IQ.Pilot Release Commit @ 763bad7

iqpilot/selfdrive/iqmodeld/egpu_policy.py

@@ -0,0 +1,117 @@
+"""
+Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos/
+"""
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+POLICY_FORMAT = 2
+QUEUE_NAMES = ("img_q", "big_img_q", "feat_q", "desire_q")
+PACKED_ORDER = ("desire", "traffic_convention", "action_t", "prev_feat")
+
+
+def packed_layout(input_spec: dict) -> tuple[dict[str, tuple[int, ...]], list[int]]:
+  dp = input_spec["desire_pulse"][0]
+  fb = input_spec["features_buffer"][0]
+  shapes = {
+    "desire": (dp[2],),
+    "traffic_convention": tuple(input_spec["traffic_convention"][0]),
+    "action_t": tuple(input_spec["action_t"][0]),
+    "prev_feat": (fb[0], math.prod(fb[2:])),
+  }
+  return shapes, [math.prod(s) for s in shapes.values()]
+
+
+def queue_shapes(input_spec: dict, frame_skip: int) -> dict[str, tuple[tuple[int, ...], str]]:
+  img = input_spec["img"][0]
+  fb = input_spec["features_buffer"][0]
+  dp = input_spec["desire_pulse"][0]
+  n_frames = img[1] // 6
+  img_buf = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
+  return {
+    "img_q": (img_buf, "uint8"),
+    "big_img_q": (img_buf, "uint8"),
+    "feat_q": ((frame_skip * fb[1], fb[0], math.prod(fb[2:])), "float32"),
+    "desire_q": ((frame_skip * dp[1], dp[0], dp[2]), "float32"),
+  }
+
+
+def make_queues(input_spec: dict, frame_skip: int, device: str) -> dict:
+  from tinygrad.tensor import Tensor
+  return {name: Tensor(np.zeros(shape, dtype=dtype), device=device).contiguous().realize()
+          for name, (shape, dtype) in queue_shapes(input_spec, frame_skip).items()}
+
+
+class PackedInputs:
+  def __init__(self, input_spec: dict):
+    from tinygrad.tensor import Tensor
+    self.shapes, self.sizes = packed_layout(input_spec)
+    self.array = np.zeros(sum(self.sizes), dtype=np.float32)
+    self.views = dict(zip(self.shapes, [v.reshape(s) for s, v in zip(self.shapes.values(), np.split(self.array, np.cumsum(self.sizes[:-1])))], strict=True))
+    self.tensor = Tensor(self.array, device="NPY").realize()
+
+
+def make_run_policy(model_runner, input_spec: dict, frame_skip: int, device: str):
+  from tinygrad.tensor import Tensor
+  shapes, sizes = packed_layout(input_spec)
+  fb = input_spec["features_buffer"][0]
+
+  def shift_and_sample(buf, new_val, sample_fn):
+    buf.assign(buf[1:].cat(new_val, dim=0).contiguous())
+    return sample_fn(buf)
+
+  def sample_skip(buf):
+    return buf[::frame_skip].contiguous().flatten(0, 1).unsqueeze(0)
+
+  def sample_desire(buf):
+    return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)
+
+  def run_policy(warped, img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_npy_inputs = packed_npy_inputs.to(device)
+    warped = warped.to(device)
+    Tensor.realize(packed_npy_inputs, warped)
+    img = shift_and_sample(img_q, warped[0:1], sample_skip)
+    big_img = shift_and_sample(big_img_q, warped[1:2], sample_skip)
+    desire, traffic_convention, action_t, prev_feat = (t.reshape(s) for t, s in zip(packed_npy_inputs.split(sizes), shapes.values(), strict=True))
+    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire)
+    feat_buf = shift_and_sample(feat_q, prev_feat.reshape(1, 1, -1), sample_skip)
+    inputs = {
+      "img": img,
+      "big_img": big_img,
+      "features_buffer": feat_buf.reshape(fb),
+      "desire_pulse": desire_buf,
+      "traffic_convention": traffic_convention,
+      "action_t": action_t,
+    }
+    out = next(iter(model_runner(inputs).values())).cast("float32")
+    return out.reshape(-1),
+
+  return run_policy
+
+
+class PolicyRunner:
+  def __init__(self, jit, input_spec: dict, frame_skip: int, hidden_slice: slice, device: str):
+    from tinygrad.tensor import Tensor
+    self._Tensor = Tensor
+    self._jit = jit
+    self._queues = make_queues(input_spec, frame_skip, device)
+    self._packed = PackedInputs(input_spec)
+    self._hidden = hidden_slice
+    self._prev_desire = np.zeros(input_spec["desire_pulse"][0][2], dtype=np.float32)
+    self._warped_shape = (2, 6, *input_spec["img"][0][2:])
+
+  def run(self, warped: np.ndarray, desire_pulse: np.ndarray, traffic_convention: np.ndarray,
+          action_t: np.ndarray) -> np.ndarray:
+    cur = desire_pulse.astype(np.float32, copy=False)
+    v = self._packed.views
+    v["desire"][:] = np.where(cur - self._prev_desire > 0.99, cur, 0)
+    self._prev_desire[:] = cur
+    v["traffic_convention"][:] = np.asarray(traffic_convention, dtype=np.float32).reshape(v["traffic_convention"].shape)
+    v["action_t"][:] = np.asarray(action_t, dtype=np.float32).reshape(v["action_t"].shape)
+    warped_t = self._Tensor(np.ascontiguousarray(warped, dtype=np.uint8).reshape(self._warped_shape), device="NPY").realize()
+    out, = self._jit(warped=warped_t, packed_npy_inputs=self._packed.tensor, **self._queues)
+    flat = out.numpy().reshape(-1)
+    v["prev_feat"][:] = flat[self._hidden].reshape(v["prev_feat"].shape)
+    return flat