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

This reverts commit dc11e5fd84.

.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

@@ -1,6 +1,3 @@
-sunnypilot Version 2026.002.000 (2026-xx-xx)
-========================
-
 sunnypilot Version 2026.001.000 (2026-05-06)
 ========================
 * What's Changed (sunnypilot/sunnypilot)

cereal/custom.capnp

@@ -194,13 +194,6 @@ struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
   aTarget @5 :Float32;
   events @6 :List(OnroadEventSP.Event);
   e2eAlerts @7 :E2eAlerts;
-  accelPersonality @8 :AccelerationPersonality;
-
-  enum AccelerationPersonality {
-    sport @0;
-    normal @1;
-    eco @2;
-  }
 
   struct DynamicExperimentalControl {
     state @0 :DynamicExperimentalControlState;

cereal/log.capnp

@@ -2054,14 +2054,16 @@ struct DriverStateV2 {
     facePosition @2 :List(Float32);
     facePositionStd @3 :List(Float32);
     faceProb @4 :Float32;
-    leftEyeProb @5 :Float32;
-    rightEyeProb @6 :Float32;
-    leftBlinkProb @7 :Float32;
-    rightBlinkProb @8 :Float32;
-    sunglassesProb @9 :Float32;
+    eyesVisibleProb @14 :Float32;
+    eyesClosedProb @15 :Float32;
     phoneProb @13 :Float32;
 
     deprecated :group {
+      leftEyeProb @5 :Float32;
+      rightEyeProb @6 :Float32;
+      leftBlinkProb @7 :Float32;
+      rightBlinkProb @8 :Float32;
+      sunglassesProb @9 :Float32;
       notReadyProb @12 :List(Float32);
       occludedProb @10 :Float32;
       readyProb @11 :List(Float32);

common/params_keys.h

@@ -135,8 +135,6 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"Version", {PERSISTENT, STRING}},
 
     // --- sunnypilot params --- //
-    {"AccelPersonality", {PERSISTENT | BACKUP, INT, std::to_string(static_cast<int>(cereal::LongitudinalPlanSP::AccelerationPersonality::NORMAL))}},
-    {"AccelPersonalityEnabled", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"ApiCache_DriveStats", {PERSISTENT, JSON}},
     {"AutoLaneChangeBsmDelay", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"AutoLaneChangeTimer", {PERSISTENT | BACKUP, INT, "0"}},

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -313,14 +313,11 @@ class LongitudinalMpc:
     lead_xv = self.extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau)
     return lead_xv
 
-  def update(self, radarstate, v_cruise, personality=log.LongitudinalPersonality.standard, a_cruise_min=None):
+  def update(self, radarstate, v_cruise, personality=log.LongitudinalPersonality.standard):
     t_follow = get_T_FOLLOW(personality)
     v_ego = self.x0[1]
     self.status = radarstate.leadOne.status or radarstate.leadTwo.status
 
-    if a_cruise_min is None:
-      a_cruise_min = CRUISE_MIN_ACCEL
-
     lead_xv_0 = self.process_lead(radarstate.leadOne)
     lead_xv_1 = self.process_lead(radarstate.leadTwo)
 
@@ -332,7 +329,7 @@ class LongitudinalMpc:
 
     # Fake an obstacle for cruise, this ensures smooth acceleration to set speed
     # when the leads are no factor.
-    v_lower = v_ego + (T_IDXS * a_cruise_min * 1.05)
+    v_lower = v_ego + (T_IDXS * CRUISE_MIN_ACCEL * 1.05)
     # TODO does this make sense when max_a is negative?
     v_upper = v_ego + (T_IDXS * CRUISE_MAX_ACCEL * 1.05)
     v_cruise_clipped = np.clip(v_cruise * np.ones(N+1), v_lower, v_upper)

selfdrive/controls/lib/longitudinal_planner.py

@@ -110,7 +110,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
     # No change cost when user is controlling the speed, or when standstill
     prev_accel_constraint = not (reset_state or sm['carState'].standstill)
 
-    accel_clip = self.get_accel_clip(v_ego) or [ACCEL_MIN, get_max_accel(v_ego)]
+    accel_clip = [ACCEL_MIN, get_max_accel(v_ego)]
     steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
     accel_clip = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_clip, self.CP)
 
@@ -138,8 +138,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
 
     self.mpc.set_weights(prev_accel_constraint, personality=sm['selfdriveState'].personality)
     self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
-    self.mpc.update(sm['radarState'], v_cruise, personality=sm['selfdriveState'].personality,
-                    a_cruise_min=self.get_cruise_min_accel(v_ego))
+    self.mpc.update(sm['radarState'], v_cruise, personality=sm['selfdriveState'].personality)
 
     self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
     self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)

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:
@@ -83,7 +75,7 @@ def parse_model_output(model_output):
     face_descs = model_output[f'face_descs_{ds_suffix}']
     parsed[f'face_descs_{ds_suffix}'] = face_descs[:, :-6]
     parsed[f'face_descs_{ds_suffix}_std'] = safe_exp(face_descs[:, -6:])
-    for key in ['face_prob', 'left_eye_prob', 'right_eye_prob','left_blink_prob', 'right_blink_prob', 'sunglasses_prob', 'using_phone_prob']:
+    for key in ['face_prob', 'eyes_visible_prob', 'eyes_closed_prob', 'using_phone_prob']:
       parsed[f'{key}_{ds_suffix}'] = sigmoid(model_output[f'{key}_{ds_suffix}'])
   return parsed
 
@@ -93,11 +85,8 @@ def fill_driver_data(msg, model_output, ds_suffix):
   msg.facePosition = model_output[f'face_descs_{ds_suffix}'][0, 3:5].tolist()
   msg.facePositionStd = model_output[f'face_descs_{ds_suffix}_std'][0, 3:5].tolist()
   msg.faceProb = model_output[f'face_prob_{ds_suffix}'][0, 0].item()
-  msg.leftEyeProb = model_output[f'left_eye_prob_{ds_suffix}'][0, 0].item()
-  msg.rightEyeProb = model_output[f'right_eye_prob_{ds_suffix}'][0, 0].item()
-  msg.leftBlinkProb = model_output[f'left_blink_prob_{ds_suffix}'][0, 0].item()
-  msg.rightBlinkProb = model_output[f'right_blink_prob_{ds_suffix}'][0, 0].item()
-  msg.sunglassesProb = model_output[f'sunglasses_prob_{ds_suffix}'][0, 0].item()
+  msg.eyesVisibleProb = model_output[f'eyes_visible_prob_{ds_suffix}'][0, 0].item()
+  msg.eyesClosedProb = model_output[f'eyes_closed_prob_{ds_suffix}'][0, 0].item()
   msg.phoneProb = model_output[f'using_phone_prob_{ds_suffix}'][0, 0].item()
 
 def get_driverstate_packet(model_output, frame_id: int, location_ts: int, exec_time: float, gpu_exec_time: float):
@@ -116,9 +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):
@@ -126,6 +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/monitoring/helpers.py

@@ -32,9 +32,8 @@ class DRIVER_MONITOR_SETTINGS:
     self._DISTRACTED_PROMPT_TIME_TILL_TERMINAL = 6.
 
     self._FACE_THRESHOLD = 0.7
-    self._EYE_THRESHOLD = 0.65
-    self._SG_THRESHOLD = 0.9
-    self._BLINK_THRESHOLD = 0.865
+    self._EYE_THRESHOLD = 0.5
+    self._BLINK_THRESHOLD = 0.5
     self._PHONE_THRESH = 0.5
 
     self._POSE_PITCH_THRESHOLD = 0.3133
@@ -111,11 +110,6 @@ class DriverProb:
     self.prob_offseter = RunningStatFilter(raw_priors=raw_priors, max_trackable=max_trackable)
     self.prob_calibrated = False
 
-class DriverBlink:
-  def __init__(self):
-    self.left = 0.
-    self.right = 0.
-
 
 # model output refers to center of undistorted+leveled image
 EFL = 598.0 # focal length in K
@@ -150,7 +144,7 @@ class DriverMonitoring:
     wheelpos_filter_raw_priors = (self.settings._WHEELPOS_DATA_AVG, self.settings._WHEELPOS_DATA_VAR, 2)
     self.wheelpos = DriverProb(raw_priors=wheelpos_filter_raw_priors, max_trackable=self.settings._WHEELPOS_MAX_COUNT)
     self.pose = DriverPose(settings=self.settings)
-    self.blink = DriverBlink()
+    self.blink_prob = 0.
     self.phone_prob = 0.
 
     self.always_on = always_on
@@ -253,7 +247,7 @@ class DriverMonitoring:
     if pitch_error > pitch_threshold or yaw_error > yaw_threshold:
       distracted_types.append(DistractedType.DISTRACTED_POSE)
 
-    if (self.blink.left + self.blink.right)*0.5 > self.settings._BLINK_THRESHOLD:
+    if self.blink_prob > self.settings._BLINK_THRESHOLD:
       distracted_types.append(DistractedType.DISTRACTED_BLINK)
 
     if self.phone_prob > self.settings._PHONE_THRESH:
@@ -294,10 +288,7 @@ class DriverMonitoring:
     self.pose.yaw_std = driver_data.faceOrientationStd[1]
     model_std_max = max(self.pose.pitch_std, self.pose.yaw_std)
     self.pose.low_std = model_std_max < self.settings._POSESTD_THRESHOLD
-    self.blink.left = driver_data.leftBlinkProb * (driver_data.leftEyeProb > self.settings._EYE_THRESHOLD) \
-                      * (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
-    self.blink.right = driver_data.rightBlinkProb * (driver_data.rightEyeProb > self.settings._EYE_THRESHOLD) \
-                      * (driver_data.sunglassesProb < self.settings._SG_THRESHOLD)
+    self.blink_prob = driver_data.eyesClosedProb * (driver_data.eyesVisibleProb > self.settings._EYE_THRESHOLD)
     self.phone_prob = driver_data.phoneProb
 
     self.distracted_types = self._get_distracted_types()

selfdrive/monitoring/test_monitoring.py

@@ -20,10 +20,8 @@ def make_msg(face_detected, distracted=False, model_uncertain=False):
   ds.leftDriverData.faceOrientation = [0., 0., 0.]
   ds.leftDriverData.facePosition = [0., 0.]
   ds.leftDriverData.faceProb = 1. * face_detected
-  ds.leftDriverData.leftEyeProb = 1.
-  ds.leftDriverData.rightEyeProb = 1.
-  ds.leftDriverData.leftBlinkProb = 1. * distracted
-  ds.leftDriverData.rightBlinkProb = 1. * distracted
+  ds.leftDriverData.eyesVisibleProb = 1.
+  ds.leftDriverData.eyesClosedProb = 1. * distracted
   ds.leftDriverData.faceOrientationStd = [1.*model_uncertain, 1.*model_uncertain, 1.*model_uncertain]
   ds.leftDriverData.facePositionStd = [1.*model_uncertain, 1.*model_uncertain]
   # TODO: test both separately when e2e is used

selfdrive/test/process_replay/model_replay.py

@@ -76,7 +76,7 @@ def generate_report(proposed, master, tmp, commit):
                      (lambda x: get_idx_if_non_empty(x.wheelOnRightProb), "wheelOnRightProb"),
                      (lambda x: get_idx_if_non_empty(x.leftDriverData.faceProb), "leftDriverData.faceProb"),
                      (lambda x: get_idx_if_non_empty(x.leftDriverData.faceOrientation, 0), "leftDriverData.faceOrientation0"),
-                     (lambda x: get_idx_if_non_empty(x.leftDriverData.leftBlinkProb), "leftDriverData.leftBlinkProb"),
+                     (lambda x: get_idx_if_non_empty(x.leftDriverData.eyesClosedProb), "leftDriverData.eyesClosedProb"),
                      (lambda x: get_idx_if_non_empty(x.leftDriverData.phoneProb), "leftDriverData.phoneProb"),
                      (lambda x: get_idx_if_non_empty(x.rightDriverData.faceProb), "rightDriverData.faceProb"),
                     ], "driverStateV2")

selfdrive/ui/mici/onroad/driver_camera_dialog.py

@@ -39,8 +39,6 @@ class BaseDriverCameraDialog(Widget):
     self._eye_fill_texture = None
     self._eye_orange_texture = None
     self._eye_size = 74
-    self._glasses_texture = None
-    self._glasses_size = 171
 
     self._load_eye_textures()
 
@@ -154,8 +152,6 @@ class BaseDriverCameraDialog(Widget):
       self._eye_fill_texture = gui_app.texture("icons_mici/onroad/eye_fill.png", self._eye_size, self._eye_size)
     if self._eye_orange_texture is None:
       self._eye_orange_texture = gui_app.texture("icons_mici/onroad/eye_orange.png", self._eye_size, self._eye_size)
-    if self._glasses_texture is None:
-      self._glasses_texture = gui_app.texture("icons_mici/onroad/glasses.png", self._glasses_size, self._glasses_size)
 
   def _draw_face_detection(self, rect: rl.Rectangle):
     dm_state = ui_state.sm["driverMonitoringState"]
@@ -202,31 +198,21 @@ class BaseDriverCameraDialog(Widget):
     eye_offset_x = 10
     eye_offset_y = 10
     eye_spacing = self._eye_size + 15
+    eyes_prob = driver_data.eyesVisibleProb
 
     left_eye_x = rect.x + eye_offset_x
     left_eye_y = rect.y + eye_offset_y
-    left_eye_prob = driver_data.leftEyeProb
 
     right_eye_x = rect.x + eye_offset_x + eye_spacing
     right_eye_y = rect.y + eye_offset_y
-    right_eye_prob = driver_data.rightEyeProb
 
     # Draw eyes with opacity based on probability
-    for eye_x, eye_y, eye_prob in [(left_eye_x, left_eye_y, left_eye_prob), (right_eye_x, right_eye_y, right_eye_prob)]:
-      fill_opacity = eye_prob
-      orange_opacity = 1.0 - eye_prob
-
+    fill_opacity = eyes_prob
+    orange_opacity = 1.0 - eyes_prob
+    for eye_x, eye_y in [(left_eye_x, left_eye_y), (right_eye_x, right_eye_y)]:
       rl.draw_texture_v(self._eye_orange_texture, (eye_x, eye_y), rl.Color(255, 255, 255, int(255 * orange_opacity)))
       rl.draw_texture_v(self._eye_fill_texture, (eye_x, eye_y), rl.Color(255, 255, 255, int(255 * fill_opacity)))
 
-    # Draw sunglasses indicator based on sunglasses probability
-    # Position glasses centered between the two eyes at top left
-    glasses_x = rect.x + eye_offset_x - 4
-    glasses_y = rect.y
-    glasses_pos = rl.Vector2(glasses_x, glasses_y)
-    glasses_prob = driver_data.sunglassesProb
-    rl.draw_texture_v(self._glasses_texture, glasses_pos, rl.Color(70, 80, 161, int(255 * glasses_prob)))
-
 
 class DriverCameraDialog(NavWidget, BaseDriverCameraDialog):
   def __init__(self):

sunnypilot/common/version.h

@@ -1 +1 @@
-#define SUNNYPILOT_VERSION "2026.002.000"
+#define SUNNYPILOT_VERSION "2026.001.000"

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/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/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/selfdrive/controls/lib/accel_personality/accel_controller.py

@@ -1,156 +0,0 @@
-"""
-Copyright (c) 2021-, rav4kumar, Haibin Wen, sunnypilot, and a number of other contributors.
-
-This file is part of sunnypilot and is licensed under the MIT License.
-See the LICENSE.md file in the root directory for more details.
-"""
-
-from cereal import custom
-import numpy as np
-from openpilot.common.realtime import DT_MDL
-from openpilot.common.params import Params
-from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
-
-AccelPersonality = custom.LongitudinalPlanSP.AccelerationPersonality
-
-
-A_MAX_BP = [0.0, 4.0, 8.0, 16.0, 40.0]
-A_MAX_V = {
-  AccelPersonality.eco:    [1.20, 1.40, 1.20, 0.40, 0.08],
-  AccelPersonality.normal: [1.80, 1.80, 1.35, 0.50, 0.15],
-  AccelPersonality.sport:  [2.20, 2.20, 1.60, 0.70, 0.25],
-}
-
-COAST_DRAG_BP = [0.0, 10.0, 25.0, 40.0]
-COAST_DRAG_V = {
-  AccelPersonality.eco:    [-0.03, -0.05, -0.08, -0.12],
-  AccelPersonality.normal: [-0.04, -0.07, -0.12, -0.18],
-  AccelPersonality.sport:  [-0.06, -0.10, -0.18, -0.28],
-}
-
-A_MIN_FLOOR_BP = [0.0, 5.0, 15.0, 40.0]
-A_MIN_FLOOR_V = {
-  AccelPersonality.eco:    [-0.20, -0.35, -0.55, -0.50],
-  AccelPersonality.normal: [-0.25, -0.45, -0.75, -0.65],
-  AccelPersonality.sport:  [-0.35, -0.65, -1.00, -0.95],
-}
-
-DEFICIT_TO_FLOOR = 8.5
-COAST_DEADBAND = 1.0
-RAMP_OFF_RANGE = 5.0
-
-A_MIN_TIGHTEN_RATE = 0.6
-A_MIN_RELAX_RATE = 0.9
-A_MAX_RATE_UP = 1.5
-A_MAX_RATE_DOWN = 0.6
-
-MIN_MAX_GAP = 0.05
-
-PARAM_REFRESH_FRAMES = max(1, int(1.0 / DT_MDL))
-
-
-class AccelPersonalityController:
-  def __init__(self):
-    self.params = Params()
-    self.frame = 0
-    self._first = True
-
-    val = self.params.get('AccelPersonality')
-    self._personality = val if val is not None else AccelPersonality.normal
-    self._enabled = self.params.get_bool('AccelPersonalityEnabled')
-
-    self._v_cruise = 0.0
-    self._a_min = -0.05
-    self._a_max = 1.50
-
-    self._cache_v: float | None = None
-    self._cache_v_cruise: float | None = None
-    self._cache_a_min = self._a_min
-    self._cache_a_max = self._a_max
-
-  def update(self, sm=None):
-    self.frame += 1
-    self._cache_v = None
-    self._cache_v_cruise = None
-
-    if sm is not None:
-      vc = sm['carState'].vCruise
-      self._v_cruise = float(vc) * (1000.0 / 3600.0) if vc != V_CRUISE_UNSET else 0.0
-
-    if self.frame % PARAM_REFRESH_FRAMES == 0:
-      val = self.params.get('AccelPersonality')
-      self._personality = val if val is not None else AccelPersonality.normal
-      new_enabled = self.params.get_bool('AccelPersonalityEnabled')
-      if new_enabled and not self._enabled:
-        self._first = True
-      self._enabled = new_enabled
-
-  def get_accel_personality(self) -> int:
-    return int(self._personality)
-
-  def is_enabled(self) -> bool:
-    return self._enabled
-
-  def get_accel_limits(self, v_ego: float) -> tuple[float, float]:
-    v_ego = max(0.0, v_ego)
-    if (self._cache_v is not None
-        and abs(self._cache_v - v_ego) < 0.01
-        and self._cache_v_cruise == self._v_cruise):
-      return self._cache_a_min, self._cache_a_max
-    self._cache_a_min, self._cache_a_max = self._step(v_ego)
-    self._cache_v = v_ego
-    self._cache_v_cruise = self._v_cruise
-    return self._cache_a_min, self._cache_a_max
-
-  def get_min_accel(self, v_ego: float) -> float:
-    return self.get_accel_limits(v_ego)[0]
-
-  def get_max_accel(self, v_ego: float) -> float:
-    return self.get_accel_limits(v_ego)[1]
-
-  def _ramp_off(self, v_ego: float) -> float:
-    if self._v_cruise <= 0.0:
-      return 1.0
-    return float(np.clip((self._v_cruise - v_ego) / RAMP_OFF_RANGE, 0.0, 1.0))
-
-  def _target_max(self, v_ego: float) -> float:
-    base = float(np.interp(v_ego, A_MAX_BP, A_MAX_V[self._personality]))
-    return base * self._ramp_off(v_ego)
-
-  def _target_min(self, v_ego: float) -> float:
-    coast = float(np.interp(v_ego, COAST_DRAG_BP, COAST_DRAG_V[self._personality]))
-    if self._v_cruise <= 0.0 or v_ego >= self._v_cruise:
-      return coast
-    floor = float(np.interp(v_ego, A_MIN_FLOOR_BP, A_MIN_FLOOR_V[self._personality]))
-    deficit = self._v_cruise - v_ego
-    t = float(np.clip(deficit / DEFICIT_TO_FLOOR, 0.0, 1.0)) ** 1.5
-    return coast + t * (floor - coast)
-
-  def _apply_coast_deadband(self, v_ego: float, t_min: float, t_max: float) -> tuple[float, float]:
-    if self._v_cruise <= 0.0 or abs(v_ego - self._v_cruise) >= COAST_DEADBAND:
-      return t_min, t_max
-    coast = float(np.interp(v_ego, COAST_DRAG_BP, COAST_DRAG_V[self._personality]))
-    return coast, max(0.05, t_max * 0.25)
-
-  def _rate_limit(self, last: float, target: float, rate_down: float, rate_up: float) -> float:
-    rate = rate_up if target > last else rate_down
-    step = rate * DT_MDL
-    return float(np.clip(target, last - step, last + step))
-
-  def _step(self, v_ego: float) -> tuple[float, float]:
-    t_max = self._target_max(v_ego)
-    t_min = self._target_min(v_ego)
-    t_min, t_max = self._apply_coast_deadband(v_ego, t_min, t_max)
-
-    if self._first:
-      self._a_min, self._a_max = t_min, t_max
-      self._first = False
-      return self._a_min, self._a_max
-
-    new_min = self._rate_limit(self._a_min, t_min, rate_down=A_MIN_TIGHTEN_RATE, rate_up=A_MIN_RELAX_RATE)
-    new_max = self._rate_limit(self._a_max, t_max, rate_down=A_MAX_RATE_DOWN, rate_up=A_MAX_RATE_UP)
-
-    new_min = min(new_min, new_max - MIN_MAX_GAP)
-
-    self._a_min, self._a_max = new_min, new_max
-    return self._a_min, self._a_max

sunnypilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -17,9 +17,6 @@ from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.speed_limit_resolve
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
 from openpilot.sunnypilot.models.helpers import get_active_bundle
 
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.accel_controller import AccelPersonalityController
-from opendbc.car.interfaces import ACCEL_MIN
-
 DecState = custom.LongitudinalPlanSP.DynamicExperimentalControl.DynamicExperimentalControlState
 LongitudinalPlanSource = custom.LongitudinalPlanSP.LongitudinalPlanSource
 
@@ -29,7 +26,6 @@ class LongitudinalPlannerSP:
     self.events_sp = EventsSP()
     self.resolver = SpeedLimitResolver()
     self.dec = DynamicExperimentalController(CP, mpc)
-    self.accel_controller = AccelPersonalityController()
     self.scc = SmartCruiseControl()
     self.resolver = SpeedLimitResolver()
     self.sla = SpeedLimitAssist(CP, CP_SP)
@@ -47,17 +43,6 @@ class LongitudinalPlannerSP:
 
     return experimental_mode and self.dec.mode() == "blended"
 
-  def get_accel_clip(self, v_ego: float) -> list[float] | None:
-    if not self.accel_controller.is_enabled():
-      return None
-    a_max = self.accel_controller.get_max_accel(v_ego)
-    return [ACCEL_MIN, max(ACCEL_MIN, a_max)]
-
-  def get_cruise_min_accel(self, v_ego: float) -> float | None:
-    if self.accel_controller.is_enabled():
-      return self.accel_controller.get_min_accel(v_ego)
-    return None
-
   def update_targets(self, sm: messaging.SubMaster, v_ego: float, a_ego: float, v_cruise: float) -> tuple[float, float]:
     CS = sm['carState']
     v_cruise_cluster_kph = min(CS.vCruiseCluster, V_CRUISE_MAX)
@@ -92,7 +77,6 @@ class LongitudinalPlannerSP:
     self.events_sp.clear()
     self.dec.update(sm)
     self.e2e_alerts_helper.update(sm, self.events_sp)
-    self.accel_controller.update(sm)
 
   def publish_longitudinal_plan_sp(self, sm: messaging.SubMaster, pm: messaging.PubMaster) -> None:
     plan_sp_send = messaging.new_message('longitudinalPlanSP')
@@ -111,8 +95,6 @@ class LongitudinalPlannerSP:
     dec.enabled = self.dec.enabled()
     dec.active = self.dec.active()
 
-    longitudinalPlanSP.accelPersonality = int(self.accel_controller.get_accel_personality())
-
     # Smart Cruise Control
     smartCruiseControl = longitudinalPlanSP.smartCruiseControl
     # Vision Control

sunnypilot/selfdrive/controls/lib/tests/test_accel_controller.py

@@ -1,147 +0,0 @@
-"""
-Copyright (c) 2021-, rav4kumar, Haibin Wen, sunnypilot, and a number of other contributors.
-
-This file is part of sunnypilot and is licensed under the MIT License.
-See the LICENSE.md file in the root directory for more details.
-
-Coverage for AccelPersonalityController:
-- live param flip via auto-refresh (no Python set_enabled() call needed)
-- V_CRUISE_UNSET guard
-- enable-transition snap to fresh target
-- per-personality accel limit deltas vs stock get_max_accel
-"""
-import numpy as np
-
-from cereal import custom
-
-from openpilot.common.params import Params
-from opendbc.car.interfaces import ACCEL_MIN
-from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
-from openpilot.selfdrive.controls.lib.longitudinal_planner import get_max_accel as stock_get_max_accel
-
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_personality.accel_controller import (
-  AccelPersonalityController,
-  PARAM_REFRESH_FRAMES,
-)
-
-
-AccelPersonality = custom.LongitudinalPlanSP.AccelerationPersonality
-
-
-class FakeCarState:
-  def __init__(self, v_cruise=30.0):
-    self.vCruise = v_cruise
-
-
-class FakeSM:
-  def __init__(self, v_cruise=30.0):
-    self._data = {'carState': FakeCarState(v_cruise)}
-
-  def __getitem__(self, k):
-    return self._data[k]
-
-
-def _print_table(title, header, rows):
-  print(f"\n--- {title} ---")
-  print(" | ".join(f"{h:>12}" for h in header))
-  print("-" * (15 * len(header)))
-  for row in rows:
-    print(" | ".join(f"{v:>12.3f}" if isinstance(v, float) else f"{v:>12}" for v in row))
-
-
-class TestAccelLiveFlip:
-  def test_enable_via_param(self):
-    Params().put_bool('AccelPersonalityEnabled', False)
-    c = AccelPersonalityController()
-    assert not c.is_enabled()
-    Params().put_bool('AccelPersonalityEnabled', True)
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM())
-    assert c.is_enabled()
-
-  def test_disable_via_param(self):
-    Params().put_bool('AccelPersonalityEnabled', True)
-    c = AccelPersonalityController()
-    assert c.is_enabled()
-    Params().put_bool('AccelPersonalityEnabled', False)
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM())
-    assert not c.is_enabled()
-
-  def test_personality_change_via_param(self):
-    Params().put('AccelPersonality', AccelPersonality.normal)
-    c = AccelPersonalityController()
-    assert c.get_accel_personality() == AccelPersonality.normal
-    Params().put('AccelPersonality', AccelPersonality.sport)
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM())
-    assert c.get_accel_personality() == AccelPersonality.sport
-
-  def test_refresh_boundary_below_threshold(self):
-    Params().put_bool('AccelPersonalityEnabled', False)
-    c = AccelPersonalityController()
-    Params().put_bool('AccelPersonalityEnabled', True)
-    for _ in range(PARAM_REFRESH_FRAMES - 1):
-      c.update(FakeSM())
-    assert not c.is_enabled()
-
-  def test_enable_transition_snaps_to_target(self):
-    Params().put_bool('AccelPersonalityEnabled', True)
-    Params().put('AccelPersonality', AccelPersonality.sport)
-    c = AccelPersonalityController()
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM(v_cruise=35.0))
-    c.get_accel_limits(25.0)
-
-    Params().put_bool('AccelPersonalityEnabled', False)
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM(v_cruise=35.0))
-    assert not c.is_enabled()
-
-    Params().put('AccelPersonality', AccelPersonality.eco)
-    Params().put_bool('AccelPersonalityEnabled', True)
-    for _ in range(PARAM_REFRESH_FRAMES + 1):
-      c.update(FakeSM(v_cruise=35.0))
-    assert c._first
-
-  def test_vcruise_unset_treated_as_zero(self):
-    Params().put_bool('AccelPersonalityEnabled', True)
-    c = AccelPersonalityController()
-    c.update(FakeSM(v_cruise=V_CRUISE_UNSET))
-    assert c._v_cruise == 0.0
-
-
-class TestAccelUsageDiff:
-  def test_accel_clip_per_personality(self, capsys):
-    rows = []
-    speeds = [3.0, 10.0, 20.0, 30.0]
-    personalities = [
-      ('eco', AccelPersonality.eco),
-      ('normal', AccelPersonality.normal),
-      ('sport', AccelPersonality.sport),
-    ]
-
-    Params().put_bool('AccelPersonalityEnabled', True)
-    sm = FakeSM(v_cruise=35.0)
-
-    any_delta = False
-    for label, p in personalities:
-      Params().put('AccelPersonality', p)
-      c = AccelPersonalityController()
-      c.update(sm)
-      for v_ego in speeds:
-        stock_hi = float(stock_get_max_accel(v_ego))
-        c_lo, c_hi = c.get_accel_limits(v_ego)
-        delta_hi = c_hi - stock_hi
-        delta_lo = c_lo - ACCEL_MIN
-        if abs(delta_hi) > 0.01 or abs(delta_lo) > 0.01:
-          any_delta = True
-        rows.append((label, v_ego, stock_hi, c_hi, delta_hi, c_lo, delta_lo))
-
-    with capsys.disabled():
-      _print_table(
-        "AccelPersonalityController: a_max stock vs controller",
-        ["personality", "v_ego", "stock_hi", "ctrl_hi", "delta_hi", "ctrl_lo", "delta_lo"],
-        rows,
-      )
-    assert any_delta

sunnypilot/sunnylink/params_metadata.json

@@ -1,26 +1,4 @@
 {
-  "AccelPersonality": {
-    "title": "Acceleration Personality",
-    "description": "Select the acceleration personality profile. Sport provides more aggressive acceleration, Eco provides gentler acceleration.",
-    "options": [
-      {
-        "value": 0,
-        "label": "Sport"
-      },
-      {
-        "value": 1,
-        "label": "Normal"
-      },
-      {
-        "value": 2,
-        "label": "Eco"
-      }
-    ]
-  },
-  "AccelPersonalityEnabled": {
-    "title": "Custom Acceleration Personality",
-    "description": "Enable custom acceleration and braking profiles that adjust max acceleration and min deceleration based on speed and selected personality."
-  },
   "AccessToken": {
     "title": "AccessTokenIsNice",
     "description": ""

sunnypilot/sunnylink/settings_ui.json

@@ -620,65 +620,6 @@
                 }
               ]
             },
-            {
-              "key": "AccelPersonalityEnabled",
-              "widget": "toggle",
-              "title": "Acceleration Personality",
-              "description": "Enable per-personality acceleration profiles. Sport allows stronger acceleration; Eco is gentler.",
-              "visibility": [
-                {
-                  "type": "capability",
-                  "field": "has_longitudinal_control",
-                  "equals": true
-                }
-              ],
-              "enablement": [
-                {
-                  "type": "capability",
-                  "field": "has_longitudinal_control",
-                  "equals": true
-                }
-              ]
-            },
-            {
-              "key": "AccelPersonality",
-              "widget": "multiple_button",
-              "title": "Acceleration Profile",
-              "description": "Sport allows the most aggressive acceleration; Eco the gentlest. Normal sits between.",
-              "options": [
-                {
-                  "value": 0,
-                  "label": "Sport"
-                },
-                {
-                  "value": 1,
-                  "label": "Normal"
-                },
-                {
-                  "value": 2,
-                  "label": "Eco"
-                }
-              ],
-              "visibility": [
-                {
-                  "type": "param",
-                  "key": "AccelPersonalityEnabled",
-                  "equals": true
-                }
-              ],
-              "enablement": [
-                {
-                  "type": "capability",
-                  "field": "has_longitudinal_control",
-                  "equals": true
-                },
-                {
-                  "type": "param",
-                  "key": "AccelPersonalityEnabled",
-                  "equals": true
-                }
-              ]
-            },
             {
               "key": "IntelligentCruiseButtonManagement",
               "widget": "toggle",

sunnypilot/sunnylink/settings_ui_src/pages/cruise.yaml

@@ -43,34 +43,6 @@ sections:
       label: Relaxed
     enablement:
     - $ref: '#/macros/longitudinal'
-  - key: AccelPersonalityEnabled
-    widget: toggle
-    title: Acceleration Personality
-    description: Enable per-personality acceleration profiles. Sport allows stronger acceleration; Eco is gentler.
-    visibility:
-    - $ref: '#/macros/longitudinal'
-    enablement:
-    - $ref: '#/macros/longitudinal'
-  - key: AccelPersonality
-    widget: multiple_button
-    title: Acceleration Profile
-    description: Sport allows the most aggressive acceleration; Eco the gentlest. Normal sits between.
-    options:
-    - value: 0
-      label: Sport
-    - value: 1
-      label: Normal
-    - value: 2
-      label: Eco
-    visibility:
-    - type: param
-      key: AccelPersonalityEnabled
-      equals: true
-    enablement:
-    - $ref: '#/macros/longitudinal'
-    - type: param
-      key: AccelPersonalityEnabled
-      equals: true
   - key: IntelligentCruiseButtonManagement
     widget: toggle
     title: Intelligent Cruise Button Management (ICBM) (Alpha)