openpilot v0.9.9 release (#35334)

* openpilot v0.9.9 release

date: 2025-06-05T19:54:08
master commit: 8aadf02b2fd91f4e1285e18c2c7feb32d93b66f5

* AGNOS 12.4 (#35558)

agnos12.4

---------

Co-authored-by: Vehicle Researcher <user@comma.ai>
Co-authored-by: Maxime Desroches <desroches.maxime@gmail.com>

tinygrad_repo/examples/handcode_opt.py

@@ -1,14 +1,14 @@
-from typing import List, Tuple
 from extra.models.resnet import ResNet50
 from extra.mcts_search import mcts_search
 from examples.mlperf.helpers import get_mlperf_bert_model
 from tinygrad import Tensor, Device, dtypes, nn
 from tinygrad.codegen.kernel import Kernel
-from tinygrad.ops import Ops, sym_infer
+from tinygrad.codegen.heuristic import hand_coded_optimizations
+from tinygrad.uop.ops import Ops, sym_infer
 from tinygrad.device import Compiled
-from tinygrad.engine.schedule import create_schedule
-from tinygrad.engine.search import time_linearizer, beam_search, bufs_from_lin
+from tinygrad.engine.search import beam_search, bufs_from_lin
 from tinygrad.helpers import DEBUG, ansilen, getenv, colored, TRACEMETA
+from extra.optimization.helpers import time_linearizer
 
 def get_sched_resnet():
   mdl = ResNet50()
@@ -18,12 +18,12 @@ def get_sched_resnet():
   # run model twice to get only what changes, these are the kernels of the model
   for _ in range(2):
     out = mdl(Tensor.empty(BS, 3, 224, 224))
-    targets = [out.lazydata]
+    targets = [out]
     if getenv("BACKWARD"):
       optim.zero_grad()
       out.sparse_categorical_crossentropy(Tensor.empty(BS, dtype=dtypes.int)).backward()
-      targets += [x.lazydata for x in optim.schedule_step()]
-    sched = create_schedule(targets)
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
     print(f"schedule length {len(sched)}")
   return sched
 
@@ -42,17 +42,16 @@ def get_sched_bert():
   next_sentence_labels = Tensor.empty((BS, 1), dtype=dtypes.float32)
 
   # run model twice to get only what changes, these are the kernels of the model
-  seen = set()
   for _ in range(2):
     lm_logits, seq_relationship_logits = mdl(input_ids, attention_mask, masked_positions, segment_ids)
-    targets = [lm_logits.lazydata, seq_relationship_logits.lazydata]
+    targets = [lm_logits, seq_relationship_logits]
     if getenv("BACKWARD"):
       optim.zero_grad()
       loss = mdl.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
       # ignore grad norm and loss scaler for now
       loss.backward()
-      targets += [x.lazydata for x in optim.schedule_step()]
-    sched = create_schedule(targets)
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
     print(f"schedule length {len(sched)}")
   return sched
 
@@ -81,11 +80,11 @@ if __name__ == "__main__":
     rawbufs = bufs_from_lin(Kernel(si.ast))
 
     # "linearize" the op into uops in different ways
-    lins: List[Tuple[Kernel, str]] = []
+    lins: list[tuple[Kernel, str]] = []
 
     # always try hand coded opt
     lin = Kernel(si.ast, opts=device.renderer)
-    lin.hand_coded_optimizations()
+    lin.apply_opts(hand_coded_optimizations(lin))
     lins.append((lin, "HC"))
 
     # maybe try tensor cores