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IQ.Pilot Release Commit @ bec7652

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IQ.Lvbs CI [bot]
2026-08-22 21:29:55 -05:00
parent 11cefcb266
commit 7786d94c33
58 changed files with 578 additions and 267 deletions
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2
iqpilot/selfdrive/iqmodeld/models/runners/__init__.py
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@@ -1,3 +1,3 @@
"""
Runner interfaces used by iqmodeld model execution.
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""

4
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/__init__.py
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@@ -1,3 +1,3 @@
"""
Tinygrad runner support for iqmodeld.
"""
Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
"""

7
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/fused_runner.py
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@@ -27,8 +27,6 @@ WARP_DEV = os.getenv('WARP_DEV')
class TinygradFusedRunner(ModelRunner):
"""Runs a fused warp+vision+policy pkl. Bundle ships one `driving_fused_*` artifact."""
uses_opencl_warp: bool = False
def __init__(self):
@@ -110,7 +108,6 @@ class TinygradFusedRunner(ModelRunner):
'feat_q': zeros_f32((self._frame_skip * (fb[1] - 1) + 1, fb[0], fb[2])),
'desire_q': zeros_f32((self._frame_skip * dp[1], dp[0], dp[2])),
}
# shapes must match the captured run_policy JIT inputs
on_shapes = self._on_meta['input_shapes']
captured = self._run_policy.captured
jit_shapes = {
@@ -135,7 +132,6 @@ class TinygradFusedRunner(ModelRunner):
self._cam_resolution = (cam_w, cam_h)
def run_fused(self, bufs: dict, transforms: dict[str, np.ndarray], numpy_inputs: NumpyDict) -> NumpyDict:
"""warp + vision + policy in one pass from raw NV12 bufs + transform matrices."""
Tensor, Device = _tinygrad_imports()
main_buf = bufs['img']
@@ -154,7 +150,6 @@ class TinygradFusedRunner(ModelRunner):
npy = lambda key: Tensor(self._npy_buffers[key], device='NPY')
# frames go on the compute device to match the captured warp JIT
frame = self._frame_tensor('img', bufs['img'])
big_frame = self._frame_tensor('big_img', bufs['big_img'])
@@ -169,8 +164,6 @@ class TinygradFusedRunner(ModelRunner):
policy_inputs['action_t'] = npy('action_t')
vision_out_t, on_out_t, off_out_t = self._run_policy(**policy_inputs)
# parse each model's output on its own sliced dict; parsing a merged dict
# would run parse_dynamic_outputs twice and double-parse plan/lead
def _slice(tensor_out, meta) -> NumpyDict:
flat = tensor_out.numpy().flatten()
return {k: flat[np.newaxis, sl] for k, sl in meta['output_slices'].items() if k != 'pad'}

7
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/supercombo_runner.py
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@@ -68,8 +68,6 @@ def _is_jit_arg_mismatch(err: BaseException) -> bool:
class TinygradSupercomboRunner(ModelRunner):
"""Runs a single combined supercombo pkl. Bundle ships one `driving_supercombo_*` artifact."""
uses_opencl_warp: bool = False
def __init__(self):
@@ -282,7 +280,6 @@ class TinygradSupercomboRunner(ModelRunner):
zeros_u8 = lambda s: Tensor(np.zeros(s, dtype=np.uint8), device=Device.DEFAULT).contiguous().realize()
zeros_f32 = lambda s: Tensor(np.zeros(s, dtype=np.float32), device=Device.DEFAULT).contiguous().realize()
# packed npy block (single NPY tensor, mutated in place via views): order matches run_policy.split
shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
sizes = [math.prod(s) for s in shapes.values()]
packed = np.zeros(sum(sizes), dtype=np.float32)
@@ -318,7 +315,6 @@ class TinygradSupercomboRunner(ModelRunner):
self._npy['traffic_convention'][:] = numpy_inputs['traffic_convention']
if 'action_t' in numpy_inputs:
self._npy['action_t'][:] = numpy_inputs['action_t']
# self._npy['prev_feat'] holds last frame's hidden_state (zeros on the first frame)
frame = self._frame_tensor('img', bufs['img'])
big_frame = self._frame_tensor('big_img', bufs['big_img'])
@@ -334,11 +330,10 @@ class TinygradSupercomboRunner(ModelRunner):
raise
flat = out.numpy().flatten()
# feed hidden_state back as prev_feat for the next frame
self._npy['prev_feat'][:] = flat[self._hidden_slice].reshape(self._npy['prev_feat'].shape)
sliced = {k: flat[np.newaxis, sl] for k, sl in self._slices.items()}
return self._parser.parse_vision_outputs(sliced) # single-pass; parse_outputs double-parses a combined dict
return self._parser.parse_vision_outputs(sliced)
def _run_model(self) -> NumpyDict:
raise RuntimeError("supercombo path goes through run_fused(), not _run_model()")

2
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/tinygrad_runner.py
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@@ -85,8 +85,6 @@ class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTiny
self.model_run = _load_program_blob(asset_name)
self._input_plan = _compile_input_plan(self.model_run.captured)
# the warp pipeline hands the runner raw uint8 YUV; a float image interface
# would silently reinterpret those bytes and drive on garbage vision
for name, spec in self._input_plan.items():
if "img" in name and spec.dtype is not dtypes.uint8:
raise ValueError(f"{asset_name}: image input {name} expects {spec.dtype}, incompatible with uint8 warp buffer")