chore: update CHANGELOG for 2026.001.005

sunnylink: add CarParams fallback for brand-specific capabilities (#1839 )
Brand-specific capabilities (hyundai_alpha_long_available, subaru_has_sng) only resolved from CarPlatformBundle, which requires manual car selection. Auto-fingerprinted vehicles had no bundle, leaving these capabilities at default false — hiding vehicle settings on the dashboard despite working on the device UI. Add _resolve_brand_capabilities() with bundle-first, CP-fallback pattern matching the device UI layouts (hyundai.py, subaru.py). Fixes https://community.sunnypilot.ai/t/5126
2026-06-13 06:54:48 +08:00 · 2026-05-13 02:22:55 -04:00 · 2026-05-13 01:54:58 -04:00 · 2026-05-13 01:54:53 -04:00 · 2026-05-10 01:19:44 -04:00 · 2026-05-10 01:14:07 -04:00
36 changed files with 593 additions and 753 deletions
--- a/.github/workflows/sunnypilot-build-model.yaml
+++ b/.github/workflows/sunnypilot-build-model.yaml
@@ -172,7 +172,7 @@ jobs:
            output_file="${{ env.MODELS_DIR }}/${base_name}_tinygrad.pkl"

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

--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,29 @@
+sunnypilot Version 2026.001.005 (2026-05-13)
+========================
+* What's Changed (sunnypilot/sunnypilot)
+  * sunnylink: add CarParams fallback for brand-specific capabilities by @sunnyhaibin
+
+sunnypilot Version 2026.001.004 (2026-05-10)
+========================
+* What's Changed (sunnypilot/sunnypilot)
+  * sunnylink: switch athena domain by @DevTekVE
+  * sunnylink: fix max time offroad values by @nayan8teen
+
+sunnypilot Version 2026.001.003 (2026-05-08)
+========================
+* What's Changed (sunnypilot/sunnypilot)
+  * manager: disable DEVELOPMENT_ONLY reset by @sunnyhaibin
+
+sunnypilot Version 2026.001.002 (2026-05-07)
+========================
+* What's Changed (sunnypilot/sunnypilot)
+  * release: ignore upstream IsReleaseBranch by @sunnyhaibin
+
+sunnypilot Version 2026.001.001 (2026-05-06)
+========================
+* What's Changed (sunnypilot/sunnypilot)
+  * ui: update gates for certain toggles by @sunnyhaibin
+
 sunnypilot Version 2026.001.000 (2026-05-06)
 ========================
 * What's Changed (sunnypilot/sunnypilot)
--- a/common/model.h
+++ b/common/model.h
@@ -0,0 +1 @@
+#define DEFAULT_MODEL "CD210 (Default)"
--- a/2
+++ b/2
--- a/selfdrive/modeld/SConscript
+++ b/selfdrive/modeld/SConscript
@@ -1,23 +1,10 @@
 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()
@@ -29,17 +16,18 @@ 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())
-if 'CUDA' in available:
+# FIXME-SP: reset when we bump tg
+if False:  # '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 OPENPILOT_HACKS=1'
+  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_backend = 'CPU' if arch == 'Darwin' else 'CPU CPU_LLVM=1'  # FIXME-SP: reset when we bump tg
  tg_flags = f'DEV={tg_backend} THREADS=0'

 def write_tg_compiled_flags(target, source, env):
@@ -66,35 +54,14 @@ for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
 image_flag = {
     'larch64': 'IMAGE=2',
 }.get(arch, 'IMAGE=0')
-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)
+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)

 def tg_compile(flags, model_name):
  pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
@@ -115,4 +82,7 @@ def tg_compile(flags, model_name):
    do_chunk,
  )

-tg_compile(tg_flags, 'dmonitoring_model')
+# Compile small models
+for model_name in ['driving_vision', 'driving_policy', 'dmonitoring_model']:
+  tg_compile(tg_flags, model_name)
+
--- a/selfdrive/modeld/compile_dm_warp.py
+++ b/selfdrive/modeld/compile_dm_warp.py
@@ -1,54 +0,0 @@
-#!/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)
--- a/selfdrive/modeld/compile_modeld.py
+++ b/selfdrive/modeld/compile_modeld.py
@@ -1,253 +0,0 @@
-#!/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)
--- a/selfdrive/modeld/compile_warp.py
+++ b/selfdrive/modeld/compile_warp.py
@@ -0,0 +1,201 @@
+#!/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()
--- a/selfdrive/modeld/dmonitoringmodeld.py
+++ b/selfdrive/modeld/dmonitoringmodeld.py
@@ -1,8 +1,12 @@
 #!/usr/bin/env python3
 import os
-from openpilot.selfdrive.modeld.helpers import MODELS_DIR, CompileConfig, set_tinygrad_backend_from_compiled_flags
+from openpilot.selfdrive.modeld.tinygrad_helpers import MODELS_DIR, 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
@@ -28,7 +32,7 @@ class ModelState:
  inputs: dict[str, np.ndarray]
  output: np.ndarray

-  def __init__(self, cam_w: int, cam_h: int):
+  def __init__(self):
    with open(METADATA_PATH, 'rb') as f:
      model_metadata = pickle.load(f)
      self.input_shapes = model_metadata['input_shapes']
@@ -40,18 +44,22 @@ 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 = get_nv12_info(cam_w, cam_h)
+    self.frame_buf_params = None
    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:
@@ -105,6 +113,9 @@ 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):
@@ -112,9 +123,6 @@ 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"])

--- a/selfdrive/modeld/get_model_metadata.py
+++ b/selfdrive/modeld/get_model_metadata.py
@@ -7,10 +7,6 @@ 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:
@@ -52,7 +48,7 @@ if __name__ == "__main__":
    'output_shapes': dict(get_name_and_shape(x) for x in model["graph"]["output"]),
  }

-  metadata_path = metadata_path_for(model_path)
+  metadata_path = model_path.parent / (model_path.stem + '_metadata.pkl')
  with open(metadata_path, 'wb') as f:
    pickle.dump(metadata, f)

--- a/selfdrive/modeld/helpers.py
+++ b/selfdrive/modeld/helpers.py
@@ -1,31 +0,0 @@
-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))
--- a/selfdrive/modeld/modeld.py
+++ b/selfdrive/modeld/modeld.py
@@ -1,8 +1,12 @@
 #!/usr/bin/env python3
 import os
-from openpilot.selfdrive.modeld.helpers import MODELS_DIR, CompileConfig, set_tinygrad_backend_from_compiled_flags
+from openpilot.selfdrive.modeld.tinygrad_helpers import MODELS_DIR, 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'
@@ -26,7 +30,6 @@ 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
@@ -38,13 +41,17 @@ 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,
@@ -79,39 +86,108 @@ 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, cam_w: int, cam_h: int):
+  def __init__(self):
    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)

-    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)
+    # 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.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 = {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())
+    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)))

  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()}
@@ -119,6 +195,18 @@ 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]
@@ -127,31 +215,30 @@ 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][:,:]

-    # 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'][:,:]
+    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]}

    if prepare_only:
-      self.warp_enqueue(**self.input_queues, frame=self.full_frames['img'], big_frame=self.full_frames['big_img'])
      return None

-    vision_output, policy_output = self.run_policy(
-      **self.input_queues, frame=self.full_frames['img'], big_frame=self.full_frames['big_img']
-    )
+    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 = 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))
+    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))
    combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
-
    if SEND_RAW_PRED:
-      combined_outputs_dict['raw_pred'] = np.concatenate([vision_output.copy(), policy_output.copy()])
+      combined_outputs_dict['raw_pred'] = np.concatenate([self.vision_output.copy(), self.policy_output.copy()])
+
    return combined_outputs_dict


@@ -163,6 +250,11 @@ 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)
@@ -186,11 +278,6 @@ 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"])
--- a/selfdrive/modeld/models/driving_policy.onnx
+++ b/selfdrive/modeld/models/driving_policy.onnx
--- a/selfdrive/modeld/models/driving_vision.onnx
+++ b/selfdrive/modeld/models/driving_vision.onnx
--- a/selfdrive/modeld/tinygrad_helpers.py
+++ b/selfdrive/modeld/tinygrad_helpers.py
@@ -0,0 +1,12 @@
+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'])
--- a/selfdrive/ui/sunnypilot/layouts/settings/models.py
+++ b/selfdrive/ui/sunnypilot/layouts/settings/models.py
@@ -10,7 +10,6 @@ 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
@@ -208,7 +207,7 @@ 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': f"{DEFAULT_MODEL} (Default)", 'short_name': "Default"})])]
+    folders_list = [TreeFolder("", [TreeNode("Default", {'display_name': tr("Default Model"), '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 "")
@@ -244,7 +243,7 @@ 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 f"{DEFAULT_MODEL} (Default)"
+    active_name = self.model_manager.activeBundle.internalName if self.model_manager and self.model_manager.activeBundle.ref else tr("Default Model")
    self.current_model_item.action_item.set_value(active_name)

    if not ui_state.is_offroad():
--- a/selfdrive/ui/sunnypilot/mici/layouts/models.py
+++ b/selfdrive/ui/sunnypilot/mici/layouts/models.py
@@ -8,7 +8,6 @@ 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
@@ -28,8 +27,7 @@ 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)
-    default_text = f"{DEFAULT_MODEL} (Default)".lower()
-    self.current_model_text = UnifiedLabel(default_text, 32, max_width=max_width, text_color=subheader_color, font_weight=FontWeight.ROMAN, scroll=True)
+    self.current_model_text = UnifiedLabel(tr("default model"), 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)
@@ -100,7 +98,7 @@ class ModelsLayoutMici(NavScroller):

    folders = self._get_grouped_bundles(favorites)
    folder_buttons = []
-    default_btn = BigButton(f"{DEFAULT_MODEL} (Default)".lower())
+    default_btn = BigButton(tr("default model"))
    default_btn.set_click_callback(self._select_default)
    folder_buttons.append(default_btn)

@@ -170,8 +168,7 @@ class ModelsLayoutMici(NavScroller):
    self._was_downloading = is_downloading

    self.current_model_info.current_model_header.set_text(tr("active 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.current_model_text.set_text(manager.activeBundle.displayName.lower() if manager.activeBundle.index > 0 else tr("default model"))
    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")

--- a/sunnypilot/common/version.h
+++ b/sunnypilot/common/version.h
@@ -1 +1 @@
-#define SUNNYPILOT_VERSION "2026.001.000"
+#define SUNNYPILOT_VERSION "2026.001.005"
--- a/sunnypilot/modeld_v2/SConscript
+++ b/sunnypilot/modeld_v2/SConscript
@@ -1,6 +1,5 @@
 import os
 import glob
-from tinygrad import Device

 Import('env', 'arch')
 lenv = env.Clone()
@@ -22,19 +21,10 @@ if PC:
  if outputs:
    lenv.Command(outputs, inputs, cmd)

-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 ''
+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')

 image_flag = {
  'larch64': 'IMAGE=2',
@@ -48,7 +38,7 @@ def tg_compile(flags, model_name):
  return lenv.Command(
    out,
    [fn + ".onnx"] + tinygrad_files,
-    f'{pythonpath_string} {tg_flags} {mac_brew_string} {image_flag} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {out}'
+    f'{pythonpath_string} {flags} {image_flag} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {out}'
  )

 # Compile models
@@ -56,9 +46,9 @@ 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")]
+script_files = [File("warp.py"), File(Dir("#selfdrive/modeld").File("compile_warp.py").abspath)]
 pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + ':' + env.Dir("#").abspath + '"'
-compile_warp_cmd = f'{pythonpath_string} {tg_flags} {mac_brew_string} {image_flag} python3 -m sunnypilot.modeld_v2.warp'
+compile_warp_cmd = f'{pythonpath_string} {tg_flags} python3 -m sunnypilot.modeld_v2.warp'

 from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
 warp_targets = []
--- a/sunnypilot/modeld_v2/modeld.py
+++ b/sunnypilot/modeld_v2/modeld.py
@@ -129,7 +129,8 @@ class ModelState(ModelStateBase):
        self.numpy_inputs[key][:] = inputs[key]

    imgs_tensors = self.warp.process(bufs, transforms)
-    self.model_runner.update_vision_inputs(imgs_tensors)
+    for name, tensor in imgs_tensors.items():
+      self.model_runner.inputs[name] = tensor
    self.model_runner.prepare_inputs(self.numpy_inputs)

    if prepare_only:
--- a/sunnypilot/modeld_v2/tests/test_warp.py
+++ b/sunnypilot/modeld_v2/tests/test_warp.py
@@ -2,11 +2,8 @@ import os
 os.environ['DEV'] = 'CPU'
 import pytest
 import numpy as np
-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
+from openpilot.selfdrive.modeld.compile_warp import get_nv12_info, CAMERA_CONFIGS
+from openpilot.sunnypilot.modeld_v2.warp import Warp, MODEL_W, MODEL_H

 VISION_NAME_PAIRS = [  # needed to account for supercombos input_imgs
  ('img', 'big_img'),
--- a/sunnypilot/modeld_v2/warp.py
+++ b/sunnypilot/modeld_v2/warp.py
@@ -6,128 +6,29 @@ 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.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
+from openpilot.selfdrive.modeld.compile_warp import (
+  CAMERA_CONFIGS, MEDMODEL_INPUT_SIZE, make_frame_prepare, make_update_both_imgs,
+  warp_pkl_path,
+)

 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, model_w=MEDMODEL_INPUT_SIZE[0], model_h=MEDMODEL_INPUT_SIZE[1], pkl_path=None):
+def compile_v2_warp(cam_w, cam_h, buffer_length):
  _, _, _, 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()
@@ -145,25 +46,25 @@ def compile_v2_warp(cam_w, cam_h, buffer_length, model_w=MEDMODEL_INPUT_SIZE[0],
    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")

-  if pkl_path is None:
-    pkl_path = v2_warp_pkl_path(cam_w, cam_h, buffer_length)
+  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, model_w=MEDMODEL_INPUT_SIZE[0], model_h=MEDMODEL_INPUT_SIZE[1]):
+  def __init__(self, buffer_length=2):
    self.buffer_length = buffer_length
-    self.model_w = model_w
-    self.model_h = model_h
-    self.img_buffer_shape = (buffer_length * 6, model_h // 2, model_w // 2)
+    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']}
@@ -191,8 +92,8 @@ 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, self.model_w, self.model_h)
-        update_both_imgs = make_update_both_imgs(frame_prepare, self.model_w, self.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)
        self.jit_cache[key] = TinyJit(update_both_imgs, prune=True)

    if key not in self._nv12_cache:
@@ -206,7 +107,7 @@ 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]
+    wide_blob = self._blob_cache[wide_ptr] if wide_ptr != road_ptr else Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
    np.copyto(self.transforms_np['img'], transforms[road].reshape(3, 3))
    np.copyto(self.transforms_np['big_img'], transforms[wide].reshape(3, 3))

@@ -215,11 +116,13 @@ class Warp:
      self.full_buffers['img'], road_blob, self.transforms['img'],
      self.full_buffers['big_img'], wide_blob, self.transforms['big_img'],
    )
-    return {road: res[1].realize(), wide: res[3].realize()}
+    out_road = res[0].realize()
+    out_wide = res[1].realize()
+
+    return {road: out_road, wide: out_wide}


 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)
--- a/sunnypilot/models/default_model.py
+++ b/sunnypilot/models/default_model.py
@@ -4,9 +4,8 @@ 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, "sunnypilot", "models", "model_name.py")
+DEFAULT_MODEL_NAME_PATH = os.path.join(BASEDIR, "common", "model.h")
 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")
@@ -26,7 +25,8 @@ def update_model_hash():

 def get_current_default_model_name():
  print("[GET DEFAULT MODEL NAME]")
-  name = DEFAULT_MODEL
+  with open(DEFAULT_MODEL_NAME_PATH) as f:
+    name = f.read().split('"')[1]
  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'DEFAULT_MODEL = "{name}"\n')
+    f.write(f'#define 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 = args.new_name
+  new_name = f"{args.new_name} (Default)"
  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()
--- a/sunnypilot/models/fetcher.py
+++ b/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_v18.json"
+  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v16.json"

  def __init__(self, params: Params):
    self.params = params
--- a/sunnypilot/models/model_name.py
+++ b/sunnypilot/models/model_name.py
@@ -1 +0,0 @@
-DEFAULT_MODEL = "POP model"
--- a/sunnypilot/models/runners/model_runner.py
+++ b/sunnypilot/models/runners/model_runner.py
@@ -132,11 +132,6 @@ 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:
    """
--- a/sunnypilot/models/runners/tinygrad/tinygrad_runner.py
+++ b/sunnypilot/models/runners/tinygrad/tinygrad_runner.py
@@ -46,13 +46,14 @@ 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]
-      self.input_to_device[name] = info[3]
-      self.inputs[name] = Tensor.zeros(*self.input_shapes[name], dtype=info[2], device=info[3]).realize()
+      self.input_to_dtype[name] = info[2]  # dtype
+      self.input_to_device[name] = info[3]  # device
+    self._policy_cached = False

  @property
  def vision_input_names(self) -> list[str]:
@@ -61,23 +62,22 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny


  def prepare_policy_inputs(self, numpy_inputs: NumpyDict):
-    for key, value in numpy_inputs.items():
-      if key in self.inputs:
-        self.inputs[key].assign(Tensor(value, device=self.inputs[key].device))
+    if not self._policy_cached:
+      for key, value in numpy_inputs.items():
+        self.inputs[key] = Tensor(value, device='NPY').realize()
+      self._policy_cached = True

  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).numpy().flatten()
+    outputs = self.model_run(**self.inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
    return self._parse_outputs(outputs)

  def _parse_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
--- a/sunnypilot/models/tests/model_hash
+++ b/sunnypilot/models/tests/model_hash
@@ -1 +1 @@
-5d4d21f1899de21137f69d74a4602c44cc5a6b04cf4e4aa9d0ec9206f8c30350
+32f57bdc91f910df1f48ddae7c59aaf6e751f9df6756da481a210577dbce8bcf
--- a/sunnypilot/selfdrive/car/sync_sunnylink_params.py
+++ b/sunnypilot/selfdrive/car/sync_sunnylink_params.py
--- a/sunnypilot/sunnylink/athena/sunnylinkd.py
+++ b/sunnypilot/sunnylink/athena/sunnylinkd.py
@@ -28,8 +28,7 @@ from websocket import (ABNF, WebSocket, WebSocketException, WebSocketTimeoutExce
                       create_connection, WebSocketConnectionClosedException)

 import cereal.messaging as messaging
-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.selfdrive.car.sync_car_list_param 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
@@ -215,7 +214,6 @@ 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:
--- a/sunnypilot/sunnylink/docs/README.md
+++ b/sunnypilot/sunnylink/docs/README.md
@@ -2,6 +2,8 @@

 > 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 |
--- a/sunnypilot/sunnylink/settings_ui.json
+++ b/sunnypilot/sunnylink/settings_ui.json
@@ -1664,13 +1664,13 @@
        {
          "id": "updates",
          "title": "Updates",
-          "description": "Control software updates",
+          "description": "Control automatic software updates",
          "items": [
            {
              "key": "DisableUpdates",
              "widget": "toggle",
              "title": "Disable Updates",
-              "description": "When enabled, software updates will be off. This requires a reboot to take effect.",
+              "description": "When enabled, automatic software updates will be off. This requires a reboot to take effect.",
              "enablement": [
                {
                  "type": "offroad_only"
--- a/sunnypilot/sunnylink/settings_ui_src/pages/software.yaml
+++ b/sunnypilot/sunnylink/settings_ui_src/pages/software.yaml
@@ -9,12 +9,12 @@ description: Software update preferences
 sections:
 - id: updates
  title: Updates
-  description: Control software updates
+  description: Control automatic software updates
  items:
  - key: DisableUpdates
    widget: toggle
    title: Disable Updates
-    description: When enabled, software updates will be off. This requires a reboot to take effect.
+    description: When enabled, automatic software updates will be off. This requires a reboot to take effect.
    enablement:
    - $ref: '#/macros/offroad'
    - $ref: '#/macros/advanced_only'
--- a/sunnypilot/system/params_migration.py
+++ b/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_sunnylink_params import CAR_LIST_JSON_OUT
+from openpilot.sunnypilot.selfdrive.car.sync_car_list_param import CAR_LIST_JSON_OUT

 ONROAD_BRIGHTNESS_MIGRATION_VERSION: str = "1.0"
 ONROAD_BRIGHTNESS_TIMER_MIGRATION_VERSION: str = "1.0"
--- a/2
+++ b/2
--- a/uv.lock
+++ b/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#346fa1e479d7324d446f32b2cbe2913897372745" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=bzip2&rev=release-bzip2#7876f40b7a3e9f0d634a60586043395169ef1a82" }

 [[package]]
 name = "capnproto"
 version = "1.0.1"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=capnproto&rev=release-capnproto#b4fd14982cbff568be0e021f55c0ef90c29da934" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=capnproto&rev=release-capnproto#bcd0c43cb9dbd3b48aad36812bae9498fb5c7be1" }

 [[package]]
 name = "casadi"
@@ -251,26 +251,26 @@ wheels = [

 [[package]]
 name = "coverage"
-version = "7.14.0"
+version = "7.13.5"
 source = { registry = "https://pypi.org/simple" }
-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" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/e0/70553e3000e345daff267cec284ce4cbf3fc141b6da229ac52775b5428f1/coverage-7.13.5.tar.gz", hash = "sha256:c81f6515c4c40141f83f502b07bbfa5c240ba25bbe73da7b33f1e5b6120ff179", size = 915967, upload-time = "2026-03-17T10:33:18.341Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/09/1e/2f996b2c8415cbb6f54b0f5ec1ee850c96d7911961afb4fc05f4a89d8c58/coverage-7.14.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7ffd19fc8aed057fd686a17a4935eef5f9859d69208f96310e893e64b9b6ccf5", size = 219967, upload-time = "2026-05-10T18:00:13.756Z" },
-    { url = "https://files.pythonhosted.org/packages/34/23/35c7aea1274aef7525bdd2dc92f710bdde6d11652239d71d1ec450067939/coverage-7.14.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:829994cfe1aeb773ca27bf246d4badc1e764893e3bfb98fff820fcecd1ca4662", size = 220329, upload-time = "2026-05-10T18:00:15.264Z" },
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 [[package]]
 name = "zeromq"
 version = "4.3.5"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zeromq&rev=release-zeromq#10f97237e00e5fabf3c1fa54a2ca1a1da39de461" }
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 [[package]]
 name = "zstandard"
@@ -1665,4 +1661,4 @@ wheels = [
 [[package]]
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 version = "1.5.6"
-source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zstd&rev=release-zstd#eb147476324db97737c31cd63e71a4f44b0d0723" }
+source = { git = "https://github.com/commaai/dependencies.git?subdirectory=zstd&rev=release-zstd#59e9ca4ecfda299d4861ea53a5fcc1eacadc5524" }
Author	SHA1	Message	Date
Jason Wen	c7e57a1bc1	chore: update CHANGELOG for 2026.001.005	2026-05-13 02:22:55 -04:00
Jason Wen	353ed2a9e1	sunnylink: add CarParams fallback for brand-specific capabilities (#1839 ) Brand-specific capabilities (hyundai_alpha_long_available, subaru_has_sng) only resolved from CarPlatformBundle, which requires manual car selection. Auto-fingerprinted vehicles had no bundle, leaving these capabilities at default false — hiding vehicle settings on the dashboard despite working on the device UI. Add _resolve_brand_capabilities() with bundle-first, CP-fallback pattern matching the device UI layouts (hyundai.py, subaru.py). Fixes https://community.sunnypilot.ai/t/5126	2026-05-13 01:54:58 -04:00
Jason Wen	1db8b82f16	version: bump to 2026.001.005	2026-05-13 01:54:53 -04:00
Jason Wen	e8964ce7ae	chore: update CHANGELOG for 2026.001.004	2026-05-10 01:19:44 -04:00
Jason Wen	ad799442a8	chore: bump version to 2026.001.004	2026-05-10 01:14:07 -04:00
Jason Wen	592f062326	ci: simplify cereal validation to sparse-checkout + pycapnp, drop scons (#1836 ) * ci: simplify cereal validation to sparse-checkout + pycapnp, drop scons build Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * more * fix: resolve cereal_dir to absolute path before passing to capnp.load Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * ci: init opendbc submodule after sparse checkout to resolve car.capnp symlink Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com> * try to break it * Revert "try to break it" This reverts commit `79ce135c5f`. * try to break it * Revert "try to break it" This reverts commit `1eaa9e79e6`. --------- Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>	2026-05-10 01:14:00 -04:00
Nayan	70fd56f69c	sunnylink: fix max time offroad values (#1835 ) fix sunnylink values	2026-05-10 01:13:57 -04:00
DevTekVE	29cd05d6ed	sunnylink: switch athena domain (#1826 ) use new domain	2026-05-10 01:13:54 -04:00
Jason Wen	b1232629c3	chore: update CHANGELOG for 2026.001.003	2026-05-08 07:28:11 -04:00
Jason Wen	8539ad0373	manager: disable DEVELOPMENT_ONLY reset (#1833 )	2026-05-08 07:27:59 -04:00
Jason Wen	f468467606	chore: version bump 2026.001.003	2026-05-08 07:27:56 -04:00
Jason Wen	4cf822a6cc	chore: update CHANGELOG for 2026.001.002	2026-05-07 11:02:40 -04:00
Jason Wen	ac8af9aa94	release: ignore upstream IsReleaseBranch (#1831 )	2026-05-07 11:01:33 -04:00
Jason Wen	1ac64f7360	chore: bump version to 2026.001.002	2026-05-07 11:01:27 -04:00
Jason Wen	505881cbc5	chore: update CHANGELOG for 2026.001.001	2026-05-06 22:15:17 -04:00
Jason Wen	a68ed2fd01	ui: update gates for certain toggles (#1830 ) * don't use upstream's * clean * update schema * fix * mismatch test and fix	2026-05-06 21:41:18 -04:00
Jason Wen	2aa179bcac	chore: bump version to 2026.001.001	2026-05-06 21:41:16 -04:00
Jason Wen	090b404fee	Update CHANGELOG.md (cherry picked from commit `b9aa1962ca`)	2026-05-05 23:00:00 -04:00
Jason Wen	4c36db0091	Revert "sunnylink: switch athena domain (#1826 )" This reverts commit `53e5ae0578`.	2026-05-05 22:08:46 -04:00
Jason Wen	c25b581ae5	Default model: CD210 model	2026-05-05 22:08:29 -04:00
Jason Wen	2316b1142c	Revert "POP model (#37727 )" This reverts commit `12f1be19cc`.	2026-05-05 22:02:50 -04:00