Revert ISP image processing + tinygrad bump (#34020)

* Revert "Replace ThneedModel with TinygradModel (#33532)" This reverts commit da952e9b64. * Revert "camerad: move E + D cams image pipelines to the IFE (#33959)" This reverts commit f2a1cce42b.
2024-11-13 19:27:11 -08:00 · 2024-11-13 19:27:11 -08:00 · d9d57e5d6f
parent 3dc970960d
commit d9d57e5d6f
27 changed files with 1020 additions and 150 deletions
--- a/release/release_files.py
+++ b/release/release_files.py
@ -55,7 +55,7 @@ whitelist = [
  "tools/joystick/",
  "tools/longitudinal_maneuvers/",

-  "tinygrad_repo/examples/openpilot/compile3.py",
+  "tinygrad_repo/openpilot/compile2.py",
  "tinygrad_repo/extra/onnx.py",
  "tinygrad_repo/extra/onnx_ops.py",
  "tinygrad_repo/extra/thneed.py",
--- a/selfdrive/modeld/SConscript
+++ b/selfdrive/modeld/SConscript
@ -13,6 +13,15 @@ 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":
@ -50,18 +59,20 @@ 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)

-# Compile tinygrad model
-# TODO this is all super hacky
-pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
-if arch == 'larch64':
-  device_string = 'QCOM=1'
-elif arch == 'Darwin' or arch == 'aarch64':
-  device_string = 'CLANG=1 IMAGE=0'
-else:
-  device_string = 'GPU=1'
+# 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"

-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)
+  lenv.Command(fn + ".thneed", [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'])
--- a/selfdrive/modeld/dmonitoringmodeld
+++ b/selfdrive/modeld/dmonitoringmodeld
@ -1,4 +1,10 @@
 #!/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 "$DIR/dmonitoringmodeld.py" "$@"
--- a/selfdrive/modeld/dmonitoringmodeld.py
+++ b/selfdrive/modeld/dmonitoringmodeld.py
@ -1,16 +1,8 @@
 #!/usr/bin/env python3
 import os
-from openpilot.system.hardware import TICI
-## TODO this is hack
-if TICI:
-  GPU_BACKEND = 'QCOM'
-else:
-  GPU_BACKEND = 'GPU'
-os.environ[GPU_BACKEND] = '1'
 import gc
 import math
 import time
-import pickle
 import ctypes
 import numpy as np
 from pathlib import Path
@ -22,11 +14,9 @@ from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
 from openpilot.common.realtime import set_realtime_priority
-from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext #, cl_from_visionbuf
+from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
+from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext
 from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid
-#from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
-from tinygrad.tensor import Tensor
-#from tinygrad.dtype import dtypes

 CALIB_LEN = 3
 MODEL_WIDTH = 1440
@ -36,7 +26,9 @@ OUTPUT_SIZE = 84 + FEATURE_LEN

 PROCESS_NAME = "selfdrive.modeld.dmonitoringmodeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
-MODEL_PKL_PATH = Path(__file__).parent / 'models/dmonitoring_model_tinygrad.pkl'
+MODEL_PATHS = {
+  ModelRunner.THNEED: Path(__file__).parent / 'models/dmonitoring_model.thneed',
+  ModelRunner.ONNX: Path(__file__).parent / 'models/dmonitoring_model.onnx'}

 class DriverStateResult(ctypes.Structure):
  _fields_ = [
@ -67,32 +59,33 @@ 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.numpy_inputs = {'calib': np.zeros((1, CALIB_LEN), dtype=np.float32),
-                         'input_img': np.zeros((1,MODEL_HEIGHT * MODEL_WIDTH), dtype=np.uint8)}
-    self.img = None
+    self.output = np.zeros(OUTPUT_SIZE, dtype=np.float32)
+    self.inputs = {
+      'input_img': np.zeros(MODEL_HEIGHT * MODEL_WIDTH, dtype=np.uint8),
+      'calib': np.zeros(CALIB_LEN, dtype=np.float32)}

-
-    with open(MODEL_PKL_PATH, "rb") as f:
-      self.model_run = pickle.load(f)
+    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'])

  def run(self, buf:VisionBuf, calib:np.ndarray) -> tuple[np.ndarray, float]:
-    self.numpy_inputs['calib'][0,:] = calib
+    self.inputs['calib'][:] = calib

-    t1 = time.perf_counter()
-    # TODO use opencl buffer directly to make tensor
    v_offset = buf.height - MODEL_HEIGHT
    h_offset = (buf.width - MODEL_WIDTH) // 2
    buf_data = buf.data.reshape(-1, buf.stride)
-    self.numpy_inputs['input_img'][:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH].reshape((1, -1))
-
-    tensor_inputs = {k: Tensor(v) for k,v in self.numpy_inputs.items()}
-    output = self.model_run(**tensor_inputs)['outputs'].numpy().flatten()
+    input_data = self.inputs['input_img'].reshape(MODEL_HEIGHT, MODEL_WIDTH)
+    input_data[:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH]

+    self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
+    t1 = time.perf_counter()
+    self.model.execute()
    t2 = time.perf_counter()
-    return output, t2 - t1
+    return self.output, t2 - t1


 def fill_driver_state(msg, ds_result: DriverStateResult):
--- a/selfdrive/modeld/modeld.py
+++ b/selfdrive/modeld/modeld.py
@ -1,12 +1,5 @@
 #!/usr/bin/env python3
 import os
-from openpilot.system.hardware import TICI
-## TODO this is hack
-if TICI:
-  GPU_BACKEND = 'QCOM'
-else:
-  GPU_BACKEND = 'GPU'
-os.environ[GPU_BACKEND] = '1'
 import time
 import pickle
 import numpy as np
@ -25,24 +18,21 @@ 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 ModelFrame, CLContext
-from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
-
-from tinygrad.tensor import Tensor
-from tinygrad.dtype import dtypes

 PROCESS_NAME = "selfdrive.modeld.modeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')

-MODEL_PATH = Path(__file__).parent / 'models/supercombo.onnx'
-MODEL_PKL_PATH = Path(__file__).parent / 'models/supercombo_tinygrad.pkl'
+MODEL_PATHS = {
+  ModelRunner.THNEED: Path(__file__).parent / 'models/supercombo.thneed',
+  ModelRunner.ONNX: Path(__file__).parent / 'models/supercombo.onnx'}
+
 METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'

-# TODO: should not hardcoded
-IMG_INPUT_SHAPE = (1, 12, 128, 256)

 class FrameMeta:
  frame_id: int = 0
@ -59,6 +49,7 @@ class ModelState:
  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 = ModelFrame(context)
@ -69,14 +60,13 @@ class ModelState:
    self.prev_desired_curv_20hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32)

    # img buffers are managed in openCL transform code
-    self.numpy_inputs = {
-      'desire': np.zeros((1, (ModelConstants.HISTORY_BUFFER_LEN+1), ModelConstants.DESIRE_LEN), dtype=np.float32),
-      'traffic_convention': np.zeros((1, ModelConstants.TRAFFIC_CONVENTION_LEN), dtype=np.float32),
-      'lateral_control_params': np.zeros((1, ModelConstants.LATERAL_CONTROL_PARAMS_LEN), dtype=np.float32),
-      'prev_desired_curv': np.zeros((1,(ModelConstants.HISTORY_BUFFER_LEN+1), ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32),
-      'features_buffer': np.zeros((1, ModelConstants.HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32),
+    self.inputs = {
+      'desire': np.zeros(ModelConstants.DESIRE_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
+      'traffic_convention': np.zeros(ModelConstants.TRAFFIC_CONVENTION_LEN, dtype=np.float32),
+      'lateral_control_params': np.zeros(ModelConstants.LATERAL_CONTROL_PARAMS_LEN, dtype=np.float32),
+      'prev_desired_curv': np.zeros(ModelConstants.PREV_DESIRED_CURV_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
+      'features_buffer': np.zeros(ModelConstants.HISTORY_BUFFER_LEN * ModelConstants.FEATURE_LEN, dtype=np.float32),
    }
-    self.img_inputs = {} # type: ignore

    with open(METADATA_PATH, 'rb') as f:
      model_metadata = pickle.load(f)
@ -86,8 +76,11 @@ class ModelState:
    self.output = np.zeros(net_output_size, dtype=np.float32)
    self.parser = Parser()

-    with open(MODEL_PKL_PATH, "rb") as f:
-      self.model_run = pickle.load(f)
+    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)

  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()}
@ -104,27 +97,18 @@ class ModelState:

    self.desire_20Hz[:-1] = self.desire_20Hz[1:]
    self.desire_20Hz[-1] = new_desire
-    self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape((1,25,4,-1)).max(axis=2)
+    self.inputs['desire'][:] = self.desire_20Hz.reshape((25,4,-1)).max(axis=1).flatten()

-    self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
-    self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
-    input_imgs_cl = self.frame.prepare(buf, transform.flatten())
-    big_input_imgs_cl = self.wide_frame.prepare(wbuf, transform_wide.flatten())
+    self.inputs['traffic_convention'][:] = inputs['traffic_convention']
+    self.inputs['lateral_control_params'][:] = inputs['lateral_control_params']

-    if TICI:
-      # The imgs tensors are backed by opencl memory, only need init once
-      if 'input_imgs' not in self.img_inputs:
-        self.img_inputs['input_imgs'] = qcom_tensor_from_opencl_address(input_imgs_cl.mem_address, IMG_INPUT_SHAPE, dtype=dtypes.uint8)
-        self.img_inputs['big_input_imgs'] = qcom_tensor_from_opencl_address(big_input_imgs_cl.mem_address, IMG_INPUT_SHAPE, dtype=dtypes.uint8)
-    else:
-      self.img_inputs['input_imgs'] = Tensor(self.frame.buffer_from_cl(input_imgs_cl)).reshape(IMG_INPUT_SHAPE)
-      self.img_inputs['big_input_imgs'] = Tensor(self.wide_frame.buffer_from_cl(big_input_imgs_cl)).reshape(IMG_INPUT_SHAPE)
+    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")))

-    tensor_inputs = {**self.img_inputs, **{k: Tensor(v) for k,v in self.numpy_inputs.items()}}
    if prepare_only:
      return None

-    self.output = self.model_run(**tensor_inputs)['outputs'].numpy().flatten()
+    self.model.execute()
    outputs = self.parser.parse_outputs(self.slice_outputs(self.output))

    self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
@ -134,9 +118,9 @@ class ModelState:
    self.prev_desired_curv_20hz[-1] = outputs['desired_curvature'][0, :]

    idxs = np.arange(-4,-100,-4)[::-1]
-    self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[idxs]
+    self.inputs['features_buffer'][:] = self.full_features_20Hz[idxs].flatten()
    # TODO model only uses last value now, once that changes we need to input strided action history buffer
-    self.numpy_inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
+    self.inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
    return outputs


@ -205,7 +189,7 @@ def main(demo=False):
  cloudlog.info("modeld got CarParams: %s", CP.carName)

  # TODO this needs more thought, use .2s extra for now to estimate other delays
-  steer_delay =  .2
+  steer_delay = CP.steerActuatorDelay + .2

  DH = DesireHelper()

--- a/selfdrive/modeld/models/commonmodel.cc
+++ b/selfdrive/modeld/models/commonmodel.cc
@ -8,7 +8,6 @@

 ModelFrame::ModelFrame(cl_device_id device_id, cl_context context) {
  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));

  q = CL_CHECK_ERR(clCreateCommandQueue(context, device_id, 0, &err));
  y_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, MODEL_WIDTH * MODEL_HEIGHT, NULL, &err));
@ -23,7 +22,7 @@ ModelFrame::ModelFrame(cl_device_id device_id, cl_context context) {
  loadyuv_init(&loadyuv, context, device_id, MODEL_WIDTH, MODEL_HEIGHT);
 }

-cl_mem* ModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3 &projection) {
+uint8_t* ModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3 &projection, cl_mem *output) {
  transform_queue(&this->transform, q,
                yuv_cl, frame_width, frame_height, frame_stride, frame_uv_offset,
                y_cl, u_cl, v_cl, MODEL_WIDTH, MODEL_HEIGHT, projection);
@ -32,19 +31,19 @@ cl_mem* ModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, in
    CL_CHECK(clEnqueueCopyBuffer(q, img_buffer_20hz_cl, img_buffer_20hz_cl, (i+1)*frame_size_bytes, i*frame_size_bytes, frame_size_bytes, 0, nullptr, nullptr));
  }
  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 &input_frames_cl;
-}
-
-uint8_t* ModelFrame::buffer_from_cl(cl_mem *in_frames) {
-  CL_CHECK(clEnqueueReadBuffer(q, *in_frames, CL_TRUE, 0, MODEL_FRAME_SIZE * 2 * sizeof(uint8_t), &input_frames[0], 0, nullptr, nullptr));
-  clFinish(q);
-  return &input_frames[0];
+    // NOTE: Since thneed is using a different command queue, this clFinish is needed to ensure the image is ready.
+    clFinish(q);
+    return NULL;
+  }
 }

 ModelFrame::~ModelFrame() {
--- a/selfdrive/modeld/models/commonmodel.h
+++ b/selfdrive/modeld/models/commonmodel.h
@ -20,8 +20,7 @@ class ModelFrame {
 public:
  ModelFrame(cl_device_id device_id, cl_context context);
  ~ModelFrame();
-  cl_mem* prepare(cl_mem yuv_cl, int width, int height, int frame_stride, int frame_uv_offset, const mat3& transform);
-  uint8_t* buffer_from_cl(cl_mem *in_frames);
+  uint8_t* prepare(cl_mem yuv_cl, int width, int height, int frame_stride, int frame_uv_offset, const mat3& transform, cl_mem *output);

  const int MODEL_WIDTH = 512;
  const int MODEL_HEIGHT = 256;
@ -33,7 +32,7 @@ private:
  Transform transform;
  LoadYUVState loadyuv;
  cl_command_queue q;
-  cl_mem y_cl, u_cl, v_cl, img_buffer_20hz_cl, last_img_cl, input_frames_cl;
+  cl_mem y_cl, u_cl, v_cl, img_buffer_20hz_cl, last_img_cl;
  cl_buffer_region region;
  std::unique_ptr<uint8_t[]> input_frames;
 };
--- a/selfdrive/modeld/models/commonmodel.pxd
+++ b/selfdrive/modeld/models/commonmodel.pxd
@ -15,5 +15,4 @@ cdef extern from "selfdrive/modeld/models/commonmodel.h":
  cppclass ModelFrame:
    int buf_size
    ModelFrame(cl_device_id, cl_context)
-    cl_mem * prepare(cl_mem, int, int, int, int, mat3)
-    unsigned char * buffer_from_cl(cl_mem*);
+    unsigned char * prepare(cl_mem, int, int, int, int, mat3, cl_mem*)
--- a/selfdrive/modeld/models/commonmodel_pyx.pyx
+++ b/selfdrive/modeld/models/commonmodel_pyx.pyx
@ -4,7 +4,6 @@
 import numpy as np
 cimport numpy as cnp
 from libc.string cimport memcpy
-from libc.stdint cimport uintptr_t

 from msgq.visionipc.visionipc cimport cl_mem
 from msgq.visionipc.visionipc_pyx cimport VisionBuf, CLContext as BaseCLContext
@ -24,13 +23,6 @@ cdef class CLMem:
    mem.mem = <cl_mem*> cmem
    return mem

-  @property
-  def mem_address(self):
-    return <uintptr_t>(self.mem)
-
-def cl_from_visionbuf(VisionBuf buf):
-  return CLMem.create(<void*>&buf.buf.buf_cl)
-
 cdef class ModelFrame:
  cdef cppModelFrame * frame

@ -40,14 +32,14 @@ cdef class ModelFrame:
  def __dealloc__(self):
    del self.frame

-  def prepare(self, VisionBuf buf, float[:] projection):
+  def prepare(self, VisionBuf buf, float[:] projection, CLMem output):
    cdef mat3 cprojection
    memcpy(cprojection.v, &projection[0], 9*sizeof(float))
-    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)
-
-  def buffer_from_cl(self, CLMem in_frames):
-    cdef unsigned char * data2
-    data2 = self.frame.buffer_from_cl(in_frames.mem)
-    return np.asarray(<cnp.uint8_t[:self.frame.buf_size]> data2)
+    cdef unsigned char * 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
+    return np.asarray(<cnp.uint8_t[:self.frame.buf_size]> data)
--- a/selfdrive/modeld/runners/init.py
+++ b/selfdrive/modeld/runners/init.py
@ -3,18 +3,18 @@ from openpilot.system.hardware import TICI
 from openpilot.selfdrive.modeld.runners.runmodel_pyx import RunModel, Runtime
 assert Runtime

-USE_TINYGRAD = int(os.getenv('USE_TINYGRAD', str(int(TICI))))
+USE_THNEED = int(os.getenv('USE_THNEED', str(int(TICI))))
 USE_SNPE = int(os.getenv('USE_SNPE', str(int(TICI))))

 class ModelRunner(RunModel):
-  TINYGRAD = 'TINYGRAD'
+  THNEED = 'THNEED'
  SNPE = 'SNPE'
  ONNX = 'ONNX'

  def __new__(cls, paths, *args, **kwargs):
-    if ModelRunner.TINYGRAD in paths and USE_TINYGRAD:
-      from openpilot.selfdrive.modeld.runners.tinygradmodel import TinygradModel as Runner
-      runner_type = ModelRunner.TINYGRAD
+    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
--- a/selfdrive/modeld/runners/runmodel_pyx.pyx
+++ b/selfdrive/modeld/runners/runmodel_pyx.pyx
@ -5,7 +5,6 @@ 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
-import numpy as np

 class Runtime:
  CPU = USE_CPU_RUNTIME
@ -22,12 +21,11 @@ cdef class RunModel:
    else:
      self.model.addInput(name, NULL, 0)

-  def setInputBuffer(self, string name, unsigned char[:] input_buffer):
-    cdef int num_floats = len(input_buffer) // sizeof(float)
-    cdef float* float_ptr = <float*> &input_buffer[0]
-    cdef float[:] float_buffer_view = <float[:num_floats]> float_ptr
-    if float_buffer_view is not None:
-      self.model.setInputBuffer(name, &float_buffer_view[0], num_floats)
+  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)
--- a/selfdrive/modeld/runners/thneedmodel.cc
+++ b/selfdrive/modeld/runners/thneedmodel.cc
@ -0,0 +1,58 @@
+#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);
+  }
+}
--- a/selfdrive/modeld/runners/thneedmodel.h
+++ b/selfdrive/modeld/runners/thneedmodel.h
@ -0,0 +1,17 @@
+#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;
+};
--- a/selfdrive/modeld/runners/thneedmodel.pxd
+++ b/selfdrive/modeld/runners/thneedmodel.pxd
@ -0,0 +1,9 @@
+# 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)
--- a/selfdrive/modeld/runners/thneedmodel_pyx.pyx
+++ b/selfdrive/modeld/runners/thneedmodel_pyx.pyx
@ -0,0 +1,14 @@
+# 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)
--- a/selfdrive/modeld/runners/tinygrad_helpers.py
+++ b/selfdrive/modeld/runners/tinygrad_helpers.py
@ -1,8 +0,0 @@
-
-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')
--- a/selfdrive/modeld/thneed/README
+++ b/selfdrive/modeld/thneed/README
@ -0,0 +1,8 @@
+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.
+
--- a/selfdrive/modeld/thneed/init.py
+++ b/selfdrive/modeld/thneed/init.py
--- a/selfdrive/modeld/thneed/serialize.cc
+++ b/selfdrive/modeld/thneed/serialize.cc
@ -0,0 +1,154 @@
+#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);
+}
--- a/selfdrive/modeld/thneed/thneed.h
+++ b/selfdrive/modeld/thneed/thneed.h
@ -0,0 +1,133 @@
+#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();
+};
+
--- a/selfdrive/modeld/thneed/thneed_common.cc
+++ b/selfdrive/modeld/thneed/thneed_common.cc
@ -0,0 +1,216 @@
+#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;
+}
--- a/selfdrive/modeld/thneed/thneed_pc.cc
+++ b/selfdrive/modeld/thneed/thneed_pc.cc
@ -0,0 +1,32 @@
+#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);
+  }
+}
--- a/selfdrive/modeld/thneed/thneed_qcom2.cc
+++ b/selfdrive/modeld/thneed/thneed_qcom2.cc
@ -0,0 +1,258 @@
+#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);
+  }
+}
--- a/selfdrive/test/test_onroad.py
+++ b/selfdrive/test/test_onroad.py
@ -36,7 +36,7 @@ CPU usage budget
 TEST_DURATION = 25
 LOG_OFFSET = 8

-MAX_TOTAL_CPU = 275.  # total for all 8 cores
+MAX_TOTAL_CPU = 265.  # 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": 22.0,
-  "selfdrive.modeld.dmonitoringmodeld": 21.0,
+  "selfdrive.modeld.modeld": 17.0,
+  "selfdrive.modeld.dmonitoringmodeld": 11.0,
  "system.hardware.hardwared": 4.0,
  "selfdrive.locationd.calibrationd": 2.0,
  "selfdrive.locationd.torqued": 5.0,
@ -361,15 +361,13 @@ class TestOnroad:
    result += "------------------------------------------------\n"
    result += "----------------- Model Timing -----------------\n"
    result += "------------------------------------------------\n"
-    # TODO: Decrease again when tinygrad speeds ups
+    # TODO: this went up when plannerd cpu usage increased, why?
    cfgs = [
-      ("modelV2", 0.050, 0.040),
+      ("modelV2", 0.050, 0.036),
      ("driverStateV2", 0.050, 0.026),
    ]
    for (s, instant_max, avg_max) in cfgs:
      ts = [getattr(m, s).modelExecutionTime for m in self.msgs[s]]
-      # TODO some tinygrad init happens in first iteration
-      ts = ts[1:]
      assert max(ts) < instant_max, f"high '{s}' execution time: {max(ts)}"
      assert np.mean(ts) < avg_max, f"high avg '{s}' execution time: {np.mean(ts)}"
      result += f"'{s}' execution time: min  {min(ts):.5f}s\n"
--- a/system/camerad/cameras/camera_qcom2.cc
+++ b/system/camerad/cameras/camera_qcom2.cc
@ -55,7 +55,7 @@ public:

  float fl_pix = 0;

-  CameraState(SpectraMaster *master, const CameraConfig &config) : camera(master, config, config.stream_type == VISION_STREAM_ROAD) {};
+  CameraState(SpectraMaster *master, const CameraConfig &config) : camera(master, config, true /*config.stream_type == VISION_STREAM_ROAD*/) {};
  ~CameraState();
  void init(VisionIpcServer *v, cl_device_id device_id, cl_context ctx);
  void update_exposure_score(float desired_ev, int exp_t, int exp_g_idx, float exp_gain);
--- a/system/hardware/tici/tests/test_power_draw.py
+++ b/system/hardware/tici/tests/test_power_draw.py
@ -31,7 +31,7 @@ class Proc:


 PROCS = [
-  Proc(['camerad'], 1.75, msgs=['roadCameraState', 'wideRoadCameraState', 'driverCameraState']),
+  Proc(['camerad'], 2.1, msgs=['roadCameraState', 'wideRoadCameraState', 'driverCameraState']),
  Proc(['modeld'], 1.12, atol=0.2, msgs=['modelV2']),
  Proc(['dmonitoringmodeld'], 0.5, msgs=['driverStateV2']),
  Proc(['encoderd'], 0.23, msgs=[]),
--- a/2
+++ b/2
@ -1 +1 @@
-Subproject commit ad119af6a511373e1c016a6525ab733f14a60c51
+Subproject commit 9dda6d260db0255750bacff61e3cee1e580567e1