New default model name: "{name}"')
   print("[DONE]")
 
@@ -51,7 +51,7 @@ if __name__ == "__main__":
     exit(0)
 
   current_name = get_current_default_model_name()
-  new_name = f"{args.new_name} (Default)"
+  new_name = args.new_name
   if current_name == new_name:
     print(f'Proposed default model name: "{new_name}"')
     confirm = input("Proposed default model name is the same as the current default model name. Confirm? (y/n): ").upper().strip()

sunnypilot/models/fetcher.py

@@ -116,7 +116,7 @@ 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_v16.json"
+  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v18.json"
 
   def __init__(self, params: Params):
     self.params = params

sunnypilot/models/model_name.py

@@ -0,0 +1 @@
+DEFAULT_MODEL = "POP model"

sunnypilot/models/runners/model_runner.py

@@ -132,6 +132,11 @@ class ModelRunner(ModularRunner):
       return list(self._model_data.input_shapes.keys())
     raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
 
+  def update_vision_inputs(self, vision_inputs: dict) -> None:
+    """Updates the vision inputs in the runner."""
+    for name, tensor in vision_inputs.items():
+      self.inputs[name] = tensor
+
   @abstractmethod
   def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
     """

sunnypilot/models/runners/tinygrad/tinygrad_runner.py

@@ -46,14 +46,13 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny
         assert "/dev/kgsl-3d0" not in str(e), "Model was built on C3 or C3X, but is being loaded on PC"
         raise
 
-    # Map input names to their required dtype and device from the loaded model
     self.input_to_dtype = {}
     self.input_to_device = {}
     for idx, name in enumerate(self.model_run.captured.expected_names):
       info = self.model_run.captured.expected_input_info[idx]
-      self.input_to_dtype[name] = info[2]  # dtype
-      self.input_to_device[name] = info[3]  # device
-    self._policy_cached = False
+      self.input_to_dtype[name] = info[2]
+      self.input_to_device[name] = info[3]
+      self.inputs[name] = Tensor.zeros(*self.input_shapes[name], dtype=info[2], device=info[3]).realize()
 
   @property
   def vision_input_names(self) -> list[str]:
@@ -62,22 +61,23 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny
 
 
   def prepare_policy_inputs(self, numpy_inputs: NumpyDict):
-    if not self._policy_cached:
-      for key, value in numpy_inputs.items():
-        self.inputs[key] = Tensor(value, device='NPY').realize()
-      self._policy_cached = True
+    for key, value in numpy_inputs.items():
+      if key in self.inputs:
+        self.inputs[key].assign(Tensor(value, device=self.inputs[key].device))
 
   def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
     """Prepares all vision and policy inputs for the model."""
     self.prepare_policy_inputs(numpy_inputs)
-    for key in self.vision_input_names:
-      if key in self.inputs:
-        self.inputs[key] = self.inputs[key].cast(self.input_to_dtype[key])
     return self.inputs
 
+  def update_vision_inputs(self, vision_inputs: dict[str, Tensor]):
+    for name, tensor in vision_inputs.items():
+      if name in self.inputs:
+        self.inputs[name].assign(tensor)
+
   def _run_model(self) -> NumpyDict:
     """Runs the Tinygrad model inference and parses the outputs."""
-    outputs = self.model_run(**self.inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
+    outputs = self.model_run(**self.inputs).numpy().flatten()
     return self._parse_outputs(outputs)
 
   def _parse_outputs(self, model_outputs: np.ndarray) -> NumpyDict:

sunnypilot/sunnylink/athena/sunnylinkd.py

@@ -28,7 +28,8 @@ from websocket import (ABNF, WebSocket, WebSocketException, WebSocketTimeoutExce
                        create_connection, WebSocketConnectionClosedException)
 
 import cereal.messaging as messaging
-from openpilot.sunnypilot.selfdrive.car.sync_car_list_param import update_car_list_param
+from openpilot.sunnypilot.models.default_model import DEFAULT_MODEL
+from openpilot.sunnypilot.selfdrive.car.sync_sunnylink_params import update_car_list_param
 from openpilot.sunnypilot.sunnylink.api import SunnylinkApi
 from openpilot.sunnypilot.sunnylink.utils import sunnylink_need_register, sunnylink_ready, get_param_as_byte, save_param_from_base64_encoded_string
 from openpilot.sunnypilot.sunnylink.capabilities import generate_capabilities, CAPABILITY_LABELS
@@ -214,6 +215,7 @@ def getParamsMetadata() -> str:
     schema = generate_schema()
     schema["capabilities"] = generate_capabilities()
     schema["capability_labels"] = CAPABILITY_LABELS
+    schema["default_model"] = DEFAULT_MODEL
     raw = json.dumps(schema, separators=(",", ":")).encode("utf-8")
     return base64.b64encode(gzip.compress(raw)).decode("utf-8")
   except Exception:

sunnypilot/sunnylink/capabilities.py

@@ -78,6 +78,38 @@ def _bundle_field(bundle: dict | None, key: str) -> str:
   return bundle.get(key, "") if isinstance(bundle, dict) else ""
 
 
+def _resolve_brand_capabilities(caps: dict, bundle_platform: str, CP) -> None:
+  """Set brand-specific capabilities from bundle platform or CarParams fallback.
+
+  Bundle (manual car selection) is a pre-fingerprint approximation.
+  CarParams (auto-fingerprint) is the authoritative post-fingerprint source.
+  Mirrors the per-brand update_settings() logic in device UI layouts.
+  """
+  brand = caps["brand"]
+
+  if brand == "hyundai":
+    if bundle_platform:
+      try:
+        unsupported = set().union(*UNSUPPORTED_LONGITUDINAL_CAR.values())
+        caps["hyundai_alpha_long_available"] = HYUNDAI_CAR[bundle_platform] not in unsupported
+      except KeyError:
+        cloudlog.exception(f"capabilities: unknown hyundai platform {bundle_platform!r}")
+    elif CP is not None:
+      caps["hyundai_alpha_long_available"] = bool(CP.alphaLongitudinalAvailable)
+
+  elif brand == "subaru":
+    if bundle_platform:
+      try:
+        flags = SUBARU_CAR[bundle_platform].config.flags
+        caps["subaru_has_sng"] = not bool(flags & (SubaruFlags.GLOBAL_GEN2 | SubaruFlags.HYBRID))
+        caps["has_stop_and_go"] = caps["subaru_has_sng"]
+      except KeyError:
+        cloudlog.exception(f"capabilities: unknown subaru platform {bundle_platform!r}")
+    elif CP is not None:
+      caps["subaru_has_sng"] = not bool(CP.flags & (SubaruFlags.GLOBAL_GEN2 | SubaruFlags.HYBRID))
+      caps["has_stop_and_go"] = caps["subaru_has_sng"]
+
+
 def generate_capabilities(params: Params | None = None) -> dict:
   """Generate a SettingsCapabilities dict from CarParams + boolean params.
 
@@ -94,7 +126,7 @@ def generate_capabilities(params: Params | None = None) -> dict:
 
   # Hardware + boolean params (no CarParams dependency)
   caps["device_type"] = HARDWARE.get_device_type()
-  caps["is_release"] = params.get_bool("IsReleaseBranch")
+  caps["is_release"] = False  # params.get_bool("IsReleaseBranch")
   caps["is_sp_release"] = params.get_bool("IsReleaseSpBranch")
   caps["is_development"] = params.get_bool("IsDevelopmentBranch")
   caps["stock_longitudinal"] = params.get_bool("ToyotaEnforceStockLongitudinal")
@@ -108,6 +140,7 @@ def generate_capabilities(params: Params | None = None) -> dict:
     caps["brand"] = bundle_brand
 
   # CarParams-derived capabilities
+  CP = None
   CP_bytes = params.get("CarParamsPersistent")
   if CP_bytes is not None:
     try:
@@ -129,6 +162,7 @@ def generate_capabilities(params: Params | None = None) -> dict:
       # Generic SnG fallback. Brand-specific opaque flags below override.
       caps["has_stop_and_go"] = bool(CP.openpilotLongitudinalControl)
     except Exception:
+      CP = None
       cloudlog.exception("capabilities: failed to deserialize CarParamsPersistent")
 
   # CarParamsSP-derived capabilities
@@ -142,23 +176,7 @@ def generate_capabilities(params: Params | None = None) -> dict:
     except Exception:
       cloudlog.exception("capabilities: failed to deserialize CarParamsSPPersistent")
 
-  # Brand-specific opaque flags. Mirror Raylib brand-settings logic so the
-  # device and the dashboard agree on per-platform availability without
-  # leaking the platform identifier over the wire.
-  if caps["brand"] == "subaru" and bundle_platform:
-    try:
-      flags = SUBARU_CAR[bundle_platform].config.flags
-      caps["subaru_has_sng"] = not bool(flags & (SubaruFlags.GLOBAL_GEN2 | SubaruFlags.HYBRID))
-      caps["has_stop_and_go"] = caps["subaru_has_sng"]
-    except KeyError:
-      cloudlog.exception(f"capabilities: unknown subaru platform {bundle_platform!r}")
-
-  if caps["brand"] == "hyundai" and bundle_platform:
-    try:
-      unsupported = set().union(*UNSUPPORTED_LONGITUDINAL_CAR.values())
-      caps["hyundai_alpha_long_available"] = HYUNDAI_CAR[bundle_platform] not in unsupported
-    except KeyError:
-      cloudlog.exception(f"capabilities: unknown hyundai platform {bundle_platform!r}")
+  _resolve_brand_capabilities(caps, bundle_platform, CP)
 
   return caps
 

sunnypilot/sunnylink/docs/README.md

@@ -2,8 +2,6 @@
 
 > One YAML file per page. Edit, run the compiler, commit. The sunnylink frontend updates automatically.
 
-For detailed architecture, capability fields, parity analysis, and dialog mappings, see [REFERENCE.md](REFERENCE.md).
-
 ## What you edit (and what's generated)
 
 | File | What | When to edit |

sunnypilot/sunnylink/settings_ui.json

@@ -574,19 +574,9 @@
               "description": "Let the model decide when to use sunnypilot ACC or sunnypilot End to End Longitudinal.",
               "visibility": [
                 {
-                  "type": "any",
-                  "conditions": [
-                    {
-                      "type": "capability",
-                      "field": "has_longitudinal_control",
-                      "equals": true
-                    },
-                    {
-                      "type": "capability",
-                      "field": "has_icbm",
-                      "equals": true
-                    }
-                  ]
+                  "type": "capability",
+                  "field": "has_longitudinal_control",
+                  "equals": true
                 }
               ],
               "enablement": [
@@ -1603,47 +1593,47 @@
                   "label": "Always On"
                 },
                 {
-                  "value": 1,
+                  "value": 5,
                   "label": "5m"
                 },
                 {
-                  "value": 2,
+                  "value": 10,
                   "label": "10m"
                 },
                 {
-                  "value": 3,
+                  "value": 15,
                   "label": "15m"
                 },
                 {
-                  "value": 4,
+                  "value": 30,
                   "label": "30m"
                 },
                 {
-                  "value": 5,
+                  "value": 60,
                   "label": "1h"
                 },
                 {
-                  "value": 6,
+                  "value": 120,
                   "label": "2h"
                 },
                 {
-                  "value": 7,
+                  "value": 180,
                   "label": "3h"
                 },
                 {
-                  "value": 8,
+                  "value": 300,
                   "label": "5h"
                 },
                 {
-                  "value": 9,
+                  "value": 600,
                   "label": "10h"
                 },
                 {
-                  "value": 10,
+                  "value": 1440,
                   "label": "24h"
                 },
                 {
-                  "value": 11,
+                  "value": 1800,
                   "label": "30h (Default)"
                 }
               ]
@@ -1674,13 +1664,13 @@
         {
           "id": "updates",
           "title": "Updates",
-          "description": "Control automatic software updates",
+          "description": "Control software updates",
           "items": [
             {
               "key": "DisableUpdates",
               "widget": "toggle",
               "title": "Disable Updates",
-              "description": "When enabled, automatic software updates will be off. This requires a reboot to take effect.",
+              "description": "When enabled, software updates will be off. This requires a reboot to take effect.",
               "enablement": [
                 {
                   "type": "offroad_only"
@@ -1731,26 +1721,6 @@
               "key": "JoystickDebugMode",
               "widget": "toggle",
               "title": "Joystick Debug Mode",
-              "visibility": [
-                {
-                  "type": "not",
-                  "condition": {
-                    "type": "any",
-                    "conditions": [
-                      {
-                        "type": "capability",
-                        "field": "is_release",
-                        "equals": true
-                      },
-                      {
-                        "type": "capability",
-                        "field": "is_sp_release",
-                        "equals": true
-                      }
-                    ]
-                  }
-                }
-              ],
               "enablement": [
                 {
                   "type": "offroad_only"
@@ -1775,19 +1745,9 @@
                     {
                       "type": "not",
                       "condition": {
-                        "type": "any",
-                        "conditions": [
-                          {
-                            "type": "capability",
-                            "field": "is_release",
-                            "equals": true
-                          },
-                          {
-                            "type": "capability",
-                            "field": "is_sp_release",
-                            "equals": true
-                          }
-                        ]
+                        "type": "capability",
+                        "field": "has_icbm",
+                        "equals": true
                       }
                     }
                   ]
@@ -1900,19 +1860,9 @@
                 {
                   "type": "not",
                   "condition": {
-                    "type": "any",
-                    "conditions": [
-                      {
-                        "type": "capability",
-                        "field": "is_release",
-                        "equals": true
-                      },
-                      {
-                        "type": "capability",
-                        "field": "is_sp_release",
-                        "equals": true
-                      }
-                    ]
+                    "type": "capability",
+                    "field": "is_sp_release",
+                    "equals": true
                   }
                 }
               ],
@@ -1947,11 +1897,6 @@
                   "condition": {
                     "type": "any",
                     "conditions": [
-                      {
-                        "type": "capability",
-                        "field": "is_release",
-                        "equals": true
-                      },
                       {
                         "type": "capability",
                         "field": "is_sp_release",

sunnypilot/sunnylink/settings_ui_src/_macros.yaml

@@ -59,12 +59,7 @@ macros:
             - type: not
               condition: {type: capability, field: tesla_has_vehicle_bus, equals: true}
 
-  # Hide everything but a clearly-marked release branch (matches Raylib
-  # _is_release_branch = is_release OR is_sp_release).
+  # Hide on sunnypilot release branches (is_release is hardcoded False everywhere; is_sp_release is the active gate).
   release_branches_hide:
     - type: not
-      condition:
-        type: any
-        conditions:
-          - {type: capability, field: is_release, equals: true}
-          - {type: capability, field: is_sp_release, equals: true}
+      condition: {type: capability, field: is_sp_release, equals: true}

sunnypilot/sunnylink/settings_ui_src/pages/cruise.yaml

@@ -21,14 +21,7 @@ sections:
     title: Dynamic Experimental Control
     description: Let the model decide when to use sunnypilot ACC or sunnypilot End to End Longitudinal.
     visibility:
-    - type: any
-      conditions:
-      - type: capability
-        field: has_longitudinal_control
-        equals: true
-      - type: capability
-        field: has_icbm
-        equals: true
+    - $ref: '#/macros/longitudinal'
     enablement:
     - $ref: '#/macros/longitudinal'
   - key: DisengageOnAccelerator

sunnypilot/sunnylink/settings_ui_src/pages/developer.yaml

@@ -26,8 +26,6 @@ sections:
   - key: JoystickDebugMode
     widget: toggle
     title: Joystick Debug Mode
-    visibility:
-    - $ref: '#/macros/release_branches_hide'
     enablement:
     - $ref: '#/macros/offroad'
   - key: AlphaLongitudinalEnabled
@@ -46,14 +44,9 @@ sections:
         equals: true
       - type: not
         condition:
-          type: any
-          conditions:
-          - type: capability
-            field: is_release
-            equals: true
-          - type: capability
-            field: is_sp_release
-            equals: true
+          type: capability
+          field: has_icbm
+          equals: true
     enablement:
     - $ref: '#/macros/not_engaged'
   - key: ShowDebugInfo
@@ -131,9 +124,6 @@ sections:
       condition:
         type: any
         conditions:
-        - type: capability
-          field: is_release
-          equals: true
         - type: capability
           field: is_sp_release
           equals: true

sunnypilot/sunnylink/settings_ui_src/pages/device.yaml

@@ -37,27 +37,27 @@ sections:
     options:
     - value: 0
       label: Always On
-    - value: 1
-      label: 5m
-    - value: 2
-      label: 10m
-    - value: 3
-      label: 15m
-    - value: 4
-      label: 30m
     - value: 5
-      label: 1h
-    - value: 6
-      label: 2h
-    - value: 7
-      label: 3h
-    - value: 8
-      label: 5h
-    - value: 9
-      label: 10h
+      label: 5m
     - value: 10
+      label: 10m
+    - value: 15
+      label: 15m
+    - value: 30
+      label: 30m
+    - value: 60
+      label: 1h
+    - value: 120
+      label: 2h
+    - value: 180
+      label: 3h
+    - value: 300
+      label: 5h
+    - value: 600
+      label: 10h
+    - value: 1440
       label: 24h
-    - value: 11
+    - value: 1800
       label: 30h (Default)
 - id: language
   title: Language

sunnypilot/sunnylink/settings_ui_src/pages/software.yaml

@@ -9,12 +9,12 @@ description: Software update preferences
 sections:
 - id: updates
   title: Updates
-  description: Control automatic software updates
+  description: Control software updates
   items:
   - key: DisableUpdates
     widget: toggle
     title: Disable Updates
-    description: When enabled, automatic software updates will be off. This requires a reboot to take effect.
+    description: When enabled, software updates will be off. This requires a reboot to take effect.
     enablement:
     - $ref: '#/macros/offroad'
     - $ref: '#/macros/advanced_only'

sunnypilot/sunnylink/tests/test_compile_settings_ui.py

@@ -15,6 +15,7 @@ compiled output once the compiler has produced it.
 """
 from __future__ import annotations
 
+import difflib
 import json
 import os
 
@@ -44,7 +45,16 @@ def committed() -> dict:
 class TestRoundtrip:
   def test_compiled_matches_committed(self, compiled, committed):
     """Compiled output must match the checked-in JSON."""
-    assert compiled == committed
+    if compiled == committed:
+      return
+    diff = "\n".join(difflib.unified_diff(
+      json.dumps(committed, indent=2).splitlines(),
+      json.dumps(compiled, indent=2).splitlines(),
+      fromfile="settings_ui.json (committed)",
+      tofile="settings_ui.json (freshly compiled)",
+      lineterm="",
+    ))
+    pytest.fail(f"settings_ui.json schema mismatch — run compile_settings_ui.py\n\n{diff}")
 
   def test_committed_file_is_canonical(self):
     """Compiled output must byte-match the checked-in file (including trailing newline).
@@ -53,7 +63,16 @@ class TestRoundtrip:
     rendered = json.dumps(schema, indent=2) + "\n"
     with open(DEFAULT_OUT) as f:
       current = f.read()
-    assert current == rendered, "settings_ui.json out of sync — run compile_settings_ui.py"
+    if current == rendered:
+      return
+    diff = "\n".join(difflib.unified_diff(
+      current.splitlines(),
+      rendered.splitlines(),
+      fromfile="settings_ui.json (on disk)",
+      tofile="settings_ui.json (freshly compiled)",
+      lineterm="",
+    ))
+    pytest.fail(f"settings_ui.json out of sync — run compile_settings_ui.py\n\n{diff}")
 
 
 class TestRefResolution:

sunnypilot/sunnylink/tests/test_settings_changes.py

@@ -181,17 +181,14 @@ class TestTorqueOptionGeneration:
 
 class TestReleaseBranchGates:
   @pytest.mark.parametrize("key", [
-    "JoystickDebugMode",
-    "AlphaLongitudinalEnabled",
     "EnableGithubRunner",
     "QuickBootToggle",
   ])
   def test_sp_dev_items_gate_on_is_sp_release(self, schema, key):
-    """SP dev items must hide on either release branch (is_release OR is_sp_release)."""
+    """sunnypilot dev items must hide on sunnypilot release branches (is_sp_release gate)."""
     item = _find_item(schema, key)
     assert item is not None, f"{key} not found in schema"
     rules = (item.get("visibility") or []) + (item.get("enablement") or [])
-    assert _references_capability_field(rules, "is_release"), f"{key} missing is_release gate"
     assert _references_capability_field(rules, "is_sp_release"), f"{key} missing is_sp_release gate"
 
 

sunnypilot/system/params_migration.py

@@ -7,7 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 import json
 
 from openpilot.common.swaglog import cloudlog
-from openpilot.sunnypilot.selfdrive.car.sync_car_list_param import CAR_LIST_JSON_OUT
+from openpilot.sunnypilot.selfdrive.car.sync_sunnylink_params import CAR_LIST_JSON_OUT
 
 ONROAD_BRIGHTNESS_MIGRATION_VERSION: str = "1.0"
 ONROAD_BRIGHTNESS_TIMER_MIGRATION_VERSION: str = "1.0"

system/manager/manager.py

@@ -35,8 +35,8 @@ def manager_init() -> None:
   params.clear_all(ParamKeyFlag.CLEAR_ON_ONROAD_TRANSITION)
   params.clear_all(ParamKeyFlag.CLEAR_ON_OFFROAD_TRANSITION)
   params.clear_all(ParamKeyFlag.CLEAR_ON_IGNITION_ON)
-  if build_metadata.release_channel:
-    params.clear_all(ParamKeyFlag.DEVELOPMENT_ONLY)
+  # if build_metadata.release_channel:
+  #   params.clear_all(ParamKeyFlag.DEVELOPMENT_ONLY)
 
   # device boot mode
   if params.get("DeviceBootMode") == 1:  # start in Always Offroad mode

uv.lock

@@ -116,12 +116,12 @@ wheels = [
 [[package]]
 name = "bzip2"
 version = "1.0.8"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=bzip2&rev=release-bzip2#7876f40b7a3e9f0d634a60586043395169ef1a82" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=bzip2&rev=release-bzip2#346fa1e479d7324d446f32b2cbe2913897372745" }
 
 [[package]]
 name = "capnproto"
 version = "1.0.1"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=capnproto&rev=release-capnproto#bcd0c43cb9dbd3b48aad36812bae9498fb5c7be1" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=capnproto&rev=release-capnproto#b4fd14982cbff568be0e021f55c0ef90c29da934" }
 
 [[package]]
 name = "casadi"
@@ -251,26 +251,26 @@ wheels = [
 
 [[package]]
 name = "coverage"
-version = "7.13.5"
+version = "7.14.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/23/7f/d0720730a397a999ffc0fd3f5bebef347338e3a47b727da66fbb228e2ff2/coverage-7.14.0.tar.gz", hash = "sha256:057a6af2f160a85384cde4ab36f0d2777bae1057bae255f95413cdd382aa5c74", size = 919489, upload-time = "2026-05-10T18:02:31.397Z" }
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 [[package]]
 name = "zeromq"
 version = "4.3.5"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zeromq&rev=release-zeromq#39acf5d6cbd996fe61e8e8727e1b403e79bc2b7b" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zeromq&rev=release-zeromq#10f97237e00e5fabf3c1fa54a2ca1a1da39de461" }
 
 [[package]]
 name = "zstandard"
@@ -1661,4 +1665,4 @@ wheels = [
 [[package]]
 name = "zstd"
 version = "1.5.6"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zstd&rev=release-zstd#59e9ca4ecfda299d4861ea53a5fcc1eacadc5524" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zstd&rev=release-zstd#eb147476324db97737c31cd63e71a4f44b0d0723" }