Very agressive performance boost for rreplay on device

Corrected the indentation within the loop for numpy_inputs assignment in `modeld.py`. This ensures proper execution of the loop and prevents potential runtime issues caused by misaligned code blocks.

Refactor TINYGRAD usage logic and simplify checks.

Consolidated the TINYGRAD flag with TICI conditions to reduce redundancy. Adjusted tensor initialization flow to handle different device setups more cleanly. This simplifies the code and improves maintainability.

Enable tinygrad integration via environment variable

Added support for using tinygrad on non-TICI devices by introducing the `USE_TINYGRAD` environment variable. Conditional logic was updated to accommodate this change, ensuring compatibility with both tinygrad and ONNX runtimes. This allows more flexibility in choosing the computation framework.

pyproject.toml

@@ -42,8 +42,7 @@ dependencies = [
 
   # modeld
   "onnx >= 1.14.0",
-  "onnxruntime >=1.16.3; platform_system == 'Linux' and platform_machine == 'aarch64'",
-  "onnxruntime-gpu >=1.16.3; platform_system == 'Linux' and platform_machine == 'x86_64'",
+  "onnxruntime >=1.16.3",
 
   # logging
   "pyzmq",

release/release_files.py

@@ -91,7 +91,7 @@ whitelist = [
   "tools/joystick/",
   "tools/longitudinal_maneuvers/",
 
-  "tinygrad_repo/openpilot/compile2.py",
+  "tinygrad_repo/examples/openpilot/compile3.py",
   "tinygrad_repo/extra/onnx.py",
   "tinygrad_repo/extra/onnx_ops.py",
   "tinygrad_repo/extra/thneed.py",

selfdrive/modeld/SConscript

@@ -13,20 +13,6 @@ common_src = [
   "transforms/transform.cc",
 ]
 
-thneed_src_common = [
-  "thneed/thneed_common.cc",
-  "thneed/serialize.cc",
-]
-
-thneed_src_qcom = thneed_src_common + ["thneed/thneed_qcom2.cc"]
-thneed_src_pc = thneed_src_common + ["thneed/thneed_pc.cc"]
-thneed_src = thneed_src_qcom if arch == "larch64" else thneed_src_pc
-
-# SNPE except on Mac and ARM Linux
-snpe_lib = []
-if arch != "Darwin" and arch != "aarch64":
-  common_src += ['runners/snpemodel.cc']
-  snpe_lib += ['SNPE']
 
 # OpenCL is a framework on Mac
 if arch == "Darwin":
@@ -45,34 +31,24 @@ snpe_rpath_pc = f"{Dir('#').abspath}/third_party/snpe/x86_64-linux-clang"
 snpe_rpath = lenvCython['RPATH'] + [snpe_rpath_qcom if arch == "larch64" else snpe_rpath_pc]
 
 cython_libs = envCython["LIBS"] + libs
-snpemodel_lib = lenv.Library('snpemodel', ['runners/snpemodel.cc'])
 commonmodel_lib = lenv.Library('commonmodel', common_src)
-
-lenvCython.Program('runners/runmodel_pyx.so', 'runners/runmodel_pyx.pyx', LIBS=cython_libs, FRAMEWORKS=frameworks)
-lenvCython.Program('runners/snpemodel_pyx.so', 'runners/snpemodel_pyx.pyx', LIBS=[snpemodel_lib, snpe_lib, *cython_libs], FRAMEWORKS=frameworks, RPATH=snpe_rpath)
 lenvCython.Program('models/commonmodel_pyx.so', 'models/commonmodel_pyx.pyx', LIBS=[commonmodel_lib, *cython_libs], FRAMEWORKS=frameworks)
-
-tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "/**", recursive=True, root_dir=env.Dir("#").abspath)]
+tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "/**", recursive=True, root_dir=env.Dir("#").abspath) if 'pycache' not in x]
 
 # Get model metadata
 fn = File("models/supercombo").abspath
 cmd = f'python3 {Dir("#selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
 lenv.Command(fn + "_metadata.pkl", [fn + ".onnx"] + tinygrad_files, cmd)
 
-# Build thneed model
-if arch == "larch64" or GetOption('pc_thneed'):
-  tinygrad_opts = []
-  if not GetOption('pc_thneed'):
-    # use FLOAT16 on device for speed + don't cache the CL kernels for space
-    tinygrad_opts += ["FLOAT16=1", "PYOPENCL_NO_CACHE=1"]
-  cmd = f"cd {Dir('#').abspath}/tinygrad_repo && " + ' '.join(tinygrad_opts) + f" python3 openpilot/compile2.py {fn}.onnx {fn}.thneed"
+# Compile tinygrad model
+pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
+if arch == 'larch64':
+  device_string = 'QCOM=1'
+else:
+  device_string = 'CLANG=1 IMAGE=0'
 
-  lenv.Command(fn + ".thneed", [fn + ".onnx"] + tinygrad_files, cmd)
+for model_name in ['supercombo', 'dmonitoring_model']:
+  fn = File(f"models/{model_name}").abspath
+  cmd = f'{pythonpath_string} {device_string} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {fn}_tinygrad.pkl'
+  lenv.Command(fn + "_tinygrad.pkl", [fn + ".onnx"] + tinygrad_files, cmd)
 
-  fn_dm = File("models/dmonitoring_model").abspath
-  cmd = f"cd {Dir('#').abspath}/tinygrad_repo && " + ' '.join(tinygrad_opts) + f" python3 openpilot/compile2.py {fn_dm}.onnx {fn_dm}.thneed"
-  lenv.Command(fn_dm + ".thneed", [fn_dm + ".onnx"] + tinygrad_files, cmd)
-
-  thneed_lib = env.SharedLibrary('thneed', thneed_src, LIBS=[gpucommon, common, 'OpenCL', 'dl'])
-  thneedmodel_lib = env.Library('thneedmodel', ['runners/thneedmodel.cc'])
-  lenvCython.Program('runners/thneedmodel_pyx.so', 'runners/thneedmodel_pyx.pyx', LIBS=envCython["LIBS"]+[thneedmodel_lib, thneed_lib, gpucommon, common, 'dl', 'OpenCL'])

selfdrive/modeld/dmonitoringmodeld

@@ -1,10 +1,4 @@
 #!/usr/bin/env bash
 
 DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null && pwd)"
-cd "$DIR/../../"
-
-if [ -f "$DIR/libthneed.so" ]; then
-  export LD_PRELOAD="$DIR/libthneed.so"
-fi
-
 exec "$DIR/dmonitoringmodeld.py" "$@"

selfdrive/modeld/dmonitoringmodeld.py

@@ -1,8 +1,17 @@
 #!/usr/bin/env python3
 import os
+from openpilot.system.hardware import TICI
+if TICI:
+  from tinygrad.tensor import Tensor
+  from tinygrad.dtype import dtypes
+  from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
+  os.environ['QCOM'] = '1'
+else:
+  from openpilot.selfdrive.modeld.runners.ort_helpers import make_onnx_cpu_runner
 import gc
 import math
 import time
+import pickle
 import ctypes
 import numpy as np
 from pathlib import Path
@@ -13,21 +22,20 @@ from cereal.messaging import PubMaster, SubMaster
 from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.realtime import set_realtime_priority
-from openpilot.common.transformations.model import dmonitoringmodel_intrinsics
+from openpilot.common.transformations.model import dmonitoringmodel_intrinsics, DM_INPUT_SIZE
 from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
 from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext, MonitoringModelFrame
-from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
 from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid
 
+MODEL_WIDTH, MODEL_HEIGHT = DM_INPUT_SIZE
 CALIB_LEN = 3
 FEATURE_LEN = 512
 OUTPUT_SIZE = 84 + FEATURE_LEN
 
 PROCESS_NAME = "selfdrive.modeld.dmonitoringmodeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
-MODEL_PATHS = {
-  ModelRunner.THNEED: Path(__file__).parent / 'models/dmonitoring_model.thneed',
-  ModelRunner.ONNX: Path(__file__).parent / 'models/dmonitoring_model.onnx'}
+MODEL_PATH = Path(__file__).parent / 'models/dmonitoring_model.onnx'
+MODEL_PKL_PATH = Path(__file__).parent / 'models/dmonitoring_model_tinygrad.pkl'
 
 class DriverStateResult(ctypes.Structure):
   _fields_ = [
@@ -58,29 +66,42 @@ class DMonitoringModelResult(ctypes.Structure):
 class ModelState:
   inputs: dict[str, np.ndarray]
   output: np.ndarray
-  model: ModelRunner
 
   def __init__(self, cl_ctx):
     assert ctypes.sizeof(DMonitoringModelResult) == OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
 
     self.frame = MonitoringModelFrame(cl_ctx)
-    self.output = np.zeros(OUTPUT_SIZE, dtype=np.float32)
-    self.inputs = {
-      'calib': np.zeros(CALIB_LEN, dtype=np.float32)}
+    self.numpy_inputs = {
+      'calib': np.zeros((1, CALIB_LEN), dtype=np.float32),
+    }
 
-    self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.GPU, False, cl_ctx)
-    self.model.addInput("input_img", None)
-    self.model.addInput("calib", self.inputs['calib'])
+    if TICI:
+      self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
+      with open(MODEL_PKL_PATH, "rb") as f:
+        self.model_run = pickle.load(f)
+    else:
+      self.onnx_cpu_runner = make_onnx_cpu_runner(MODEL_PATH)
 
   def run(self, buf:VisionBuf, calib:np.ndarray, transform:np.ndarray) -> tuple[np.ndarray, float]:
-    self.inputs['calib'][:] = calib
-
-    self.model.setInputBuffer("input_img", self.frame.prepare(buf, transform.flatten(), None).view(np.float32))
+    self.numpy_inputs['calib'][0,:] = calib
 
     t1 = time.perf_counter()
-    self.model.execute()
+
+    input_img_cl = self.frame.prepare(buf, transform.flatten())
+    if TICI:
+      # The imgs tensors are backed by opencl memory, only need init once
+      if 'input_img' not in self.tensor_inputs:
+        self.tensor_inputs['input_img'] = qcom_tensor_from_opencl_address(input_img_cl.mem_address, (1, MODEL_WIDTH*MODEL_HEIGHT), dtype=dtypes.uint8)
+    else:
+      self.numpy_inputs['input_img'] = self.frame.buffer_from_cl(input_img_cl).reshape((1, MODEL_WIDTH*MODEL_HEIGHT))
+
+    if TICI:
+      output = self.model_run(**self.tensor_inputs).numpy().flatten()
+    else:
+      output = self.onnx_cpu_runner.run(None, self.numpy_inputs)[0].flatten()
+
     t2 = time.perf_counter()
-    return self.output, t2 - t1
+    return output, t2 - t1
 
 
 def fill_driver_state(msg, ds_result: DriverStateResult):

selfdrive/modeld/modeld

@@ -1,10 +1,4 @@
 #!/usr/bin/env bash
 
 DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null && pwd)"
-cd "$DIR/../../"
-
-if [ -f "$DIR/libthneed.so" ]; then
-  export LD_PRELOAD="$DIR/libthneed.so"
-fi
-
 exec "$DIR/modeld.py" "$@"

selfdrive/modeld/modeld.py

@@ -1,5 +1,16 @@
 #!/usr/bin/env python3
 import os
+from openpilot.system.hardware import TICI
+
+#
+USE_TINYGRAD = os.getenv('USE_TINYGRAD', True) or TICI
+if USE_TINYGRAD:
+  from tinygrad.tensor import Tensor
+  from tinygrad.dtype import dtypes
+  from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
+  os.environ['QCOM'] = '1'
+else:
+  from openpilot.selfdrive.modeld.runners.ort_helpers import make_onnx_cpu_runner, ORT_TYPES_TO_NP_TYPES
 import time
 import pickle
 import numpy as np
@@ -12,30 +23,25 @@ from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from opendbc.car.car_helpers import get_demo_car_params
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
-from openpilot.common.realtime import DT_MDL
-from openpilot.common.numpy_fast import interp
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.common.realtime import config_realtime_process
 from openpilot.common.transformations.camera import DEVICE_CAMERAS
 from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.system import sentry
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
-from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
 from openpilot.selfdrive.modeld.parse_model_outputs import Parser
 from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
 from openpilot.selfdrive.modeld.constants import ModelConstants
 from openpilot.selfdrive.modeld.models.commonmodel_pyx import DrivingModelFrame, CLContext
 
+
 PROCESS_NAME = "selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 
-MODEL_PATHS = {
-  ModelRunner.THNEED: Path(__file__).parent / 'models/supercombo.thneed',
-  ModelRunner.ONNX: Path(__file__).parent / 'models/supercombo.onnx'}
-
+MODEL_PATH = Path(__file__).parent / 'models/supercombo.onnx'
+MODEL_PKL_PATH = Path(__file__).parent / 'models/supercombo_tinygrad.pkl'
 METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'
 
-
 class FrameMeta:
   frame_id: int = 0
   timestamp_sof: int = 0
@@ -46,48 +52,45 @@ class FrameMeta:
       self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof
 
 class ModelState:
-  frame: DrivingModelFrame
-  wide_frame: DrivingModelFrame
+  frames: dict[str, DrivingModelFrame]
   inputs: dict[str, np.ndarray]
   output: np.ndarray
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
-  model: ModelRunner
 
   def __init__(self, context: CLContext):
-    self.frame = DrivingModelFrame(context)
-    self.wide_frame = DrivingModelFrame(context)
+    self.frames = {'input_imgs': DrivingModelFrame(context), 'big_input_imgs': DrivingModelFrame(context)}
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
     self.full_features_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
-    self.desire_20Hz =  np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
+    self.desire_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
 
     # img buffers are managed in openCL transform code
-    self.inputs = {}
+    self.numpy_inputs = {}
 
     with open(METADATA_PATH, 'rb') as f:
       model_metadata = pickle.load(f)
+    self.input_shapes =  model_metadata['input_shapes']
 
-    for key, shape in model_metadata['input_shapes'].items():
-      if key not in ["input_imgs", "big_input_imgs"]:
-        self.inputs[key] = np.zeros(shape, dtype=np.float32).flatten()
+    for key, shape in self.input_shapes.items():
+      if key not in self.frames: # Managed by opencl
+        self.numpy_inputs[key] = np.zeros(shape, dtype=np.float32)
 
     self.output_slices = model_metadata['output_slices']
     net_output_size = model_metadata['output_shapes']['outputs'][1]
     self.output = np.zeros(net_output_size, dtype=np.float32)
     self.parser = Parser()
 
-    self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.GPU, False, context)
-    self.model.addInput("input_imgs", None)
-    self.model.addInput("big_input_imgs", None)
-    for k,v in self.inputs.items():
-      self.model.addInput(k, v)
+    if USE_TINYGRAD:
+      self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
+      with open(MODEL_PKL_PATH, "rb") as f:
+        self.model_run = pickle.load(f)
+    else:
+      self.onnx_cpu_runner = make_onnx_cpu_runner(MODEL_PATH)
+      self.onnx_model_metadata = {input.name: input.type for input in self.onnx_cpu_runner.get_inputs()}
 
-    num_elements = model_metadata['input_shapes']['features_buffer'][1]
+    num_elements = self.numpy_inputs['features_buffer'].shape[1]
     step_size = int(-100 / num_elements)
     self.full_features_20Hz_idxs = np.arange(step_size, step_size * (num_elements + 1), step_size)[::-1]
-
-    desired_shape = int(self.inputs['desire'].shape[0] / self.desire_20Hz.shape[1])
-    middle_dim = int(self.desire_20Hz.shape[0] / desired_shape)
-    self.desire_reshape_dims = (desired_shape, middle_dim, -1)
+    self.desire_reshape_dims = (self.numpy_inputs['desire'].shape[0], self.numpy_inputs['desire'].shape[1], -1, self.numpy_inputs['desire'].shape[2])
 
   def slice_outputs(self, model_outputs: np.ndarray) -> dict[str, np.ndarray]:
     parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in self.output_slices.items()}
@@ -104,41 +107,50 @@ class ModelState:
 
     self.desire_20Hz[:-1] = self.desire_20Hz[1:]
     self.desire_20Hz[-1] = new_desire
-    self.inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=1).flatten()
+    self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=2)
 
-    self.inputs['traffic_convention'][:] = inputs['traffic_convention']
+    self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
+    imgs_cl = {'input_imgs': self.frames['input_imgs'].prepare(buf, transform.flatten()),
+               'big_input_imgs': self.frames['big_input_imgs'].prepare(wbuf, transform_wide.flatten())}
 
-    self.model.setInputBuffer("input_imgs", self.frame.prepare(buf, transform.flatten(), self.model.getCLBuffer("input_imgs")))
-    self.model.setInputBuffer("big_input_imgs", self.wide_frame.prepare(wbuf, transform_wide.flatten(), self.model.getCLBuffer("big_input_imgs")))
+    if USE_TINYGRAD:
+      # The imgs tensors are backed by opencl memory, only need init once
+      for key in imgs_cl:
+        if not TICI or key not in self.tensor_inputs:
+          index = self.model_run.captured.expected_names.index(key)
+          _, _, dtype, device = self.model_run.captured.expected_st_vars_dtype_device[index]
+          if TICI:
+            self.tensor_inputs[key] = qcom_tensor_from_opencl_address(imgs_cl[key].mem_address, self.input_shapes[key], dtype=dtype)
+          else:
+            shape = self.frames[key].buffer_from_cl(imgs_cl[key]).reshape(self.input_shapes[key])
+            self.tensor_inputs[key] = Tensor(shape, device=device, dtype=dtype).realize()
+    else:
+      for key in imgs_cl:
+        dtype = self.onnx_model_metadata[key]
+        self.numpy_inputs[key] = self.frames[key].buffer_from_cl(imgs_cl[key]).astype(ORT_TYPES_TO_NP_TYPES[dtype]).reshape(self.input_shapes[key])
 
     if prepare_only:
       return None
 
-    self.model.execute()
+    if USE_TINYGRAD:
+      self.output = self.model_run(**self.tensor_inputs).numpy().flatten()
+    else:
+      self.output = self.onnx_cpu_runner.run(None, self.numpy_inputs)[0].flatten()
+
     outputs = self.parser.parse_outputs(self.slice_outputs(self.output))
 
     self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
     self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
 
-    # idxs = np.arange(-4,-100,-4)[::-1]
-    self.inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs].flatten()
-
-    if "lat_planner_solution" in outputs:
-      if "lat_planner_state" in self.inputs.keys():
-        self.inputs['lat_planner_state'][2] = interp(DT_MDL, ModelConstants.T_IDXS, outputs['lat_planner_solution'][0, :, 2])
-        self.inputs['lat_planner_state'][3] = interp(DT_MDL, ModelConstants.T_IDXS, outputs['lat_planner_solution'][0, :, 3])
-
+    self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs]
     if "desired_curvature" in outputs:
-      input_name_prev = None
-      if "prev_desired_curvs" in self.inputs.keys():
-        input_name_prev = 'prev_desired_curvs'
-      elif "prev_desired_curv" in self.inputs.keys():
-        input_name_prev = 'prev_desired_curv'
-
-      if input_name_prev is not None:
-        len = outputs['desired_curvature'][0].size
-        self.inputs[input_name_prev][:-len] = self.inputs[input_name_prev][len:]
-        self.inputs[input_name_prev][-len:] = outputs['desired_curvature'][0, :len]
+      if "prev_desired_curvs" in self.numpy_inputs.keys():
+        self.numpy_inputs['prev_desired_curvs'][:-1] = self.numpy_inputs['prev_desired_curvs'][1:]
+        self.numpy_inputs['prev_desired_curvs'][-1] = outputs['desired_curvature'][:, 0:1, None]  # Reshape to (1,1,1)
+      if "prev_desired_curv" in self.numpy_inputs.keys():
+        # First shift everything
+        self.numpy_inputs['prev_desired_curv'][:-ModelConstants.PREV_DESIRED_CURV_LEN] = self.numpy_inputs['prev_desired_curv'][ModelConstants.PREV_DESIRED_CURV_LEN:]
+        self.numpy_inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = outputs['desired_curvature'][:, :1].reshape(1, -1, 1)
     return outputs
 
 
@@ -249,6 +261,10 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["roadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
+    lateral_control_params = None #TODO-SP: hardcoded ,this shouldnt' be here this way. We should do it more dynamically
+    if "lateral_control_params" in model.numpy_inputs.keys(): #TODO-SP: hardcoded ,this shouldnt' be here this way. We should do it more dynamically
+      lateral_control_params = np.array([sm["carState"].vEgo, steer_delay], dtype=np.float32)
+
     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))]
@@ -280,18 +296,8 @@ def main(demo=False):
       'desire': vec_desire,
       'traffic_convention': traffic_convention,
       }
-
-    if "lateral_control_params" in model.inputs.keys():
-      inputs['lateral_control_params'] = np.array([v_ego, steer_delay], dtype=np.float32)
-
-    if "driving_style" in model.inputs.keys():
-      inputs['driving_style'] = np.array([1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], dtype=np.float32)
-
-    if "nav_features" in model.inputs.keys():
-      inputs['nav_features'] = np.zeros(ModelConstants.NAV_FEATURE_LEN, dtype=np.float32)  # Get size from shape
-
-    if "nav_instructions" in model.inputs.keys():
-      inputs['nav_instructions'] = np.zeros(ModelConstants.NAV_INSTRUCTION_LEN, dtype=np.float32)  # Get size from shape
+    if "lateral_control_params" in model.numpy_inputs.keys():
+      inputs['lateral_control_params'] = lateral_control_params
 
     mt1 = time.perf_counter()
     model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
@@ -320,7 +326,6 @@ def main(demo=False):
       pm.send('modelV2', modelv2_send)
       pm.send('drivingModelData', drivingdata_send)
       pm.send('cameraOdometry', posenet_send)
-
     last_vipc_frame_id = meta_main.frame_id
 
 

selfdrive/modeld/models/commonmodel.cc

@@ -6,8 +6,8 @@
 #include "common/clutil.h"
 
 DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context) : ModelFrame(device_id, context) {
-  input_frames = std::make_unique<float[]>(buf_size);
-  //input_frames_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
+  input_frames = std::make_unique<uint8_t[]>(buf_size);
+  input_frames_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
   img_buffer_20hz_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, 5*frame_size_bytes, NULL, &err));
   region.origin = 4 * frame_size_bytes;
   region.size = frame_size_bytes;
@@ -17,7 +17,7 @@ DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context)
   init_transform(device_id, context, MODEL_WIDTH, MODEL_HEIGHT);
 }
 
-float* DrivingModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection, cl_mem* output) {
+cl_mem* DrivingModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) {
   run_transform(yuv_cl, MODEL_WIDTH, MODEL_HEIGHT, frame_width, frame_height, frame_stride, frame_uv_offset, projection);
 
   for (int i = 0; i < 4; i++) {
@@ -25,19 +25,12 @@ float* DrivingModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_heig
   }
   loadyuv_queue(&loadyuv, q, y_cl, u_cl, v_cl, last_img_cl);
 
-  if (output == NULL) {
-    CL_CHECK(clEnqueueReadBuffer(q, img_buffer_20hz_cl, CL_TRUE, 0, frame_size_bytes, &input_frames[0], 0, nullptr, nullptr));
-    CL_CHECK(clEnqueueReadBuffer(q, last_img_cl, CL_TRUE, 0, frame_size_bytes, &input_frames[MODEL_FRAME_SIZE], 0, nullptr, nullptr));
-    clFinish(q);
-    return &input_frames[0];
-  } else {
-    copy_queue(&loadyuv, q, img_buffer_20hz_cl, *output, 0, 0, frame_size_bytes);
-    copy_queue(&loadyuv, q, last_img_cl, *output, 0, frame_size_bytes, frame_size_bytes);
+  copy_queue(&loadyuv, q, img_buffer_20hz_cl, input_frames_cl, 0, 0, frame_size_bytes);
+  copy_queue(&loadyuv, q, last_img_cl, input_frames_cl, 0, frame_size_bytes, frame_size_bytes);
 
-    // NOTE: Since thneed is using a different command queue, this clFinish is needed to ensure the image is ready.
-    clFinish(q);
-    return NULL;
-  }
+  // NOTE: Since thneed is using a different command queue, this clFinish is needed to ensure the image is ready.
+  clFinish(q);
+  return &input_frames_cl;
 }
 
 DrivingModelFrame::~DrivingModelFrame() {
@@ -50,17 +43,16 @@ DrivingModelFrame::~DrivingModelFrame() {
 
 
 MonitoringModelFrame::MonitoringModelFrame(cl_device_id device_id, cl_context context) : ModelFrame(device_id, context) {
-  input_frames = std::make_unique<float[]>(buf_size);
-  //input_frame_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
+  input_frames = std::make_unique<uint8_t[]>(buf_size);
+  input_frame_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
 
   init_transform(device_id, context, MODEL_WIDTH, MODEL_HEIGHT);
 }
-float* MonitoringModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection, cl_mem* output) {
+
+cl_mem* MonitoringModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) {
   run_transform(yuv_cl, MODEL_WIDTH, MODEL_HEIGHT, frame_width, frame_height, frame_stride, frame_uv_offset, projection);
-  CL_CHECK(clEnqueueReadBuffer(q, y_cl, CL_TRUE, 0, MODEL_FRAME_SIZE * sizeof(float), input_frames.get(), 0, nullptr, nullptr));
   clFinish(q);
-  //return &y_cl;
-  return input_frames.get();
+  return &y_cl;
 }
 
 MonitoringModelFrame::~MonitoringModelFrame() {

selfdrive/modeld/models/commonmodel.h

@@ -23,14 +23,12 @@ public:
     q = CL_CHECK_ERR(clCreateCommandQueue(context, device_id, 0, &err));
   }
   virtual ~ModelFrame() {}
-  virtual float* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection, cl_mem* output) { return NULL; }
-  /*
+  virtual cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) { return NULL; }
   uint8_t* buffer_from_cl(cl_mem *in_frames, int buffer_size) {
     CL_CHECK(clEnqueueReadBuffer(q, *in_frames, CL_TRUE, 0, buffer_size, input_frames.get(), 0, nullptr, nullptr));
     clFinish(q);
     return &input_frames[0];
   }
-  */
 
   int MODEL_WIDTH;
   int MODEL_HEIGHT;
@@ -41,7 +39,7 @@ protected:
   cl_mem y_cl, u_cl, v_cl;
   Transform transform;
   cl_command_queue q;
-  std::unique_ptr<float[]> input_frames;
+  std::unique_ptr<uint8_t[]> input_frames;
 
   void init_transform(cl_device_id device_id, cl_context context, int model_width, int model_height) {
     y_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, model_width * model_height, NULL, &err));
@@ -68,17 +66,17 @@ class DrivingModelFrame : public ModelFrame {
 public:
   DrivingModelFrame(cl_device_id device_id, cl_context context);
   ~DrivingModelFrame();
-  float* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection, cl_mem* output);
+  cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);
 
   const int MODEL_WIDTH = 512;
   const int MODEL_HEIGHT = 256;
   const int MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 / 2;
   const int buf_size = MODEL_FRAME_SIZE * 2;
-  const size_t frame_size_bytes = MODEL_FRAME_SIZE * sizeof(float);
+  const size_t frame_size_bytes = MODEL_FRAME_SIZE * sizeof(uint8_t);
 
 private:
   LoadYUVState loadyuv;
-  cl_mem img_buffer_20hz_cl, last_img_cl;//, input_frames_cl;
+  cl_mem img_buffer_20hz_cl, last_img_cl, input_frames_cl;
   cl_buffer_region region;
 };
 
@@ -86,7 +84,7 @@ class MonitoringModelFrame : public ModelFrame {
 public:
   MonitoringModelFrame(cl_device_id device_id, cl_context context);
   ~MonitoringModelFrame();
-  float* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection, cl_mem* output);
+  cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);
 
   const int MODEL_WIDTH = 1440;
   const int MODEL_HEIGHT = 960;
@@ -94,5 +92,5 @@ public:
   const int buf_size = MODEL_FRAME_SIZE;
 
 private:
-  // cl_mem input_frame_cl;
+  cl_mem input_frame_cl;
 };

selfdrive/modeld/models/commonmodel.pxd

@@ -14,8 +14,8 @@ cdef extern from "common/clutil.h":
 cdef extern from "selfdrive/modeld/models/commonmodel.h":
   cppclass ModelFrame:
     int buf_size
-    # unsigned char * buffer_from_cl(cl_mem*, int);
-    float * prepare(cl_mem, int, int, int, int, mat3, cl_mem*)
+    unsigned char * buffer_from_cl(cl_mem*, int);
+    cl_mem * prepare(cl_mem, int, int, int, int, mat3)
 
   cppclass DrivingModelFrame:
     int buf_size

selfdrive/modeld/models/commonmodel_pyx.pyx

@@ -39,24 +39,17 @@ cdef class ModelFrame:
   def __dealloc__(self):
     del self.frame
 
-  def prepare(self, VisionBuf buf, float[:] projection, CLMem output):
+  def prepare(self, VisionBuf buf, float[:] projection):
     cdef mat3 cprojection
     memcpy(cprojection.v, &projection[0], 9*sizeof(float))
-    cdef float * data
-    if output is None:
-      data = self.frame.prepare(buf.buf.buf_cl, buf.width, buf.height, buf.stride, buf.uv_offset, cprojection, NULL)
-    else:
-      data = self.frame.prepare(buf.buf.buf_cl, buf.width, buf.height, buf.stride, buf.uv_offset, cprojection, output.mem)
-    if not data:
-      return None
+    cdef cl_mem * data
+    data = self.frame.prepare(buf.buf.buf_cl, buf.width, buf.height, buf.stride, buf.uv_offset, cprojection)
+    return CLMem.create(data)
 
-    return np.asarray(<cnp.float32_t[:self.buf_size]> data)
-    # return CLMem.create(data)
-
-  # def buffer_from_cl(self, CLMem in_frames):
-  #   cdef unsigned char * data2
-  #   data2 = self.frame.buffer_from_cl(in_frames.mem, self.buf_size)
-  #   return np.asarray(<cnp.uint8_t[:self.buf_size]> data2)
+  def buffer_from_cl(self, CLMem in_frames):
+    cdef unsigned char * data2
+    data2 = self.frame.buffer_from_cl(in_frames.mem, self.buf_size)
+    return np.asarray(<cnp.uint8_t[:self.buf_size]> data2)
 
 
 cdef class DrivingModelFrame(ModelFrame):
@@ -74,3 +67,4 @@ cdef class MonitoringModelFrame(ModelFrame):
     self._frame = new cppMonitoringModelFrame(context.device_id, context.context)
     self.frame = <cppModelFrame*>(self._frame)
     self.buf_size = self._frame.buf_size
+

selfdrive/modeld/models/supercombo.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b31b504bc0b440d3bc72967507a00eb4f112285626fbfb3135011500325ee6d6
-size 51452435
+oid sha256:72d3d6f8d3c98f5431ec86be77b6350d7d4f43c25075c0106f1d1e7ec7c77668
+size 49096168

selfdrive/modeld/runners/__init__.py

@@ -1,27 +0,0 @@
-import os
-from openpilot.system.hardware import TICI
-from openpilot.selfdrive.modeld.runners.runmodel_pyx import RunModel, Runtime
-assert Runtime
-
-USE_THNEED = int(os.getenv('USE_THNEED', str(int(TICI))))
-USE_SNPE = int(os.getenv('USE_SNPE', str(int(TICI))))
-
-class ModelRunner(RunModel):
-  THNEED = 'THNEED'
-  SNPE = 'SNPE'
-  ONNX = 'ONNX'
-
-  def __new__(cls, paths, *args, **kwargs):
-    if ModelRunner.THNEED in paths and USE_THNEED:
-      from openpilot.selfdrive.modeld.runners.thneedmodel_pyx import ThneedModel as Runner
-      runner_type = ModelRunner.THNEED
-    elif ModelRunner.SNPE in paths and USE_SNPE:
-      from openpilot.selfdrive.modeld.runners.snpemodel_pyx import SNPEModel as Runner
-      runner_type = ModelRunner.SNPE
-    elif ModelRunner.ONNX in paths:
-      from openpilot.selfdrive.modeld.runners.onnxmodel import ONNXModel as Runner
-      runner_type = ModelRunner.ONNX
-    else:
-      raise Exception("Couldn't select a model runner, make sure to pass at least one valid model path")
-
-    return Runner(str(paths[runner_type]), *args, **kwargs)

selfdrive/modeld/runners/onnxmodel.py

@@ -1,71 +0,0 @@
-import os
-import onnx
-import sys
-import numpy as np
-from typing import Any
-
-from openpilot.selfdrive.modeld.runners.runmodel_pyx import RunModel
-from openpilot.selfdrive.modeld.runners.ort_helpers import convert_fp16_to_fp32, ORT_TYPES_TO_NP_TYPES
-
-
-def create_ort_session(path, fp16_to_fp32):
-  os.environ["OMP_NUM_THREADS"] = "4"
-  os.environ["OMP_WAIT_POLICY"] = "PASSIVE"
-
-  import onnxruntime as ort
-  print("Onnx available providers: ", ort.get_available_providers(), file=sys.stderr)
-  options = ort.SessionOptions()
-  options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
-
-  provider: str | tuple[str, dict[Any, Any]]
-  if 'OpenVINOExecutionProvider' in ort.get_available_providers() and 'ONNXCPU' not in os.environ:
-    provider = 'OpenVINOExecutionProvider'
-  elif 'CUDAExecutionProvider' in ort.get_available_providers() and 'ONNXCPU' not in os.environ:
-    options.intra_op_num_threads = 2
-    provider = ('CUDAExecutionProvider', {'cudnn_conv_algo_search': 'EXHAUSTIVE'})
-  else:
-    options.intra_op_num_threads = 2
-    options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
-    options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-    provider = 'CPUExecutionProvider'
-
-  model_data = convert_fp16_to_fp32(onnx.load(path)) if fp16_to_fp32 else path
-  print("Onnx selected provider: ", [provider], file=sys.stderr)
-  ort_session = ort.InferenceSession(model_data, options, providers=[provider])
-  print("Onnx using ", ort_session.get_providers(), file=sys.stderr)
-  return ort_session
-
-
-class ONNXModel(RunModel):
-  def __init__(self, path, output, runtime, use_tf8, cl_context):
-    self.inputs = {}
-    self.output = output
-
-    self.session = create_ort_session(path, fp16_to_fp32=True)
-    self.input_names = [x.name for x in self.session.get_inputs()]
-    self.input_shapes = {x.name: [1, *x.shape[1:]] for x in self.session.get_inputs()}
-    self.input_dtypes = {x.name: ORT_TYPES_TO_NP_TYPES[x.type] for x in self.session.get_inputs()}
-
-    # run once to initialize CUDA provider
-    if "CUDAExecutionProvider" in self.session.get_providers():
-      self.session.run(None, {k: np.zeros(self.input_shapes[k], dtype=self.input_dtypes[k]) for k in self.input_names})
-    print("ready to run onnx model", self.input_shapes, file=sys.stderr)
-
-  def addInput(self, name, buffer):
-    assert name in self.input_names
-    self.inputs[name] = buffer
-
-  def setInputBuffer(self, name, buffer):
-    assert name in self.inputs
-    self.inputs[name] = buffer
-
-  def getCLBuffer(self, name):
-    return None
-
-  def execute(self):
-    inputs = {k: v.view(self.input_dtypes[k]) for k,v in self.inputs.items()}
-    inputs = {k: v.reshape(self.input_shapes[k]).astype(self.input_dtypes[k]) for k,v in inputs.items()}
-    outputs = self.session.run(None, inputs)
-    assert len(outputs) == 1, "Only single model outputs are supported"
-    self.output[:] = outputs[0]
-    return self.output

selfdrive/modeld/runners/run.h

@@ -1,4 +0,0 @@
-#pragma once
-
-#include "selfdrive/modeld/runners/runmodel.h"
-#include "selfdrive/modeld/runners/snpemodel.h"

selfdrive/modeld/runners/runmodel.h

@@ -1,49 +0,0 @@
-#pragma once
-
-#include <string>
-#include <vector>
-#include <memory>
-#include <cassert>
-
-#include "common/clutil.h"
-#include "common/swaglog.h"
-
-#define USE_CPU_RUNTIME 0
-#define USE_GPU_RUNTIME 1
-#define USE_DSP_RUNTIME 2
-
-struct ModelInput {
-  const std::string name;
-  float *buffer;
-  int size;
-
-  ModelInput(const std::string _name, float *_buffer, int _size) : name(_name), buffer(_buffer), size(_size) {}
-  virtual void setBuffer(float *_buffer, int _size) {
-    assert(size == _size || size == 0);
-    buffer = _buffer;
-    size = _size;
-  }
-};
-
-class RunModel {
-public:
-  std::vector<std::unique_ptr<ModelInput>> inputs;
-
-  virtual ~RunModel() {}
-  virtual void execute() {}
-  virtual void* getCLBuffer(const std::string name) { return nullptr; }
-
-  virtual void addInput(const std::string name, float *buffer, int size) {
-    inputs.push_back(std::unique_ptr<ModelInput>(new ModelInput(name, buffer, size)));
-  }
-  virtual void setInputBuffer(const std::string name, float *buffer, int size) {
-    for (auto &input : inputs) {
-      if (name == input->name) {
-        input->setBuffer(buffer, size);
-        return;
-      }
-    }
-    LOGE("Tried to update input `%s` but no input with this name exists", name.c_str());
-    assert(false);
-  }
-};

selfdrive/modeld/runners/runmodel.pxd

@@ -1,14 +0,0 @@
-# distutils: language = c++
-
-from libcpp.string cimport string
-
-cdef extern from "selfdrive/modeld/runners/runmodel.h":
-  cdef int USE_CPU_RUNTIME
-  cdef int USE_GPU_RUNTIME
-  cdef int USE_DSP_RUNTIME
-
-  cdef cppclass RunModel:
-    void addInput(string, float*, int)
-    void setInputBuffer(string, float*, int)
-    void * getCLBuffer(string)
-    void execute()

selfdrive/modeld/runners/runmodel_pyx.pxd

@@ -1,6 +0,0 @@
-# distutils: language = c++
-
-from .runmodel cimport RunModel as cppRunModel
-
-cdef class RunModel:
-  cdef cppRunModel * model

selfdrive/modeld/runners/runmodel_pyx.pyx

@@ -1,37 +0,0 @@
-# distutils: language = c++
-# cython: c_string_encoding=ascii, language_level=3
-
-from libcpp.string cimport string
-
-from .runmodel cimport USE_CPU_RUNTIME, USE_GPU_RUNTIME, USE_DSP_RUNTIME
-from selfdrive.modeld.models.commonmodel_pyx cimport CLMem
-
-class Runtime:
-  CPU = USE_CPU_RUNTIME
-  GPU = USE_GPU_RUNTIME
-  DSP = USE_DSP_RUNTIME
-
-cdef class RunModel:
-  def __dealloc__(self):
-    del self.model
-
-  def addInput(self, string name, float[:] buffer):
-    if buffer is not None:
-      self.model.addInput(name, &buffer[0], len(buffer))
-    else:
-      self.model.addInput(name, NULL, 0)
-
-  def setInputBuffer(self, string name, float[:] buffer):
-    if buffer is not None:
-      self.model.setInputBuffer(name, &buffer[0], len(buffer))
-    else:
-      self.model.setInputBuffer(name, NULL, 0)
-
-  def getCLBuffer(self, string name):
-    cdef void * cl_buf = self.model.getCLBuffer(name)
-    if not cl_buf:
-      return None
-    return CLMem.create(cl_buf)
-
-  def execute(self):
-    self.model.execute()

selfdrive/modeld/runners/snpemodel.cc

@@ -1,116 +0,0 @@
-#pragma clang diagnostic ignored "-Wexceptions"
-
-#include "selfdrive/modeld/runners/snpemodel.h"
-
-#include <cstring>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "common/util.h"
-#include "common/timing.h"
-
-void PrintErrorStringAndExit() {
-  std::cerr << zdl::DlSystem::getLastErrorString() << std::endl;
-  std::exit(EXIT_FAILURE);
-}
-
-SNPEModel::SNPEModel(const std::string path, float *_output, size_t _output_size, int runtime, bool _use_tf8, cl_context context) {
-  output = _output;
-  output_size = _output_size;
-  use_tf8 = _use_tf8;
-
-#ifdef QCOM2
-  if (runtime == USE_GPU_RUNTIME) {
-    snpe_runtime = zdl::DlSystem::Runtime_t::GPU;
-  } else if (runtime == USE_DSP_RUNTIME) {
-    snpe_runtime = zdl::DlSystem::Runtime_t::DSP;
-  } else {
-    snpe_runtime = zdl::DlSystem::Runtime_t::CPU;
-  }
-  assert(zdl::SNPE::SNPEFactory::isRuntimeAvailable(snpe_runtime));
-#endif
-  model_data = util::read_file(path);
-  assert(model_data.size() > 0);
-
-  // load model
-  std::unique_ptr<zdl::DlContainer::IDlContainer> container = zdl::DlContainer::IDlContainer::open((uint8_t*)model_data.data(), model_data.size());
-  if (!container) { PrintErrorStringAndExit(); }
-  LOGW("loaded model with size: %lu", model_data.size());
-
-  // create model runner
-  zdl::SNPE::SNPEBuilder snpe_builder(container.get());
-  while (!snpe) {
-#ifdef QCOM2
-    snpe = snpe_builder.setOutputLayers({})
-                       .setRuntimeProcessor(snpe_runtime)
-                       .setUseUserSuppliedBuffers(true)
-                       .setPerformanceProfile(zdl::DlSystem::PerformanceProfile_t::HIGH_PERFORMANCE)
-                       .build();
-#else
-    snpe = snpe_builder.setOutputLayers({})
-                       .setUseUserSuppliedBuffers(true)
-                       .setPerformanceProfile(zdl::DlSystem::PerformanceProfile_t::HIGH_PERFORMANCE)
-                       .build();
-#endif
-    if (!snpe) std::cerr << zdl::DlSystem::getLastErrorString() << std::endl;
-  }
-
-  // create output buffer
-  zdl::DlSystem::UserBufferEncodingFloat ub_encoding_float;
-  zdl::DlSystem::IUserBufferFactory &ub_factory = zdl::SNPE::SNPEFactory::getUserBufferFactory();
-
-  const auto &output_tensor_names_opt = snpe->getOutputTensorNames();
-  if (!output_tensor_names_opt) throw std::runtime_error("Error obtaining output tensor names");
-  const auto &output_tensor_names = *output_tensor_names_opt;
-  assert(output_tensor_names.size() == 1);
-  const char *output_tensor_name = output_tensor_names.at(0);
-  const zdl::DlSystem::TensorShape &buffer_shape = snpe->getInputOutputBufferAttributes(output_tensor_name)->getDims();
-  if (output_size != 0) {
-    assert(output_size == buffer_shape[1]);
-  } else {
-    output_size = buffer_shape[1];
-  }
-  std::vector<size_t> output_strides = {output_size * sizeof(float), sizeof(float)};
-  output_buffer = ub_factory.createUserBuffer(output, output_size * sizeof(float), output_strides, &ub_encoding_float);
-  output_map.add(output_tensor_name, output_buffer.get());
-}
-
-void SNPEModel::addInput(const std::string name, float *buffer, int size) {
-  const int idx = inputs.size();
-  const auto &input_tensor_names_opt = snpe->getInputTensorNames();
-  if (!input_tensor_names_opt) throw std::runtime_error("Error obtaining input tensor names");
-  const auto &input_tensor_names = *input_tensor_names_opt;
-  const char *input_tensor_name = input_tensor_names.at(idx);
-  const bool input_tf8 = use_tf8 && strcmp(input_tensor_name, "input_img") == 0;  // TODO: This is a terrible hack, get rid of this name check both here and in onnx_runner.py
-  LOGW("adding index %d: %s", idx, input_tensor_name);
-
-  zdl::DlSystem::UserBufferEncodingFloat ub_encoding_float;
-  zdl::DlSystem::UserBufferEncodingTf8 ub_encoding_tf8(0, 1./255); // network takes 0-1
-  zdl::DlSystem::IUserBufferFactory &ub_factory = zdl::SNPE::SNPEFactory::getUserBufferFactory();
-  zdl::DlSystem::UserBufferEncoding *input_encoding = input_tf8 ? (zdl::DlSystem::UserBufferEncoding*)&ub_encoding_tf8 : (zdl::DlSystem::UserBufferEncoding*)&ub_encoding_float;
-
-  const auto &buffer_shape_opt = snpe->getInputDimensions(input_tensor_name);
-  const zdl::DlSystem::TensorShape &buffer_shape = *buffer_shape_opt;
-  size_t size_of_input = input_tf8 ? sizeof(uint8_t) : sizeof(float);
-  std::vector<size_t> strides(buffer_shape.rank());
-  strides[strides.size() - 1] = size_of_input;
-  size_t product = 1;
-  for (size_t i = 0; i < buffer_shape.rank(); i++) product *= buffer_shape[i];
-  size_t stride = strides[strides.size() - 1];
-  for (size_t i = buffer_shape.rank() - 1; i > 0; i--) {
-    stride *= buffer_shape[i];
-    strides[i-1] = stride;
-  }
-
-  auto input_buffer = ub_factory.createUserBuffer(buffer, product*size_of_input, strides, input_encoding);
-  input_map.add(input_tensor_name, input_buffer.get());
-  inputs.push_back(std::unique_ptr<SNPEModelInput>(new SNPEModelInput(name, buffer, size, std::move(input_buffer))));
-}
-
-void SNPEModel::execute() {
-  if (!snpe->execute(input_map, output_map)) {
-    PrintErrorStringAndExit();
-  }
-}

selfdrive/modeld/runners/snpemodel.h

@@ -1,52 +0,0 @@
-#pragma once
-#pragma clang diagnostic ignored "-Wdeprecated-declarations"
-
-#include <memory>
-#include <string>
-#include <utility>
-
-#include <DlContainer/IDlContainer.hpp>
-#include <DlSystem/DlError.hpp>
-#include <DlSystem/ITensor.hpp>
-#include <DlSystem/ITensorFactory.hpp>
-#include <DlSystem/IUserBuffer.hpp>
-#include <DlSystem/IUserBufferFactory.hpp>
-#include <SNPE/SNPE.hpp>
-#include <SNPE/SNPEBuilder.hpp>
-#include <SNPE/SNPEFactory.hpp>
-
-#include "selfdrive/modeld/runners/runmodel.h"
-
-struct SNPEModelInput : public ModelInput {
-  std::unique_ptr<zdl::DlSystem::IUserBuffer> snpe_buffer;
-
-  SNPEModelInput(const std::string _name, float *_buffer, int _size, std::unique_ptr<zdl::DlSystem::IUserBuffer> _snpe_buffer) : ModelInput(_name, _buffer, _size), snpe_buffer(std::move(_snpe_buffer)) {}
-  void setBuffer(float *_buffer, int _size) {
-    ModelInput::setBuffer(_buffer, _size);
-    assert(snpe_buffer->setBufferAddress(_buffer) == true);
-  }
-};
-
-class SNPEModel : public RunModel {
-public:
-  SNPEModel(const std::string path, float *_output, size_t _output_size, int runtime, bool use_tf8 = false, cl_context context = NULL);
-  void addInput(const std::string name, float *buffer, int size);
-  void execute();
-
-private:
-  std::string model_data;
-
-#ifdef QCOM2
-  zdl::DlSystem::Runtime_t snpe_runtime;
-#endif
-
-  // snpe model stuff
-  std::unique_ptr<zdl::SNPE::SNPE> snpe;
-  zdl::DlSystem::UserBufferMap input_map;
-  zdl::DlSystem::UserBufferMap output_map;
-  std::unique_ptr<zdl::DlSystem::IUserBuffer> output_buffer;
-
-  bool use_tf8;
-  float *output;
-  size_t output_size;
-};

selfdrive/modeld/runners/snpemodel.pxd

@@ -1,9 +0,0 @@
-# distutils: language = c++
-
-from libcpp.string cimport string
-
-from msgq.visionipc.visionipc cimport cl_context
-
-cdef extern from "selfdrive/modeld/runners/snpemodel.h":
-  cdef cppclass SNPEModel:
-    SNPEModel(string, float*, size_t, int, bool, cl_context)

selfdrive/modeld/runners/snpemodel_pyx.pyx

@@ -1,17 +0,0 @@
-# distutils: language = c++
-# cython: c_string_encoding=ascii, language_level=3
-
-import os
-from libcpp cimport bool
-from libcpp.string cimport string
-
-from .snpemodel cimport SNPEModel as cppSNPEModel
-from selfdrive.modeld.models.commonmodel_pyx cimport CLContext
-from selfdrive.modeld.runners.runmodel_pyx cimport RunModel
-from selfdrive.modeld.runners.runmodel cimport RunModel as cppRunModel
-
-os.environ['ADSP_LIBRARY_PATH'] = "/data/pythonpath/third_party/snpe/dsp/"
-
-cdef class SNPEModel(RunModel):
-  def __cinit__(self, string path, float[:] output, int runtime, bool use_tf8, CLContext context):
-    self.model = <cppRunModel *> new cppSNPEModel(path, &output[0], len(output), runtime, use_tf8, context.context)

selfdrive/modeld/runners/thneedmodel.cc

@@ -1,58 +0,0 @@
-#include "selfdrive/modeld/runners/thneedmodel.h"
-
-#include <string>
-
-#include "common/swaglog.h"
-
-ThneedModel::ThneedModel(const std::string path, float *_output, size_t _output_size, int runtime, bool luse_tf8, cl_context context) {
-  thneed = new Thneed(true, context);
-  thneed->load(path.c_str());
-  thneed->clexec();
-
-  recorded = false;
-  output = _output;
-}
-
-void* ThneedModel::getCLBuffer(const std::string name) {
-  int index = -1;
-  for (int i = 0; i < inputs.size(); i++) {
-    if (name == inputs[i]->name) {
-      index = i;
-      break;
-    }
-  }
-
-  if (index == -1) {
-    LOGE("Tried to get CL buffer for input `%s` but no input with this name exists", name.c_str());
-    assert(false);
-  }
-
-  if (thneed->input_clmem.size() >= inputs.size()) {
-    return &thneed->input_clmem[inputs.size() - index - 1];
-  } else {
-    return nullptr;
-  }
-}
-
-void ThneedModel::execute() {
-  if (!recorded) {
-    thneed->record = true;
-    float *input_buffers[inputs.size()];
-    for (int i = 0; i < inputs.size(); i++) {
-      input_buffers[inputs.size() - i - 1] = inputs[i]->buffer;
-    }
-
-    thneed->copy_inputs(input_buffers);
-    thneed->clexec();
-    thneed->copy_output(output);
-    thneed->stop();
-
-    recorded = true;
-  } else {
-    float *input_buffers[inputs.size()];
-    for (int i = 0; i < inputs.size(); i++) {
-      input_buffers[inputs.size() - i - 1] = inputs[i]->buffer;
-    }
-    thneed->execute(input_buffers, output);
-  }
-}

selfdrive/modeld/runners/thneedmodel.h

@@ -1,17 +0,0 @@
-#pragma once
-
-#include <string>
-
-#include "selfdrive/modeld/runners/runmodel.h"
-#include "selfdrive/modeld/thneed/thneed.h"
-
-class ThneedModel : public RunModel {
-public:
-  ThneedModel(const std::string path, float *_output, size_t _output_size, int runtime, bool use_tf8 = false, cl_context context = NULL);
-  void *getCLBuffer(const std::string name);
-  void execute();
-private:
-  Thneed *thneed = NULL;
-  bool recorded;
-  float *output;
-};

selfdrive/modeld/runners/thneedmodel.pxd

@@ -1,9 +0,0 @@
-# distutils: language = c++
-
-from libcpp.string cimport string
-
-from msgq.visionipc.visionipc cimport cl_context
-
-cdef extern from "selfdrive/modeld/runners/thneedmodel.h":
-  cdef cppclass ThneedModel:
-    ThneedModel(string, float*, size_t, int, bool, cl_context)

selfdrive/modeld/runners/thneedmodel_pyx.pyx

@@ -1,14 +0,0 @@
-# distutils: language = c++
-# cython: c_string_encoding=ascii, language_level=3
-
-from libcpp cimport bool
-from libcpp.string cimport string
-
-from .thneedmodel cimport ThneedModel as cppThneedModel
-from selfdrive.modeld.models.commonmodel_pyx cimport CLContext
-from selfdrive.modeld.runners.runmodel_pyx cimport RunModel
-from selfdrive.modeld.runners.runmodel cimport RunModel as cppRunModel
-
-cdef class ThneedModel(RunModel):
-  def __cinit__(self, string path, float[:] output, int runtime, bool use_tf8, CLContext context):
-    self.model = <cppRunModel *> new cppThneedModel(path, &output[0], len(output), runtime, use_tf8, context.context)

selfdrive/modeld/runners/tinygrad_helpers.py

@@ -0,0 +1,8 @@
+
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import to_mv
+
+def qcom_tensor_from_opencl_address(opencl_address, shape, dtype):
+  cl_buf_desc_ptr = to_mv(opencl_address, 8).cast('Q')[0]
+  rawbuf_ptr = to_mv(cl_buf_desc_ptr, 0x100).cast('Q')[20] # offset 0xA0 is a raw gpu pointer.
+  return Tensor.from_blob(rawbuf_ptr, shape, dtype=dtype, device='QCOM')

selfdrive/modeld/thneed/README

@@ -1,8 +0,0 @@
-thneed is an SNPE accelerator. I know SNPE is already an accelerator, but sometimes things need to go even faster..
-
-It runs on the local device, and caches a single model run. Then it replays it, but fast.
-
-thneed slices through abstraction layers like a fish.
-
-You need a thneed.
-

selfdrive/modeld/thneed/serialize.cc

@@ -1,154 +0,0 @@
-#include <cassert>
-#include <set>
-
-#include "third_party/json11/json11.hpp"
-#include "common/util.h"
-#include "common/clutil.h"
-#include "common/swaglog.h"
-#include "selfdrive/modeld/thneed/thneed.h"
-using namespace json11;
-
-extern map<cl_program, string> g_program_source;
-
-void Thneed::load(const char *filename) {
-  LOGD("Thneed::load: loading from %s\n", filename);
-
-  string buf = util::read_file(filename);
-  int jsz = *(int *)buf.data();
-  string jsonerr;
-  string jj(buf.data() + sizeof(int), jsz);
-  Json jdat = Json::parse(jj, jsonerr);
-
-  map<cl_mem, cl_mem> real_mem;
-  real_mem[NULL] = NULL;
-
-  int ptr = sizeof(int)+jsz;
-  for (auto &obj : jdat["objects"].array_items()) {
-    auto mobj = obj.object_items();
-    int sz = mobj["size"].int_value();
-    cl_mem clbuf = NULL;
-
-    if (mobj["buffer_id"].string_value().size() > 0) {
-      // image buffer must already be allocated
-      clbuf = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())];
-      assert(mobj["needs_load"].bool_value() == false);
-    } else {
-      if (mobj["needs_load"].bool_value()) {
-        clbuf = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, sz, &buf[ptr], NULL);
-        if (debug >= 1) printf("loading %p %d @ 0x%X\n", clbuf, sz, ptr);
-        ptr += sz;
-      } else {
-        // TODO: is there a faster way to init zeroed out buffers?
-        void *host_zeros = calloc(sz, 1);
-        clbuf = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, sz, host_zeros, NULL);
-        free(host_zeros);
-      }
-    }
-    assert(clbuf != NULL);
-
-    if (mobj["arg_type"] == "image2d_t" || mobj["arg_type"] == "image1d_t") {
-      cl_image_desc desc = {0};
-      desc.image_type = (mobj["arg_type"] == "image2d_t") ? CL_MEM_OBJECT_IMAGE2D : CL_MEM_OBJECT_IMAGE1D_BUFFER;
-      desc.image_width = mobj["width"].int_value();
-      desc.image_height = mobj["height"].int_value();
-      desc.image_row_pitch = mobj["row_pitch"].int_value();
-      assert(sz == desc.image_height*desc.image_row_pitch);
-#ifdef QCOM2
-      desc.buffer = clbuf;
-#else
-      // TODO: we are creating unused buffers on PC
-      clReleaseMemObject(clbuf);
-#endif
-      cl_image_format format = {0};
-      format.image_channel_order = CL_RGBA;
-      format.image_channel_data_type = mobj["float32"].bool_value() ? CL_FLOAT : CL_HALF_FLOAT;
-
-      cl_int errcode;
-
-#ifndef QCOM2
-      if (mobj["needs_load"].bool_value()) {
-        clbuf = clCreateImage(context, CL_MEM_COPY_HOST_PTR | CL_MEM_READ_WRITE, &format, &desc, &buf[ptr-sz], &errcode);
-      } else {
-        clbuf = clCreateImage(context, CL_MEM_READ_WRITE, &format, &desc, NULL, &errcode);
-      }
-#else
-      clbuf = clCreateImage(context, CL_MEM_READ_WRITE, &format, &desc, NULL, &errcode);
-#endif
-      if (clbuf == NULL) {
-        LOGE("clError: %s create image %zux%zu rp %zu with buffer %p\n", cl_get_error_string(errcode),
-             desc.image_width, desc.image_height, desc.image_row_pitch, desc.buffer);
-      }
-      assert(clbuf != NULL);
-    }
-
-    real_mem[*(cl_mem*)(mobj["id"].string_value().data())] = clbuf;
-  }
-
-  map<string, cl_program> g_programs;
-  for (const auto &[name, source] : jdat["programs"].object_items()) {
-    if (debug >= 1) printf("building %s with size %zu\n", name.c_str(), source.string_value().size());
-    g_programs[name] = cl_program_from_source(context, device_id, source.string_value());
-  }
-
-  for (auto &obj : jdat["inputs"].array_items()) {
-    auto mobj = obj.object_items();
-    int sz = mobj["size"].int_value();
-    cl_mem aa = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())];
-    input_clmem.push_back(aa);
-    input_sizes.push_back(sz);
-    LOGD("Thneed::load: adding input %s with size %d\n", mobj["name"].string_value().data(), sz);
-
-    cl_int cl_err;
-    void *ret = clEnqueueMapBuffer(command_queue, aa, CL_TRUE, CL_MAP_WRITE, 0, sz, 0, NULL, NULL, &cl_err);
-    if (cl_err != CL_SUCCESS) LOGE("clError: %s map %p %d\n", cl_get_error_string(cl_err), aa, sz);
-    assert(cl_err == CL_SUCCESS);
-    inputs.push_back(ret);
-  }
-
-  for (auto &obj : jdat["outputs"].array_items()) {
-    auto mobj = obj.object_items();
-    int sz = mobj["size"].int_value();
-    LOGD("Thneed::save: adding output with size %d\n", sz);
-    // TODO: support multiple outputs
-    output = real_mem[*(cl_mem*)(mobj["buffer_id"].string_value().data())];
-    assert(output != NULL);
-  }
-
-  for (auto &obj : jdat["binaries"].array_items()) {
-    string name = obj["name"].string_value();
-    size_t length = obj["length"].int_value();
-    if (debug >= 1) printf("binary %s with size %zu\n", name.c_str(), length);
-    g_programs[name] = cl_program_from_binary(context, device_id, (const uint8_t*)&buf[ptr], length);
-    ptr += length;
-  }
-
-  for (auto &obj : jdat["kernels"].array_items()) {
-    auto gws = obj["global_work_size"];
-    auto lws = obj["local_work_size"];
-    auto kk = shared_ptr<CLQueuedKernel>(new CLQueuedKernel(this));
-
-    kk->name = obj["name"].string_value();
-    kk->program = g_programs[kk->name];
-    kk->work_dim = obj["work_dim"].int_value();
-    for (int i = 0; i < kk->work_dim; i++) {
-      kk->global_work_size[i] = gws[i].int_value();
-      kk->local_work_size[i] = lws[i].int_value();
-    }
-    kk->num_args = obj["num_args"].int_value();
-    for (int i = 0; i < kk->num_args; i++) {
-      string arg = obj["args"].array_items()[i].string_value();
-      int arg_size = obj["args_size"].array_items()[i].int_value();
-      kk->args_size.push_back(arg_size);
-      if (arg_size == 8) {
-        cl_mem val = *(cl_mem*)(arg.data());
-        val = real_mem[val];
-        kk->args.push_back(string((char*)&val, sizeof(val)));
-      } else {
-        kk->args.push_back(arg);
-      }
-    }
-    kq.push_back(kk);
-  }
-
-  clFinish(command_queue);
-}

selfdrive/modeld/thneed/thneed.h

@@ -1,133 +0,0 @@
-#pragma once
-
-#ifndef __user
-#define __user __attribute__(())
-#endif
-
-#include <cstdint>
-#include <cstdlib>
-#include <memory>
-#include <string>
-#include <vector>
-
-#include <CL/cl.h>
-
-#include "third_party/linux/include/msm_kgsl.h"
-
-using namespace std;
-
-cl_int thneed_clSetKernelArg(cl_kernel kernel, cl_uint arg_index, size_t arg_size, const void *arg_value);
-
-namespace json11 {
-  class Json;
-}
-class Thneed;
-
-class GPUMalloc {
-  public:
-    GPUMalloc(int size, int fd);
-    ~GPUMalloc();
-    void *alloc(int size);
-  private:
-    uint64_t base;
-    int remaining;
-};
-
-class CLQueuedKernel {
-  public:
-    CLQueuedKernel(Thneed *lthneed) { thneed = lthneed; }
-    CLQueuedKernel(Thneed *lthneed,
-                   cl_kernel _kernel,
-                   cl_uint _work_dim,
-                   const size_t *_global_work_size,
-                   const size_t *_local_work_size);
-    cl_int exec();
-    void debug_print(bool verbose);
-    int get_arg_num(const char *search_arg_name);
-    cl_program program;
-    string name;
-    cl_uint num_args;
-    vector<string> arg_names;
-    vector<string> arg_types;
-    vector<string> args;
-    vector<int> args_size;
-    cl_kernel kernel = NULL;
-    json11::Json to_json() const;
-
-    cl_uint work_dim;
-    size_t global_work_size[3] = {0};
-    size_t local_work_size[3] = {0};
-  private:
-    Thneed *thneed;
-};
-
-class CachedIoctl {
-  public:
-    virtual void exec() {}
-};
-
-class CachedSync: public CachedIoctl {
-  public:
-    CachedSync(Thneed *lthneed, string ldata) { thneed = lthneed; data = ldata; }
-    void exec();
-  private:
-    Thneed *thneed;
-    string data;
-};
-
-class CachedCommand: public CachedIoctl {
-  public:
-    CachedCommand(Thneed *lthneed, struct kgsl_gpu_command *cmd);
-    void exec();
-  private:
-    void disassemble(int cmd_index);
-    struct kgsl_gpu_command cache;
-    unique_ptr<kgsl_command_object[]> cmds;
-    unique_ptr<kgsl_command_object[]> objs;
-    Thneed *thneed;
-    vector<shared_ptr<CLQueuedKernel> > kq;
-};
-
-class Thneed {
-  public:
-    Thneed(bool do_clinit=false, cl_context _context = NULL);
-    void stop();
-    void execute(float **finputs, float *foutput, bool slow=false);
-    void wait();
-
-    vector<cl_mem> input_clmem;
-    vector<void *> inputs;
-    vector<size_t> input_sizes;
-    cl_mem output = NULL;
-
-    cl_context context = NULL;
-    cl_command_queue command_queue;
-    cl_device_id device_id;
-    int context_id;
-
-    // protected?
-    bool record = false;
-    int debug;
-    int timestamp;
-
-#ifdef QCOM2
-    unique_ptr<GPUMalloc> ram;
-    vector<unique_ptr<CachedIoctl> > cmds;
-    int fd;
-#endif
-
-    // all CL kernels
-    void copy_inputs(float **finputs, bool internal=false);
-    void copy_output(float *foutput);
-    cl_int clexec();
-    vector<shared_ptr<CLQueuedKernel> > kq;
-
-    // pending CL kernels
-    vector<shared_ptr<CLQueuedKernel> > ckq;
-
-    // loading
-    void load(const char *filename);
-  private:
-    void clinit();
-};
-

selfdrive/modeld/thneed/thneed_common.cc

@@ -1,216 +0,0 @@
-#include "selfdrive/modeld/thneed/thneed.h"
-
-#include <cassert>
-#include <cstring>
-#include <map>
-
-#include "common/clutil.h"
-#include "common/timing.h"
-
-map<pair<cl_kernel, int>, string> g_args;
-map<pair<cl_kernel, int>, int> g_args_size;
-map<cl_program, string> g_program_source;
-
-void Thneed::stop() {
-  //printf("Thneed::stop: recorded %lu commands\n", cmds.size());
-  record = false;
-}
-
-void Thneed::clinit() {
-  device_id = cl_get_device_id(CL_DEVICE_TYPE_DEFAULT);
-  if (context == NULL) context = CL_CHECK_ERR(clCreateContext(NULL, 1, &device_id, NULL, NULL, &err));
-  //cl_command_queue_properties props[3] = {CL_QUEUE_PROPERTIES, CL_QUEUE_PROFILING_ENABLE, 0};
-  cl_command_queue_properties props[3] = {CL_QUEUE_PROPERTIES, 0, 0};
-  command_queue = CL_CHECK_ERR(clCreateCommandQueueWithProperties(context, device_id, props, &err));
-  printf("Thneed::clinit done\n");
-}
-
-cl_int Thneed::clexec() {
-  if (debug >= 1) printf("Thneed::clexec: running %lu queued kernels\n", kq.size());
-  for (auto &k : kq) {
-    if (record) ckq.push_back(k);
-    cl_int ret = k->exec();
-    assert(ret == CL_SUCCESS);
-  }
-  return clFinish(command_queue);
-}
-
-void Thneed::copy_inputs(float **finputs, bool internal) {
-  for (int idx = 0; idx < inputs.size(); ++idx) {
-    if (debug >= 1) printf("copying %lu -- %p -> %p (cl %p)\n", input_sizes[idx], finputs[idx], inputs[idx], input_clmem[idx]);
-
-    if (internal) {
-      // if it's internal, using memcpy is fine since the buffer sync is cached in the ioctl layer
-      if (finputs[idx] != NULL) memcpy(inputs[idx], finputs[idx], input_sizes[idx]);
-    } else {
-      if (finputs[idx] != NULL) CL_CHECK(clEnqueueWriteBuffer(command_queue, input_clmem[idx], CL_TRUE, 0, input_sizes[idx], finputs[idx], 0, NULL, NULL));
-    }
-  }
-}
-
-void Thneed::copy_output(float *foutput) {
-  if (output != NULL) {
-    size_t sz;
-    clGetMemObjectInfo(output, CL_MEM_SIZE, sizeof(sz), &sz, NULL);
-    if (debug >= 1) printf("copying %lu for output %p -> %p\n", sz, output, foutput);
-    CL_CHECK(clEnqueueReadBuffer(command_queue, output, CL_TRUE, 0, sz, foutput, 0, NULL, NULL));
-  } else {
-    printf("CAUTION: model output is NULL, does it have no outputs?\n");
-  }
-}
-
-// *********** CLQueuedKernel ***********
-
-CLQueuedKernel::CLQueuedKernel(Thneed *lthneed,
-                               cl_kernel _kernel,
-                               cl_uint _work_dim,
-                               const size_t *_global_work_size,
-                               const size_t *_local_work_size) {
-  thneed = lthneed;
-  kernel = _kernel;
-  work_dim = _work_dim;
-  assert(work_dim <= 3);
-  for (int i = 0; i < work_dim; i++) {
-    global_work_size[i] = _global_work_size[i];
-    local_work_size[i] = _local_work_size[i];
-  }
-
-  char _name[0x100];
-  clGetKernelInfo(kernel, CL_KERNEL_FUNCTION_NAME, sizeof(_name), _name, NULL);
-  name = string(_name);
-  clGetKernelInfo(kernel, CL_KERNEL_NUM_ARGS, sizeof(num_args), &num_args, NULL);
-
-  // get args
-  for (int i = 0; i < num_args; i++) {
-    char arg_name[0x100] = {0};
-    clGetKernelArgInfo(kernel, i, CL_KERNEL_ARG_NAME, sizeof(arg_name), arg_name, NULL);
-    arg_names.push_back(string(arg_name));
-    clGetKernelArgInfo(kernel, i, CL_KERNEL_ARG_TYPE_NAME, sizeof(arg_name), arg_name, NULL);
-    arg_types.push_back(string(arg_name));
-
-    args.push_back(g_args[make_pair(kernel, i)]);
-    args_size.push_back(g_args_size[make_pair(kernel, i)]);
-  }
-
-  // get program
-  clGetKernelInfo(kernel, CL_KERNEL_PROGRAM, sizeof(program), &program, NULL);
-}
-
-int CLQueuedKernel::get_arg_num(const char *search_arg_name) {
-  for (int i = 0; i < num_args; i++) {
-    if (arg_names[i] == search_arg_name) return i;
-  }
-  printf("failed to find %s in %s\n", search_arg_name, name.c_str());
-  assert(false);
-}
-
-cl_int CLQueuedKernel::exec() {
-  if (kernel == NULL) {
-    kernel = clCreateKernel(program, name.c_str(), NULL);
-    arg_names.clear();
-    arg_types.clear();
-
-    for (int j = 0; j < num_args; j++) {
-      char arg_name[0x100] = {0};
-      clGetKernelArgInfo(kernel, j, CL_KERNEL_ARG_NAME, sizeof(arg_name), arg_name, NULL);
-      arg_names.push_back(string(arg_name));
-      clGetKernelArgInfo(kernel, j, CL_KERNEL_ARG_TYPE_NAME, sizeof(arg_name), arg_name, NULL);
-      arg_types.push_back(string(arg_name));
-
-      cl_int ret;
-      if (args[j].size() != 0) {
-        assert(args[j].size() == args_size[j]);
-        ret = thneed_clSetKernelArg(kernel, j, args[j].size(), args[j].data());
-      } else {
-        ret = thneed_clSetKernelArg(kernel, j, args_size[j], NULL);
-      }
-      assert(ret == CL_SUCCESS);
-    }
-  }
-
-  if (thneed->debug >= 1) {
-    debug_print(thneed->debug >= 2);
-  }
-
-  return clEnqueueNDRangeKernel(thneed->command_queue,
-    kernel, work_dim, NULL, global_work_size, local_work_size, 0, NULL, NULL);
-}
-
-void CLQueuedKernel::debug_print(bool verbose) {
-  printf("%p %56s -- ", kernel, name.c_str());
-  for (int i = 0; i < work_dim; i++) {
-    printf("%4zu ", global_work_size[i]);
-  }
-  printf(" -- ");
-  for (int i = 0; i < work_dim; i++) {
-    printf("%4zu ", local_work_size[i]);
-  }
-  printf("\n");
-
-  if (verbose) {
-    for (int i = 0; i < num_args; i++) {
-      string arg = args[i];
-      printf("  %s %s", arg_types[i].c_str(), arg_names[i].c_str());
-      void *arg_value = (void*)arg.data();
-      int arg_size = arg.size();
-      if (arg_size == 0) {
-        printf(" (size) %d", args_size[i]);
-      } else if (arg_size == 1) {
-        printf(" = %d", *((char*)arg_value));
-      } else if (arg_size == 2) {
-        printf(" = %d", *((short*)arg_value));
-      } else if (arg_size == 4) {
-        if (arg_types[i] == "float") {
-          printf(" = %f", *((float*)arg_value));
-        } else {
-          printf(" = %d", *((int*)arg_value));
-        }
-      } else if (arg_size == 8) {
-        cl_mem val = (cl_mem)(*((uintptr_t*)arg_value));
-        printf(" = %p", val);
-        if (val != NULL) {
-          cl_mem_object_type obj_type;
-          clGetMemObjectInfo(val, CL_MEM_TYPE, sizeof(obj_type), &obj_type, NULL);
-          if (arg_types[i] == "image2d_t" || arg_types[i] == "image1d_t" || obj_type == CL_MEM_OBJECT_IMAGE2D) {
-            cl_image_format format;
-            size_t width, height, depth, array_size, row_pitch, slice_pitch;
-            cl_mem buf;
-            clGetImageInfo(val, CL_IMAGE_FORMAT, sizeof(format), &format, NULL);
-            assert(format.image_channel_order == CL_RGBA);
-            assert(format.image_channel_data_type == CL_HALF_FLOAT || format.image_channel_data_type == CL_FLOAT);
-            clGetImageInfo(val, CL_IMAGE_WIDTH, sizeof(width), &width, NULL);
-            clGetImageInfo(val, CL_IMAGE_HEIGHT, sizeof(height), &height, NULL);
-            clGetImageInfo(val, CL_IMAGE_ROW_PITCH, sizeof(row_pitch), &row_pitch, NULL);
-            clGetImageInfo(val, CL_IMAGE_DEPTH, sizeof(depth), &depth, NULL);
-            clGetImageInfo(val, CL_IMAGE_ARRAY_SIZE, sizeof(array_size), &array_size, NULL);
-            clGetImageInfo(val, CL_IMAGE_SLICE_PITCH, sizeof(slice_pitch), &slice_pitch, NULL);
-            assert(depth == 0);
-            assert(array_size == 0);
-            assert(slice_pitch == 0);
-
-            clGetImageInfo(val, CL_IMAGE_BUFFER, sizeof(buf), &buf, NULL);
-            size_t sz = 0;
-            if (buf != NULL) clGetMemObjectInfo(buf, CL_MEM_SIZE, sizeof(sz), &sz, NULL);
-            printf(" image %zu x %zu rp %zu @ %p buffer %zu", width, height, row_pitch, buf, sz);
-          } else {
-            size_t sz;
-            clGetMemObjectInfo(val, CL_MEM_SIZE, sizeof(sz), &sz, NULL);
-            printf(" buffer %zu", sz);
-          }
-        }
-      }
-      printf("\n");
-    }
-  }
-}
-
-cl_int thneed_clSetKernelArg(cl_kernel kernel, cl_uint arg_index, size_t arg_size, const void *arg_value) {
-  g_args_size[make_pair(kernel, arg_index)] = arg_size;
-  if (arg_value != NULL) {
-    g_args[make_pair(kernel, arg_index)] = string((char*)arg_value, arg_size);
-  } else {
-    g_args[make_pair(kernel, arg_index)] = string("");
-  }
-  cl_int ret = clSetKernelArg(kernel, arg_index, arg_size, arg_value);
-  return ret;
-}

selfdrive/modeld/thneed/thneed_pc.cc

@@ -1,32 +0,0 @@
-#include "selfdrive/modeld/thneed/thneed.h"
-
-#include <cassert>
-
-#include "common/clutil.h"
-#include "common/timing.h"
-
-Thneed::Thneed(bool do_clinit, cl_context _context) {
-  context = _context;
-  if (do_clinit) clinit();
-  char *thneed_debug_env = getenv("THNEED_DEBUG");
-  debug = (thneed_debug_env != NULL) ? atoi(thneed_debug_env) : 0;
-}
-
-void Thneed::execute(float **finputs, float *foutput, bool slow) {
-  uint64_t tb, te;
-  if (debug >= 1) tb = nanos_since_boot();
-
-  // ****** copy inputs
-  copy_inputs(finputs);
-
-  // ****** run commands
-  clexec();
-
-  // ****** copy outputs
-  copy_output(foutput);
-
-  if (debug >= 1) {
-    te = nanos_since_boot();
-    printf("model exec in %lu us\n", (te-tb)/1000);
-  }
-}

selfdrive/modeld/thneed/thneed_qcom2.cc

@@ -1,258 +0,0 @@
-#include "selfdrive/modeld/thneed/thneed.h"
-
-#include <dlfcn.h>
-#include <sys/mman.h>
-
-#include <cassert>
-#include <cerrno>
-#include <cstring>
-#include <map>
-#include <string>
-
-#include "common/clutil.h"
-#include "common/timing.h"
-
-Thneed *g_thneed = NULL;
-int g_fd = -1;
-
-void hexdump(uint8_t *d, int len) {
-  assert((len%4) == 0);
-  printf("  dumping %p len 0x%x\n", d, len);
-  for (int i = 0; i < len/4; i++) {
-    if (i != 0 && (i%0x10) == 0) printf("\n");
-    printf("%8x ", d[i]);
-  }
-  printf("\n");
-}
-
-// *********** ioctl interceptor ***********
-
-extern "C" {
-
-int (*my_ioctl)(int filedes, unsigned long request, void *argp) = NULL;
-#undef ioctl
-int ioctl(int filedes, unsigned long request, void *argp) {
-  request &= 0xFFFFFFFF;  // needed on QCOM2
-  if (my_ioctl == NULL) my_ioctl = reinterpret_cast<decltype(my_ioctl)>(dlsym(RTLD_NEXT, "ioctl"));
-  Thneed *thneed = g_thneed;
-
-  // save the fd
-  if (request == IOCTL_KGSL_GPUOBJ_ALLOC) g_fd = filedes;
-
-  // note that this runs always, even without a thneed object
-  if (request == IOCTL_KGSL_DRAWCTXT_CREATE) {
-    struct kgsl_drawctxt_create *create = (struct kgsl_drawctxt_create *)argp;
-    create->flags &= ~KGSL_CONTEXT_PRIORITY_MASK;
-    create->flags |= 6 << KGSL_CONTEXT_PRIORITY_SHIFT;   // priority from 1-15, 1 is max priority
-    printf("IOCTL_KGSL_DRAWCTXT_CREATE: creating context with flags 0x%x\n", create->flags);
-  }
-
-  if (thneed != NULL) {
-    if (request == IOCTL_KGSL_GPU_COMMAND) {
-      struct kgsl_gpu_command *cmd = (struct kgsl_gpu_command *)argp;
-      if (thneed->record) {
-        thneed->timestamp = cmd->timestamp;
-        thneed->context_id = cmd->context_id;
-        thneed->cmds.push_back(unique_ptr<CachedCommand>(new CachedCommand(thneed, cmd)));
-      }
-      if (thneed->debug >= 1) {
-        printf("IOCTL_KGSL_GPU_COMMAND(%2zu): flags: 0x%lx    context_id: %u  timestamp: %u  numcmds: %d  numobjs: %d\n",
-            thneed->cmds.size(),
-            cmd->flags,
-            cmd->context_id, cmd->timestamp, cmd->numcmds, cmd->numobjs);
-      }
-    } else if (request == IOCTL_KGSL_GPUOBJ_SYNC) {
-      struct kgsl_gpuobj_sync *cmd = (struct kgsl_gpuobj_sync *)argp;
-      struct kgsl_gpuobj_sync_obj *objs = (struct kgsl_gpuobj_sync_obj *)(cmd->objs);
-
-      if (thneed->debug >= 2) {
-        printf("IOCTL_KGSL_GPUOBJ_SYNC count:%d ", cmd->count);
-        for (int i = 0; i < cmd->count; i++) {
-          printf(" -- offset:0x%lx len:0x%lx id:%d op:%d  ", objs[i].offset, objs[i].length, objs[i].id, objs[i].op);
-        }
-        printf("\n");
-      }
-
-      if (thneed->record) {
-        thneed->cmds.push_back(unique_ptr<CachedSync>(new
-              CachedSync(thneed, string((char *)objs, sizeof(struct kgsl_gpuobj_sync_obj)*cmd->count))));
-      }
-    } else if (request == IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID) {
-      struct kgsl_device_waittimestamp_ctxtid *cmd = (struct kgsl_device_waittimestamp_ctxtid *)argp;
-      if (thneed->debug >= 1) {
-        printf("IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID: context_id: %d  timestamp: %d  timeout: %d\n",
-            cmd->context_id, cmd->timestamp, cmd->timeout);
-      }
-    } else if (request == IOCTL_KGSL_SETPROPERTY) {
-      if (thneed->debug >= 1) {
-        struct kgsl_device_getproperty *prop = (struct kgsl_device_getproperty *)argp;
-        printf("IOCTL_KGSL_SETPROPERTY: 0x%x sizebytes:%zu\n", prop->type, prop->sizebytes);
-        if (thneed->debug >= 2) {
-          hexdump((uint8_t *)prop->value, prop->sizebytes);
-          if (prop->type == KGSL_PROP_PWR_CONSTRAINT) {
-            struct kgsl_device_constraint *constraint = (struct kgsl_device_constraint *)prop->value;
-            hexdump((uint8_t *)constraint->data, constraint->size);
-          }
-        }
-      }
-    } else if (request == IOCTL_KGSL_DRAWCTXT_CREATE || request == IOCTL_KGSL_DRAWCTXT_DESTROY) {
-      // this happens
-    } else if (request == IOCTL_KGSL_GPUOBJ_ALLOC || request == IOCTL_KGSL_GPUOBJ_FREE) {
-      // this happens
-    } else {
-      if (thneed->debug >= 1) {
-        printf("other ioctl %lx\n", request);
-      }
-    }
-  }
-
-  int ret = my_ioctl(filedes, request, argp);
-  // NOTE: This error message goes into stdout and messes up pyenv
-  // if (ret != 0) printf("ioctl returned %d with errno %d\n", ret, errno);
-  return ret;
-}
-
-}
-
-// *********** GPUMalloc ***********
-
-GPUMalloc::GPUMalloc(int size, int fd) {
-  struct kgsl_gpuobj_alloc alloc;
-  memset(&alloc, 0, sizeof(alloc));
-  alloc.size = size;
-  alloc.flags = 0x10000a00;
-  ioctl(fd, IOCTL_KGSL_GPUOBJ_ALLOC, &alloc);
-  void *addr = mmap64(NULL, alloc.mmapsize, 0x3, 0x1, fd, alloc.id*0x1000);
-  assert(addr != MAP_FAILED);
-
-  base = (uint64_t)addr;
-  remaining = size;
-}
-
-GPUMalloc::~GPUMalloc() {
-  // TODO: free the GPU malloced area
-}
-
-void *GPUMalloc::alloc(int size) {
-  void *ret = (void*)base;
-  size = (size+0xff) & (~0xFF);
-  assert(size <= remaining);
-  remaining -= size;
-  base += size;
-  return ret;
-}
-
-// *********** CachedSync, at the ioctl layer ***********
-
-void CachedSync::exec() {
-  struct kgsl_gpuobj_sync cmd;
-
-  cmd.objs = (uint64_t)data.data();
-  cmd.obj_len = data.length();
-  cmd.count = data.length() / sizeof(struct kgsl_gpuobj_sync_obj);
-
-  int ret = ioctl(thneed->fd, IOCTL_KGSL_GPUOBJ_SYNC, &cmd);
-  assert(ret == 0);
-}
-
-// *********** CachedCommand, at the ioctl layer ***********
-
-CachedCommand::CachedCommand(Thneed *lthneed, struct kgsl_gpu_command *cmd) {
-  thneed = lthneed;
-  assert(cmd->numsyncs == 0);
-
-  memcpy(&cache, cmd, sizeof(cache));
-
-  if (cmd->numcmds > 0) {
-    cmds = make_unique<struct kgsl_command_object[]>(cmd->numcmds);
-    memcpy(cmds.get(), (void *)cmd->cmdlist, sizeof(struct kgsl_command_object)*cmd->numcmds);
-    cache.cmdlist = (uint64_t)cmds.get();
-    for (int i = 0; i < cmd->numcmds; i++) {
-      void *nn = thneed->ram->alloc(cmds[i].size);
-      memcpy(nn, (void*)cmds[i].gpuaddr, cmds[i].size);
-      cmds[i].gpuaddr = (uint64_t)nn;
-    }
-  }
-
-  if (cmd->numobjs > 0) {
-    objs = make_unique<struct kgsl_command_object[]>(cmd->numobjs);
-    memcpy(objs.get(), (void *)cmd->objlist, sizeof(struct kgsl_command_object)*cmd->numobjs);
-    cache.objlist = (uint64_t)objs.get();
-    for (int i = 0; i < cmd->numobjs; i++) {
-      void *nn = thneed->ram->alloc(objs[i].size);
-      memset(nn, 0, objs[i].size);
-      objs[i].gpuaddr = (uint64_t)nn;
-    }
-  }
-
-  kq = thneed->ckq;
-  thneed->ckq.clear();
-}
-
-void CachedCommand::exec() {
-  cache.timestamp = ++thneed->timestamp;
-  int ret = ioctl(thneed->fd, IOCTL_KGSL_GPU_COMMAND, &cache);
-
-  if (thneed->debug >= 1) printf("CachedCommand::exec got %d\n", ret);
-
-  if (thneed->debug >= 2) {
-    for (auto &it : kq) {
-      it->debug_print(false);
-    }
-  }
-
-  assert(ret == 0);
-}
-
-// *********** Thneed ***********
-
-Thneed::Thneed(bool do_clinit, cl_context _context) {
-  // TODO: QCOM2 actually requires a different context
-  //context = _context;
-  if (do_clinit) clinit();
-  assert(g_fd != -1);
-  fd = g_fd;
-  ram = make_unique<GPUMalloc>(0x80000, fd);
-  timestamp = -1;
-  g_thneed = this;
-  char *thneed_debug_env = getenv("THNEED_DEBUG");
-  debug = (thneed_debug_env != NULL) ? atoi(thneed_debug_env) : 0;
-}
-
-void Thneed::wait() {
-  struct kgsl_device_waittimestamp_ctxtid wait;
-  wait.context_id = context_id;
-  wait.timestamp = timestamp;
-  wait.timeout = -1;
-
-  uint64_t tb = nanos_since_boot();
-  int wret = ioctl(fd, IOCTL_KGSL_DEVICE_WAITTIMESTAMP_CTXTID, &wait);
-  uint64_t te = nanos_since_boot();
-
-  if (debug >= 1) printf("wait %d after %lu us\n", wret, (te-tb)/1000);
-}
-
-void Thneed::execute(float **finputs, float *foutput, bool slow) {
-  uint64_t tb, te;
-  if (debug >= 1) tb = nanos_since_boot();
-
-  // ****** copy inputs
-  copy_inputs(finputs, true);
-
-  // ****** run commands
-  int i = 0;
-  for (auto &it : cmds) {
-    ++i;
-    if (debug >= 1) printf("run %2d @ %7lu us: ", i, (nanos_since_boot()-tb)/1000);
-    it->exec();
-    if ((i == cmds.size()) || slow) wait();
-  }
-
-  // ****** copy outputs
-  copy_output(foutput);
-
-  if (debug >= 1) {
-    te = nanos_since_boot();
-    printf("model exec in %lu us\n", (te-tb)/1000);
-  }
-}

selfdrive/modeld/transforms/loadyuv.cl

@@ -1,7 +1,7 @@
 #define UV_SIZE ((TRANSFORMED_WIDTH/2)*(TRANSFORMED_HEIGHT/2))
 
 __kernel void loadys(__global uchar8 const * const Y,
-                     __global float * out,
+                     __global uchar * out,
                      int out_offset)
 {
     const int gid = get_global_id(0);
@@ -10,13 +10,12 @@ __kernel void loadys(__global uchar8 const * const Y,
     const int ox = ois % TRANSFORMED_WIDTH;
 
     const uchar8 ys = Y[gid];
-    const float8 ysf = convert_float8(ys);
 
     // 02
     // 13
 
-    __global float* outy0;
-    __global float* outy1;
+    __global uchar* outy0;
+    __global uchar* outy1;
     if ((oy & 1) == 0) {
       outy0 = out + out_offset; //y0
       outy1 = out + out_offset + UV_SIZE*2; //y2
@@ -25,23 +24,24 @@ __kernel void loadys(__global uchar8 const * const Y,
       outy1 = out + out_offset + UV_SIZE*3; //y3
     }
 
-    vstore4(ysf.s0246, 0, outy0 + (oy/2) * (TRANSFORMED_WIDTH/2) + ox/2);
-    vstore4(ysf.s1357, 0, outy1 + (oy/2) * (TRANSFORMED_WIDTH/2) + ox/2);
+    vstore4(ys.s0246, 0, outy0 + (oy/2) * (TRANSFORMED_WIDTH/2) + ox/2);
+    vstore4(ys.s1357, 0, outy1 + (oy/2) * (TRANSFORMED_WIDTH/2) + ox/2);
 }
 
 __kernel void loaduv(__global uchar8 const * const in,
-                     __global float8 * out,
+                     __global uchar8 * out,
                      int out_offset)
 {
   const int gid = get_global_id(0);
   const uchar8 inv = in[gid];
-  const float8 outv  = convert_float8(inv);
-  out[gid + out_offset / 8] = outv;
+  out[gid + out_offset / 8] = inv;
 }
 
-__kernel void copy(__global float8 * inout,
-                   int in_offset)
+__kernel void copy(__global uchar8 * in,
+                   __global uchar8 * out,
+                   int in_offset,
+                   int out_offset)
 {
   const int gid = get_global_id(0);
-  inout[gid] = inout[gid + in_offset / 8];
-}
+  out[gid + out_offset / 8] = in[gid + in_offset / 8];
+}

selfdrive/test/test_onroad.py

@@ -36,7 +36,7 @@ CPU usage budget
 TEST_DURATION = 25
 LOG_OFFSET = 8
 
-MAX_TOTAL_CPU = 265.  # total for all 8 cores
+MAX_TOTAL_CPU = 275.  # total for all 8 cores
 PROCS = {
   # Baseline CPU usage by process
   "selfdrive.controls.controlsd": 16.0,
@@ -50,8 +50,8 @@ PROCS = {
   "selfdrive.locationd.paramsd": 9.0,
   "./sensord": 7.0,
   "selfdrive.controls.radard": 2.0,
-  "selfdrive.modeld.modeld": 17.0,
-  "selfdrive.modeld.dmonitoringmodeld": 11.0,
+  "selfdrive.modeld.modeld": 22.0,
+  "selfdrive.modeld.dmonitoringmodeld": 21.0,
   "system.hardware.hardwared": 4.0,
   "selfdrive.locationd.calibrationd": 2.0,
   "selfdrive.locationd.torqued": 5.0,
@@ -371,10 +371,9 @@ class TestOnroad:
     result += "------------------------------------------------\n"
     result += "----------------- Model Timing -----------------\n"
     result += "------------------------------------------------\n"
-    # TODO: this went up when plannerd cpu usage increased, why?
     cfgs = [
-      ("modelV2", 0.050, 0.036),
-      ("driverStateV2", 0.050, 0.026),
+      ("modelV2", 0.045, 0.035),
+      ("driverStateV2", 0.045, 0.035),
     ]
     for (s, instant_max, avg_max) in cfgs:
       ts = [getattr(m, s).modelExecutionTime for m in self.msgs[s]]

system/hardware/tici/tests/test_power_draw.py

@@ -33,7 +33,7 @@ class Proc:
 PROCS = [
   Proc(['camerad'], 1.75, msgs=['roadCameraState', 'wideRoadCameraState', 'driverCameraState']),
   Proc(['modeld'], 1.12, atol=0.2, msgs=['modelV2']),
-  Proc(['dmonitoringmodeld'], 0.65, msgs=['driverStateV2']),
+  Proc(['dmonitoringmodeld'], 0.6, msgs=['driverStateV2']),
   Proc(['encoderd'], 0.23, msgs=[]),
 ]
 

system/manager/process_config.py

@@ -73,10 +73,12 @@ procs = [
   PythonProcess("micd", "system.micd", iscar),
   PythonProcess("timed", "system.timed", always_run, enabled=not PC),
 
+  # TODO Make python process once TG allows opening QCOM from child proc
   NativeProcess("dmonitoringmodeld", "selfdrive/modeld", ["./dmonitoringmodeld"], driverview, enabled=(not PC or WEBCAM)),
   NativeProcess("encoderd", "system/loggerd", ["./encoderd"], only_onroad),
   NativeProcess("stream_encoderd", "system/loggerd", ["./encoderd", "--stream"], notcar),
   NativeProcess("loggerd", "system/loggerd", ["./loggerd"], logging),
+  # TODO Make python process once TG allows opening QCOM from child proc
   NativeProcess("modeld", "selfdrive/modeld", ["./modeld"], only_onroad),
   NativeProcess("sensord", "system/sensord", ["./sensord"], only_onroad, enabled=not PC),
   NativeProcess("ui", "selfdrive/ui", ["./ui"], always_run, watchdog_max_dt=(5 if not PC else None)),

tools/replay/framereader.cc

@@ -119,7 +119,13 @@ VideoDecoder::~VideoDecoder() {
 }
 
 bool VideoDecoder::open(AVCodecParameters *codecpar, bool hw_decoder) {
-  const AVCodec *decoder = avcodec_find_decoder(codecpar->codec_id);
+  const AVCodec *decoder = avcodec_find_decoder_by_name("h264_mediacodec");
+  if (!decoder) {
+    decoder = avcodec_find_decoder_by_name("h264_qcom");
+  }
+  if (!decoder) {
+    decoder = avcodec_find_decoder(codecpar->codec_id);
+  }
   if (!decoder) return false;
 
   decoder_ctx = avcodec_alloc_context3(decoder);
@@ -127,6 +133,23 @@ bool VideoDecoder::open(AVCodecParameters *codecpar, bool hw_decoder) {
     rError("Failed to allocate or initialize codec context");
     return false;
   }
+
+  // More aggressive settings focused on reducing lag
+  decoder_ctx->thread_count = static_cast<int>(std::min(std::thread::hardware_concurrency(), 16u));
+  decoder_ctx->thread_type = FF_THREAD_FRAME | FF_THREAD_SLICE;
+    
+  // Very aggressive frame dropping
+  decoder_ctx->flags |= AV_CODEC_FLAG_LOW_DELAY;
+  decoder_ctx->flags2 |= AV_CODEC_FLAG2_FAST;
+  decoder_ctx->skip_frame = AVDISCARD_BIDIR;  // More aggressive frame skipping
+  decoder_ctx->skip_loop_filter = AVDISCARD_ALL;
+  decoder_ctx->workaround_bugs = FF_BUG_AUTODETECT;
+    
+  // Minimize buffering
+  decoder_ctx->max_b_frames = 0;
+  decoder_ctx->strict_std_compliance = FF_COMPLIANCE_UNOFFICIAL;  // Allow faster non-standard optimizations
+  decoder_ctx->flags |= AV_CODEC_FLAG_OUTPUT_CORRUPT;  // Output frames even if slightly corrupted
+
   width = (decoder_ctx->width + 3) & ~3;
   height = decoder_ctx->height;