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github-actions[bot] 10e05bbf32 sunnypilot v2026.08.22-4716
version: sunnypilot v2026.003.000 (feature-branch)
date: 2026-08-22T07:35:47
master commit: 6b5a506b79
2026-08-22 07:35:47 +00:00
7 changed files with 16 additions and 117 deletions
-9
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@@ -16,15 +16,6 @@ MASTER_SP_BRANCHES = ['master']
RELEASE_BRANCHES = ['release-tizi-staging', 'release-mici-staging', 'release-tizi', 'release-mici', 'nightly'] RELEASE_BRANCHES = ['release-tizi-staging', 'release-mici-staging', 'release-tizi', 'release-mici', 'nightly']
TESTED_BRANCHES = RELEASE_BRANCHES + ['devel-staging', 'nightly-dev'] + RELEASE_SP_BRANCHES + TESTED_SP_BRANCHES TESTED_BRANCHES = RELEASE_BRANCHES + ['devel-staging', 'nightly-dev'] + RELEASE_SP_BRANCHES + TESTED_SP_BRANCHES
CHESTNUT_BRANCHES = {
"staging": "staging-chestnut",
"dev": "dev-chestnut",
"release-mici": "release-chestnut",
"release-tizi": "release-chestnut",
"release-mici-staging": "release-chestnut-staging",
"release-tizi-staging": "release-chestnut-staging",
}
SP_BRANCH_MIGRATIONS = { SP_BRANCH_MIGRATIONS = {
("tici", "staging-c3-new"): "staging-tici", ("tici", "staging-c3-new"): "staging-tici",
("tici", "dev-c3-new"): "staging-tici", ("tici", "dev-c3-new"): "staging-tici",
@@ -18,7 +18,7 @@
"_comment": "Set extra field to the failed reason." "_comment": "Set extra field to the failed reason."
}, },
"Offroad_ChestnutBranch": { "Offroad_ChestnutBranch": {
"text": "Chestnut detected! Switch to the %1 branch to use chestnut-class models.", "text": "Chestnut detected! Switch to the release-chestnut branch to use chestnut-class models.",
"severity": 0 "severity": 0
}, },
"Offroad_UnregisteredHardware": { "Offroad_UnregisteredHardware": {
+1 -1
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@@ -1 +1 @@
#define SUNNYPILOT_VERSION "2026.08.22-4721" #define SUNNYPILOT_VERSION "2026.08.22-4716"
@@ -7,7 +7,6 @@ See the LICENSE.md file in the root directory for more details.
""" """
import argparse import argparse
import math
import os import os
import tempfile import tempfile
import time import time
@@ -67,15 +66,14 @@ def get_policy_npy_shapes(input_shapes: dict, is_supercombo: bool = False) -> tu
if desire_key: if desire_key:
shapes['desire'] = (input_shapes[desire_key][2],) shapes['desire'] = (input_shapes[desire_key][2],)
if is_supercombo and 'features_buffer' in input_shapes:
fb = input_shapes['features_buffer']
shapes['prev_feat'] = (fb[0], fb[2])
for key, shape in input_shapes.items(): for key, shape in input_shapes.items():
if key not in (desire_key, 'features_buffer') and 'img' not in key: if key not in (desire_key, 'features_buffer') and 'img' not in key:
shapes[key] = tuple(shape) shapes[key] = tuple(shape)
if is_supercombo and 'features_buffer' in input_shapes:
fb = input_shapes['features_buffer']
feat_dim = math.prod(fb[2:])
shapes['prev_feat'] = (fb[0], feat_dim)
sizes = [int(np.prod(size)) for size in shapes.values()] sizes = [int(np.prod(size)) for size in shapes.values()]
return shapes, sizes return shapes, sizes
@@ -119,9 +117,8 @@ def generate_queues_and_npy(input_shapes: dict, frame_skip: int, device: str = D
} }
if features_buffer: if features_buffer:
feat_dim = math.prod(features_buffer[2:])
feat_q_len = frame_skip * features_buffer[1] if is_supercombo else frame_skip * (features_buffer[1] - 1) + 1 feat_q_len = frame_skip * features_buffer[1] if is_supercombo else frame_skip * (features_buffer[1] - 1) + 1
queues['feat_q'] = Tensor(np.zeros((feat_q_len, features_buffer[0], feat_dim), queues['feat_q'] = Tensor(np.zeros((feat_q_len, features_buffer[0], features_buffer[2]),
dtype=np.float32), device=device).contiguous().realize() dtype=np.float32), device=device).contiguous().realize()
queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')}) queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')})
@@ -186,14 +183,14 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
warped_dev = warped.to(Device.DEFAULT) warped_dev = warped.to(Device.DEFAULT)
Tensor.realize(packed_npy_inputs_dev, warped_dev) Tensor.realize(packed_npy_inputs_dev, warped_dev)
img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn) img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn).realize()
big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn) big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn).realize()
unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)] unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)]
unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True)) unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True))
desire_dev = unpacked_dict['desire'] desire_dev = unpacked_dict['desire']
desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn) desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()
inputs = {desire_key: desire_buf} inputs = {desire_key: desire_buf}
for key, tensor_val in unpacked_dict.items(): for key, tensor_val in unpacked_dict.items():
@@ -202,22 +199,19 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice,
if 'prev_feat' in unpacked_dict: if 'prev_feat' in unpacked_dict:
prev_feat_dev = unpacked_dict['prev_feat'] prev_feat_dev = unpacked_dict['prev_feat']
feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn) inputs['features_buffer'] = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()
inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
if vision_runner: if vision_runner:
vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize() vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize()
if 'features_buffer' not in inputs: if 'features_buffer' not in inputs:
new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0) new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0)
feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize() inputs['features_buffer'] = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners] policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0]) return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])
inputs.update({road_key: img, wide_key: big_img}) inputs.update({road_key: img, wide_key: big_img})
if 'features_buffer' not in inputs: if 'features_buffer' not in inputs:
feat_buf = sample_skip_fn(feat_q) inputs['features_buffer'] = sample_skip_fn(feat_q)
inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb)
policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize() policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
if 'features_buffer' not in inputs and features_slice is not None: if 'features_buffer' not in inputs and features_slice is not None:
@@ -195,85 +195,3 @@ class TestReadFileChunkedToDisk(OpenpilotTestCase):
assert out.parent == Path(d) assert out.parent == Path(d)
assert out.read_bytes() == payload assert out.read_bytes() == payload
class Test4DFeaturesBuffer(OpenpilotTestCase):
def test_get_policy_npy_shapes_4d(self):
from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes
input_shapes = {
'desire_pulse': (1, 25, 8),
'features_buffer': (1, 24, 32, 512), # compare 4d to 3d for regression
'traffic_convention': (1, 2),
'action_t': (1, 2)
}
shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=True)
assert shapes['prev_feat'] == (1, 16384)
assert sizes == [8, 2, 2, 16384]
def test_get_policy_npy_shapes_3d(self):
from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes
input_shapes = {
'desire_pulse': (1, 25, 8),
'features_buffer': (1, 24, 512),
'traffic_convention': (1, 2),
'action_t': (1, 2)
}
shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=True)
assert shapes['prev_feat'] == (1, 512)
assert sizes == [8, 2, 2, 512]
class TestStockCompileModeldEquivalence(OpenpilotTestCase):
def test_get_policy_npy_shapes_matches_stock(self):
from openpilot.selfdrive.modeld.compile_modeld import get_policy_npy_shapes as stock_get_policy_npy_shapes
from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes as sunny_get_policy_npy_shapes
stock_input_shapes = {
'desire_pulse': (1, 25, 8),
'features_buffer': (1, 24, 512), # see below comment
'traffic_convention': (1, 2),
'action_t': (1, 2),
}
stock_shapes, stock_sizes = stock_get_policy_npy_shapes(stock_input_shapes)
sunny_shapes, sunny_sizes = sunny_get_policy_npy_shapes(stock_input_shapes, is_supercombo=True)
assert sunny_shapes == stock_shapes
assert sunny_sizes == stock_sizes
assert sunny_shapes['prev_feat'] == (1, 512)
def test_make_input_queues_full_stock_equivalence(self):
from openpilot.selfdrive.modeld.compile_modeld import make_input_queues as stock_make_input_queues
from openpilot.sunnypilot.modeld_v2.compile_modeld import make_supercombo_input_queues as sunny_make_supercombo_input_queues
input_shapes = {
'img': (1, 12, 128, 256),
'desire_pulse': (1, 25, 8),
'features_buffer': (1, 24, 512), # when https://github.com/commaai/openpilot/pull/38681 merges, update to 1,24,32,512
'traffic_convention': (1, 2),
'action_t': (1, 2),
}
frame_skip = 4
stock_queues, stock_npy = stock_make_input_queues(input_shapes, frame_skip, device='NPY')
sunny_queues, sunny_npy = sunny_make_supercombo_input_queues(input_shapes, frame_skip, device='NPY')
assert set(sunny_queues.keys()) == set(stock_queues.keys())
for key in stock_queues:
assert sunny_queues[key].shape == stock_queues[key].shape, \
f"Queue shape mismatch for {key}: sunny {sunny_queues[key].shape} != stock {stock_queues[key].shape}"
assert set(sunny_npy.keys()) == set(stock_npy.keys())
for key in stock_npy:
assert sunny_npy[key].shape == stock_npy[key].shape, \
f"Numpy array shape mismatch for {key}: sunny {sunny_npy[key].shape} != stock {stock_npy[key].shape}"
def test_make_warp_queues_stock_equivalence(self):
from openpilot.selfdrive.modeld.compile_modeld import make_warp_input_queues as stock_make_warp_queues
from openpilot.sunnypilot.modeld_v2.compile_modeld import make_warp_queues as sunny_make_warp_queues
stock_vision_shapes = {'img': (1, 12, 128, 256)} # for now?
stock_queues, stock_npy = stock_make_warp_queues(stock_vision_shapes, frame_skip=4, device='NPY')
sunny_queues, sunny_npy = sunny_make_warp_queues(device='NPY')
assert set(sunny_npy.keys()) == set(stock_npy.keys()) == {'tfm', 'big_tfm'}
for key in sunny_npy:
assert sunny_npy[key].shape == stock_npy[key].shape == (3, 3)
+1 -1
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@@ -141,7 +141,7 @@ class ModelCache:
class ModelFetcher: class ModelFetcher:
"""Handles fetching and caching of model data from remote source""" """Handles fetching and caching of model data from remote source"""
MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v20.json" MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v20.json"
MODEL_URL_USBGPU = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_usbgpu_v22.json" MODEL_URL_USBGPU = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_usbgpu_v21.json"
def __init__(self, params: Params): def __init__(self, params: Params):
self.params = params self.params = params
+2 -6
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@@ -27,7 +27,7 @@ from openpilot.common.swaglog import cloudlog
from openpilot.sunnypilot.system.statsd import statlog from openpilot.sunnypilot.system.statsd import statlog
from openpilot.system.hardware.power_monitoring import PowerMonitoring from openpilot.system.hardware.power_monitoring import PowerMonitoring
from openpilot.system.hardware.fan_controller import FanController from openpilot.system.hardware.fan_controller import FanController
from openpilot.common.version import terms_version, training_version, get_build_metadata, terms_version_sp, CHESTNUT_BRANCHES from openpilot.common.version import terms_version, training_version, get_build_metadata, terms_version_sp
ThermalStatus = log.DeviceState.ThermalStatus ThermalStatus = log.DeviceState.ThermalStatus
@@ -301,11 +301,7 @@ def hardware_thread(end_event, hw_queue) -> None:
set_usb_state(msg.deviceState, last_hw_state.usb_state) set_usb_state(msg.deviceState, last_hw_state.usb_state)
chestnut.update(started_ts is None, last_hw_state.usb_state) chestnut.update(started_ts is None, last_hw_state.usb_state)
current_channel = get_build_metadata().channel set_offroad_alert_if_changed("Offroad_ChestnutBranch", msg.deviceState.chestnutPresent and not big_model_available)
chestnut_target = CHESTNUT_BRANCHES.get(current_channel)
chestnut_needs_switch = msg.deviceState.chestnutPresent and not big_model_available and chestnut_target is not None
set_offroad_alert_if_changed("Offroad_ChestnutBranch", chestnut_needs_switch,
extra_text=chestnut_target if chestnut_needs_switch else None)
# this subset is only used for offroad # this subset is only used for offroad
temp_sources = [ temp_sources = [