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2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Kumar
|
b1aee24150 | ||
|
rav4kumar
|
6c5b7c4c27 |
@@ -42,7 +42,7 @@ jobs:
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||||
WARP_PKL="openpilot/sunnypilot/modeld_v2/models/driving_warp_${res}_tinygrad.pkl"
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echo "Compiling $WARP_PKL on QCOM"
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taskset -c 7 env DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1 python3 openpilot/sunnypilot/modeld_v2/compile_warp.py \
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--frame ${FRAME_PARAM} --warp-to ${MODEL_SIZE} --split-frames --output "${WARP_PKL}"
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--frame ${FRAME_PARAM} --warp-to ${MODEL_SIZE} --output "${WARP_PKL}"
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BIG_WARP_PKL="openpilot/sunnypilot/modeld_v2/models/big_driving_warp_${res}_tinygrad.pkl"
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echo "Compiling $BIG_WARP_PKL on AMD"
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+1
-4
@@ -4,7 +4,7 @@
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||||
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||||
A supported vehicle is one that just works when you install a comma device. All supported cars provide a better experience than any stock system. Supported vehicles reference the US market unless otherwise specified.
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|
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# 354 Supported Cars
|
||||
# 351 Supported Cars
|
||||
|
||||
|Make|Model|Supported Package|ACC|No ACC accel below|No ALC below|Steering Torque|Resume from stop|<a href="##"><img width=2000></a>Hardware Needed<br> |Video|Setup Video|
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||||
|---|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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@@ -153,21 +153,18 @@ A supported vehicle is one that just works when you install a comma device. All
|
||||
|Hyundai|Sonata 2018-19|Smart Cruise Control (SCC)|Stock|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Sonata 2018-19">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Sonata 2020-23|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Sonata 2020-23">Buy Here</a></sub></details>|<a href="https://www.youtube.com/watch?v=ix63r9kE3Fw" target="_blank"><img height="18px" src="assets/icon-youtube.svg" /></a>||
|
||||
|Hyundai|Sonata Hybrid 2020-23|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Sonata Hybrid 2020-23">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Sonata Hybrid (without HDA II) 2024-26|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Sonata Hybrid (without HDA II) 2024-26">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Staria 2023|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai K connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Staria 2023">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson 2021|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|19 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai L connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson 2021">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson 2022|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai N connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson 2022">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson 2023-24|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai N connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson 2023-24">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson Diesel 2019|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai L connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson Diesel 2019">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson Hybrid 2022-24|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai N connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson Hybrid 2022-24">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson Hybrid (without HDA II) 2025-26|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai N connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson Hybrid (without HDA II) 2025-26">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Tucson Plug-in Hybrid 2024|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai N connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Tucson Plug-in Hybrid 2024">Buy Here</a></sub></details>|||
|
||||
|Hyundai|Veloster 2019-20|Smart Cruise Control (SCC)|Stock|5 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Hyundai Veloster 2019-20">Buy Here</a></sub></details>|||
|
||||
|Jeep|Grand Cherokee 2016-18|Adaptive Cruise Control (ACC)|Stock|0 mph|9 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 FCA connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Jeep Grand Cherokee 2016-18">Buy Here</a></sub></details>|<a href="https://www.youtube.com/watch?v=eLR9o2JkuRk" target="_blank"><img height="18px" src="assets/icon-youtube.svg" /></a>||
|
||||
|Jeep|Grand Cherokee 2019-21|Adaptive Cruise Control (ACC)|Stock|0 mph|39 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 FCA connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Jeep Grand Cherokee 2019-21">Buy Here</a></sub></details>|||
|
||||
|Kia|Carnival 2022-24|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia Carnival 2022-24">Buy Here</a></sub></details>|||
|
||||
|Kia|Carnival (China only) 2023|Smart Cruise Control (SCC)|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai K connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia Carnival (China only) 2023">Buy Here</a></sub></details>|||
|
||||
|Kia|Carnival Hybrid (with HDA II) 2025-26|Highway Driving Assist II|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai Q connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia Carnival Hybrid (with HDA II) 2025-26">Buy Here</a></sub></details>|||
|
||||
|Kia|Ceed 2019-21|Smart Cruise Control (SCC)|Stock|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai E connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia Ceed 2019-21">Buy Here</a></sub></details>|||
|
||||
|Kia|Ceed Plug-in Hybrid 2021|Smart Cruise Control (SCC)|Stock|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai B connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia Ceed Plug-in Hybrid 2021">Buy Here</a></sub></details>|||
|
||||
|Kia|EV6 (Southeast Asia only) 2022-24|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[](##)|[](##)|<details><summary>Parts</summary><sub>- 1 Hyundai P connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Kia EV6 (Southeast Asia only) 2022-24">Buy Here</a></sub></details>|||
|
||||
|
||||
+1
-1
Submodule opendbc_repo updated: f087f898a1...48611ae9d6
@@ -22,6 +22,7 @@ from openpilot.common.transformations.orientation import rot_from_euler
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from enum import IntEnum
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|
||||
if gui_app.sunnypilot_ui():
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from openpilot.selfdrive.ui.sunnypilot.mici.onroad.augmented_road_view import AugmentedRoadViewSP
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from openpilot.selfdrive.ui.sunnypilot.mici.onroad.hud_renderer import HudRendererSP as HudRenderer
|
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from openpilot.selfdrive.ui.sunnypilot.ui_state import OnroadTimerStatus
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|
||||
@@ -156,9 +157,10 @@ class BookmarkIcon(Widget):
|
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rl.draw_texture_ex(icon, rl.Vector2(icon_x, icon_y), 0.0, 1.0, rl.Color(255, 255, 255, round(255 * alpha)))
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|
||||
|
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class AugmentedRoadView(CameraView):
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class AugmentedRoadView(CameraView, AugmentedRoadViewSP):
|
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def __init__(self, bookmark_callback=None, stream_type: VisionStreamType = VisionStreamType.VISION_STREAM_NARROW_ROAD):
|
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super().__init__("camerad", stream_type)
|
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AugmentedRoadViewSP.__init__(self)
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self._bookmark_callback = bookmark_callback
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self._set_placeholder_color(rl.BLACK)
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@@ -275,7 +277,7 @@ class AugmentedRoadView(CameraView):
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if show_navigation:
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self._navigation.render(rl.Rectangle(self._content_rect.x + self._content_rect.width, self.rect.y, PANEL_WIDTH, self.rect.height))
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else:
|
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self._confidence_ball.render(self.rect)
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AugmentedRoadViewSP.render_side_panel(self, self.rect)
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|
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self._bookmark_icon.render(self.rect)
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@@ -0,0 +1,95 @@
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"""
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Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
|
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|
||||
This file is part of sunnypilot and is licensed under the MIT License.
|
||||
See the LICENSE.md file in the root directory for more details.
|
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"""
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import math
|
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import pyray as rl
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from openpilot.cereal import log
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.selfdrive.ui.mici.onroad import SIDE_PANEL_WIDTH
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from openpilot.selfdrive.ui.ui_state import ui_state
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from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost import get_boost_max
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from openpilot.system.ui.lib.application import gui_app
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from openpilot.system.ui.widgets import Widget
|
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|
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BAR_WIDTH = 26
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TRACK_TOP_RATIO = 0.08
|
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TRACK_BOTTOM_RATIO = 0.92
|
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CAP_BLEND_START = 0.45
|
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MAX_BOOST_BLEND = 0.55
|
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FADE_THRESHOLD = 0.01
|
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|
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COLOR_TRACK = rl.Color(255, 255, 255, 28)
|
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COLOR_FILL = rl.Color(0, 230, 120, 255)
|
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|
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|
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def get_boost_fraction(boost: float, max_boost: float) -> float:
|
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if max_boost <= 0.0:
|
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return 0.0
|
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return min(max(boost / max_boost, 0.0), 1.0)
|
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|
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|
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def lighten(color: rl.Color, amount: float) -> rl.Color:
|
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amount = min(max(amount, 0.0), 1.0)
|
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return rl.Color(int(color.r + (255 - color.r) * amount),
|
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int(color.g + (255 - color.g) * amount),
|
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int(color.b + (255 - color.b) * amount), color.a)
|
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|
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|
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def get_boost_color(fraction: float) -> rl.Color:
|
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blend = max(0.0, (fraction - CAP_BLEND_START) / (1.0 - CAP_BLEND_START))
|
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return lighten(COLOR_FILL, MAX_BOOST_BLEND * blend)
|
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|
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|
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class AccelBoostIndicator(Widget):
|
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def __init__(self, panel_width: float = SIDE_PANEL_WIDTH, bar_width: float = BAR_WIDTH):
|
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super().__init__()
|
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self._panel_width = panel_width
|
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self._bar_width = bar_width
|
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fps = max(gui_app.target_fps, 1)
|
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self._boost_filter = FirstOrderFilter(0.0, 0.15, 1 / fps)
|
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self._alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / fps)
|
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self._max_boost_filter = FirstOrderFilter(get_boost_max(), 0.25, 1 / fps)
|
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|
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@property
|
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def holding_panel(self) -> bool:
|
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return self._alpha_filter.x > FADE_THRESHOLD
|
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|
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def _update_state(self):
|
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sm = ui_state.sm
|
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visible = (ui_state.accel_boost and ui_state.has_longitudinal_control and sm.alive['selfdriveState'] and
|
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sm.alive['longitudinalPlan'] and sm['selfdriveState'].enabled)
|
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plan = sm['longitudinalPlan']
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applied = visible and plan.longitudinalPlanSource == log.LongitudinalPlan.LongitudinalPlanSource.e2e
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self._boost_filter.update(float(plan.accelBoost) if applied else 0.0)
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self._alpha_filter.update(1.0 if visible else 0.0)
|
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|
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if sm.alive['selfdriveState']:
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self._max_boost_filter.update(get_boost_max(sm['selfdriveState'].personality))
|
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|
||||
def _render(self, _):
|
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alpha = self._alpha_filter.x
|
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if alpha < FADE_THRESHOLD:
|
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return
|
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|
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panel_x = max(self.rect.x, self.rect.x + self.rect.width - self._panel_width)
|
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bar_x = panel_x + (self._panel_width - self._bar_width) / 2
|
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track_top = self.rect.y + self.rect.height * TRACK_TOP_RATIO
|
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track = rl.Rectangle(bar_x, track_top, self._bar_width,
|
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max(1.0, self.rect.height * (TRACK_BOTTOM_RATIO - TRACK_TOP_RATIO)))
|
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rl.draw_rectangle_rounded(track, 0.5, 12,
|
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rl.Color(COLOR_TRACK.r, COLOR_TRACK.g, COLOR_TRACK.b, int(COLOR_TRACK.a * alpha)))
|
||||
|
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fraction = get_boost_fraction(self._boost_filter.x, self._max_boost_filter.x)
|
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fill_h = fraction * track.height
|
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if fill_h < 1.0:
|
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return
|
||||
|
||||
fill_color = get_boost_color(fraction)
|
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tip = track.y + track.height - fill_h
|
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rl.begin_scissor_mode(int(track.x), int(tip), int(math.ceil(track.width)) + 1, int(math.ceil(fill_h)))
|
||||
rl.draw_rectangle_rounded(track, 0.5, 12, rl.Color(fill_color.r, fill_color.g, fill_color.b, int(235 * alpha)))
|
||||
rl.end_scissor_mode()
|
||||
@@ -0,0 +1,19 @@
|
||||
"""
|
||||
Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
|
||||
|
||||
This file is part of sunnypilot and is licensed under the MIT License.
|
||||
See the LICENSE.md file in the root directory for more details.
|
||||
"""
|
||||
import pyray as rl
|
||||
|
||||
from openpilot.selfdrive.ui.sunnypilot.mici.onroad.accel_boost_indicator import AccelBoostIndicator
|
||||
|
||||
|
||||
class AugmentedRoadViewSP:
|
||||
def __init__(self):
|
||||
self._accel_boost_indicator = AccelBoostIndicator()
|
||||
|
||||
def render_side_panel(self, rect: rl.Rectangle) -> None:
|
||||
self._accel_boost_indicator.render(rect)
|
||||
if not self._accel_boost_indicator.holding_panel:
|
||||
self._confidence_ball.render(rect)
|
||||
@@ -0,0 +1,36 @@
|
||||
"""
|
||||
Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
|
||||
|
||||
This file is part of sunnypilot and is licensed under the MIT License.
|
||||
See the LICENSE.md file in the root directory for more details.
|
||||
"""
|
||||
from openpilot.cereal import log
|
||||
from openpilot.common.test import OpenpilotTestCase
|
||||
from openpilot.selfdrive.ui.sunnypilot.mici.onroad.accel_boost_indicator import (
|
||||
CAP_BLEND_START, COLOR_FILL, get_boost_color, get_boost_fraction,
|
||||
)
|
||||
from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost import ACCEL_BOOST_MAX, get_boost_max
|
||||
|
||||
|
||||
class TestAccelBoostIndicator(OpenpilotTestCase):
|
||||
def test_bar_fills_at_the_active_profile_cap(self):
|
||||
for personality in (log.LongitudinalPersonality.aggressive, log.LongitudinalPersonality.standard,
|
||||
log.LongitudinalPersonality.relaxed):
|
||||
cap = get_boost_max(personality)
|
||||
assert get_boost_fraction(0.0, cap) == 0.0
|
||||
assert get_boost_fraction(cap * 0.5, cap) == 0.5
|
||||
assert get_boost_fraction(cap, cap) == 1.0
|
||||
|
||||
assert get_boost_fraction(max(ACCEL_BOOST_MAX), min(ACCEL_BOOST_MAX)) == 1.0
|
||||
assert get_boost_fraction(0.0, 0.0) == 0.0
|
||||
assert get_boost_fraction(-1.0, get_boost_max()) == 0.0
|
||||
|
||||
def test_fill_lightens_as_boost_builds(self):
|
||||
base = get_boost_color(0.0)
|
||||
assert (base.r, base.g, base.b) == (COLOR_FILL.r, COLOR_FILL.g, COLOR_FILL.b)
|
||||
assert (get_boost_color(CAP_BLEND_START).r, get_boost_color(CAP_BLEND_START).b) == (COLOR_FILL.r, COLOR_FILL.b)
|
||||
|
||||
full = get_boost_color(1.0)
|
||||
assert full.r > base.r
|
||||
assert full.b > base.b
|
||||
assert full.g > full.r and full.g > full.b
|
||||
@@ -44,6 +44,7 @@ class UIStateSP:
|
||||
self.screensaver = ScreenSaverSP(params=self.params)
|
||||
self.screensaver_enabled: bool = False
|
||||
|
||||
self.accel_boost: bool = False
|
||||
self.active_bundle = None
|
||||
self.model_runner_tinygrad: bool = False
|
||||
self.active_runner_tinygrad: bool = False
|
||||
@@ -171,6 +172,7 @@ class UIStateSP:
|
||||
# stock only counts the default big model's compiled pkl. a downloaded big bundle runs on the
|
||||
# chestnut just the same, so ChestnutState has to see it as available too.
|
||||
self.chestnut_compiled = self.chestnut_compiled or self.model_runner_tinygrad
|
||||
self.accel_boost = self.params.get_bool("AccelBoostSP")
|
||||
self.blindspot = self.params.get_bool("BlindSpot")
|
||||
self.chevron_metrics = self.params.get("ChevronInfo")
|
||||
self.custom_interactive_timeout = self.params.get("InteractivityTimeout", return_default=True)
|
||||
|
||||
@@ -17,24 +17,16 @@ def main():
|
||||
parser.add_argument("--frame", required=True, help="width,height,stride,y_height,uv_height,buffer_size")
|
||||
parser.add_argument("--warp-to", required=True, help="model input WxH")
|
||||
parser.add_argument("--output", required=True)
|
||||
parser.add_argument("--split-frames", action="store_true", help="one input per camera (frame, big_frame), bound to the camera buffers by pointer")
|
||||
args = parser.parse_args()
|
||||
|
||||
from examples.openpilot.compile_warp import make_frame_prepare, parse_frame, parse_size
|
||||
from tinygrad import Device, Tensor, TinyJit
|
||||
frame = parse_frame(args.frame)
|
||||
warp = make_frame_prepare(frame, *parse_size(args.warp_to))
|
||||
run = TinyJit(lambda input_frame, M_inv: Tensor.stack(*(warp(input_frame[i], M_inv[i]) for i in range(2))), prune=True)
|
||||
rng = np.random.default_rng(42)
|
||||
frames = rng.integers(0, 256, (2, frame.size), dtype=np.uint8)
|
||||
m_inv = Tensor((rng.standard_normal((2, 3, 3)) * 8).astype(np.float32), device=Device.DEFAULT).realize()
|
||||
if args.split_frames:
|
||||
run = TinyJit(lambda frame, big_frame, M_inv: Tensor.stack(warp(frame, M_inv[0]), warp(big_frame, M_inv[1])), prune=True)
|
||||
inputs = {"frame": Tensor(frames[0], device=Device.DEFAULT).realize(), "big_frame": Tensor(frames[1], device=Device.DEFAULT).realize(), "M_inv": m_inv}
|
||||
specs = dict.fromkeys(("frame", "big_frame"), ((frame.size,), "|u1", Device.DEFAULT))
|
||||
else:
|
||||
run = TinyJit(lambda input_frame, M_inv: Tensor.stack(*(warp(input_frame[i], M_inv[i]) for i in range(2))), prune=True)
|
||||
inputs = {"input_frame": Tensor(frames, device=Device.DEFAULT).realize(), "M_inv": m_inv}
|
||||
specs = {"input_frame": ((2, frame.size), "|u1", Device.DEFAULT)}
|
||||
inputs = {"input_frame": Tensor(rng.integers(0, 256, (2, frame.size), dtype=np.uint8), device=Device.DEFAULT).realize(),
|
||||
"M_inv": Tensor((rng.standard_normal((2, 3, 3)) * 8).astype(np.float32), device=Device.DEFAULT).realize()}
|
||||
with record_kernel_linear() as recorded:
|
||||
run(**inputs)
|
||||
run(**inputs)
|
||||
@@ -42,7 +34,7 @@ def main():
|
||||
assert len(recorded) == 1, f"expected one captured kernel list, got {len(recorded)}"
|
||||
part = serialize_jit(run, recorded[0], check)
|
||||
PlainJit(*part)
|
||||
specs |= {"M_inv": ((2, 3, 3), "<f4", Device.DEFAULT)}
|
||||
specs = {"input_frame": ((2, frame.size), "|u1", Device.DEFAULT), "M_inv": ((2, 3, 3), "<f4", Device.DEFAULT)}
|
||||
write_bundle(args.output, {"run": part}, {"input_specs": specs, "metadata": {}})
|
||||
print(f"Saved warp to {args.output} ({len(part[0]['calls'])} kernels)")
|
||||
|
||||
|
||||
@@ -12,8 +12,6 @@ from functools import partial
|
||||
from openpilot.common.basedir import BASEDIR
|
||||
from openpilot.sunnypilot.modeld_v2.helpers import _dtype, read_parts
|
||||
|
||||
SPLIT_FRAMES = ("big_frame", "frame")
|
||||
|
||||
|
||||
def pack_layout(shapes: dict[str, tuple], frame_size: int) -> tuple[dict[str, int], int]:
|
||||
offsets, offset = {}, 0
|
||||
@@ -41,31 +39,29 @@ def frame_part(warp_path: str, parts: dict, extras: dict) -> dict:
|
||||
(model, model_blob), (warp, warp_blob) = parts["run"], read_parts(warp_path)[0]["run"]
|
||||
states = sorted(name for name in extras["input_specs"] if f"next_{name}" in extras["metadata"]["output_shapes"])
|
||||
host = {name: shape for name, (shape, _, _) in extras["input_specs"].items() if name not in states and name != "new_img"}
|
||||
frame_specs = {s["name"]: s for s in warp["inputs"] if s["name"] in SPLIT_FRAMES}
|
||||
frame_size = next(s["numel"] for s in warp["inputs"] if s["name"] in ("input_frame", "frame")) // (1 if frame_specs else 2)
|
||||
offsets, packed = pack_layout(host, 0 if frame_specs else frame_size)
|
||||
frame_size = next(s["numel"] for s in warp["inputs"] if s["name"] == "input_frame") // 2
|
||||
offsets, packed = pack_layout(host, frame_size)
|
||||
shift, upload, u8 = len(model_blob), len(model["buffers"]) + len(warp["buffers"]), repr(_dtype("dtypes.uint8"))
|
||||
|
||||
def view(name: str, nbytes: int, dtype: str) -> dict:
|
||||
return {"buffer": upload, "offset": offsets[name], "nbytes": nbytes, "dtype": dtype}
|
||||
outputs = sorted(s["slot"] for s in model["inputs"] if s["name"] is None)
|
||||
names = sorted(["host", *states, *frame_specs])
|
||||
names = sorted(["host", *states])
|
||||
slots = {s["slot"]: names.index(s["name"]) for s in model["inputs"] if s["name"] in states} | \
|
||||
{slot: len(names) + i for i, slot in enumerate(outputs)}
|
||||
returned = warp["returns"]["tensors"][0]
|
||||
model_inputs = {s["slot"]: {"input": slots[s["slot"]]} if s["slot"] in slots else
|
||||
{k: v for k, v in returned.items() if k != "shape"} | {"buffer": returned["buffer"] + len(model["buffers"])} if s["name"] == "new_img" else
|
||||
view(s["name"], s["numel"] * _dtype(s["dtype"]).itemsize, s["dtype"]) for s in model["inputs"]}
|
||||
warp_inputs = {s["slot"]: {"input": names.index(s["name"])} if s["name"] in frame_specs else
|
||||
view("frames" if s["name"] == "input_frame" else "tfm", s["numel"] * _dtype(s["dtype"]).itemsize, s["dtype"]) for s in warp["inputs"]}
|
||||
warp_inputs = {s["slot"]: view("frames" if s["name"] == "input_frame" else "tfm", s["numel"] * _dtype(s["dtype"]).itemsize, s["dtype"])
|
||||
for s in warp["inputs"]}
|
||||
|
||||
def remap(calls: list, inputs: dict, buffers: int, lib: int) -> list:
|
||||
return [call | ({"lib": [call["lib"][0] + lib, call["lib"][1] + lib]} if "lib" in call else {}) |
|
||||
{"args": [dict(inputs[a["input"]]) if "input" in a else a | {"buffer": a["buffer"] + buffers} for a in call["args"]]} for call in calls]
|
||||
header = {"input_names": names, "returns": {"kind": "NoneType", "tensors": []}, "check": None,
|
||||
"inputs": [{"slot": names.index("host"), "name": "host", "dtype": u8, "numel": packed, "device": "NPY"}] +
|
||||
[s | {"slot": slots[s["slot"]]} for s in model["inputs"] if s["slot"] in slots] +
|
||||
[s | {"slot": names.index(name)} for name, s in frame_specs.items()],
|
||||
[s | {"slot": slots[s["slot"]]} for s in model["inputs"] if s["slot"] in slots],
|
||||
"buffers": model["buffers"] + [b | ({"init": [b["init"][0] + shift, b["init"][1] + shift]} if "init" in b else {}) for b in warp["buffers"]] +
|
||||
[{"nbytes": packed, "device": model["inputs"][0]["device"], "dtype": u8}],
|
||||
"calls": [{"kind": "copy", "args": [{"buffer": upload, "offset": 0, "nbytes": packed, "dtype": u8}, {"input": names.index("host")}]}] +
|
||||
|
||||
@@ -270,42 +270,16 @@ def _program_uop(call: dict, lib: bytes):
|
||||
UOp(Ops.SOURCE, arg=""), UOp(Ops.BINARY, arg=lib)), arg=info)
|
||||
|
||||
|
||||
def _input_key(args: tuple, kwargs: dict) -> tuple | None:
|
||||
from tinygrad import Tensor
|
||||
if args:
|
||||
return None
|
||||
key = []
|
||||
for name, value in sorted(kwargs.items()):
|
||||
if not isinstance(value, (Tensor, dict)):
|
||||
return None
|
||||
for sub, tensor in [(None, value)] if isinstance(value, Tensor) else sorted(value.items()):
|
||||
if not isinstance(tensor, Tensor) or not tensor.uop.is_realized:
|
||||
return None
|
||||
key.append((name, sub, tensor.uop))
|
||||
return tuple(key)
|
||||
|
||||
|
||||
class _Jit:
|
||||
def __init__(self, captured, input_names: list, buffers: list):
|
||||
self.captured, self.input_names, self.buffers = captured, input_names, buffers
|
||||
|
||||
def __getstate__(self):
|
||||
return {k: v for k, v in self.__dict__.items() if k != "_bound"}
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
import tinygrad.engine.jit as jit
|
||||
# modeld binds the same buffers every frame (the camera buffers cycle through a few): bind each set once, not per call
|
||||
bound, key = self.__dict__.setdefault("_bound", {}), _input_key(args, kwargs)
|
||||
if key is None or (hit := bound.get(key)) is None:
|
||||
input_uops, var_vals, names, _ = jit._prepare_jit_inputs(args, kwargs)
|
||||
if names != self.input_names:
|
||||
raise ValueError(f"expected inputs {self.input_names}, got {names}")
|
||||
hit = (input_uops, var_vals)
|
||||
if key is not None:
|
||||
if len(bound) >= 16:
|
||||
bound.clear()
|
||||
bound[key] = hit
|
||||
return self.captured(*hit)
|
||||
input_uops, var_vals, names, _ = jit._prepare_jit_inputs(args, kwargs)
|
||||
if names != self.input_names:
|
||||
raise ValueError(f"expected inputs {self.input_names}, got {names}")
|
||||
return self.captured(input_uops, var_vals)
|
||||
|
||||
|
||||
class PlainJit(_Jit):
|
||||
@@ -337,7 +311,7 @@ class PlainJit(_Jit):
|
||||
calls.append(_program_uop(call, bytes(blob[slice(*call["lib"])])).call(*arguments))
|
||||
else:
|
||||
calls.append(arguments[0].store_call(arguments[1]))
|
||||
# allocate gpu buffers
|
||||
# stand-ins for JIT inputs: copy staging to GPU is decided from the input buffers, the real ones bind per call
|
||||
stand_ins = {slot: UOp.from_buffer(Buffer(_device(spec["device"], device_map), spec["numel"] * _dtype(spec["dtype"]).itemsize).ensure_allocated(),
|
||||
_dtype(spec["dtype"])) for slot, spec in inputs.items()}
|
||||
input_uops = tuple(stand_ins.get(slot, next(iter(stand_ins.values()), None)) for slot in range(max(stand_ins, default=-1) + 1))
|
||||
|
||||
@@ -11,7 +11,7 @@ import pickle
|
||||
import numpy as np
|
||||
|
||||
from openpilot.common.basedir import BASEDIR
|
||||
from openpilot.sunnypilot.modeld_v2.frame_fusion import pack_layout, SPLIT_FRAMES
|
||||
from openpilot.sunnypilot.modeld_v2.frame_fusion import pack_layout
|
||||
from openpilot.sunnypilot.modeld_v2.helpers import load_model
|
||||
from openpilot.sunnypilot.modeld_v2.compile_modeld import (POLICY_INPUTS, derive_frame_skip,
|
||||
make_split_input_queues, make_supercombo_input_queues)
|
||||
@@ -169,18 +169,16 @@ class NativeTinygradAdapter(BaseModelAdapter):
|
||||
self.warp_frame_size = self.frame_copy_size
|
||||
self._init_common()
|
||||
self.run_model = self.jits['run']
|
||||
self.split = [name for name in SPLIT_FRAMES if name in self.run_model.input_names] # the warp reads the camera buffers in place
|
||||
self.frame_inputs: dict[str, Tensor] = {}
|
||||
|
||||
self.input_queues = {name: Tensor(np.zeros(shape, dtype=dtype), device=self.model_device).realize()
|
||||
for name, (shape, dtype) in self.input_shapes.items() if name in self.state_pairs}
|
||||
shapes = {name: shape for name, (shape, _) in self.input_shapes.items() if name not in self.state_pairs and name != 'new_img'}
|
||||
offsets, packed = pack_layout(shapes, 0 if self.split else self.warp_frame_size)
|
||||
offsets, packed = pack_layout(shapes, self.warp_frame_size)
|
||||
self.packed_input = np.zeros(packed, dtype=np.uint8)
|
||||
self.host = Tensor(self.packed_input, device='NPY')
|
||||
self.numpy_inputs = {name: np.ndarray(shape, dtype=np.float32, buffer=self.packed_input, offset=offsets[name])
|
||||
for name, shape in ({'tfm': (2, 3, 3)} | shapes).items()}
|
||||
self.frames = None if self.split else self.packed_input[offsets['frames']:].reshape(2, self.warp_frame_size)
|
||||
self.frames = self.packed_input[offsets['frames']:].reshape(2, self.warp_frame_size)
|
||||
|
||||
self.outputs = {name: Tensor(np.zeros(shape, dtype=dtype), device=device).realize() for name, (shape, dtype, device) in self.jits['output_specs'].items()}
|
||||
for name, next_name in self.state_pairs.items():
|
||||
@@ -189,30 +187,17 @@ class NativeTinygradAdapter(BaseModelAdapter):
|
||||
|
||||
def copy_frames(self, bufs):
|
||||
for i, key in enumerate(self._vision_input_names):
|
||||
if key not in bufs:
|
||||
continue
|
||||
if not self.split:
|
||||
if key in bufs:
|
||||
data = bufs[key].data if hasattr(bufs[key], 'data') else bufs[key]
|
||||
np.copyto(self.frames[i], np.frombuffer(data, dtype=np.uint8, count=self.warp_frame_size))
|
||||
elif (name := ('frame', 'big_frame')[i]) in self.split:
|
||||
if not hasattr(bufs[key], 'data'): # a numpy frame (warmup, tests): cached memory the GPU must not read in place, so upload it
|
||||
self.frame_inputs[name] = Tensor(np.ascontiguousarray(bufs[key][:self.warp_frame_size]), device=self.model_device).realize()
|
||||
continue
|
||||
ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data # visionipc buffers are coherent: map once per buffer, no copy
|
||||
if (key, ptr) not in self._blob_cache:
|
||||
self._blob_cache[(key, ptr)] = Tensor.from_blob(ptr, (self.warp_frame_size,), dtype='uint8', device=self.model_device)
|
||||
self.frame_inputs[name] = self._blob_cache[(key, ptr)]
|
||||
|
||||
def reset_warmup_buffers(self) -> None:
|
||||
self.packed_input[:] = 0
|
||||
self.frame_inputs.clear()
|
||||
self._blob_cache.clear()
|
||||
self.run_model.__dict__.pop('_bound', None) # warmup's uploaded frames: drop their bindings with them
|
||||
for key in self.state_pairs:
|
||||
self.input_queues[key].assign(0).realize()
|
||||
|
||||
def run(self):
|
||||
self.run_model(host=self.host, **self.input_queues, **self.frame_inputs, output_buffers=self.outputs)
|
||||
self.run_model(host=self.host, **self.input_queues, output_buffers=self.outputs)
|
||||
return self.outputs['outputs']
|
||||
|
||||
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d776c72125156b5dd3eb59768409401e28c8a52a3a619cd3b9f02c10a156fda6
|
||||
size 3699072
|
||||
oid sha256:4b22fd6bcb3a3e8a642e6866449dd9c30c12e694ff0741cad9e34e4b00d8c5fa
|
||||
size 3698799
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a3f13ed12e087817a86afd48b2496c66c4f61a1925065b676b2945e81fabaacd
|
||||
size 7924864
|
||||
oid sha256:9a17fd6acece4fab23784ef0f3f8bba28a049e9599e5e9e5ee7b36e4b1d24a6f
|
||||
size 7924847
|
||||
|
||||
@@ -144,21 +144,6 @@ class TestPlainArtifact(OpenpilotTestCase):
|
||||
for name in ("outputs", "next_state_feat_q"):
|
||||
assert np.array_equal(inputs["output_buffers"][name].numpy(), expected["output_buffers"][name].numpy()), name
|
||||
|
||||
split_path = os.path.join(self.tmp.name, "warp_split_tinygrad.pkl")
|
||||
with mock.patch.object(sys, "argv", ["compile_warp", "--frame", FRAME, "--warp-to", f"{MODEL_W}x{MODEL_H}", "--output", split_path, "--split-frames"]):
|
||||
compile_warp.main()
|
||||
split = load_model(path, cache=False, extend=functools.partial(frame_part, split_path))
|
||||
assert split["run"].input_names == ["big_frame", "frame", "host", "state_feat_q"]
|
||||
offsets, packed = pack_layout(HOST, 0)
|
||||
host = np.zeros(packed, np.uint8)
|
||||
for name, shape in ({"tfm": (2, 3, 3)} | HOST).items():
|
||||
np.ndarray(shape, np.float32, host, offsets[name])[...] = tfm.numpy() if name == "tfm" else inputs[name].numpy()
|
||||
out = {"outputs": Tensor.zeros(OUT).contiguous().realize(), "next_state_feat_q": Tensor.zeros(STATE).contiguous().realize()}
|
||||
split["run"](host=Tensor(host, device="NPY"), frame=Tensor(frames.numpy()[0]).realize(), big_frame=Tensor(frames.numpy()[1]).realize(),
|
||||
state_feat_q=expected["state_feat_q"], output_buffers=out)
|
||||
for name in ("outputs", "next_state_feat_q"): # the camera buffers bound in place give the packed upload's result exactly
|
||||
assert np.array_equal(out[name].numpy(), expected["output_buffers"][name].numpy()), name
|
||||
|
||||
def test_tinygrad_api_the_loader_reads(self):
|
||||
import tinygrad.engine.jit as jit
|
||||
from tinygrad.device import Buffer, TinyELF
|
||||
|
||||
Reference in New Issue
Block a user