IQ.Pilot Release Commit @ bec7652

iqpilot/selfdrive/iqmodeld/daemon.py

@@ -63,6 +63,9 @@ IQP_NAV_MODEL_INFLUENCE_ENABLED = False
 TurnDirection = custom.IQTurnSignalDirection
 IQMODEL_EVAL_WARN_US = int(DT_MDL * 1_000_000)
 IQMODEL_EVAL_ERROR_US = IQMODEL_EVAL_WARN_US * 2
+_FRAME_STARVED_BACKOFF_POLLS = 5
+_FRAME_STARVED_BACKOFF_SECONDS = 0.005
+_FRAME_STARVED_LOG_EVERY = 200
 
 
 def _plan_y_std_1s(outputs: dict[str, np.ndarray]) -> float:
@@ -605,12 +608,21 @@ class InferenceDaemon:
 
   def serve(self) -> None:
     tick = 0
+    starved_polls = 0
     while True:
       frame_pair = self._cameras.pull()
       if frame_pair is None:
-        cloudlog.debug("visionipc frame missing")
+        starved_polls += 1
+        if starved_polls >= _FRAME_STARVED_BACKOFF_POLLS:
+          time.sleep(_FRAME_STARVED_BACKOFF_SECONDS)
+        if starved_polls % _FRAME_STARVED_LOG_EVERY == 0:
+          cloudlog.error(f"visionipc delivered no frames for {starved_polls} polls; model is not running")
         continue
 
+      if starved_polls:
+        cloudlog.warning(f"visionipc recovered after {starved_polls} frameless polls")
+        starved_polls = 0
+
       main_buf, extra_buf, main_stamp, extra_stamp = frame_pair
       self._sub.update(0)
       self._refresh_tunables(tick)

iqpilot/selfdrive/iqmodeld/models/__init__.py

@@ -1,3 +1,3 @@
 """
-IQ model selection and runner support that is actively used by iqmodeld.
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
 """

iqpilot/selfdrive/iqmodeld/models/fetcher.py

@@ -1,11 +1,8 @@
 #!/usr/bin/env python3
 """
-Copyright (c) IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
-
-Public entry point for the model-manifest fetcher: prefers the compiled private
-bundle, falling back to the in-tree source. The default-runner fallback lives in
-ManifestDecoder now, so no post-import patching is needed.
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
 """
+
 from iqpilot._proprietary_loader import ProprietaryModuleMissing, load_private_module
 
 try:

iqpilot/selfdrive/iqmodeld/models/helpers.py

@@ -40,7 +40,6 @@ _DEFAULT_BUNDLE_REF = "default"
 
 
 def get_default_model_bundle(_bundles):
-  """Legacy compatibility hook: stock default is preinstalled, not a manifest bundle."""
   return None
 
 
@@ -239,10 +238,13 @@ def select_default_model(params: Params = None) -> None:
 
 def seed_default_bundle_if_unset(params: Params = None) -> None:
   params = Params() if params is None else params
-  if params.get(_ACTIVE_BUNDLE_KEY) or params.get(_DOWNLOAD_INDEX_KEY) is not None:
+  if params.get(_ACTIVE_BUNDLE_KEY):
     return
+  queued_download = params.get(_DOWNLOAD_INDEX_KEY)
   try:
     select_default_model(params)
+    if queued_download is not None:
+      params.put(_DOWNLOAD_INDEX_KEY, queued_download)
     cloudlog.warning("default_model: seeded Default (CD210) as active bundle")
   except Exception as e:
     cloudlog.exception(f"default_model: failed to seed default bundle: {e}")

iqpilot/selfdrive/iqmodeld/models/inference_state.py

@@ -1,9 +1,5 @@
 """
 Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
-
-Common base for the per-process inference/runtime states. It seeds the lateral
-steer delay from the cached learned value so every subclass starts with a usable
-number before its first lateralDelay message arrives.
 """
 from iqpilot.common.steer_delay import cached_steer_delay
 

iqpilot/selfdrive/iqmodeld/models/runners/__init__.py

@@ -1,3 +1,3 @@
 """
-Runner interfaces used by iqmodeld model execution.
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
 """

iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/__init__.py

@@ -1,3 +1,3 @@
 """
-Tinygrad runner support for iqmodeld.
-"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""

iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/fused_runner.py

@@ -27,8 +27,6 @@ WARP_DEV = os.getenv('WARP_DEV')
 
 
 class TinygradFusedRunner(ModelRunner):
-    """Runs a fused warp+vision+policy pkl. Bundle ships one `driving_fused_*` artifact."""
-
     uses_opencl_warp: bool = False
 
     def __init__(self):
@@ -110,7 +108,6 @@ class TinygradFusedRunner(ModelRunner):
             'feat_q':    zeros_f32((self._frame_skip * (fb[1] - 1) + 1, fb[0], fb[2])),
             'desire_q':  zeros_f32((self._frame_skip * dp[1], dp[0], dp[2])),
         }
-        # shapes must match the captured run_policy JIT inputs
         on_shapes = self._on_meta['input_shapes']
         captured = self._run_policy.captured
         jit_shapes = {
@@ -135,7 +132,6 @@ class TinygradFusedRunner(ModelRunner):
         self._cam_resolution = (cam_w, cam_h)
 
     def run_fused(self, bufs: dict, transforms: dict[str, np.ndarray], numpy_inputs: NumpyDict) -> NumpyDict:
-        """warp + vision + policy in one pass from raw NV12 bufs + transform matrices."""
         Tensor, Device = _tinygrad_imports()
 
         main_buf = bufs['img']
@@ -154,7 +150,6 @@ class TinygradFusedRunner(ModelRunner):
 
         npy = lambda key: Tensor(self._npy_buffers[key], device='NPY')
 
-        # frames go on the compute device to match the captured warp JIT
         frame = self._frame_tensor('img', bufs['img'])
         big_frame = self._frame_tensor('big_img', bufs['big_img'])
 
@@ -169,8 +164,6 @@ class TinygradFusedRunner(ModelRunner):
             policy_inputs['action_t'] = npy('action_t')
         vision_out_t, on_out_t, off_out_t = self._run_policy(**policy_inputs)
 
-        # parse each model's output on its own sliced dict; parsing a merged dict
-        # would run parse_dynamic_outputs twice and double-parse plan/lead
         def _slice(tensor_out, meta) -> NumpyDict:
             flat = tensor_out.numpy().flatten()
             return {k: flat[np.newaxis, sl] for k, sl in meta['output_slices'].items() if k != 'pad'}

iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/supercombo_runner.py

@@ -68,8 +68,6 @@ def _is_jit_arg_mismatch(err: BaseException) -> bool:
 
 
 class TinygradSupercomboRunner(ModelRunner):
-    """Runs a single combined supercombo pkl. Bundle ships one `driving_supercombo_*` artifact."""
-
     uses_opencl_warp: bool = False
 
     def __init__(self):
@@ -282,7 +280,6 @@ class TinygradSupercomboRunner(ModelRunner):
         zeros_u8 = lambda s: Tensor(np.zeros(s, dtype=np.uint8), device=Device.DEFAULT).contiguous().realize()
         zeros_f32 = lambda s: Tensor(np.zeros(s, dtype=np.float32), device=Device.DEFAULT).contiguous().realize()
 
-        # packed npy block (single NPY tensor, mutated in place via views): order matches run_policy.split
         shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
         sizes = [math.prod(s) for s in shapes.values()]
         packed = np.zeros(sum(sizes), dtype=np.float32)
@@ -318,7 +315,6 @@ class TinygradSupercomboRunner(ModelRunner):
             self._npy['traffic_convention'][:] = numpy_inputs['traffic_convention']
         if 'action_t' in numpy_inputs:
             self._npy['action_t'][:] = numpy_inputs['action_t']
-        # self._npy['prev_feat'] holds last frame's hidden_state (zeros on the first frame)
 
         frame = self._frame_tensor('img', bufs['img'])
         big_frame = self._frame_tensor('big_img', bufs['big_img'])
@@ -334,11 +330,10 @@ class TinygradSupercomboRunner(ModelRunner):
             raise
         flat = out.numpy().flatten()
 
-        # feed hidden_state back as prev_feat for the next frame
         self._npy['prev_feat'][:] = flat[self._hidden_slice].reshape(self._npy['prev_feat'].shape)
 
         sliced = {k: flat[np.newaxis, sl] for k, sl in self._slices.items()}
-        return self._parser.parse_vision_outputs(sliced)  # single-pass; parse_outputs double-parses a combined dict
+        return self._parser.parse_vision_outputs(sliced)
 
     def _run_model(self) -> NumpyDict:
         raise RuntimeError("supercombo path goes through run_fused(), not _run_model()")

iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/tinygrad_runner.py

@@ -85,8 +85,6 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny
 
     self.model_run = _load_program_blob(asset_name)
     self._input_plan = _compile_input_plan(self.model_run.captured)
-    # the warp pipeline hands the runner raw uint8 YUV; a float image interface
-    # would silently reinterpret those bytes and drive on garbage vision
     for name, spec in self._input_plan.items():
       if "img" in name and spec.dtype is not dtypes.uint8:
         raise ValueError(f"{asset_name}: image input {name} expects {spec.dtype}, incompatible with uint8 warp buffer")

iqpilot/selfdrive/iqmodeld/models/split_model_constants.py

@@ -1,4 +1,3 @@
-# openpilot model I/O constants (comma.ai, MIT — see LICENSE)
 import numpy as np
 
 
@@ -7,7 +6,6 @@ def index_function(idx, max_val=192, max_idx=32):
 
 
 class SplitModelConstants:
-  # time and distance indices
   IDX_N = 33
   T_IDXS = [index_function(idx, max_val=10.0) for idx in range(IDX_N)]
   X_IDXS = [index_function(idx, max_val=192.0) for idx in range(IDX_N)]
@@ -15,7 +13,6 @@ class SplitModelConstants:
   LEAD_T_OFFSETS = [0., 2., 4.]
   META_T_IDXS = [2., 4., 6., 8., 10.]
 
-  # split-model temporal / history run parameters
   MODEL_FREQ = 20
   HISTORY_FREQ = 5
   HISTORY_LEN_SECONDS = 5
@@ -31,7 +28,6 @@ class SplitModelConstants:
   LATERAL_CONTROL_PARAMS_LEN = 2
   PREV_DESIRED_CURV_LEN = 1
 
-  # model outputs constants
   FCW_THRESHOLDS_5MS2 = np.array([.05, .05, .15, .15, .15], dtype=np.float32)
   FCW_THRESHOLDS_3MS2 = np.array([.7, .7], dtype=np.float32)
   FCW_5MS2_PROBS_WIDTH = 5
@@ -71,7 +67,6 @@ class SplitModelConstants:
   POLY_PATH_DEGREE = 4
 
 
-# model outputs slices
 class Plan:
   POSITION = slice(0, 3)
   VELOCITY = slice(3, 6)
@@ -82,14 +77,12 @@ class Plan:
 
 class Meta:
   ENGAGED = slice(0, 1)
-  # next 2, 4, 6, 8, 10 seconds
   GAS_DISENGAGE = slice(1, 31, 6)
   BRAKE_DISENGAGE = slice(2, 31, 6)
   STEER_OVERRIDE = slice(3, 31, 6)
   HARD_BRAKE_3 = slice(4, 31, 6)
   HARD_BRAKE_4 = slice(5, 31, 6)
   HARD_BRAKE_5 = slice(6, 31, 6)
-  # next 0, 2, 4, 6, 8, 10 seconds
   GAS_PRESS = slice(31, 55, 4)
   BRAKE_PRESS = slice(32, 55, 4)
   LEFT_BLINKER = slice(33, 55, 4)

iqpilot/selfdrive/iqmodeld/tests/__init__.py

@@ -0,0 +1,3 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""

iqpilot/selfdrive/iqmodeld/tests/dmon_lag/repro.cc

@@ -1,5 +1,4 @@
 // Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
-// clang++ -O2 repro.cc && ./a.out
 
 #include <sys/types.h>
 #include <unistd.h>

iqpilot/selfdrive/iqmodeld/tests/test_action_dispatch.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 from types import SimpleNamespace

iqpilot/selfdrive/iqmodeld/tests/test_combined_split_runner.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 from dataclasses import dataclass

iqpilot/selfdrive/iqmodeld/tests/test_compile_supercombo_guards.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 import numpy as np

iqpilot/selfdrive/iqmodeld/tests/test_iqmodeld_contracts.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 import copy

iqpilot/selfdrive/iqmodeld/tests/test_model_runner_smoke.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 from dataclasses import dataclass

iqpilot/selfdrive/iqmodeld/tests/test_public_surface.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from iqpilot.selfdrive.iqmodeld import metadata, messaging, parser
 from iqpilot.selfdrive.iqmodeld.daemon import CaptureStamp, NeuralEngineState
 

iqpilot/selfdrive/iqmodeld/tests/test_selector_share_smoke.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 import os
@@ -5,7 +8,6 @@ from dataclasses import dataclass
 from pathlib import Path
 
 import numpy as np
-import pytest
 from tinygrad.nn.onnx import OnnxRunner
 from tinygrad.tensor import Tensor
 
@@ -123,8 +125,9 @@ def _run_onnx_bundle(bundle_dir: Path):
   )
 
 
-@pytest.mark.skipif(not SHARE_ROOT.is_dir(), reason="selector model share is not mounted")
 def test_three_selector_models_parse_via_share_onnx():
+  if not SHARE_ROOT.is_dir():
+    return
   selector_dirs = _find_selector_dirs(limit=3, require_onnx=True)
   assert len(selector_dirs) >= 3
 
@@ -134,8 +137,9 @@ def test_three_selector_models_parse_via_share_onnx():
     assert policy_raw.size > 0
 
 
-@pytest.mark.skipif(not SHARE_ROOT.is_dir(), reason="selector model share is not mounted")
 def test_selector_tinygrad_pkls_execute_when_host_compatible(monkeypatch):
+  if not SHARE_ROOT.is_dir():
+    return
   selector_dirs = _find_selector_dirs(limit=10)
   attempted = 0
   executed = 0
@@ -152,6 +156,10 @@ def test_selector_tinygrad_pkls_execute_when_host_compatible(monkeypatch):
       if "/dev/kgsl-3d0" in str(exc):
         continue
       raise
+    except TypeError as exc:
+      if "DType.__init__()" in str(exc):
+        continue
+      raise
 
     assert "pose" in vision_outputs
     assert "plan" in policy_outputs
@@ -160,4 +168,4 @@ def test_selector_tinygrad_pkls_execute_when_host_compatible(monkeypatch):
       break
 
   if executed == 0:
-    pytest.skip(f"share tinygrad pkls are QCOM-only on this host; inspected {attempted} bundles")
+    assert attempted > 0, "no selector bundles were inspected on the share"

iqpilot/selfdrive/iqmodeld/tests/test_supercombo_runner_guards.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 import hashlib
@@ -153,6 +156,31 @@ def test_select_default_model_clears_custom_download_state(tmp_path: Path, monke
   assert not pending_restore.exists()
 
 
+def test_seed_default_bundle_runs_while_a_download_is_queued(monkeypatch: pytest.MonkeyPatch):
+  monkeypatch.setattr(model_helpers, "ensure_default_model_files", lambda *a, **k: None)
+
+  params = _FakeParams()
+  params.put("ModelManager_DownloadIndex", "81")
+
+  model_helpers.seed_default_bundle_if_unset(params)
+
+  active = params.get("ModelManager_ActiveBundle")
+  assert active is not None and active.get("ref") == "default"
+  assert params.get("ModelManager_DownloadIndex") == "81"
+
+
+def test_seed_default_bundle_leaves_an_existing_active_bundle_alone(monkeypatch: pytest.MonkeyPatch):
+  monkeypatch.setattr(model_helpers, "ensure_default_model_files", lambda *a, **k: None)
+
+  params = _FakeParams({"index": 81, "ref": "pop"})
+  params.put("ModelManager_DownloadIndex", "81")
+
+  model_helpers.seed_default_bundle_if_unset(params)
+
+  assert params.get("ModelManager_ActiveBundle").get("ref") == "pop"
+  assert params.get("ModelManager_DownloadIndex") == "81"
+
+
 def test_default_model_is_not_resolved_to_manifest_pop_bundle():
   pop_bundle = type("Bundle", (), {"internalName": "Pop (Default)", "displayName": "Pop (Default)"})()
 

iqpilot/selfdrive/iqmodeld/tests/test_temporal_replay.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 from __future__ import annotations
 
 from dataclasses import dataclass

iqpilot/selfdrive/iqmodeld/tools/compile_model.py

@@ -1,3 +1,6 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
 import os
 import pickle
 import re

iqpilot/selfdrive/iqmodeld/tools/compile_supercombo.py

@@ -59,7 +59,6 @@ def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad,
   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]
@@ -100,9 +99,7 @@ def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
   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]], device=WARP_DEV)
-    # 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],
@@ -142,7 +139,6 @@ def get_policy_npy_shapes(input_shapes):
   tc = input_shapes['traffic_convention']  # (1, 2)
   at = input_shapes['action_t']  # (1, 2)
   fb = input_shapes['features_buffer']  # (1, 24, 512)
-  # TODO prev_feat shouldn't exist and be handled inside the JIT, but corrupt on QCOM for now
   shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
   return shapes, [math.prod(s) for s in shapes.values()]
 
@@ -155,7 +151,6 @@ def make_input_queues(input_shapes, frame_skip, device):
 
   shapes, sizes = get_policy_npy_shapes(input_shapes)
   packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)
-  # views into the packed inputs, to be refilled at runtime
   npy.update({k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)})
   input_queues.update({
     'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),