mirror of
https://github.com/sunnypilot/sunnypilot.git
synced 2026-11-13 06:53:43 +08:00
Compare commits
7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 James Vecellio-Grant
|
43744ac5b8 | ||
|
James Vecellio-Grant
|
812b8b27a8 | ||
|
discountchubbs
|
3cfe730f8c | ||
|
discountchubbs
|
6d34938afe | ||
|
Jason Wen
|
61af0a0512 | ||
|
github-actions[bot]
|
066c0e2957 | ||
|
Jason Wen
|
26b8501e7b |
@@ -42,7 +42,7 @@ jobs:
|
||||
WARP_PKL="openpilot/sunnypilot/modeld_v2/models/driving_warp_${res}_tinygrad.pkl"
|
||||
echo "Compiling $WARP_PKL on QCOM"
|
||||
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 \
|
||||
--frame ${FRAME_PARAM} --warp-to ${MODEL_SIZE} --output "${WARP_PKL}"
|
||||
--frame ${FRAME_PARAM} --warp-to ${MODEL_SIZE} --split-frames --output "${WARP_PKL}"
|
||||
|
||||
BIG_WARP_PKL="openpilot/sunnypilot/modeld_v2/models/big_driving_warp_${res}_tinygrad.pkl"
|
||||
echo "Compiling $BIG_WARP_PKL on AMD"
|
||||
|
||||
+4
-1
@@ -4,7 +4,7 @@
|
||||
|
||||
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.
|
||||
|
||||
# 351 Supported Cars
|
||||
# 354 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|
|
||||
|---|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||
@@ -153,18 +153,21 @@ 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: 48611ae9d6...f087f898a1
@@ -17,16 +17,24 @@ 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)
|
||||
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()}
|
||||
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)}
|
||||
with record_kernel_linear() as recorded:
|
||||
run(**inputs)
|
||||
run(**inputs)
|
||||
@@ -34,7 +42,7 @@ 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 = {"input_frame": ((2, frame.size), "|u1", Device.DEFAULT), "M_inv": ((2, 3, 3), "<f4", Device.DEFAULT)}
|
||||
specs |= {"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)")
|
||||
|
||||
|
||||
@@ -12,6 +12,8 @@ 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
|
||||
@@ -39,29 +41,31 @@ 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_size = next(s["numel"] for s in warp["inputs"] if s["name"] == "input_frame") // 2
|
||||
offsets, packed = pack_layout(host, frame_size)
|
||||
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)
|
||||
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])
|
||||
names = sorted(["host", *states, *frame_specs])
|
||||
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"]: 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"]: {"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"]}
|
||||
|
||||
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": 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()],
|
||||
"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")}]}] +
|
||||
|
||||
@@ -270,16 +270,42 @@ 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
|
||||
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)
|
||||
# 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)
|
||||
|
||||
|
||||
class PlainJit(_Jit):
|
||||
@@ -311,7 +337,7 @@ class PlainJit(_Jit):
|
||||
calls.append(_program_uop(call, bytes(blob[slice(*call["lib"])])).call(*arguments))
|
||||
else:
|
||||
calls.append(arguments[0].store_call(arguments[1]))
|
||||
# stand-ins for JIT inputs: copy staging to GPU is decided from the input buffers, the real ones bind per call
|
||||
# allocate gpu buffers
|
||||
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
|
||||
from openpilot.sunnypilot.modeld_v2.frame_fusion import pack_layout, SPLIT_FRAMES
|
||||
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)
|
||||
@@ -169,16 +169,18 @@ 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, self.warp_frame_size)
|
||||
offsets, packed = pack_layout(shapes, 0 if self.split else 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 = self.packed_input[offsets['frames']:].reshape(2, self.warp_frame_size)
|
||||
self.frames = None if self.split else 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():
|
||||
@@ -187,17 +189,30 @@ class NativeTinygradAdapter(BaseModelAdapter):
|
||||
|
||||
def copy_frames(self, bufs):
|
||||
for i, key in enumerate(self._vision_input_names):
|
||||
if key in bufs:
|
||||
if key not in bufs:
|
||||
continue
|
||||
if not self.split:
|
||||
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, output_buffers=self.outputs)
|
||||
self.run_model(host=self.host, **self.input_queues, **self.frame_inputs, output_buffers=self.outputs)
|
||||
return self.outputs['outputs']
|
||||
|
||||
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4b22fd6bcb3a3e8a642e6866449dd9c30c12e694ff0741cad9e34e4b00d8c5fa
|
||||
size 3698799
|
||||
oid sha256:d776c72125156b5dd3eb59768409401e28c8a52a3a619cd3b9f02c10a156fda6
|
||||
size 3699072
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9a17fd6acece4fab23784ef0f3f8bba28a049e9599e5e9e5ee7b36e4b1d24a6f
|
||||
size 7924847
|
||||
oid sha256:a3f13ed12e087817a86afd48b2496c66c4f61a1925065b676b2945e81fabaacd
|
||||
size 7924864
|
||||
|
||||
@@ -144,6 +144,21 @@ 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