#!/usr/bin/env python3 from collections.abc import Callable import ctypes from functools import cached_property import json import os import struct from openpilot.system.hardware import HARDWARE, TICI os.environ['GMMU'] = '0' os.environ['DEV'] = 'QCOM' if TICI else 'LLVM' from tinygrad.device import Device from tinygrad.tensor import Tensor import time import pickle import numpy as np import cereal.messaging as messaging from cereal import car, log from pathlib import Path from setproctitle import setproctitle from cereal.messaging import PubMaster, SubMaster from cereal.services import SERVICE_LIST from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf from openpilot.common.swaglog import cloudlog from openpilot.common.params import Params from openpilot.common.filter_simple import FirstOrderFilter from openpilot.common.file_chunker import file_chunked_exists, open_file_chunked from openpilot.common.realtime import config_realtime_process, DT_MDL from openpilot.common.transformations.camera import DEVICE_CAMERAS from openpilot.common.transformations.model import get_warp_matrix from openpilot.system.camerad.cameras.nv12_info import get_nv12_info from openpilot.system import sentry from opendbc.car.car_helpers import get_demo_car_params from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan_tomb_raider, smooth_value from openpilot.selfdrive.modeld.camera_offset import CameraOffset, DEFAULT_CAMERA_HEIGHT from openpilot.selfdrive.modeld.parse_model_outputs import Parser from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output from openpilot.selfdrive.modeld.constants import ModelConstants, Plan from openpilot.selfdrive.modeld.compile_modeld import ( ARTIFACT_FORMAT_VERSION, FAST_POLICY_INPUTS, FAST_WARP_INPUTS, IMAGE_HISTORY_IN_POLICY, IMAGE_HISTORY_IN_WARP, LEGACY_WARP_INPUTS, _detect_vision_keys, derive_frame_skip, make_split_input_queues, make_supercombo_input_queues, ) from openpilot.selfdrive.modeld.helpers import get_tg_input_devices, load_oob, tinygrad_dev_config, usbgpu_present from openpilot.selfdrive.modeld.usbgpu_link import wait_usbgpu_link from openpilot.starpilot.assets.model_manager import ( ModelManager, load_model_artifact_metadata, model_accelerator_artifact_available, model_accelerator_artifact_installed, model_accelerator_artifact_path, model_uses_external_gpu, ) from openpilot.starpilot.common.model_lab import ( MODEL_LAB_RUNTIME_PARAM, compose_model_outputs, hybrid_action_values, load_model_lab_config, model_lab_manifest_eligible, ) from openpilot.starpilot.common.model_versions import is_tinygrad_model_version from openpilot.starpilot.common.starpilot_variables import get_starpilot_toggles, MODELS_PATH, params_memory PROCESS_NAME = "selfdrive.modeld.modeld" SEND_RAW_PRED = os.getenv('SEND_RAW_PRED') BUILTIN_MODEL_KEY = "rdf43" BUILTIN_MODEL_ALIASES = {BUILTIN_MODEL_KEY, "rdf"} MODEL_ID_ALIASES = {"sc": "sc2"} LAT_SMOOTH_SECONDS = 0.1 LONG_SMOOTH_SECONDS = 0.3 SMOOTH_SECONDS_STEP = 0.005 def _model_smooth_seconds(params, key, default): if not params.get_bool("DeveloperUI") or params.get_bool("SafeMode"): return default value = params.get_float(key, return_default=True, default=default) return round(min(max(value, SMOOTH_SECONDS_STEP), 2.0) / SMOOTH_SECONDS_STEP) * SMOOTH_SECONDS_STEP def _should_publish_model_output(model_output, vipc_dropped_frames: int, external_gpu_active: bool = False) -> bool: return model_output is not None and vipc_dropped_frames == 0 MIN_LAT_CONTROL_SPEED = 0.3 BIG_MODEL_LOAD_WAIT_TIMEOUT_MS = 30000 BIG_MODEL_RUN_WAIT_TIMEOUT_MS = 3000 EXTERNAL_GPU_POWER_READY_MV = 10000 EXTERNAL_GPU_EGMP_READY_MV = 12500 EXTERNAL_GPU_POWER_STABLE_SECONDS = 3.0 EXTERNAL_GPU_POWER_LOG_INTERVAL_SECONDS = 10.0 LAT_SMOOTH_BP = [2.0, 8.0] def _set_hcq_wait_timeout(timeout_ms: int) -> None: """Update tinygrad's cached HCQ timeout for the external-GPU load/run phase.""" os.environ["HCQDEV_WAIT_TIMEOUT_MS"] = str(timeout_ms) # tinygrad.getenv is cached. Updating os.environ alone leaves the first value # in effect for the lifetime of modeld. from tinygrad.helpers import getenv getenv.cache_clear() def _external_gpu_power_voltage(device_type: str, panda_states, peripheral_state) -> int | None: if device_type == "tici": voltage = int(peripheral_state.voltage) return voltage if peripheral_state.pandaType != log.PandaState.PandaType.unknown and voltage > 0 else None voltages = [ int(state.voltage) for state in panda_states if state.pandaType != log.PandaState.PandaType.unknown and int(state.voltage) > 0 ] return max(voltages, default=None) def _external_gpu_power_ready(voltage: int | None, now: float, stable_since: float | None, minimum_voltage: int = EXTERNAL_GPU_POWER_READY_MV) -> tuple[bool, float | None]: if voltage is None or voltage < minimum_voltage: return False, None stable_since = now if stable_since is None else stable_since return now - stable_since >= EXTERNAL_GPU_POWER_STABLE_SECONDS, stable_since def _egmp_ready_bus(CP) -> int | None: if CP is None or CP.brand != "hyundai": return None # These platforms use the accessory-mode ECU-disable startup sequence. from opendbc.car.hyundai.hyundaicanfd import CanBus from opendbc.car.hyundai.values import CAR if CP.carFingerprint not in (CAR.HYUNDAI_IONIQ_5_PE, CAR.HYUNDAI_IONIQ_6, CAR.KIA_EV9): return None return CanBus(CP).ECAN def _egmp_vehicle_ready(can_messages, bus: int) -> bool: return any( msg.address == 0x35 and msg.src == bus and len(msg.dat) > 3 and bytes(msg.dat)[3] & 0x40 for msg in can_messages ) def wait_for_external_gpu_power_ready(CP=None) -> None: """Wait out vehicle startup power transitions before initializing Chestnut.""" device_type = HARDWARE.get_device_type() egmp_bus = _egmp_ready_bus(CP) services = ["pandaStates", "peripheralState"] + (["can"] if egmp_bus is not None else []) sm = SubMaster(services) vehicle_ready = egmp_bus is None stable_since = None last_log = 0.0 while True: sm.update(1000) now = time.monotonic() if egmp_bus is not None and sm.updated["can"] and _egmp_vehicle_ready(sm["can"], egmp_bus): if not vehicle_ready: cloudlog.warning("e-GMP vehicle entered READY; waiting for external GPU power to stabilize") vehicle_ready = True voltage = _external_gpu_power_voltage(device_type, sm["pandaStates"], sm["peripheralState"]) minimum_voltage = EXTERNAL_GPU_EGMP_READY_MV if egmp_bus is not None else EXTERNAL_GPU_POWER_READY_MV ready, stable_since = _external_gpu_power_ready( voltage, now, stable_since if vehicle_ready else None, minimum_voltage, ) if vehicle_ready and ready: cloudlog.warning(f"vehicle power stable at {voltage / 1000:.2f} V; starting external GPU load") return if now - last_log >= EXTERNAL_GPU_POWER_LOG_INTERVAL_SECONDS: detail = "unavailable" if voltage is None else f"{voltage / 1000:.2f} V" if not vehicle_ready: cloudlog.warning(f"external GPU load deferred: vehicle power is {detail}; waiting for e-GMP READY") else: cloudlog.warning(f"external GPU load deferred: vehicle power is {detail}; waiting for " + f"{minimum_voltage / 1000:.1f} V to remain stable") last_log = now def get_lateral_smooth_seconds(v_ego: float, maximum: float = 0.0) -> float: return float(np.interp(v_ego, LAT_SMOOTH_BP, [maximum, 0.0])) def get_car_lateral_smooth_seconds(brand: str, v_ego: float, maximum: float) -> float: if brand in ("rivian", "subaru"): return get_lateral_smooth_seconds(v_ego, maximum) return maximum class ChestnutState: """Publish bounded external-GPU and ASM2464 telemetry from modeld.""" def __init__(self, pm: PubMaster, big: bool): self.pm = pm self.big = big self.valid = True self.sends = 0 self.metrics = {} @cached_property def power_limit(self) -> int: smu = Device["AMD"].iface.dev_impl.smu return smu._send_msg(smu.smu_mod.PPSMC_MSG_GetPptLimit, 0, read_back_arg=True, timeout=100) def send(self) -> None: msg = messaging.new_message("chestnutState") state = msg.chestnutState self.sends += 1 # SMU metrics are relatively expensive, so update them at 0.1 Hz while # publishing the cached values with the 10 Hz ASM link telemetry. if self.big and "AMD" in Device._opened_devices and self.sends % 100 == 1: try: smu = Device["AMD"].iface.dev_impl.smu metrics_t = smu.smu_mod.SmuMetricsExternal_t smu._send_msg(smu.smu_mod.PPSMC_MSG_TransferTableSmu2Dram, smu.smu_mod.TABLE_SMU_METRICS, timeout=100) metrics_buf = bytearray(smu.adev.vram.view(smu.driver_table_paddr, ctypes.sizeof(metrics_t))[:]) metrics = metrics_t.from_buffer(metrics_buf).SmuMetrics self.metrics = { "tempC": metrics.AvgTemperature[smu.smu_mod.TEMP_HOTSPOT], "memoryTempC": metrics.AvgTemperature[smu.smu_mod.TEMP_MEM], "powerDrawW": metrics.AverageSocketPower, "powerLimitW": self.power_limit, "gpuUsagePercent": metrics.AverageGfxActivity, "gpuClockMhz": metrics.AverageGfxclkFrequencyPostDs, "fanSpeedRpm": metrics.AvgFanRpm, } self.valid = True except Exception: if self.valid: cloudlog.exception("chestnut state read failed") self.valid = False self.metrics.clear() if self.big: for key, value in self.metrics.items(): setattr(state, key, value) asm_valid = False if "AMD" in Device._opened_devices: try: asm = Device["AMD"].iface.pci_dev.usb state.pcieLtssm = asm.read(0xB450, 1)[0] state.supplyVoltage, state.supplyCurrent = struct.unpack(" str: try: val = params.get(key) except Exception: return default if val is None: return default if isinstance(val, bytes): try: return val.decode("utf-8") except Exception: return default if isinstance(val, (dict, list)): return default return str(val) def _get_default_param_str(params: Params, key: str) -> str: try: val = params.get_default_value(key) except Exception: return "" if val is None: return "" if isinstance(val, bytes): try: return val.decode("utf-8") except Exception: return "" return str(val) def _resolve_mirrored_param(params: Params, primary_key: str, secondary_key: str) -> str: primary_val = _get_param_str(params, primary_key).strip() secondary_val = _get_param_str(params, secondary_key).strip() if primary_val == secondary_val: return secondary_val or primary_val primary_default = _get_default_param_str(params, primary_key).strip() secondary_default = _get_default_param_str(params, secondary_key).strip() primary_non_default = bool(primary_val) and primary_val != primary_default secondary_non_default = bool(secondary_val) and secondary_val != secondary_default if secondary_non_default: return secondary_val if primary_non_default: return primary_val return secondary_val or primary_val def _canonical_model_id(model_id: str) -> str: key = (model_id or "").strip().lower() if key in BUILTIN_MODEL_ALIASES: return BUILTIN_MODEL_KEY return MODEL_ID_ALIASES.get(key, key) def _select_builtin_model(params: Params) -> None: params.put("Model", BUILTIN_MODEL_KEY) params.put("DrivingModel", BUILTIN_MODEL_KEY) params.put("DrivingModelName", "Regret Driven Framework V4") def _close_tinygrad_disk_cache_connection() -> None: """Close tinygrad's cache connection without replacing its thread-local holder.""" import tinygrad.helpers as tinygrad_helpers holder = getattr(tinygrad_helpers, "_db_connection", None) if holder is None: return has_thread_local_connection = hasattr(holder, "conn") connection = getattr(holder, "conn", holder if hasattr(holder, "close") else None) if connection is None: return try: connection.close() except Exception: cloudlog.exception("failed to close tinygrad disk cache connection") finally: if has_thread_local_connection: try: del holder.conn except AttributeError: pass else: tinygrad_helpers._db_connection = None def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action, lat_action_t: float, long_action_t: float, v_ego: float, mlsim: bool, is_v9: bool, is_v14: bool, is_v15: bool, starpilot_toggles, lat_smooth_seconds=LAT_SMOOTH_SECONDS, long_smooth_seconds=LONG_SMOOTH_SECONDS, is_v16: bool = False) -> log.ModelDataV2.Action: if is_v14 or is_v15 or is_v16: desired_curv_unscaled, desired_accel = model_output['action'][0] if is_v15 or is_v16: desired_curvature = float(desired_curv_unscaled) / max(1.0, v_ego) ** 2 else: desired_curvature = float(desired_curv_unscaled) / 100.0 should_stop = (v_ego < 0.3 and desired_accel < 0.1) desired_accel = smooth_value(float(desired_accel), prev_action.desiredAcceleration, long_smooth_seconds) if v_ego > MIN_LAT_CONTROL_SPEED: desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, lat_smooth_seconds) else: desired_curvature = prev_action.desiredCurvature return log.ModelDataV2.Action(desiredCurvature=float(desired_curvature), desiredAcceleration=float(desired_accel), shouldStop=bool(should_stop)) plan = model_output['plan'][0] if 'planplus' in model_output: recovery_power = getattr(starpilot_toggles, "recovery_power", 1.0) plan = plan + recovery_power * model_output['planplus'][0] cloudlog.error(f"planplus applied: shape {model_output['planplus'].shape}, RECOVERY_POWER {recovery_power}") v_ego_stopping = getattr(starpilot_toggles, "vEgoStopping", 0.3) desired_accel, should_stop = get_accel_from_plan_tomb_raider(plan[:,Plan.VELOCITY][:,0], plan[:,Plan.ACCELERATION][:,0], ModelConstants.T_IDXS, action_t=long_action_t, vEgoStopping=v_ego_stopping) desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, long_smooth_seconds) if is_v9: # V9: use desired_curvature if present; otherwise do NOT fall back to plan if 'desired_curvature' in model_output: desired_curvature = float(model_output['desired_curvature'][0, 0]) else: desired_curvature = prev_action.desiredCurvature else: desired_curvature = get_curvature_from_output(model_output, plan, v_ego, lat_action_t, mlsim=mlsim) if v_ego > MIN_LAT_CONTROL_SPEED: desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, lat_smooth_seconds) else: desired_curvature = prev_action.desiredCurvature return log.ModelDataV2.Action(desiredCurvature=float(desired_curvature), desiredAcceleration=float(desired_accel), shouldStop=bool(should_stop)) class FrameMeta: frame_id: int = 0 timestamp_sof: int = 0 timestamp_eof: int = 0 def __init__(self, vipc=None): if vipc is not None: self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof MAX_OOB_OPCODE_SIZE = 1024 * 1024 * 1024 def _is_oob_artifact_header(header: bytes) -> bool: if len(header) < 10: return False opcode_size = struct.unpack(" dict: """Adapt the current metadata-only artifact envelope to StarPilot's explicit schema.""" if artifact.get("format_version") is not None: if artifact["format_version"] != ARTIFACT_FORMAT_VERSION: raise ValueError( f"Unsupported model artifact format {artifact.get('format_version')!r}; " f"expected {ARTIFACT_FORMAT_VERSION}" ) return artifact metadata = artifact.get("metadata") if not isinstance(metadata, dict) or "run_policy" not in artifact: raise ValueError("Unsupported metadata-only model artifact") if "model" in metadata: model_type = "supercombo" policy_order = [] policy_shapes = metadata["model"]["input_shapes"] else: policy_order = [key for key in metadata if key != "vision"] if not policy_order or "vision" not in metadata: raise ValueError("Split model artifact is missing vision or policy metadata") model_type = "vision_policy" if policy_order == ["policy"] else "vision_multi_policy" policy_shapes = metadata[policy_order[0]]["input_shapes"] return { **artifact, "format_version": ARTIFACT_FORMAT_VERSION, "model_type": model_type, "policy_order": policy_order, "frame_skip": derive_frame_skip(policy_shapes), "image_history_pipeline": IMAGE_HISTORY_IN_POLICY, "warp_input_keys": FAST_WARP_INPUTS, "policy_input_keys": FAST_POLICY_INPUTS, } class ModelState: prev_desire: np.ndarray def _build_policy_inputs(self, input_shapes: dict[str, tuple[int, ...]]) -> tuple[dict[str, np.ndarray], str | None]: numpy_inputs: dict[str, np.ndarray] = {} desire_key = next((key for key in input_shapes if key.startswith("desire")), None) if desire_key is not None: numpy_inputs[desire_key] = np.zeros(input_shapes[desire_key], dtype=np.float32) for key, shape in input_shapes.items(): if key == desire_key or key == "features_buffer" or "img" in key: continue numpy_inputs[key] = np.zeros(shape, dtype=np.float32) prev_desired_curv_key = next( (key for key in ("prev_desired_curv", "prev_desired_curvs") if key in input_shapes), None, ) return numpy_inputs, prev_desired_curv_key def __init__(self, cam_w: int, cam_h: int, external_gpu_active: bool = False, model_id_override: str | None = None, write_model_version: bool = True, model_version_override: str | None = None, model_path_override: Path | None = None, force_external_gpu: bool = False): params = Params() selected_model = model_id_override or _resolve_mirrored_param(params, "Model", "DrivingModel") or BUILTIN_MODEL_KEY model_id = _canonical_model_id(selected_model) requires_external_gpu = model_uses_external_gpu(model_id) if force_external_gpu and not external_gpu_active: raise RuntimeError("an external GPU artifact cannot run without Chestnut") if requires_external_gpu and not external_gpu_active: cloudlog.error(f"Model {model_id} requires an external GPU; falling back to {BUILTIN_MODEL_KEY}") model_id = BUILTIN_MODEL_KEY use_builtin = model_id == BUILTIN_MODEL_KEY and model_path_override is None loaded_builtin = use_builtin if model_path_override is not None: model_path = Path(model_path_override) elif use_builtin: model_path = Path(__file__).parent / "models" / "driving_tinygrad.pkl" else: model_path = MODELS_PATH / f"{model_id}_driving_tinygrad.pkl" if not file_chunked_exists(model_path) and not use_builtin and model_path_override is None: cloudlog.error(f"Missing model artifact {model_path}, downloading {model_id}...") try: ModelManager(params, params_memory).download_model(model_id) except Exception: cloudlog.exception(f"Failed to download model {model_id}") if not file_chunked_exists(model_path) and not use_builtin and model_path_override is None: fallback_path = Path(__file__).parent / "models" / "driving_tinygrad.pkl" if file_chunked_exists(fallback_path): cloudlog.error(f"Falling back to builtin model artifact after {model_id} download failed") model_path = fallback_path loaded_builtin = True requires_external_gpu = False if not file_chunked_exists(model_path): raise FileNotFoundError(model_path) self.model_id = BUILTIN_MODEL_KEY if loaded_builtin else model_id self.uses_external_gpu = external_gpu_active and (requires_external_gpu or force_external_gpu) and not loaded_builtin artifact = _normalize_model_artifact(_load_model_artifact(model_path)) self.model_type = artifact["model_type"] self.metadata = artifact["metadata"] self.policy_order = artifact.get("policy_order", []) self.frame_skip = int(artifact["frame_skip"]) self.image_history_pipeline = artifact.get("image_history_pipeline", IMAGE_HISTORY_IN_WARP) self.warp_input_keys = tuple(artifact.get("warp_input_keys", LEGACY_WARP_INPUTS)) self.policy_input_keys = tuple(artifact["policy_input_keys"]) self.run_policy = artifact["run_policy"] self.warp_enqueue = artifact[(cam_w, cam_h)] self.can_prepare_only = self.image_history_pipeline == IMAGE_HISTORY_IN_WARP if self.model_type == "supercombo": input_shapes = self.metadata["model"]["input_shapes"] self.output_slices = self.metadata["model"]["output_slices"] self.input_queues, self.npy = make_supercombo_input_queues(input_shapes, self.frame_skip, self.QUEUE_DEV) self.policy_input_shapes = input_shapes else: vision_shapes = self.metadata["vision"]["input_shapes"] primary_policy = "on_policy" if "on_policy" in self.policy_order else "policy" self.policy_input_shapes = self.metadata[primary_policy]["input_shapes"] self.input_queues, self.npy = make_split_input_queues( vision_shapes, self.policy_input_shapes, self.frame_skip, self.QUEUE_DEV, ) input_shapes = vision_shapes self.road_key, self.wide_key = _detect_vision_keys(input_shapes) self.vision_input_names = [self.road_key, self.wide_key] self.numpy_inputs, self.prev_desired_curv_key = self._build_policy_inputs(self.policy_input_shapes) self.desire_key = next(key for key in self.numpy_inputs if key.startswith("desire")) self.off_policy_enabled = "off_policy" in self.policy_order self.off_policy_numpy_inputs = dict(self.numpy_inputs) if self.off_policy_enabled else {} self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32) self.parser = Parser() self.aux_parser = Parser(ignore_missing=True) self.frame_buf_size = get_nv12_info(cam_w, cam_h)[3] self._blob_cache: dict[tuple[str, int], Tensor] = {} model_version = str(model_version_override or "").strip() if not model_version: model_version = _resolve_mirrored_param(params, "ModelVersion", "DrivingModelVersion") if not model_version: model_version = str(artifact.get("behavior_version") or "") if not model_version: versions_path = MODELS_PATH / ".model_versions.json" if versions_path.is_file(): try: model_version = str(json.loads(versions_path.read_text()).get(model_id) or "") except Exception: pass if loaded_builtin: model_version = str(artifact.get("behavior_version") or "v15") self.policy_generation = model_version or ("v15" if loaded_builtin else "v8") self.is_v9 = self.policy_generation == "v9" self.is_v14 = self.policy_generation == "v14" self.is_v15 = self.policy_generation == "v15" self.is_v16 = self.policy_generation == "v16" self.mlsim = is_tinygrad_model_version(self.policy_generation) if write_model_version: params.put("ModelVersion", self.policy_generation) params.put("DrivingModelVersion", self.policy_generation) if self.prev_desired_curv_key is not None: self.full_prev_desired_curv = np.zeros( (1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32, ) self.temporal_idxs = slice( -1 - (ModelConstants.TEMPORAL_SKIP * (ModelConstants.INPUT_HISTORY_BUFFER_LEN - 1)), None, ModelConstants.TEMPORAL_SKIP, ) @property def QUEUE_DEV(self) -> str: if not hasattr(self, "_queue_dev"): devices = get_tg_input_devices(PROCESS_NAME, usbgpu=self.uses_external_gpu) self._warp_dev = devices["WARP_DEV"] self._queue_dev = devices["QUEUE_DEV"] return self._queue_dev @property def WARP_DEV(self) -> str: if not hasattr(self, "_warp_dev"): _ = self.QUEUE_DEV return self._warp_dev @staticmethod def slice_outputs(model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]: return {key: model_outputs[np.newaxis, value] for key, value in output_slices.items()} def _set_optional_input(self, name: str, inputs: dict[str, np.ndarray]) -> None: if name not in self.numpy_inputs or name not in inputs: return self.numpy_inputs[name][:] = inputs[name] if name in self.npy: self.npy[name][:] = self.numpy_inputs[name] def _parse_split_outputs(self, outputs: list[np.ndarray]) -> dict[str, np.ndarray]: vision_output, *policy_outputs = outputs parsed = self.parser.parse_vision_outputs( self.slice_outputs(vision_output, self.metadata["vision"]["output_slices"]) ) policy_results: dict[str, dict[str, np.ndarray]] = {} for key, output in zip(self.policy_order, policy_outputs, strict=True): sliced = self.slice_outputs(output, self.metadata[key]["output_slices"]) policy_results[key] = ( self.aux_parser.parse_off_policy_outputs(sliced) if key == "off_policy" else self.parser.parse_policy_outputs(sliced) ) for key in self.policy_order: if key not in ("on_policy", "policy"): parsed.update(policy_results[key]) primary_key = "on_policy" if "on_policy" in policy_results else "policy" parsed.update(policy_results[primary_key]) return parsed def _reset_state(self) -> None: if self.model_type == "supercombo": self.input_queues, self.npy = make_supercombo_input_queues( self.policy_input_shapes, self.frame_skip, self.QUEUE_DEV, ) else: vision_shapes = self.metadata["vision"]["input_shapes"] self.input_queues, self.npy = make_split_input_queues( vision_shapes, self.policy_input_shapes, self.frame_skip, self.QUEUE_DEV, ) for value in self.numpy_inputs.values(): value.fill(0) self.prev_desire.fill(0) if self.prev_desired_curv_key is not None: self.full_prev_desired_curv.fill(0) self._blob_cache.clear() def warmup(self) -> None: dummy_frames = { key: np.zeros(self.frame_buf_size, dtype=np.uint8) for key in self.vision_input_names } # A host pointer is not a valid camera buffer for every warp backend. Match # upstream and substitute realized device buffers for warmup only. self._blob_cache.update({ (key, value.ctypes.data): Tensor.zeros(value.shape, dtype="uint8", device=self.WARP_DEV).realize() for key, value in dummy_frames.items() }) eye = np.eye(3, dtype=np.float32) inputs = {self.desire_key: np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)} for name, value in self.numpy_inputs.items(): if name in (self.desire_key, self.prev_desired_curv_key): continue shape = value.shape[1:] if value.ndim > 1 and value.shape[0] == 1 else value.shape inputs[name] = np.zeros(shape, dtype=value.dtype) self.run(dummy_frames, dict.fromkeys(self.vision_input_names, eye), inputs, False) self._reset_state() def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray], inputs: dict[str, np.ndarray], prepare_only: bool, after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray] | None: frames: dict[str, Tensor] = {} for key, buf in bufs.items(): ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data cache_key = (key, ptr) if cache_key not in self._blob_cache: self._blob_cache[cache_key] = Tensor.from_blob( ptr, (self.frame_buf_size,), dtype="uint8", device=self.WARP_DEV, ) frames[key] = self._blob_cache[cache_key] inputs[self.desire_key][0] = 0 self.numpy_inputs[self.desire_key].fill(0) self.numpy_inputs[self.desire_key].reshape(-1, ModelConstants.DESIRE_LEN)[-1] = inputs[self.desire_key] self.npy["desire"][:] = np.where( inputs[self.desire_key] - self.prev_desire > 0.99, inputs[self.desire_key], 0, ) self.prev_desire[:] = inputs[self.desire_key] for name in self.numpy_inputs: if name not in (self.desire_key, self.prev_desired_curv_key): self._set_optional_input(name, inputs) self.npy["tfm"][:] = transforms[self.road_key] self.npy["big_tfm"][:] = transforms[self.wide_key] warp_output = self.warp_enqueue( **{key: self.input_queues[key] for key in self.warp_input_keys}, frame=frames[self.road_key], big_frame=frames[self.wide_key], ) if self.image_history_pipeline == IMAGE_HISTORY_IN_POLICY: output_tensors = self.run_policy( **{key: self.input_queues[key] for key in self.policy_input_keys}, warped=warp_output, ) else: img, big_img = warp_output if prepare_only: return None output_tensors = self.run_policy( **{key: self.input_queues[key] for key in self.policy_input_keys}, img=img, big_img=big_img, ) if after_enqueue is not None: after_enqueue() outputs = [output.numpy().flatten() for output in output_tensors] if self.uses_external_gpu and any(not np.isfinite(output).all() for output in outputs): raise RuntimeError("external GPU model output not finite") if self.model_type == "supercombo": model_output = outputs[0] parsed = self.parser.parse_outputs(self.slice_outputs(model_output, self.output_slices)) if "prev_feat" in self.npy and "hidden_state" in self.output_slices: self.npy["prev_feat"][:] = model_output[self.output_slices["hidden_state"]] else: parsed = self._parse_split_outputs(outputs) if self.prev_desired_curv_key is not None and "desired_curvature" in parsed: self.full_prev_desired_curv[0, :-1] = self.full_prev_desired_curv[0, 1:] self.full_prev_desired_curv[0, -1, :] = parsed["desired_curvature"][0, :] history = self.full_prev_desired_curv[0, self.temporal_idxs] self.numpy_inputs[self.prev_desired_curv_key][:] = 0 * history if self.mlsim else history if self.prev_desired_curv_key in self.npy: self.npy[self.prev_desired_curv_key][:] = self.numpy_inputs[self.prev_desired_curv_key] if SEND_RAW_PRED: parsed["raw_pred"] = np.concatenate([output.copy() for output in outputs]) return parsed def _load_model_state(cam_w: int, cam_h: int, selected_model: str, external_gpu_requested: bool, params: Params) -> ModelState: try: return ModelState(cam_w, cam_h, external_gpu_requested) except Exception: if selected_model == BUILTIN_MODEL_KEY: raise cloudlog.exception(f"Failed to load model {selected_model}; falling back to {BUILTIN_MODEL_KEY}") _select_builtin_model(params) if external_gpu_requested: from tinygrad.helpers import DEV device_config = tinygrad_dev_config(False, TICI) DEV.value = device_config os.environ["DEV"] = device_config return ModelState(cam_w, cam_h, False) def _load_external_gpu_model(cam_w: int, cam_h: int, selected_model: str, CP=None, demo: bool = False) -> ModelState | None: """Load and warm the USB-GPU model without running another tinygrad model concurrently.""" candidate = None try: if not demo: wait_for_external_gpu_power_ready(CP) _set_hcq_wait_timeout(BIG_MODEL_LOAD_WAIT_TIMEOUT_MS) wait_usbgpu_link() candidate = ModelState( cam_w, cam_h, True, model_id_override=selected_model, write_model_version=False, ) if not candidate.uses_external_gpu: raise RuntimeError("external GPU model resolved to the builtin model") candidate.warmup() return candidate except Exception: cloudlog.exception("external GPU model load or warmup failed") return None finally: _close_tinygrad_disk_cache_connection() _set_hcq_wait_timeout(BIG_MODEL_RUN_WAIT_TIMEOUT_MS) def _model_versions() -> dict[str, str]: versions_path = MODELS_PATH / ".model_versions.json" try: payload = json.loads(versions_path.read_text()) return {str(key): str(value) for key, value in payload.items()} if isinstance(payload, dict) else {} except (OSError, TypeError, ValueError): return {} def _load_model_lab_model(cam_w: int, cam_h: int, model_id: str, version: str) -> ModelState: if not model_accelerator_artifact_available(model_id) or not model_accelerator_artifact_installed(model_id): raise RuntimeError(f"Model Laboratory AMD artifact is unavailable for {model_id}") candidate = ModelState( cam_w, cam_h, True, model_id_override=model_id, write_model_version=False, model_version_override=version, model_path_override=model_accelerator_artifact_path(model_id), force_external_gpu=True, ) if candidate.model_id != _canonical_model_id(model_id) or not candidate.uses_external_gpu: raise RuntimeError(f"Model Laboratory failed to load {model_id} on AMD") return candidate def _isolate_next_model_artifact_load() -> int: from tinygrad.uop.ops import Ops, UOpMetaClass buffer_keys = [key for key in UOpMetaClass.ucache if key[0] is Ops.BUFFER] for key in buffer_keys: UOpMetaClass.ucache.pop(key, None) return len(buffer_keys) def _load_model_lab_models(cam_w: int, cam_h: int, lateral_id: str, longitudinal_id: str, lateral_version: str, longitudinal_version: str, CP=None, demo: bool = False) -> tuple[ModelState, ModelState] | None: try: if not demo: wait_for_external_gpu_power_ready(CP) _set_hcq_wait_timeout(BIG_MODEL_LOAD_WAIT_TIMEOUT_MS) wait_usbgpu_link() _isolate_next_model_artifact_load() lateral = _load_model_lab_model(cam_w, cam_h, lateral_id, lateral_version) lateral.warmup() evicted = _isolate_next_model_artifact_load() cloudlog.info(f"Model Laboratory isolated {evicted} realized buffer UOps before loading the second model") longitudinal = _load_model_lab_model(cam_w, cam_h, longitudinal_id, longitudinal_version) longitudinal.warmup() return lateral, longitudinal except Exception: cloudlog.exception("Model Laboratory AMD model load or warmup failed") return None finally: _close_tinygrad_disk_cache_connection() _set_hcq_wait_timeout(BIG_MODEL_RUN_WAIT_TIMEOUT_MS) def _model_outputs_finite(*outputs: dict[str, np.ndarray]) -> bool: return all( np.isfinite(value).all() for output in outputs for value in output.values() if isinstance(value, np.ndarray) ) def _model_lab_runtime_request(params: Params, chestnut_ready: bool) -> tuple[dict, str | None]: config = load_model_lab_config(params) if not config["enabled"]: return config, None if not chestnut_ready: return config, "Chestnut is not connected and firmware-ready" lateral_id = _canonical_model_id(config["lateralModel"]) longitudinal_id = _canonical_model_id(config["longitudinalModel"]) config.update({"lateralModel": lateral_id, "longitudinalModel": longitudinal_id}) if not lateral_id or not longitudinal_id: return config, "both model roles must be selected" if lateral_id == longitudinal_id: return config, "the lateral and longitudinal models must be different" versions = _model_versions() for role, model_id in (("lateral", lateral_id), ("longitudinal", longitudinal_id)): metadata = load_model_artifact_metadata(model_id) version = versions.get(model_id, "") if not model_lab_manifest_eligible(metadata, version): return config, f"{role} model {model_id} is not a compatible small model" if not model_accelerator_artifact_available(model_id): return config, f"{role} model {model_id} has no precompiled AMD artifact in the manifest" if not model_accelerator_artifact_installed(model_id): return config, f"{role} model {model_id} AMD artifact is not installed by Model Manager" return config, None def _set_model_lab_runtime(params: Params, *, requested: bool, active: bool, config: dict | None = None, error: str = "") -> None: config = config or {} params.put(MODEL_LAB_RUNTIME_PARAM, { "requested": bool(requested), "active": bool(active), "lateralModel": str(config.get("lateralModel") or ""), "longitudinalModel": str(config.get("longitudinalModel") or ""), "schedule": "sequential_20hz" if requested else "", "executionDevice": "AMD" if active else "", "error": str(error or ""), }) def _runner_frame_args(model: ModelState, buf_main, buf_extra, model_transform_main: np.ndarray, model_transform_extra: np.ndarray, vec_desire: np.ndarray, traffic_convention: np.ndarray, lat_action_t: float, long_action_t: float, prev_action: log.ModelDataV2.Action, v_ego: float, lateral_control_params: np.ndarray) -> tuple[dict, dict, dict[str, np.ndarray]]: bufs = { model.road_key: buf_main, model.wide_key: buf_extra, } transforms = { model.road_key: model_transform_main, model.wide_key: model_transform_extra, } inputs: dict[str, np.ndarray] = { model.desire_key: vec_desire, "traffic_convention": traffic_convention, } if "action_t" in model.numpy_inputs or (model.off_policy_enabled and "action_t" in model.off_policy_numpy_inputs): inputs["action_t"] = np.array([lat_action_t, long_action_t], dtype=np.float32) if "prev_action" in model.numpy_inputs or (model.off_policy_enabled and "prev_action" in model.off_policy_numpy_inputs): inputs["prev_action"] = np.array([ prev_action.desiredCurvature * max(1.0, v_ego) ** 2, prev_action.desiredAcceleration, ], dtype=np.float32) if "lateral_control_params" in model.numpy_inputs: inputs["lateral_control_params"] = lateral_control_params return bufs, transforms, inputs def main(demo=False): cloudlog.warning("modeld init") sentry.set_tag("daemon", PROCESS_NAME) cloudlog.bind(daemon=PROCESS_NAME) setproctitle(PROCESS_NAME) config_realtime_process(7, 54) params = Params() selected_model = _canonical_model_id(_resolve_mirrored_param(params, "Model", "DrivingModel") or BUILTIN_MODEL_KEY) usbgpu_present_now = usbgpu_present() model_lab_config, model_lab_error = _model_lab_runtime_request(params, usbgpu_present_now) model_lab_requested = bool(model_lab_config["enabled"]) model_lab_ready = model_lab_requested and model_lab_error is None external_model_selected = model_uses_external_gpu(selected_model) external_artifact = MODELS_PATH / f"{selected_model}_driving_tinygrad.pkl" external_artifact_ready = external_model_selected and file_chunked_exists(external_artifact) external_gpu_requested = usbgpu_present_now and (external_model_selected or model_lab_ready) params.put_bool("UsbGpuPresent", usbgpu_present_now) params.put_bool("UsbGpuCompiled", external_artifact_ready or model_lab_ready) params.put_bool("UsbGpuActive", False) params.put_bool("UsbGpuLoading", external_gpu_requested) _set_model_lab_runtime( params, requested=model_lab_requested, active=False, config=model_lab_config, error=model_lab_error or "", ) # visionipc clients while True: available_streams = VisionIpcClient.available_streams("camerad", block=False) if available_streams: use_extra_client = VisionStreamType.VISION_STREAM_WIDE_ROAD in available_streams and VisionStreamType.VISION_STREAM_ROAD in available_streams main_wide_camera = VisionStreamType.VISION_STREAM_ROAD not in available_streams break time.sleep(.1) vipc_client_main_stream = VisionStreamType.VISION_STREAM_WIDE_ROAD if main_wide_camera else VisionStreamType.VISION_STREAM_ROAD vipc_client_main = VisionIpcClient("camerad", vipc_client_main_stream, True) vipc_client_extra = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_WIDE_ROAD, False) cloudlog.warning(f"vision stream set up, main_wide_camera: {main_wide_camera}, use_extra_client: {use_extra_client}") while not vipc_client_main.connect(False): time.sleep(0.1) while use_extra_client and not vipc_client_extra.connect(False): time.sleep(0.1) cloudlog.warning(f"connected main cam with buffer size: {vipc_client_main.buffer_len} ({vipc_client_main.width} x {vipc_client_main.height})") 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})") start_time = time.monotonic() cloudlog.warning("loading model") model = None small_model = None big_model = None model_lab_longitudinal = None model_lab_active = False model_lab_timings: list[float] = [] CP = None if model_lab_ready: if demo: CP = get_demo_car_params() else: CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams) small_model = ModelState( vipc_client_main.width, vipc_client_main.height, False, model_id_override=BUILTIN_MODEL_KEY, write_model_version=False, ) versions = _model_versions() lateral_id = model_lab_config["lateralModel"] longitudinal_id = model_lab_config["longitudinalModel"] pair = _load_model_lab_models( vipc_client_main.width, vipc_client_main.height, lateral_id, longitudinal_id, versions[lateral_id], versions[longitudinal_id], CP, demo, ) if pair is not None: model, model_lab_longitudinal = pair model_lab_active = True params.put("ModelVersion", model.policy_generation) params.put("DrivingModelVersion", model.policy_generation) else: model_lab_error = "one or both precompiled AMD models failed to load; using the built-in model" cloudlog.error(f"Model Laboratory unavailable: {model_lab_error}") model = small_model params.put("ModelVersion", model.policy_generation) params.put("DrivingModelVersion", model.policy_generation) elif external_gpu_requested: if demo: CP = get_demo_car_params() else: CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams) big_model = _load_external_gpu_model( vipc_client_main.width, vipc_client_main.height, selected_model, CP, demo, ) small_model = ModelState( vipc_client_main.width, vipc_client_main.height, False, model_id_override=BUILTIN_MODEL_KEY, write_model_version=False, ) model = big_model if big_model is not None else small_model if big_model is not None: params.put("ModelVersion", model.policy_generation) params.put("DrivingModelVersion", model.policy_generation) else: model = _load_model_state(vipc_client_main.width, vipc_client_main.height, selected_model, False, params) external_gpu_active = model_lab_active or model.uses_external_gpu params.put_bool("UsbGpuCompiled", external_artifact_ready or model_lab_ready) params.put_bool("UsbGpuActive", external_gpu_active) params.put_bool("UsbGpuLoading", False) _set_model_lab_runtime( params, requested=model_lab_requested, active=model_lab_active, config=model_lab_config, error=model_lab_error or "", ) cloudlog.warning(f"models loaded in {time.monotonic() - start_time:.1f}s, modeld starting") # messaging publish_services = ["modelV2", "drivingModelData", "cameraOdometry", "starpilotModelV2"] if external_gpu_requested: publish_services.append("chestnutState") pm = PubMaster(publish_services) sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "carControl", "liveDelay", "starpilotPlan"]) publish_state = PublishState() chestnut_state = ChestnutState(pm, external_gpu_active) if external_gpu_requested else None # setup filter to track dropped frames frame_dropped_filter = FirstOrderFilter(0., 10., 1. / ModelConstants.MODEL_FREQ) frame_id = 0 last_vipc_frame_id = 0 run_count = 0 model_transform_main = np.zeros((3, 3), dtype=np.float32) model_transform_extra = np.zeros((3, 3), dtype=np.float32) live_calib_seen = False buf_main, buf_extra = None, None meta_main = FrameMeta() meta_extra = FrameMeta() camera_offset = CameraOffset() camera_offset.set_target(params.get_float("CameraOffset", return_default=True)) if CP is None: if demo: CP = get_demo_car_params() else: CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams) cloudlog.info("modeld got CarParams: %s", CP.brand) lat_smooth_seconds = _model_smooth_seconds(params, "LatSmoothSeconds", LAT_SMOOTH_SECONDS) long_smooth_seconds = _model_smooth_seconds(params, "LongSmoothSeconds", LONG_SMOOTH_SECONDS) long_delay = CP.longitudinalActuatorDelay + long_smooth_seconds prev_action = log.ModelDataV2.Action() DH = DesireHelper() starpilot_toggles = get_starpilot_toggles(sm) while True: # Keep receiving frames until we are at least 1 frame ahead of previous extra frame while meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000: buf_main = vipc_client_main.recv() meta_main = FrameMeta(vipc_client_main) if buf_main is None: break if buf_main is None: cloudlog.debug("vipc_client_main no frame") continue if use_extra_client: # Keep receiving extra frames until frame id matches main camera while True: buf_extra = vipc_client_extra.recv() meta_extra = FrameMeta(vipc_client_extra) if buf_extra is None or meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000: break if buf_extra is None: cloudlog.debug("vipc_client_extra no frame") continue if abs(meta_main.timestamp_sof - meta_extra.timestamp_sof) > 10000000: cloudlog.error(f"frames out of sync! main: {meta_main.frame_id} ({meta_main.timestamp_sof / 1e9:.5f}),\ extra: {meta_extra.frame_id} ({meta_extra.timestamp_sof / 1e9:.5f})") else: # Use single camera buf_extra = buf_main meta_extra = meta_main sm.update(0) long_smooth_seconds = _model_smooth_seconds(params, "LongSmoothSeconds", LONG_SMOOTH_SECONDS) long_delay = CP.longitudinalActuatorDelay + long_smooth_seconds desire = DH.desire is_rhd = sm["driverMonitoringState"].isRHD frame_id = sm["roadCameraState"].frameId v_ego = max(sm["carState"].vEgo, 0.) lat_smooth_default = CP.lateralSmoothSeconds if (CP.brand == "rivian" or CP.lateralSmoothSeconds > 0.0) else LAT_SMOOTH_SECONDS lat_smooth_maximum = _model_smooth_seconds(params, "LatSmoothSeconds", lat_smooth_default) lat_smooth_seconds = get_car_lateral_smooth_seconds(CP.brand, v_ego, lat_smooth_maximum) lat_delay = sm["liveDelay"].lateralDelay + lat_smooth_seconds lateral_control_params = np.array([v_ego, lat_delay], dtype=np.float32) if sm.frame % 60 == 0: camera_offset.set_target(params.get_float("CameraOffset", return_default=True)) if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']: device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32) dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))] model_transform_main = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics if main_wide_camera else dc.fcam.intrinsics, False).astype(np.float32) model_transform_extra = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics, True).astype(np.float32) camera_height = sm["liveCalibration"].height[0] if sm["liveCalibration"].height else DEFAULT_CAMERA_HEIGHT model_transform_main, model_transform_extra = camera_offset.update( model_transform_main, model_transform_extra, str(sm["deviceState"].deviceType), str(sm["roadCameraState"].sensor), camera_height, main_wide_camera, ) live_calib_seen = True traffic_convention = np.zeros(2) traffic_convention[int(is_rhd)] = 1 vec_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32) if desire >= 0 and desire < ModelConstants.DESIRE_LEN: vec_desire[desire] = 1 # tracked dropped frames vipc_dropped_frames = max(0, meta_main.frame_id - last_vipc_frame_id - 1) frames_dropped = frame_dropped_filter.update(min(vipc_dropped_frames, 10)) if run_count < 10: # let frame drops warm up frame_dropped_filter.x = 0. frames_dropped = 0. run_count = run_count + 1 if model_lab_active and run_count % ModelConstants.MODEL_FREQ == 0 and not usbgpu_present(): model_lab_active = False model_lab_longitudinal = None if small_model is None: raise RuntimeError("Model Laboratory has no built-in fallback model") model = small_model external_gpu_active = False model_lab_error = "Chestnut disconnected; using the built-in model" params.put_bool("UsbGpuPresent", False) params.put_bool("UsbGpuActive", False) params.put("ModelVersion", model.policy_generation) params.put("DrivingModelVersion", model.policy_generation) _set_model_lab_runtime( params, requested=model_lab_requested, active=False, config=model_lab_config, error=model_lab_error, ) if chestnut_state is not None: chestnut_state.big = False cloudlog.error(f"Model Laboratory stopped: {model_lab_error}") frame_drop_ratio = frames_dropped / (1 + frames_dropped) dropped_frame = vipc_dropped_frames > 0 if dropped_frame and (model.can_prepare_only or (model_lab_longitudinal is not None and model_lab_longitudinal.can_prepare_only)): cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames") frame_delay = DT_MDL # Average time elapsed since the current frame finished exposing. action_delay = DT_MDL / 2 # Target the midpoint between current output and the next model step. lat_action_t = lat_delay + frame_delay + action_delay long_action_t = long_delay + frame_delay + action_delay lateral_model_output = None longitudinal_model_output = None mt1 = time.perf_counter() try: send_chestnut = ( chestnut_state is not None and run_count % round(ModelConstants.MODEL_FREQ / SERVICE_LIST["chestnutState"].frequency) == 0 ) if model_lab_longitudinal is not None: lateral_bufs, lateral_transforms, lateral_inputs = _runner_frame_args( model, buf_main, buf_extra, model_transform_main, model_transform_extra, vec_desire, traffic_convention, lat_action_t, long_action_t, prev_action, v_ego, lateral_control_params, ) lateral_model_output = model.run( lateral_bufs, lateral_transforms, lateral_inputs, model.can_prepare_only and dropped_frame, ) longitudinal_bufs, longitudinal_transforms, longitudinal_inputs = _runner_frame_args( model_lab_longitudinal, buf_main, buf_extra, model_transform_main, model_transform_extra, vec_desire, traffic_convention, lat_action_t, long_action_t, prev_action, v_ego, lateral_control_params, ) longitudinal_model_output = model_lab_longitudinal.run( longitudinal_bufs, longitudinal_transforms, longitudinal_inputs, model_lab_longitudinal.can_prepare_only and dropped_frame, chestnut_state.send if send_chestnut else None, ) if ( lateral_model_output is not None and longitudinal_model_output is not None and not _model_outputs_finite(lateral_model_output, longitudinal_model_output) ): raise RuntimeError("Model Laboratory produced non-finite output") model_output = ( compose_model_outputs(lateral_model_output, longitudinal_model_output, longitudinal_model_output) if lateral_model_output is not None and longitudinal_model_output is not None else None ) else: bufs, transforms, inputs = _runner_frame_args( model, buf_main, buf_extra, model_transform_main, model_transform_extra, vec_desire, traffic_convention, lat_action_t, long_action_t, prev_action, v_ego, lateral_control_params, ) model_output = model.run( bufs, transforms, inputs, model.can_prepare_only and dropped_frame, chestnut_state.send if send_chestnut else None, ) lateral_model_output = model_output except Exception: if model_lab_active: cloudlog.exception("Model Laboratory inference failed, falling back to the built-in model") if small_model is None: raise RuntimeError("Model Laboratory has no built-in fallback model") from None model = small_model model_lab_longitudinal = None model_lab_active = False model_lab_error = "Model Laboratory inference failed; using the built-in model" _set_model_lab_runtime( params, requested=model_lab_requested, active=False, config=model_lab_config, error=model_lab_error, ) else: if not external_gpu_active or small_model is None: raise cloudlog.exception("external GPU model failed, falling back to builtin model") model = small_model big_model = None params.put_bool("UsbGpuActive", False) external_gpu_active = False params.put("ModelVersion", model.policy_generation) params.put("DrivingModelVersion", model.policy_generation) params.put_bool("UsbGpuLoading", False) if chestnut_state is not None: chestnut_state.big = False run_count = 0 model_output = None mt2 = time.perf_counter() model_execution_time = mt2 - mt1 if model_lab_active and model_lab_longitudinal is not None: model_lab_timings.append(model_execution_time * 1000) if run_count % (ModelConstants.MODEL_FREQ * 10) == 0: timing_summary = "/".join(( f"p50:{np.percentile(model_lab_timings, 50):.1f}", f"p95:{np.percentile(model_lab_timings, 95):.1f}", f"max:{max(model_lab_timings):.1f}ms", )) cloudlog.warning(f"Model Laboratory timing (two AMD models at 20 Hz): {timing_summary}") model_lab_timings = [] if model_output is not None and vipc_dropped_frames > 0: cloudlog.error(f"suppressing model output after dropping {vipc_dropped_frames} frames") if _should_publish_model_output(model_output, vipc_dropped_frames, external_gpu_active): modelv2_send = messaging.new_message('modelV2') starpilot_modelv2_send = messaging.new_message('starpilotModelV2') drivingdata_send = messaging.new_message('drivingModelData') posenet_send = messaging.new_message('cameraOdometry') if model_lab_active and longitudinal_model_output is not None: lateral_action = get_action_from_model( lateral_model_output, prev_action, lat_action_t, long_action_t, v_ego, model.mlsim, model.is_v9, model.is_v14, model.is_v15, starpilot_toggles, lat_smooth_seconds, long_smooth_seconds, is_v16=model.is_v16, ) longitudinal_action = get_action_from_model( longitudinal_model_output, prev_action, lat_action_t, long_action_t, v_ego, model_lab_longitudinal.mlsim, model_lab_longitudinal.is_v9, model_lab_longitudinal.is_v14, model_lab_longitudinal.is_v15, starpilot_toggles, lat_smooth_seconds, long_smooth_seconds, is_v16=model_lab_longitudinal.is_v16, ) action = log.ModelDataV2.Action(**hybrid_action_values(lateral_action, longitudinal_action)) else: action = get_action_from_model( model_output, prev_action, lat_action_t, long_action_t, v_ego, model.mlsim, model.is_v9, model.is_v14, model.is_v15, starpilot_toggles, lat_smooth_seconds, long_smooth_seconds, is_v16=model.is_v16, ) prev_action = action fill_model_msg(drivingdata_send, modelv2_send, model_output, action, publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id, frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen) desire_state = modelv2_send.modelV2.meta.desireState l_lane_change_prob = desire_state[log.Desire.laneChangeLeft] r_lane_change_prob = desire_state[log.Desire.laneChangeRight] lane_change_prob = l_lane_change_prob + r_lane_change_prob DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, sm['starpilotPlan'], starpilot_toggles, sm['carControl'].enabled) modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction starpilot_modelv2_send.starpilotModelV2.turnDirection = DH.turn_direction drivingdata_send.drivingModelData.meta.laneChangeState = DH.lane_change_state drivingdata_send.drivingModelData.meta.laneChangeDirection = DH.lane_change_direction fill_pose_msg(posenet_send, model_output, meta_main.frame_id, vipc_dropped_frames, meta_main.timestamp_eof, live_calib_seen) pm.send('modelV2', modelv2_send) pm.send('starpilotModelV2', starpilot_modelv2_send) pm.send('drivingModelData', drivingdata_send) pm.send('cameraOdometry', posenet_send) last_vipc_frame_id = meta_main.frame_id # Update planner-driven parameters if sm.updated['starpilotPlan']: starpilot_toggles = get_starpilot_toggles(sm) if __name__ == "__main__": try: import argparse parser = argparse.ArgumentParser() parser.add_argument('--demo', action='store_true', help='A boolean for demo mode.') args = parser.parse_args() main(demo=args.demo) except KeyboardInterrupt: cloudlog.warning(f"child {PROCESS_NAME} got SIGINT") except Exception: sentry.capture_exception() raise