mirror of https://github.com/commaai/openpilot.git
Replace ThneedModel with TinygradModel (#33532)
* squash * bump tg * bump tg * debump tinygrad * bump tinygrad * bump tg * Skip init iteration * fixes * cleanups * skip first test sample * typos * linter unhappy * update cpu usage * OPENCL just zeros for now * imports * Try printing * Runs again, but slower * unused import * Allow more buffer with tg and all on gpu * bump tinygrad --------- Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com> Co-authored-by: Bruce Wayne <harald.the.engineer@gmail.com>
This commit is contained in:
Mitchell Goff
GitHub
parent
127c922aed
commit
da952e9b64
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@ -55,7 +55,7 @@ whitelist = [
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"tools/joystick/",
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"tools/longitudinal_maneuvers/",
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"tinygrad_repo/openpilot/compile2.py",
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"tinygrad_repo/examples/openpilot/compile3.py",
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"tinygrad_repo/extra/onnx.py",
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"tinygrad_repo/extra/onnx_ops.py",
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"tinygrad_repo/extra/thneed.py",
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@ -13,15 +13,6 @@ common_src = [
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"transforms/transform.cc",
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]
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thneed_src_common = [
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"thneed/thneed_common.cc",
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"thneed/serialize.cc",
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]
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thneed_src_qcom = thneed_src_common + ["thneed/thneed_qcom2.cc"]
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thneed_src_pc = thneed_src_common + ["thneed/thneed_pc.cc"]
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thneed_src = thneed_src_qcom if arch == "larch64" else thneed_src_pc
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# SNPE except on Mac and ARM Linux
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snpe_lib = []
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if arch != "Darwin" and arch != "aarch64":
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@ -59,20 +50,18 @@ fn = File("models/supercombo").abspath
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cmd = f'python3 {Dir("#selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
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lenv.Command(fn + "_metadata.pkl", [fn + ".onnx"] + tinygrad_files, cmd)
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# Build thneed model
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if arch == "larch64" or GetOption('pc_thneed'):
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tinygrad_opts = []
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if not GetOption('pc_thneed'):
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# use FLOAT16 on device for speed + don't cache the CL kernels for space
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tinygrad_opts += ["FLOAT16=1", "PYOPENCL_NO_CACHE=1"]
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cmd = f"cd {Dir('#').abspath}/tinygrad_repo && " + ' '.join(tinygrad_opts) + f" python3 openpilot/compile2.py {fn}.onnx {fn}.thneed"
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# Compile tinygrad model
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# TODO this is all super hacky
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pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
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if arch == 'larch64':
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device_string = 'QCOM=1'
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elif arch == 'Darwin' or arch == 'aarch64':
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device_string = 'CLANG=1 IMAGE=0'
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else:
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device_string = 'GPU=1'
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lenv.Command(fn + ".thneed", [fn + ".onnx"] + tinygrad_files, cmd)
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for model_name in ['supercombo', 'dmonitoring_model']:
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fn = File(f"models/{model_name}").abspath
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cmd = f'{pythonpath_string} {device_string} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {fn}_tinygrad.pkl'
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lenv.Command(fn + "_tinygrad.pkl", [fn + ".onnx"] + tinygrad_files, cmd)
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fn_dm = File("models/dmonitoring_model").abspath
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cmd = f"cd {Dir('#').abspath}/tinygrad_repo && " + ' '.join(tinygrad_opts) + f" python3 openpilot/compile2.py {fn_dm}.onnx {fn_dm}.thneed"
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lenv.Command(fn_dm + ".thneed", [fn_dm + ".onnx"] + tinygrad_files, cmd)
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thneed_lib = env.SharedLibrary('thneed', thneed_src, LIBS=[gpucommon, common, 'OpenCL', 'dl'])
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thneedmodel_lib = env.Library('thneedmodel', ['runners/thneedmodel.cc'])
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lenvCython.Program('runners/thneedmodel_pyx.so', 'runners/thneedmodel_pyx.pyx', LIBS=envCython["LIBS"]+[thneedmodel_lib, thneed_lib, gpucommon, common, 'dl', 'OpenCL'])
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@ -1,10 +1,4 @@
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#!/usr/bin/env bash
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DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null && pwd)"
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cd "$DIR/../../"
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if [ -f "$DIR/libthneed.so" ]; then
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export LD_PRELOAD="$DIR/libthneed.so"
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fi
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exec "$DIR/dmonitoringmodeld.py" "$@"
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@ -1,8 +1,16 @@
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#!/usr/bin/env python3
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import os
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from openpilot.system.hardware import TICI
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## TODO this is hack
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if TICI:
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GPU_BACKEND = 'QCOM'
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else:
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GPU_BACKEND = 'GPU'
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os.environ[GPU_BACKEND] = '1'
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import gc
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import math
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import time
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import pickle
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import ctypes
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import numpy as np
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from pathlib import Path
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@ -14,9 +22,11 @@ from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
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from openpilot.common.swaglog import cloudlog
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from openpilot.common.params import Params
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from openpilot.common.realtime import set_realtime_priority
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from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
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from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext
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from openpilot.selfdrive.modeld.models.commonmodel_pyx import CLContext #, cl_from_visionbuf
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from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid
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#from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
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from tinygrad.tensor import Tensor
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#from tinygrad.dtype import dtypes
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CALIB_LEN = 3
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MODEL_WIDTH = 1440
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@ -26,9 +36,7 @@ OUTPUT_SIZE = 84 + FEATURE_LEN
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PROCESS_NAME = "selfdrive.modeld.dmonitoringmodeld"
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SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
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MODEL_PATHS = {
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ModelRunner.THNEED: Path(__file__).parent / 'models/dmonitoring_model.thneed',
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ModelRunner.ONNX: Path(__file__).parent / 'models/dmonitoring_model.onnx'}
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MODEL_PKL_PATH = Path(__file__).parent / 'models/dmonitoring_model_tinygrad.pkl'
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class DriverStateResult(ctypes.Structure):
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_fields_ = [
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@ -59,33 +67,32 @@ class DMonitoringModelResult(ctypes.Structure):
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class ModelState:
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inputs: dict[str, np.ndarray]
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output: np.ndarray
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model: ModelRunner
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def __init__(self, cl_ctx):
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assert ctypes.sizeof(DMonitoringModelResult) == OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
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self.output = np.zeros(OUTPUT_SIZE, dtype=np.float32)
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self.inputs = {
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'input_img': np.zeros(MODEL_HEIGHT * MODEL_WIDTH, dtype=np.uint8),
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'calib': np.zeros(CALIB_LEN, dtype=np.float32)}
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self.numpy_inputs = {'calib': np.zeros((1, CALIB_LEN), dtype=np.float32),
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'input_img': np.zeros((1,MODEL_HEIGHT * MODEL_WIDTH), dtype=np.uint8)}
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self.img = None
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self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.GPU, False, cl_ctx)
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self.model.addInput("input_img", None)
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self.model.addInput("calib", self.inputs['calib'])
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with open(MODEL_PKL_PATH, "rb") as f:
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self.model_run = pickle.load(f)
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def run(self, buf:VisionBuf, calib:np.ndarray) -> tuple[np.ndarray, float]:
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self.inputs['calib'][:] = calib
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self.numpy_inputs['calib'][0,:] = calib
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t1 = time.perf_counter()
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# TODO use opencl buffer directly to make tensor
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v_offset = buf.height - MODEL_HEIGHT
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h_offset = (buf.width - MODEL_WIDTH) // 2
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buf_data = buf.data.reshape(-1, buf.stride)
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input_data = self.inputs['input_img'].reshape(MODEL_HEIGHT, MODEL_WIDTH)
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input_data[:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH]
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self.numpy_inputs['input_img'][:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH].reshape((1, -1))
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tensor_inputs = {k: Tensor(v) for k,v in self.numpy_inputs.items()}
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output = self.model_run(**tensor_inputs)['outputs'].numpy().flatten()
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self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
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t1 = time.perf_counter()
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self.model.execute()
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t2 = time.perf_counter()
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return self.output, t2 - t1
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return output, t2 - t1
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def fill_driver_state(msg, ds_result: DriverStateResult):
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@ -1,5 +1,12 @@
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#!/usr/bin/env python3
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import os
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from openpilot.system.hardware import TICI
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## TODO this is hack
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if TICI:
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GPU_BACKEND = 'QCOM'
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else:
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GPU_BACKEND = 'GPU'
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os.environ[GPU_BACKEND] = '1'
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import time
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import pickle
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import numpy as np
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@ -18,21 +25,24 @@ from openpilot.common.transformations.camera import DEVICE_CAMERAS
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from openpilot.common.transformations.model import get_warp_matrix
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from openpilot.system import sentry
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from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
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from openpilot.selfdrive.modeld.runners import ModelRunner, Runtime
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from openpilot.selfdrive.modeld.parse_model_outputs import Parser
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from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState
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from openpilot.selfdrive.modeld.constants import ModelConstants
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from openpilot.selfdrive.modeld.models.commonmodel_pyx import ModelFrame, CLContext
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from openpilot.selfdrive.modeld.runners.tinygrad_helpers import qcom_tensor_from_opencl_address
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from tinygrad.tensor import Tensor
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from tinygrad.dtype import dtypes
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PROCESS_NAME = "selfdrive.modeld.modeld"
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SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
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MODEL_PATHS = {
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ModelRunner.THNEED: Path(__file__).parent / 'models/supercombo.thneed',
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ModelRunner.ONNX: Path(__file__).parent / 'models/supercombo.onnx'}
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MODEL_PATH = Path(__file__).parent / 'models/supercombo.onnx'
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MODEL_PKL_PATH = Path(__file__).parent / 'models/supercombo_tinygrad.pkl'
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METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'
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# TODO: should not hardcoded
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IMG_INPUT_SHAPE = (1, 12, 128, 256)
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class FrameMeta:
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frame_id: int = 0
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@ -49,7 +59,6 @@ class ModelState:
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inputs: dict[str, np.ndarray]
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output: np.ndarray
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prev_desire: np.ndarray # for tracking the rising edge of the pulse
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model: ModelRunner
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def __init__(self, context: CLContext):
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self.frame = ModelFrame(context)
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@ -60,13 +69,14 @@ class ModelState:
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self.prev_desired_curv_20hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32)
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# img buffers are managed in openCL transform code
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self.inputs = {
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'desire': np.zeros(ModelConstants.DESIRE_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
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'traffic_convention': np.zeros(ModelConstants.TRAFFIC_CONVENTION_LEN, dtype=np.float32),
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'lateral_control_params': np.zeros(ModelConstants.LATERAL_CONTROL_PARAMS_LEN, dtype=np.float32),
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'prev_desired_curv': np.zeros(ModelConstants.PREV_DESIRED_CURV_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
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'features_buffer': np.zeros(ModelConstants.HISTORY_BUFFER_LEN * ModelConstants.FEATURE_LEN, dtype=np.float32),
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self.numpy_inputs = {
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'desire': np.zeros((1, (ModelConstants.HISTORY_BUFFER_LEN+1), ModelConstants.DESIRE_LEN), dtype=np.float32),
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'traffic_convention': np.zeros((1, ModelConstants.TRAFFIC_CONVENTION_LEN), dtype=np.float32),
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'lateral_control_params': np.zeros((1, ModelConstants.LATERAL_CONTROL_PARAMS_LEN), dtype=np.float32),
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'prev_desired_curv': np.zeros((1,(ModelConstants.HISTORY_BUFFER_LEN+1), ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32),
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'features_buffer': np.zeros((1, ModelConstants.HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32),
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}
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self.img_inputs = {} # type: ignore
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with open(METADATA_PATH, 'rb') as f:
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model_metadata = pickle.load(f)
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@ -76,11 +86,8 @@ class ModelState:
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self.output = np.zeros(net_output_size, dtype=np.float32)
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self.parser = Parser()
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self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.GPU, False, context)
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self.model.addInput("input_imgs", None)
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self.model.addInput("big_input_imgs", None)
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for k,v in self.inputs.items():
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self.model.addInput(k, v)
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with open(MODEL_PKL_PATH, "rb") as f:
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self.model_run = pickle.load(f)
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def slice_outputs(self, model_outputs: np.ndarray) -> dict[str, np.ndarray]:
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parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in self.output_slices.items()}
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@ -97,18 +104,27 @@ class ModelState:
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self.desire_20Hz[:-1] = self.desire_20Hz[1:]
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self.desire_20Hz[-1] = new_desire
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self.inputs['desire'][:] = self.desire_20Hz.reshape((25,4,-1)).max(axis=1).flatten()
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self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape((1,25,4,-1)).max(axis=2)
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self.inputs['traffic_convention'][:] = inputs['traffic_convention']
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self.inputs['lateral_control_params'][:] = inputs['lateral_control_params']
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self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
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self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
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input_imgs_cl = self.frame.prepare(buf, transform.flatten())
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big_input_imgs_cl = self.wide_frame.prepare(wbuf, transform_wide.flatten())
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self.model.setInputBuffer("input_imgs", self.frame.prepare(buf, transform.flatten(), self.model.getCLBuffer("input_imgs")))
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self.model.setInputBuffer("big_input_imgs", self.wide_frame.prepare(wbuf, transform_wide.flatten(), self.model.getCLBuffer("big_input_imgs")))
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if TICI:
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# The imgs tensors are backed by opencl memory, only need init once
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if 'input_imgs' not in self.img_inputs:
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self.img_inputs['input_imgs'] = qcom_tensor_from_opencl_address(input_imgs_cl.mem_address, IMG_INPUT_SHAPE, dtype=dtypes.uint8)
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self.img_inputs['big_input_imgs'] = qcom_tensor_from_opencl_address(big_input_imgs_cl.mem_address, IMG_INPUT_SHAPE, dtype=dtypes.uint8)
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else:
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self.img_inputs['input_imgs'] = Tensor(self.frame.buffer_from_cl(input_imgs_cl)).reshape(IMG_INPUT_SHAPE)
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self.img_inputs['big_input_imgs'] = Tensor(self.wide_frame.buffer_from_cl(big_input_imgs_cl)).reshape(IMG_INPUT_SHAPE)
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tensor_inputs = {**self.img_inputs, **{k: Tensor(v) for k,v in self.numpy_inputs.items()}}
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if prepare_only:
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return None
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self.model.execute()
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self.output = self.model_run(**tensor_inputs)['outputs'].numpy().flatten()
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outputs = self.parser.parse_outputs(self.slice_outputs(self.output))
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self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
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@ -118,9 +134,9 @@ class ModelState:
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self.prev_desired_curv_20hz[-1] = outputs['desired_curvature'][0, :]
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idxs = np.arange(-4,-100,-4)[::-1]
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self.inputs['features_buffer'][:] = self.full_features_20Hz[idxs].flatten()
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self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[idxs]
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# TODO model only uses last value now, once that changes we need to input strided action history buffer
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self.inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
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self.numpy_inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
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return outputs
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@ -189,7 +205,7 @@ def main(demo=False):
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cloudlog.info("modeld got CarParams: %s", CP.carName)
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# TODO this needs more thought, use .2s extra for now to estimate other delays
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steer_delay = CP.steerActuatorDelay + .2
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steer_delay = .2
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DH = DesireHelper()
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@ -8,6 +8,7 @@
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ModelFrame::ModelFrame(cl_device_id device_id, cl_context context) {
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input_frames = std::make_unique<uint8_t[]>(buf_size);
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input_frames_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
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q = CL_CHECK_ERR(clCreateCommandQueue(context, device_id, 0, &err));
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y_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, MODEL_WIDTH * MODEL_HEIGHT, NULL, &err));
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@ -22,7 +23,7 @@ ModelFrame::ModelFrame(cl_device_id device_id, cl_context context) {
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loadyuv_init(&loadyuv, context, device_id, MODEL_WIDTH, MODEL_HEIGHT);
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}
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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) {
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cl_mem* ModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3 &projection) {
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transform_queue(&this->transform, q,
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yuv_cl, frame_width, frame_height, frame_stride, frame_uv_offset,
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y_cl, u_cl, v_cl, MODEL_WIDTH, MODEL_HEIGHT, projection);
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@ -31,19 +32,19 @@ uint8_t* ModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, i
|
|||
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 NULL;
|
||||
}
|
||||
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];
|
||||
}
|
||||
|
||||
ModelFrame::~ModelFrame() {
|
||||
|
|
|
|||
|
|
@ -20,7 +20,8 @@ class ModelFrame {
|
|||
public:
|
||||
ModelFrame(cl_device_id device_id, cl_context context);
|
||||
~ModelFrame();
|
||||
uint8_t* prepare(cl_mem yuv_cl, int width, int height, int frame_stride, int frame_uv_offset, const mat3& transform, cl_mem *output);
|
||||
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);
|
||||
|
||||
const int MODEL_WIDTH = 512;
|
||||
const int MODEL_HEIGHT = 256;
|
||||
|
|
@ -32,7 +33,7 @@ private:
|
|||
Transform transform;
|
||||
LoadYUVState loadyuv;
|
||||
cl_command_queue q;
|
||||
cl_mem y_cl, u_cl, v_cl, img_buffer_20hz_cl, last_img_cl;
|
||||
cl_mem y_cl, u_cl, v_cl, img_buffer_20hz_cl, last_img_cl, input_frames_cl;
|
||||
cl_buffer_region region;
|
||||
std::unique_ptr<uint8_t[]> input_frames;
|
||||
};
|
||||
|
|
@ -15,4 +15,5 @@ cdef extern from "selfdrive/modeld/models/commonmodel.h":
|
|||
cppclass ModelFrame:
|
||||
int buf_size
|
||||
ModelFrame(cl_device_id, cl_context)
|
||||
unsigned char * prepare(cl_mem, int, int, int, int, mat3, cl_mem*)
|
||||
cl_mem * prepare(cl_mem, int, int, int, int, mat3)
|
||||
unsigned char * buffer_from_cl(cl_mem*);
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
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
|
||||
|
|
@ -23,6 +24,13 @@ 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
|
||||
|
||||
|
|
@ -32,14 +40,14 @@ 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 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)
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -3,18 +3,18 @@ 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_TINYGRAD = int(os.getenv('USE_TINYGRAD', str(int(TICI))))
|
||||
USE_SNPE = int(os.getenv('USE_SNPE', str(int(TICI))))
|
||||
|
||||
class ModelRunner(RunModel):
|
||||
THNEED = 'THNEED'
|
||||
TINYGRAD = 'TINYGRAD'
|
||||
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
|
||||
if ModelRunner.TINYGRAD in paths and USE_TINYGRAD:
|
||||
from openpilot.selfdrive.modeld.runners.tinygradmodel import TinygradModel as Runner
|
||||
runner_type = ModelRunner.TINYGRAD
|
||||
elif ModelRunner.SNPE in paths and USE_SNPE:
|
||||
from openpilot.selfdrive.modeld.runners.snpemodel_pyx import SNPEModel as Runner
|
||||
runner_type = ModelRunner.SNPE
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ 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
|
||||
|
|
@ -21,11 +22,12 @@ cdef class RunModel:
|
|||
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 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 getCLBuffer(self, string name):
|
||||
cdef void * cl_buf = self.model.getCLBuffer(name)
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
@ -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;
|
||||
};
|
||||
|
|
@ -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)
|
||||
|
|
@ -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)
|
||||
|
|
@ -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')
|
||||
|
|
@ -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.
|
||||
|
||||
|
|
@ -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);
|
||||
}
|
||||
|
|
@ -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();
|
||||
};
|
||||
|
||||
|
|
@ -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;
|
||||
}
|
||||
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
@ -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);
|
||||
}
|
||||
}
|
||||
|
|
@ -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,
|
||||
|
|
@ -361,13 +361,15 @@ class TestOnroad:
|
|||
result += "------------------------------------------------\n"
|
||||
result += "----------------- Model Timing -----------------\n"
|
||||
result += "------------------------------------------------\n"
|
||||
# TODO: this went up when plannerd cpu usage increased, why?
|
||||
# TODO: Decrease again when tinygrad speeds ups
|
||||
cfgs = [
|
||||
("modelV2", 0.050, 0.036),
|
||||
("modelV2", 0.050, 0.040),
|
||||
("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"
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit 9dda6d260db0255750bacff61e3cee1e580567e1
|
||||
Subproject commit ad119af6a511373e1c016a6525ab733f14a60c51
|
||||
Loading…
Reference in New Issue