Merge branch 'master' into better_syn

hotfix: add device to decompositions ctx (#11721 )
fast_idiv requires it for checking if a dtype is supported. Without this, codegen creates non reproducible output without a complete os.environ. since `is_dtype_supported` will open devices based on the env var unless the device is specified by the caller.
2025-08-18 17:35:27 -07:00 · 2025-08-19 03:31:16 +03:00 · 2025-08-18 13:29:12 -07:00 · 2025-08-18 13:28:53 -07:00 · 2025-08-18 13:13:52 -07:00 · 2025-08-18 13:13:11 -07:00
146 changed files with 3148 additions and 2170 deletions
@@ -62,8 +62,6 @@ jobs:
      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
      run: METAL=1 python3.11 test/external/external_model_benchmark.py
-    - name: Run huggingface_onnx test
-      run: METAL=1 python3.11 extra/huggingface_onnx/run_models.py test --debug FacebookAI/xlm-roberta-large
    - name: Test speed vs torch
      run: BIG=2 MPS=1 python3.11 test/test_speed_v_torch.py | tee torch_speed.txt
    - name: Test tensor cores
@@ -325,7 +323,7 @@ jobs:
      run: BENCHMARK_LOG=resnet_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - name: Run 10 MLPerf Bert training steps (6 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
+      run: BENCHMARK_LOG=bert_10steps_6gpu NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (NVIDIA Training)
@@ -576,7 +574,7 @@ jobs:
      run: BENCHMARK_LOG=resnet_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py | tee train_resnet.txt
    - name: Run 10 MLPerf Bert training steps (6 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
+      run: BENCHMARK_LOG=bert_10steps_6gpu AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD MLPerf)
@@ -605,18 +603,18 @@ jobs:
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
      run: test/external/process_replay/reset.py
-    - name: validate openpilot 0.9.7
-      run: PYTHONPATH=. FLOAT16=0 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
-    - name: benchmark openpilot 0.9.7
-      run: BENCHMARK_LOG=openpilot_0_9_7 PYTHONPATH=. QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_0_9_7.txt
-    - name: benchmark openpilot w IMAGE=2 0.9.7
-      run: BENCHMARK_LOG=openpilot_0_9_7_image PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx | tee openpilot_image_0_9_7.txt
-    - name: openpilot compile3 0.9.7
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx
-    - name: openpilot compile3 0.9.7+ tomb raider
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/e8bea2c78ffa92685ece511e9b554122aaf1a79d/selfdrive/modeld/models/supercombo.onnx
-    - name: openpilot dmonitoring compile3 0.9.7
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/dmonitoring_model.onnx
+    - name: benchmark openpilot 0.9.9 driving_vision
+      run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+    - name: benchmark openpilot 0.9.9 driving_policy
+      run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+    - name: benchmark openpilot 0.9.9 dmonitoring
+      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
+    - name: openpilot compile3 0.9.9 driving_vision
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+    - name: openpilot compile3 0.9.9 driving_policy
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+    - name: openpilot compile3 0.9.9 dmonitoring
+      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 Space Lab policy + vision
      run: |
        PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
@@ -697,7 +695,7 @@ jobs:
    #   run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
    - name: Run 10 MLPerf Bert training steps (1 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee am_train_bert_one_gpu.txt
+      run: BENCHMARK_LOG=bert_10steps AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee am_train_bert_one_gpu.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AM Driver)
@@ -755,7 +753,7 @@ jobs:
      run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
    - name: Run 10 MLPerf Bert training steps (1 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt
+      run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (NV Driver)
@@ -1,7 +1,7 @@
 name: Unit Tests
 env:
  # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '10'
+  DOWNLOAD_CACHE_VERSION: '11'
  PYTHON_CACHE_VERSION: '2'
  CAPTURE_PROCESS_REPLAY: 1
  GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -46,6 +46,11 @@ jobs:
      with:
        deps: docs
        pydeps: "capstone"
+    - name: Build wheel and show size
+      run: |
+        pip install build
+        python -m build --wheel --outdir dist
+        ls -lh dist/*.whl
    - name: Use as an external package
      run: |
        mkdir $HOME/test_external_dir
@@ -329,7 +334,6 @@ jobs:
      run: |
        pip3 install --upgrade --force-reinstall ruff==0.11.0
        python3 -m ruff check .
-        python3 -m ruff check extra/onnx.py
        python3 -m ruff check examples/mlperf/ --ignore E501
    - name: Lint tinygrad with pylint
      run: python -m pylint tinygrad/
@@ -337,7 +341,6 @@ jobs:
      run: |
        python -m mypy --strict-equality --lineprecision-report .
        cat lineprecision.txt
-        python -m mypy --strict-equality extra/onnx.py

  unittest:
    name: Unit Tests
@@ -375,8 +378,8 @@ jobs:
        PYTHONPATH=. python extra/optimization/extract_dataset.py
        gzip -c /tmp/sops > extra/datasets/sops.gz
        DEBUG=1 MIN_ASTS=1 PYTHONPATH=. python extra/optimization/get_action_space.py
-    - name: Repo line count < 16000 lines
-      run: MAX_LINE_COUNT=16000 python sz.py
+    - name: Repo line count < 17000 lines
+      run: MAX_LINE_COUNT=17000 python sz.py

  fuzzing:
    name: Fuzzing
@@ -542,6 +545,8 @@ jobs:
        run: PYTHONPATH="." GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
      - name: Test MLPerf stuff
        run: GPU=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
+      - name: Test llama 3 training
+        run: MAX_BUFFER_SIZE=0 PYTHONPATH="." DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -975,6 +980,7 @@ jobs:
        with:
          key: macos-${{ matrix.backend }}-minimal
          deps: testing_minimal
+          pydeps: "capstone"
          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
      - name: Set env
        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
@@ -18,11 +18,11 @@ Group UOps into kernels.

 ---

-## tinygrad/opt
+## tinygrad/codegen/opt

 Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.

-::: tinygrad.opt.get_optimized_ast
+::: tinygrad.codegen.opt.get_optimized_ast
    options:
        members: false
        show_labels: false
@@ -47,8 +47,8 @@ Reboot after making these changes or restart the `displayservice.service` servic

 The [default tinybox image](https://github.com/tinygrad/tinyos) ships with tinygrad and PyTorch. While we develop tinygrad, the box is universal hardware. Use whatever framework you desire, run notebooks, download demos, install more things, train, inference, live, laugh, love, you aren't paying per hour for this box so the only limit is your imagination.

-## tinychat
+## Building the OS image

-Since LLMs are so popular, we ship with a built in tinygrad based chatbot using a LLaMA-3 finetune. Visit the IP (not the BMC IP) of your tinybox in a web browser on your computer or phone, and you'll find a friendly looking chat interface. This chatbot also provides an OpenAI compatible LLM API on that port, so you can script it.
+The OS image is built using `ubuntu-image` from <https://github.com/tinygrad/tinyos>.

-The conversations you have with this chatbot are between you and your tinybox. Also, the history in the web app is saved on the client, not the tinybox.
+After cloning, run `make green` or `make red` to build a tinybox green or tinybox red image respectively.
@@ -1,12 +1,12 @@
 # model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
-from typing import List, Callable
+from typing import Callable
 from tinygrad import Tensor, TinyJit, nn, GlobalCounters
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist

 class Model:
  def __init__(self):
-    self.layers: List[Callable[[Tensor], Tensor]] = [
+    self.layers: list[Callable[[Tensor], Tensor]] = [
      nn.Conv2d(1, 32, 5), Tensor.relu,
      nn.Conv2d(32, 32, 5), Tensor.relu,
      nn.BatchNorm(32), Tensor.max_pool2d,
@@ -21,17 +21,15 @@ if __name__ == "__main__":
  X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))

  model = Model()
-  opt = nn.optim.Adam(nn.state.get_parameters(model))
+  opt = (nn.optim.Adam if not getenv("MUON") else nn.optim.Muon)(nn.state.get_parameters(model))

  @TinyJit
  @Tensor.train()
  def train_step() -> Tensor:
    opt.zero_grad()
    samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
-    # TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
    loss = model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
-    opt.step()
-    return loss
+    return loss.realize(*opt.schedule_step())

  @TinyJit
  def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
@@ -1318,29 +1318,33 @@ def train_llama3():
  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)

+  if getenv("FAKEDATA"):
+    for v in get_parameters(model):
+      v = v.assign(Tensor.empty(v.shape))
+
  if (DP := getenv("DP", 1)) > 1:
    device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
    for v in get_parameters(model):
      v.shard_(device, axis=None)

-  # TODO: MP
-  # if (GPUS := getenv("GPUS", 1)) > 1:
-  #   device = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS))
-  #   for k,v in get_state_dict(model).items():
-  #     if 'scale' in k: v.shard_(device, axis=None)  # from quantized
-  #     # elif '.attention.wq' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wk' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wv' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wo' in k: v.shard_(device, axis=1)
-  #     # elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
-  #     # elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
-  #     # elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
-  #     # elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
-  #     elif 'output.weight' in k: v.shard_(device, axis=0)  # 243.32
-  #     else:
-  #       # print(k)
-  #       # attention_norm, ffn_norm, norm
-  #       v.shard_(device, axis=None)
+  if (MP := getenv("MP", 1)) > 1:
+    device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
+    for k,v in get_state_dict(model).items():
+      if 'scale' in k: v.shard_(device, axis=None)  # from quantized
+      elif '.attention.wq' in k: v.shard_(device, axis=0)
+      elif '.attention.wk' in k: v.shard_(device, axis=0)
+      elif '.attention.wv' in k: v.shard_(device, axis=0)
+      elif '.attention.wo' in k: v.shard_(device, axis=1)
+      elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
+      elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
+      elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
+      elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
+      elif 'output.weight' in k: v.shard_(device, axis=0)
+      else:
+        # attention_norm, ffn_norm, norm
+        v.shard_(device, axis=None)
+      # prevents memory spike on device 0
+      v.realize()

  optim = AdamW(get_parameters(model), lr=0.0,
                b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
@@ -1355,6 +1359,9 @@ def train_llama3():
      if (DP := getenv("DP", 1)) > 1:
        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
        batch = batch.shard(device, 0)
+      if (MP := getenv("MP", 1)) > 1:
+        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
+        batch = batch.shard(device)
      logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
      loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
      loss.backward()
@@ -1391,11 +1398,12 @@ def train_llama3():
    t = time.perf_counter()
    GlobalCounters.reset()
    loss, lr = train_step(model, tokens, grad_acc)
+    loss = loss.float().item()
    # above as tqdm.write f-string
-    tqdm.write(f"{loss.item():.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
+    tqdm.write(f"{loss:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
    if (fname:=getenv("LOSS_FILE", "")):
      with open(fname, "a") as f:
-        f.write(f"{i} {loss.item():.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
+        f.write(f"{i} {loss:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")

    if getenv("CKPT") and (i % 200 == 0 or i == 10):
      tqdm.write("saving checkpoint")
@@ -4,6 +4,8 @@ export PYTHONPATH="." AMD=1
 export MODEL="bert"
 export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128

+export IGNORE_OOB=1
+
 export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
 # export BEAM_LOG_SURPASS_MAX=1
@@ -5,6 +5,8 @@ export MODEL="bert"
 export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1

+export IGNORE_OOB=1
+
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
 export BASEDIR="/raid/datasets/wiki"
@@ -8,6 +8,8 @@ export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
 export TRAIN_STEPS=3900

+export IGNORE_OOB=1
+
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
 export BASEDIR="/raid/datasets/wiki"
@@ -11,6 +11,8 @@ export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
 export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
 export TRAIN_STEPS=3900

+export IGNORE_OOB=1
+
 export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
 export BASEDIR="/raid/datasets/wiki"
@@ -2,9 +2,9 @@

 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -2,9 +2,9 @@

 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -5,9 +5,9 @@ set -o pipefail  # Make pipeline fail if any command fails
 export PYTHONPATH="." NV=1
 export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_green"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -2,9 +2,9 @@

 export PYTHONPATH="." AMD=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -2,9 +2,9 @@

 export PYTHONPATH="." AMD=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -5,9 +5,9 @@ set -o pipefail  # Make pipeline fail if any command fails
 export PYTHONPATH="." AMD=1
 export MODEL="bert"
 export SUBMISSION_PLATFORM="tinybox_red"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90

-export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
+export IGNORE_OOB=1

 export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
 export IGNORE_JIT_FIRST_BEAM=1
@@ -1,8 +1,7 @@
 # https://arxiv.org/pdf/2409.02060
-import time
+import time, functools
 import numpy as np
 np.set_printoptions(suppress=True, linewidth=1000)
-import functools
 from tinygrad import Tensor, nn, Device, GlobalCounters
 from tinygrad.helpers import Timing, getenv
 from extra.models.llama import Transformer, convert_from_huggingface
@@ -17,7 +16,7 @@ class MixtureFeedForward:
  def __call__(self, x:Tensor) -> Tensor:
    assert x.shape[0] == 1, "only BS=1"
    assert x.shape[1] == 1, "only length=1"
-    g = self.gate(x).float().softmax(-1)
+    g = self.gate(x).softmax(-1)

    g = g.squeeze() # (BS, length, num_experts) -> (num_experts,)
    probs, sel = g.topk(self.activated_experts)
@@ -25,7 +24,7 @@ class MixtureFeedForward:
    # run MoE
    x_up_gate = x.dot(self.gate_proj[sel].permute(0,2,1)).silu() * x.dot(self.up_proj[sel].permute(0,2,1))
    x_down = x_up_gate.dot(self.down_proj[sel].permute(0,2,1))
-    return (x_down.float() * probs.reshape(self.activated_experts, 1, 1)).sum(axis=0)
+    return (x_down * probs.reshape(self.activated_experts, 1, 1)).sum(axis=0)

 # model is bf16, 1.3B active, 6.9B total
 # M3 Max is 400 GB/s, so 400/2.6 = ~154 tok/s
@@ -71,8 +71,8 @@ def bbox_iou(box1, box2):
  # get the coordinates of the intersection rectangle
  inter_rect_x1 = np.maximum(b1_x1, b2_x1)
  inter_rect_y1 = np.maximum(b1_y1, b2_y1)
-  inter_rect_x2 = np.maximum(b1_x2, b2_x2)
-  inter_rect_y2 = np.maximum(b1_y2, b2_y2)
+  inter_rect_x2 = np.minimum(b1_x2, b2_x2)
+  inter_rect_y2 = np.minimum(b1_y2, b2_y2)
  #Intersection area
  inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, 99999) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, 99999)
  #Union Area
@@ -297,13 +297,13 @@ class Darknet:
          # Get the number of weights of batchnorm
          num_bn_biases = math.prod(bn.bias.shape)
          # Load weights
-          bn_biases = Tensor(weights[ptr:ptr + num_bn_biases])
+          bn_biases = Tensor(weights[ptr:ptr + num_bn_biases].astype(np.float32))
          ptr += num_bn_biases
-          bn_weights = Tensor(weights[ptr:ptr+num_bn_biases])
+          bn_weights = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
          ptr += num_bn_biases
-          bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases])
+          bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
          ptr += num_bn_biases
-          bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases])
+          bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
          ptr += num_bn_biases
          # Cast the loaded weights into dims of model weights
          bn_biases = bn_biases.reshape(shape=tuple(bn.bias.shape))
@@ -319,7 +319,7 @@ class Darknet:
          # load biases of the conv layer
          num_biases = math.prod(conv.bias.shape)
          # Load weights
-          conv_biases = Tensor(weights[ptr: ptr+num_biases])
+          conv_biases = Tensor(weights[ptr: ptr+num_biases].astype(np.float32))
          ptr += num_biases
          # Reshape
          conv_biases = conv_biases.reshape(shape=tuple(conv.bias.shape))
@@ -327,7 +327,7 @@ class Darknet:
          conv.bias = conv_biases
        # Load weighys for conv layers
        num_weights = math.prod(conv.weight.shape)
-        conv_weights = Tensor(weights[ptr:ptr+num_weights])
+        conv_weights = Tensor(weights[ptr:ptr+num_weights].astype(np.float32))
        ptr += num_weights
        conv_weights = conv_weights.reshape(shape=tuple(conv.weight.shape))
        conv.weight = conv_weights
@@ -371,7 +371,7 @@ class Darknet:
 if __name__ == "__main__":
  model = Darknet(fetch('https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg').read_bytes())
  print("Loading weights file (237MB). This might take a while…")
-  model.load_weights('https://pjreddie.com/media/files/yolov3.weights')
+  model.load_weights('https://github.com/shadiakiki1986/yolov3.weights/releases/download/3.0.1/yolov3.weights')
  if len(sys.argv) > 1:
    url = sys.argv[1]
  else:
@@ -1,6 +1,16 @@
-import re, ctypes, sys
+import re, ctypes, sys, importlib

-from tinygrad.runtime.autogen.am import am, mp_11_0, mp_13_0_0, nbio_4_3_0, mmhub_3_0_0, gc_11_0_0, osssys_6_0_0
+from tinygrad.runtime.support.am.amdev import AMDev, AMRegister
+class AMDFake(AMDev):
+  def __init__(self, devfmt, vram, doorbell, mmio, dma_regions=None):
+    self.devfmt, self.vram, self.doorbell64, self.mmio, self.dma_regions = devfmt, vram, doorbell, mmio, dma_regions
+    self._run_discovery()
+    self._build_regs()
+
+amdev = importlib.import_module("tinygrad.runtime.support.am.amdev")
+amdev.AMDev = AMDFake
+
+from tinygrad.runtime.ops_amd import PCIIface

 def parse_amdgpu_logs(log_content, register_names=None):
  register_map = register_names
@@ -23,16 +33,11 @@ def parse_amdgpu_logs(log_content, register_names=None):
  return processed_log

 def main():
-  regs_offset = {13: {0: [3072, 37784576]}, 28: {0: [93184, 37754880], 1: [201327616, 201461760], 2: [209716224, 209850368], 3: [218104832, 218238976], 4: [226493440, 226627584], 5: [234882048, 235016192], 6: [243270656, 243404800]}, 21: {0: [28672, 12582912, 37795840, 130023424, 306184192], 1: [201326592, 201463808, 201465856, 204210176, 204472320], 2: [209715200, 209852416, 209854464, 212598784, 212860928], 3: [218103808, 218241024, 218243072, 220987392, 221249536], 4: [226492416, 226629632, 226631680, 229376000, 229638144], 5: [234881024, 235018240, 235020288, 237764608, 238026752], 6: [243269632, 243406848, 243408896, 246153216, 246415360]}, 22: {0: [18, 192, 13504, 36864, 37764096]}, 1: {0: [4704, 40960, 114688, 37760000]}, 2: {0: [3872, 37790720]}, 11: {0: [70656, 38103040]}, 12: {0: [106496, 37783552]}, 15: {0: [90112, 14417920, 14680064, 14942208, 38009856]}, 16: {0: [90112, 14417920, 14680064, 14942208, 38009856]}, 14: {0: [0, 20, 3360, 66560, 37859328, 67371008]}, 26: {0: [0, 20, 3360, 66560, 37859328, 67371008]}, 23: {0: [4256, 37789696]}, 33: {0: [0, 20, 3360, 66560, 37859328, 67371008]}, 25: {0: []}, 3: {0: [4704, 40960, 114688, 37760000]}, 4: {0: [4704, 40960, 114688, 37760000]}, 24: {0: [92160, 92672, 37752832, 54788096]}, 27: {0: [91648, 37751808], 1: [201339904, 201458176], 2: [209728512, 209846784], 3: [218117120, 218235392], 4: [226505728, 226624000], 5: [234894336, 235012608], 6: [243282944, 243401216]}, 29: {0: [201342976, 201344000, 205520896, 205537280], 1: [209731584, 209732608, 213909504, 213925888], 2: [218120192, 218121216, 222298112, 222314496], 3: [226508800, 226509824, 230686720, 230703104], 4: [234897408, 234898432, 239075328, 239091712], 5: [243286016, 243287040, 247463936, 247480320]}, 17: {0: [30720, 32256], 1: [31488, 73728]}}
-
  reg_names = {}
-  def _prepare_registers(modules):
-    for base, m in modules:
-      for k, regval in m.__dict__.items():
-        if k.startswith("reg") and not k.endswith("_BASE_IDX") and (base_idx:=getattr(m, f"{k}_BASE_IDX", None)) is not None:
-          reg_names[regs_offset[am.__dict__.get(f"{base}_HWIP")][0][base_idx] + regval] = k
-
-  _prepare_registers([("MP0", mp_13_0_0), ("NBIO", nbio_4_3_0), ("MMHUB", mmhub_3_0_0), ("GC", gc_11_0_0), ("OSSSYS", osssys_6_0_0)])
+  dev = PCIIface(None, 0)
+  for x, y in dev.dev_impl.__dict__.items():
+    if isinstance(y, AMRegister):
+      for inst, addr in y.addr.keys(): reg_names[addr] = f"{x}, xcc={inst}"

  with open(sys.argv[1], 'r') as f:
    log_content = log_content_them = f.read()
@@ -1,5 +1,5 @@
 from typing import Tuple, List, NamedTuple, Any, Dict, Optional, Union, DefaultDict, cast
-from tinygrad.opt.kernel import Ops, MemOp, UOp
+from tinygrad.codegen.opt.kernel import Ops, MemOp, UOp
 from tinygrad.uop.ops import BinaryOps, UnaryOps
 from tinygrad.dtype import DType, dtypes
 from tinygrad.helpers import DEBUG
@@ -3,7 +3,7 @@ from platform import system
 from typing import Tuple, Dict, List, Optional
 from tinygrad import dtypes
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
-from tinygrad.opt.kernel import Ops, UOp
+from tinygrad.codegen.opt.kernel import Ops, UOp
 from tinygrad.helpers import CI
 from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage

@@ -1,7 +1,7 @@
 from typing import List
 import struct
 from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage
-from tinygrad.opt.kernel import Ops, UOp
+from tinygrad.codegen.opt.kernel import Ops, UOp
 from tinygrad import dtypes
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
 from tinygrad.runtime.ops_cuda import arch
@@ -2,7 +2,7 @@ import yaml
 from typing import Tuple, Set, Dict
 from tinygrad import dtypes
 from tinygrad.codegen.assembly import AssemblyCodegen, Register
-from tinygrad.opt.kernel import Ops
+from tinygrad.codegen.opt.kernel import Ops
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
 from tinygrad.runtime.ops_gpu import ROCM_LLVM_PATH

@@ -2,7 +2,7 @@ from typing import Dict, List, Final, Callable, DefaultDict
 from collections import defaultdict
 from tinygrad.uop.ops import UnaryOps, BinaryOps, TernaryOps, Op
 from tinygrad.helpers import DType, PtrDType, dtypes, ImageDType, DEBUG, getenv
-from tinygrad.opt.kernel import  UOp, Ops
+from tinygrad.codegen.opt.kernel import  UOp, Ops
 from triton.compiler import compile as triton_compile
 import linecache
 import math
@@ -2,11 +2,11 @@ from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
 from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite, AxisType, PatternMatcher, UPat
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.dtype import AddrSpace
-from tinygrad.schedule.kernelize import merge_views, view_left
 from tinygrad.helpers import getenv, colored, prod, unwrap
 from tinygrad.shape.shapetracker import ShapeTracker, View
 from tinygrad.shape.view import strides_for_shape
-from tinygrad.opt.kernel import axis_colors
+from tinygrad.codegen.opt.kernel import axis_colors
+from tinygrad.codegen.opt.swizzler import merge_views, view_left

 def to_colored(full_shape, axis_types): return '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])

@@ -5,9 +5,9 @@ from typing import Optional, List, Tuple, cast, Dict, Final, DefaultDict, Self
 from tinygrad.engine.realize import get_program

 # for copied uops
-from tinygrad.opt.kernel import Kernel, KernelOptError
+from tinygrad.codegen.opt.kernel import Kernel, KernelOptError
 from tinygrad.uop.ops import UOp, Ops, BinaryOps, UnaryOps, TernaryOps, KernelInfo
-from tinygrad.opt.search import Opt, OptOps
+from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad import Device, dtypes, Tensor
 from tinygrad.dtype import PtrDType, DType, DTYPES_DICT
 from tinygrad.shape.shapetracker import ShapeTracker
@@ -2,7 +2,7 @@ import numpy as np
 from tinygrad import dtypes, Tensor
 from tinygrad.helpers import getenv, get_single_element
 from tinygrad.dtype import _to_np_dtype
-from tinygrad.opt.kernel import OptOps
+from tinygrad.codegen.opt.kernel import OptOps
 from tinygrad.engine.realize import lower_schedule

 dtype_in = dtypes.half if getenv("HALF") else dtypes.bfloat16 if getenv("BFLOAT16") else dtypes.float
@@ -1,6 +1,6 @@
 from tinygrad import Tensor, dtypes, Device
 from tinygrad.helpers import getenv, DEBUG
-from tinygrad.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from dataclasses import replace

@@ -37,7 +37,7 @@ B = Tensor.rand(K, N, device="CPU")
 C = (A.reshape(M, 1, K) * B.permute(1,0).reshape(1, N, K)).sum(axis=2)

 sched = C.schedule()
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.device import CompilerOptions
 lin = Kernel(sched[-1].ast, CompilerOptions(has_local=False, supports_float4=False))
 lin.to_program()
@@ -8,7 +8,6 @@ bert_train_params = {
  "BS": 96,
  "EVAL_BS": 96,
  "FUSE_ARANGE": 1,
-  "FUSE_ARANGE_UINT": 0,
  "BASEDIR": "/raid/datasets/wiki",
 }

@@ -0,0 +1,61 @@
+# HuggingFace ONNX
+
+Tool for discovering, downloading, and validating ONNX models from HuggingFace.
+
+## Extra Dependencies
+
+```bash
+pip install huggingface_hub pyyaml requests onnx onnxruntime numpy
+```
+
+## Huggingface Manager (discovering and downloading)
+
+The `huggingface_manager.py` script discovers top ONNX models from HuggingFace, collects metadata, and optionally downloads them.
+
+```bash
+# Download top 50 models sorted by downloads
+python huggingface_manager.py --limit 50 --download
+
+# Just collect metadata (no download)
+python huggingface_manager.py --limit 100
+
+# Sort by likes instead of downloads
+python huggingface_manager.py --limit 20 --sort likes --download
+
+# Custom output file
+python huggingface_manager.py --limit 10 --output my_models.yaml
+```
+
+### Output Format
+
+The tool generates a YAML file with the following structure:
+
+```yaml
+repositories:
+  "model-name":
+    url: "https://huggingface.co/model-name"
+    download_path: "/path/to/models/..."  # when --download used
+    files:
+      - file: "model.onnx"
+        size: "90.91MB"
+total_size: "2.45GB"
+created_at: "2024-01-15T10:30:00Z"
+```
+
+## Run Models (validation)
+
+The `run_models.py` script validates ONNX models against ONNX Runtime for correctness.
+
+```bash
+# Validate models from a YAML configuration file
+python run_models.py --validate huggingface_repos.yaml
+
+# Debug specific repository (downloads and validates all ONNX models)
+python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2
+
+# Debug specific model file
+python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2/onnx/model.onnx
+
+# Debug with model truncation for debugging and validating intermediate results
+DEBUGONNX=1 python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2/onnx/model.onnx --truncate 10
+```
@@ -1,85 +0,0 @@
-import yaml, time, requests, argparse
-from pathlib import Path
-from huggingface_hub import list_models, HfApi
-from tinygrad.helpers import tqdm
-
-HUGGINGFACE_URL = "https://huggingface.co"
-SKIPPED_FILES = [
-  "fp16", "int8", "uint8", "quantized",      # numerical accuracy issues
-  "avx2", "arm64", "avx512", "avx512_vnni",  # numerical accuracy issues
-  "q4", "q4f16", "bnb4",                     # unimplemented quantization
-  "model_O4",                                # requires non cpu ort runner and MemcpyFromHost op
-  "merged",                                  # TODO implement attribute with graph type and Loop op
-]
-SKIPPED_REPO_PATHS = [
-  # Invalid model-index
-  "AdamCodd/vit-base-nsfw-detector",
-  # TODO: implement attribute with graph type and Loop op
-  "minishlab/potion-base-8M", "minishlab/M2V_base_output", "minishlab/potion-retrieval-32M",
-  # TODO: implement SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, GroupQueryAttention
-  "HuggingFaceTB/SmolLM2-360M-Instruct",
-  # TODO: implement SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, RotaryEmbedding, MultiHeadAttention
-  "HuggingFaceTB/SmolLM2-1.7B-Instruct",
-  # TODO: implmement RandomNormalLike
-  "stabilityai/stable-diffusion-xl-base-1.0", "stabilityai/sdxl-turbo", 'SimianLuo/LCM_Dreamshaper_v7',
-  # TODO: implement NonZero
-  "mangoapps/fb_zeroshot_mnli_onnx",
-  # TODO huge Concat in here with 1024 (1, 3, 32, 32) Tensors, and maybe a MOD bug with const folding
-  "briaai/RMBG-2.0",
-]
-
-def get_top_repos(n: int, sort: str) -> list[str]: # list["FacebookAI/xlm-roberta-large", ...]
-  print(f"** Getting top {n} models sorted by {sort} **")
-  repos = []
-  i = 0
-  for model in list_models(filter="onnx", sort=sort):
-    if model.id in SKIPPED_REPO_PATHS: continue
-    print(f"{i+1}/{n}: {model.id} ({getattr(model, sort)})")
-    repos.append(model.id)
-    i += 1
-    if i == n: break
-  return repos
-
-def get_metadata(repos:list[str]) -> dict:
-  api = HfApi()
-  repos_metadata = {"repositories": {}}
-  total_size = 0
-
-  # TODO: speed head requests up with async?
-  for repo in tqdm(repos, desc="Getting metadata"):
-    files_metadata = []
-    model_info = api.model_info(repo)
-
-    for file in model_info.siblings:
-      filename = file.rfilename
-      if not (filename.endswith('.onnx') or filename.endswith('.onnx_data')): continue
-      if any(skip_str in filename for skip_str in SKIPPED_FILES): continue
-      head = requests.head(f"{HUGGINGFACE_URL}/{repo}/resolve/main/{filename}", allow_redirects=True)
-      file_size = file.size or int(head.headers.get('Content-Length', 0))
-      files_metadata.append({"file": filename, "size": f"{file_size/1e6:.2f}MB"})
-      total_size += file_size
-
-    repos_metadata["repositories"][repo] = {
-      "url": f"{HUGGINGFACE_URL}/{repo}",
-      "download_path": None,
-      "files": files_metadata,
-    }
-  repos_metadata['total_size'] = f"{total_size/1e9:.2f}GB"
-  repos_metadata['created_at'] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
-  return repos_metadata
-
-if __name__ == "__main__":
-  sort = "downloads" # recent 30 days downloads
-  huggingface_onnx_dir = Path(__file__).parent
-
-  parser = argparse.ArgumentParser(description="Produces a YAML file with metadata of top huggingface onnx models")
-  parser.add_argument("--limit", type=int, required=True, help="Number of top repositories to process (e.g., 100)")
-  parser.add_argument("--output", type=str, default="huggingface_repos.yaml", help="Output YAML file name to save the report")
-  args = parser.parse_args()
-
-  top_repos = get_top_repos(args.limit, sort)
-  metadata = get_metadata(top_repos)
-  yaml_path = huggingface_onnx_dir / args.output
-  with open(yaml_path, 'w') as f:
-    yaml.dump(metadata, f, sort_keys=False)
-    print(f"YAML saved to: {str(yaml_path)}")
@@ -1,29 +0,0 @@
-import yaml, argparse
-from pathlib import Path
-from huggingface_hub import snapshot_download
-
-def download_models(yaml_file: str, download_dir: str) -> None:
-  with open(yaml_file, 'r') as f: metadata = yaml.safe_load(f)
-  n = len(metadata["repositories"])
-
-  for i, (model_id, model_data) in enumerate(metadata["repositories"].items()):
-    print(f"Downloading {i+1}/{n}: {model_id}...")
-    allow_patterns = [file_info["file"] for file_info in model_data["files"]]
-    root_path = Path(snapshot_download(repo_id=model_id, allow_patterns=allow_patterns, cache_dir=download_dir))
-    # download configs too (the sizes are small)
-    snapshot_download(repo_id=model_id, allow_patterns=["*config.json"], cache_dir=download_dir)
-    print(f"Downloaded model files to: {root_path}")
-    model_data["download_path"] = str(root_path)
-
-  # Save the updated metadata back to the YAML file
-  with open(yaml_file, 'w') as f: yaml.dump(metadata, f, sort_keys=False)
-  print("Download completed according to YAML file.")
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser(description="Download models from Huggingface Hub based on a YAML configuration file.")
-  parser.add_argument("input", type=str, help="Path to the input YAML configuration file containing model information.")
-  args = parser.parse_args()
-
-  models_folder = Path(__file__).parent / "models"
-  models_folder.mkdir(parents=True, exist_ok=True)
-  download_models(args.input, str(models_folder))
@@ -0,0 +1,230 @@
+import yaml
+import time
+import requests
+import argparse
+from pathlib import Path
+from huggingface_hub import list_models, HfApi, snapshot_download
+from tinygrad.helpers import _ensure_downloads_dir
+DOWNLOADS_DIR = _ensure_downloads_dir() / "models"
+from tinygrad.helpers import tqdm
+
+def snapshot_download_with_retry(*, repo_id: str, allow_patterns: list[str]|tuple[str, ...]|None=None, cache_dir: str|Path|None=None,
+                                 tries: int=2, **kwargs) -> Path:
+  for attempt in range(tries):
+    try:
+      return Path(snapshot_download(
+        repo_id=repo_id,
+        allow_patterns=allow_patterns,
+        cache_dir=str(cache_dir) if cache_dir is not None else None,
+        **kwargs
+      ))
+    except Exception as e:
+      if attempt == tries-1: raise
+      time.sleep(1)
+
+# Constants for filtering models
+HUGGINGFACE_URL = "https://huggingface.co"
+SKIPPED_FILES = [
+  "fp16", "int8", "uint8", "quantized",      # numerical accuracy issues
+  "avx2", "arm64", "avx512", "avx512_vnni",  # numerical accuracy issues
+  "q4", "q4f16", "bnb4",                     # unimplemented quantization
+  "model_O4",                                # requires non cpu ort runner and MemcpyFromHost op
+  "merged",                                  # TODO implement attribute with graph type and Loop op
+]
+
+SKIPPED_REPO_PATHS = [
+  # Invalid model-index
+  "AdamCodd/vit-base-nsfw-detector",
+  # TODO: implement attribute with graph type and Loop op
+  "minishlab/potion-base-8M", "minishlab/M2V_base_output", "minishlab/potion-retrieval-32M",
+  # TODO: implement SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, GroupQueryAttention
+  "HuggingFaceTB/SmolLM2-360M-Instruct",
+  # TODO: implement SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, RotaryEmbedding, MultiHeadAttention
+  "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+  # TODO: implement RandomNormalLike
+  "stabilityai/stable-diffusion-xl-base-1.0", "stabilityai/sdxl-turbo", 'SimianLuo/LCM_Dreamshaper_v7',
+  # TODO: implement NonZero
+  "mangoapps/fb_zeroshot_mnli_onnx",
+  # TODO huge Concat in here with 1024 (1, 3, 32, 32) Tensors, and maybe a MOD bug with const folding
+  "briaai/RMBG-2.0",
+]
+
+
+class HuggingFaceONNXManager:
+  def __init__(self):
+    self.base_dir = Path(__file__).parent
+    self.models_dir = DOWNLOADS_DIR
+    self.api = HfApi()
+
+  def discover_models(self, limit: int, sort: str = "downloads") -> list[str]:
+    print(f"Discovering top {limit} ONNX models sorted by {sort}...")
+    repos = []
+    i = 0
+
+    for model in list_models(filter="onnx", sort=sort):
+      if model.id in SKIPPED_REPO_PATHS:
+        continue
+
+      print(f"  {i+1}/{limit}: {model.id} ({getattr(model, sort)})")
+      repos.append(model.id)
+      i += 1
+      if i == limit:
+        break
+
+    print(f"Found {len(repos)} suitable ONNX models")
+    return repos
+
+  def collect_metadata(self, repos: list[str]) -> dict:
+    print(f"Collecting metadata for {len(repos)} repositories...")
+    metadata = {"repositories": {}}
+    total_size = 0
+
+    for repo in tqdm(repos, desc="Collecting metadata"):
+      try:
+        files_metadata = []
+        model_info = self.api.model_info(repo)
+
+        for file in model_info.siblings:
+          filename = file.rfilename
+          if not (filename.endswith('.onnx') or filename.endswith('.onnx_data')):
+            continue
+          if any(skip_str in filename for skip_str in SKIPPED_FILES):
+            continue
+
+          # Get file size from API or HEAD request
+          try:
+            head = requests.head(
+              f"{HUGGINGFACE_URL}/{repo}/resolve/main/{filename}",
+              allow_redirects=True,
+              timeout=10
+            )
+            file_size = file.size or int(head.headers.get('Content-Length', 0))
+          except requests.RequestException:
+            file_size = file.size or 0
+
+          files_metadata.append({
+            "file": filename,
+            "size": f"{file_size/1e6:.2f}MB"
+          })
+          total_size += file_size
+
+        if files_metadata:  # Only add repos with valid ONNX files
+          metadata["repositories"][repo] = {
+            "url": f"{HUGGINGFACE_URL}/{repo}",
+            "download_path": None,
+            "files": files_metadata,
+          }
+
+      except Exception as e:
+        print(f"WARNING: Failed to collect metadata for {repo}: {e}")
+        continue
+
+    metadata['total_size'] = f"{total_size/1e9:.2f}GB"
+    metadata['created_at'] = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+
+    print(f"Collected metadata for {len(metadata['repositories'])} repositories")
+    print(f"Total estimated download size: {metadata['total_size']}")
+
+    return metadata
+
+  def download_models(self, metadata: dict) -> dict:
+    self.models_dir.mkdir(parents=True, exist_ok=True)
+
+    repos = metadata["repositories"]
+    n = len(repos)
+
+    print(f"Downloading {n} repositories to {self.models_dir}...")
+
+    for i, (model_id, model_data) in enumerate(repos.items()):
+      print(f"  Downloading {i+1}/{n}: {model_id}...")
+
+      try:
+        # Download ONNX model files
+        allow_patterns = [file_info["file"] for file_info in model_data["files"]]
+        root_path = snapshot_download_with_retry(
+          repo_id=model_id,
+          allow_patterns=allow_patterns,
+          cache_dir=str(self.models_dir)
+        )
+
+        # Download config files (usually small)
+        snapshot_download_with_retry(
+          repo_id=model_id,
+          allow_patterns=["*config.json"],
+          cache_dir=str(self.models_dir)
+        )
+
+        model_data["download_path"] = str(root_path)
+        print(f"    Downloaded to: {root_path}")
+
+      except Exception as e:
+        print(f"    ERROR: Failed to download {model_id}: {e}")
+        model_data["download_path"] = None
+        continue
+
+    successful_downloads = sum(1 for repo in repos.values() if repo["download_path"] is not None)
+    print(f"Successfully downloaded {successful_downloads}/{n} repositories")
+    print(f"All models saved to: {self.models_dir}")
+
+    return metadata
+
+  def save_metadata(self, metadata: dict, output_file: str):
+    yaml_path = self.base_dir / output_file
+    with open(yaml_path, 'w') as f:
+      yaml.dump(metadata, f, sort_keys=False)
+    print(f"Metadata saved to: {yaml_path}")
+
+  def discover_and_download(self, limit: int, output_file: str = "huggingface_repos.yaml",
+                          sort: str = "downloads", download: bool = True):
+    print(f"Starting HuggingFace ONNX workflow...")
+    print(f"   Limit: {limit} models")
+    print(f"   Sort by: {sort}")
+    print(f"   Download: {'Yes' if download else 'No'}")
+    print(f"   Output: {output_file}")
+    print("-" * 50)
+
+    repos = self.discover_models(limit, sort)
+
+    metadata = self.collect_metadata(repos)
+
+    if download:
+      metadata = self.download_models(metadata)
+
+    self.save_metadata(metadata, output_file)
+
+    print("-" * 50)
+    print("Workflow completed successfully!")
+    if download:
+      successful = sum(1 for repo in metadata["repositories"].values()
+                     if repo["download_path"] is not None)
+      print(f"{successful}/{len(metadata['repositories'])} models downloaded")
+
+    return metadata
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(
+    description="HuggingFace ONNX Model Manager - Discover, collect metadata, and download ONNX models",
+  )
+
+  parser.add_argument("--limit", type=int, help="Number of top repositories to process")
+  parser.add_argument("--output", type=str, default="huggingface_repos.yaml",
+                     help="Output YAML file name (default: huggingface_repos.yaml)")
+  parser.add_argument("--sort", type=str, default="downloads",
+                     choices=["downloads", "likes", "created", "modified"],
+                     help="Sort criteria for model discovery (default: downloads)")
+
+  parser.add_argument("--download", action="store_true", default=False,
+                     help="Download models after collecting metadata")
+
+  args = parser.parse_args()
+
+  if not args.limit: parser.error("--limit is required")
+
+  manager = HuggingFaceONNXManager()
+  manager.discover_and_download(
+    limit=args.limit,
+    output_file=args.output,
+    sort=args.sort,
+    download=args.download
+  )
@@ -1,10 +1,11 @@
-import onnx, yaml, tempfile, time, collections, pprint, argparse, json
+import onnx, yaml, tempfile, time, argparse, json
 from pathlib import Path
+from typing import Any
 from tinygrad.frontend.onnx import OnnxRunner
-from extra.onnx import get_onnx_ops
 from extra.onnx_helpers import validate, get_example_inputs
+from extra.huggingface_onnx.huggingface_manager import DOWNLOADS_DIR, snapshot_download_with_retry

-def get_config(root_path: Path):
+def get_config(root_path: Path) -> dict[str, Any]:
  ret = {}
  for path in root_path.rglob("*config.json"):
    config = json.load(path.open())
@@ -12,19 +13,19 @@ def get_config(root_path: Path):
      ret.update(config)
  return ret

-def run_huggingface_validate(onnx_model_path, config, rtol, atol):
-  onnx_runner = OnnxRunner(onnx_model_path)
-  inputs = get_example_inputs(onnx_runner.graph_inputs, config)
-  validate(onnx_model_path, inputs, rtol=rtol, atol=atol)
-
-def get_tolerances(file_name): # -> rtol, atol
+def get_tolerances(file_name: str) -> tuple[float, float]:
  # TODO very high rtol atol
  if "fp16" in file_name: return 9e-2, 9e-2
  if any(q in file_name for q in ["int8", "uint8", "quantized"]): return 4, 4
  return 4e-3, 3e-2

+def run_huggingface_validate(onnx_model_path: str | Path, config: dict[str, Any], rtol: float, atol: float):
+  onnx_runner = OnnxRunner(onnx_model_path)
+  inputs = get_example_inputs(onnx_runner.graph_inputs, config)
+  validate(onnx_model_path, inputs, rtol=rtol, atol=atol)
+
 def validate_repos(models:dict[str, tuple[Path, Path]]):
-  print(f"** Validating {len(model_paths)} models **")
+  print(f"** Validating {len(models)} models **")
  for model_id, (root_path, relative_path) in models.items():
    print(f"validating model {model_id}")
    model_path = root_path / relative_path
@@ -36,25 +37,6 @@ def validate_repos(models:dict[str, tuple[Path, Path]]):
    et = time.time() - st
    print(f"passed, took {et:.2f}s")

-def retrieve_op_stats(models:dict[str, tuple[Path, Path]]) -> dict:
-  ret = {}
-  op_counter = collections.Counter()
-  unsupported_ops = collections.defaultdict(set)
-  supported_ops = get_onnx_ops()
-  print(f"** Retrieving stats from {len(model_paths)} models **")
-  for model_id, (root_path, relative_path) in models.items():
-    print(f"examining {model_id}")
-    model_path = root_path / relative_path
-    onnx_runner = OnnxRunner(model_path)
-    for node in onnx_runner.graph_nodes:
-      op_counter[node.op] += 1
-      if node.op not in supported_ops:
-        unsupported_ops[node.op].add(model_id)
-    del onnx_runner
-  ret["unsupported_ops"] = {k:list(v) for k, v in unsupported_ops.items()}
-  ret["op_counter"] = op_counter.most_common()
-  return ret
-
 def debug_run(model_path, truncate, config, rtol, atol):
  if truncate != -1:
    model = onnx.load(model_path)
@@ -71,12 +53,9 @@ def debug_run(model_path, truncate, config, rtol, atol):
    run_huggingface_validate(model_path, config, rtol, atol)

 if __name__ == "__main__":
-  parser = argparse.ArgumentParser(description="Huggingface ONNX Model Validator and Ops Checker")
-  parser.add_argument("input", type=str, help="Path to the input YAML configuration file containing model information.")
-  parser.add_argument("--check_ops", action="store_true", default=False,
-                      help="Check support for ONNX operations in models from the YAML file")
-  parser.add_argument("--validate", action="store_true", default=False,
-                      help="Validate correctness of models from the YAML file")
+  parser = argparse.ArgumentParser(description="Huggingface ONNX Model Validator")
+  parser.add_argument("--validate", type=str, default="",
+                      help="Validate correctness of models from the specified YAML configuration file")
  parser.add_argument("--debug", type=str, default="",
                      help="""Validates without explicitly needing a YAML or models pre-installed.
                      provide repo id (e.g. "minishlab/potion-base-8M") to validate all onnx models inside the repo
@@ -85,13 +64,13 @@ if __name__ == "__main__":
  parser.add_argument("--truncate", type=int, default=-1, help="Truncate the ONNX model so intermediate results can be validated")
  args = parser.parse_args()

-  if not (args.check_ops or args.validate or args.debug):
-    parser.error("Please provide either --validate, --check_ops, or --debug.")
+  if not (args.validate or args.debug):
+    parser.error("Please provide either --validate <yaml_file> or --debug <repo_id>.")
  if args.truncate != -1 and not args.debug:
    parser.error("--truncate and --debug should be used together for debugging")

-  if args.check_ops or args.validate:
-    with open(args.input, 'r') as f:
+  if args.validate:
+    with open(args.validate, 'r') as f:
      data = yaml.safe_load(f)
      assert all(repo["download_path"] is not None for repo in data["repositories"].values()), "please run `download_models.py` for this yaml"
      model_paths = {
@@ -101,22 +80,16 @@ if __name__ == "__main__":
        if model["file"].endswith(".onnx")
      }

-    if args.check_ops:
-      pprint.pprint(retrieve_op_stats(model_paths))
-
-    if args.validate:
-      validate_repos(model_paths)
+    validate_repos(model_paths)

  if args.debug:
-    from huggingface_hub import snapshot_download
-    download_dir = Path(__file__).parent / "models"
    path:list[str] = args.debug.split("/")
    if len(path) == 2:
      # repo id
      # validates all onnx models inside repo
      repo_id = "/".join(path)
-      root_path = Path(snapshot_download(repo_id=repo_id, allow_patterns=["*.onnx", "*.onnx_data"], cache_dir=download_dir))
-      snapshot_download(repo_id=repo_id, allow_patterns=["*config.json"], cache_dir=download_dir)
+      root_path = snapshot_download_with_retry(repo_id=repo_id, allow_patterns=["*.onnx", "*.onnx_data"], cache_dir=DOWNLOADS_DIR)
+      snapshot_download_with_retry(repo_id=repo_id, allow_patterns=["*config.json"], cache_dir=DOWNLOADS_DIR)
      config = get_config(root_path)
      for onnx_model in root_path.rglob("*.onnx"):
        rtol, atol = get_tolerances(onnx_model.name)
@@ -128,8 +101,8 @@ if __name__ == "__main__":
      onnx_model = path[-1]
      assert path[-1].endswith(".onnx")
      repo_id, relative_path = "/".join(path[:2]), "/".join(path[2:])
-      root_path = Path(snapshot_download(repo_id=repo_id, allow_patterns=[relative_path], cache_dir=download_dir))
-      snapshot_download(repo_id=repo_id, allow_patterns=["*config.json"], cache_dir=download_dir)
+      root_path = snapshot_download_with_retry(repo_id=repo_id, allow_patterns=[relative_path], cache_dir=DOWNLOADS_DIR)
+      snapshot_download_with_retry(repo_id=repo_id, allow_patterns=["*config.json"], cache_dir=DOWNLOADS_DIR)
      config = get_config(root_path)
      rtol, atol = get_tolerances(onnx_model)
      print(f"validating {relative_path} with truncate={args.truncate}, {rtol=}, {atol=}")
@@ -4,9 +4,9 @@ import numpy as np
 np.set_printoptions(suppress=True)
 import math, functools, time, random, statistics
 from tinygrad.helpers import DEBUG, getenv, CACHELEVEL, diskcache_get, diskcache_put, colored, Profiling
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.device import Buffer, Device, CompileError
-from tinygrad.opt.search import _ensure_buffer_alloc, get_kernel_actions, _time_program
+from tinygrad.codegen.opt.search import _ensure_buffer_alloc, get_kernel_actions, _time_program
 from tinygrad.engine.realize import get_program

 class MCTSNode:
@@ -181,13 +181,11 @@ class Transformer:
  def forward(self, tokens:Tensor, start_pos:Union[Variable,int], temperature:float, top_k:int, top_p:float, alpha_f:float, alpha_p:float):
    _bsz, seqlen = tokens.shape
    h = self.tok_embeddings(tokens)
-
-    self.freqs_cis = self.freqs_cis.cast(h.dtype).contiguous()
-    freqs_cis = self.freqs_cis[:, start_pos:start_pos+seqlen, :, :, :]
+    freqs_cis = self.freqs_cis.cast(h.dtype)[:, start_pos:start_pos+seqlen, :, :, :]

    mask = Tensor.full((1, 1, seqlen, start_pos+seqlen), float("-inf"), dtype=h.dtype, device=h.device).triu(start_pos+1) if seqlen > 1 else None
    for layer in self.layers: h = layer(h, start_pos, freqs_cis, mask)
-    logits = self.output(self.norm(h)).float()
+    logits = self.output(self.norm(h))
    if math.isnan(temperature): return logits

    return sample(logits[:, -1, :].flatten(), temperature, top_k, top_p, alpha_f, alpha_p)
@@ -1,7 +1,6 @@
 from tinygrad import Tensor
 from tinygrad.tensor import _to_np_dtype
-from tinygrad.frontend.onnx import OnnxRunner
-from extra.onnx import OnnxValue
+from tinygrad.frontend.onnx import OnnxRunner, OnnxValue
 import numpy as np
 import onnxruntime as ort

@@ -5,9 +5,9 @@ from tinygrad.nn import Linear
 from tinygrad.tensor import Tensor
 from tinygrad.nn.optim import Adam
 from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
-from tinygrad.opt.search import actions
+from tinygrad.codegen.opt.search import actions
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, assert_same_lin
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.helpers import getenv

 # stuff needed to unpack a kernel
@@ -17,7 +17,7 @@ from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
 from tinygrad.uop.ops import Variable
 inf, nan = float('inf'), float('nan')
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps

 INNER = 256
 class PolicyNet:
@@ -10,11 +10,11 @@ from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
 from tinygrad.uop.ops import Variable
 inf, nan = float('inf'), float('nan')
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps

 # more stuff
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.search import actions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import actions
 from extra.optimization.helpers import lin_to_feats
 from extra.optimization.pretrain_valuenet import ValueNet
 from tinygrad.nn.optim import Adam
@@ -1,8 +1,8 @@
 import random
 from extra.optimization.helpers import load_worlds, ast_str_to_lin
-from tinygrad.opt.search import actions
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import actions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from tinygrad.helpers import tqdm

 tactions = set()
@@ -1,6 +1,6 @@
 # stuff needed to unpack a kernel
 from tinygrad import Variable
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
 from tinygrad.dtype import dtypes, PtrDType
 from tinygrad.shape.shapetracker import ShapeTracker
@@ -11,7 +11,7 @@ inf, nan = float('inf'), float('nan')
 UOps = Ops

 # kernel unpacker
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 def ast_str_to_ast(ast_str:str) -> UOp: return eval(ast_str)
 def ast_str_to_lin(ast_str:str, opts=None): return Kernel(ast_str_to_ast(ast_str), opts=opts)
 def kern_str_to_lin(kern_str:str, opts=None):
@@ -103,7 +103,7 @@ def lin_to_feats(lin:Kernel, use_sts=True):
  return ret

 from tinygrad.device import Device, Buffer
-from tinygrad.opt.search import _ensure_buffer_alloc, _time_program
+from tinygrad.codegen.opt.search import _ensure_buffer_alloc, _time_program
 from tinygrad.helpers import to_function_name, CACHELEVEL, diskcache_get, diskcache_put

 def time_linearizer(lin:Kernel, rawbufs:list[Buffer], allow_test_size=True, max_global_size=65536, cnt=3, disable_cache=False, clear_l2=False) -> float:  # noqa: E501
@@ -1,4 +1,4 @@
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 from tqdm import tqdm, trange
 import math
 import random
@@ -14,7 +14,7 @@ from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
 from tinygrad.uop.ops import Variable
 inf, nan = float('inf'), float('nan')
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps

 from extra.optimization.helpers import lin_to_feats, MAX_DIMS

@@ -3,7 +3,7 @@ import numpy as np
 import math, random
 from tinygrad.tensor import Tensor
 from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
-from tinygrad.opt.search import actions, bufs_from_lin, get_kernel_actions
+from tinygrad.codegen.opt.search import actions, bufs_from_lin, get_kernel_actions
 from tinygrad.nn.optim import Adam
 from extra.optimization.extract_policynet import PolicyNet
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
@@ -1,6 +1,6 @@
 from typing import List, Tuple
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.search import get_kernel_actions, actions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import get_kernel_actions, actions

 _net = None
 def beam_q_estimate(beam:List[Tuple[Kernel, float]]) -> List[Tuple[Kernel, float]]:
@@ -4,8 +4,8 @@ from extra.optimization.helpers import ast_str_to_lin, time_linearizer
 from tinygrad import dtypes
 from tinygrad.helpers import BEAM, getenv
 from tinygrad.device import Device, Compiled
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.search import beam_search, bufs_from_lin
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import beam_search, bufs_from_lin


 if __name__ == '__main__':
@@ -6,8 +6,8 @@ from copy import deepcopy
 from tinygrad.helpers import getenv, colored
 from tinygrad.tensor import Tensor
 from tinygrad.nn.state import get_parameters, get_state_dict, safe_save, safe_load, load_state_dict
-from tinygrad.opt.search import bufs_from_lin, actions, get_kernel_actions
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import bufs_from_lin, actions, get_kernel_actions
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, time_linearizer
 from extra.optimization.extract_policynet import PolicyNet
 from extra.optimization.pretrain_valuenet import ValueNet
@@ -1,5 +1,5 @@
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
-from tinygrad.opt.search import bufs_from_lin, get_kernel_actions
+from tinygrad.codegen.opt.search import bufs_from_lin, get_kernel_actions

 if __name__ == "__main__":
  ast_strs = load_worlds()
@@ -6,8 +6,8 @@ from tinygrad.helpers import getenv, BEAM
 from tinygrad.engine.jit import TinyJit
 from tinygrad.engine.realize import CompiledRunner, ExecItem, ScheduleItem, lower_schedule_item, get_program
 from tinygrad.renderer import ProgramSpec
-from tinygrad.opt.kernel import Kernel, Opt, OptOps
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 import numpy as np

 def move_jit_captured_to_dev(captured, device="DSP"):
@@ -223,15 +223,18 @@ def max_unpool2d(self:torch.Tensor, indices:torch.Tensor, output_size):

@torch.library.impl("aten::arange", "privateuseone")
 def arange(end, dtype=None, device=None, pin_memory=None):
-  return wrap(Tensor.arange(0, end, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
+  has_float = isinstance(end, float)
+  return wrap(Tensor.arange(0, end, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))

@torch.library.impl("aten::arange.start", "privateuseone")
 def arange_start(start, end, dtype=None, device=None, pin_memory=None):
-  return wrap(Tensor.arange(start, end, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
+  has_float = any(isinstance(x, float) for x in (start, end))
+  return wrap(Tensor.arange(start, end, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))

@torch.library.impl("aten::arange.start_step", "privateuseone")
 def arange_start_step(start, end, step, dtype=None, device=None, pin_memory=None):
-  return wrap(Tensor.arange(start, end, step, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
+  has_float = any(isinstance(x, float) for x in (start, end, step))
+  return wrap(Tensor.arange(start, end, step, dtype=_from_torch_dtype(dtype or (torch.get_default_dtype() if has_float else torch.int64))))

@torch.library.impl("aten::convolution_overrideable", "privateuseone")
 def convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups):
@@ -368,6 +371,7 @@ from torch._decomp import get_decompositions
 decomps = [
  aten.native_batch_norm, aten.native_batch_norm_backward,
  aten.native_layer_norm_backward,
+  aten.linalg_cross,
  aten.addmm,
  aten.addcmul,
  aten.addcdiv,
@@ -135,7 +135,7 @@ class TestTorchBackend(unittest.TestCase):
    print(c.cpu())

  def test_maxpool2d_backward(self):
-    x = torch.arange(3*3, device=device).reshape(1, 1, 3, 3).requires_grad_(True)
+    x = torch.arange(3*3, dtype=torch.float32, device=device).reshape(1, 1, 3, 3).requires_grad_(True)
    torch.nn.functional.max_pool2d(x, kernel_size=2, stride=1).sum().backward()
    np.testing.assert_equal(x.grad.squeeze().cpu().numpy(), [[0, 0, 0], [0, 1, 1], [0, 1, 1]])

@@ -203,6 +203,12 @@ class TestTorchBackend(unittest.TestCase):
    b = torch.linalg.det(a)
    np.testing.assert_equal(b.cpu().numpy(), 120.0)

+  def test_linalg_cross(self):
+    a = torch.tensor([[1, 0, 0], [0, 1, 0]], dtype=torch.float32, device=device)
+    b = torch.tensor([[0, 0, 1]], dtype=torch.float32, device=device)
+    cross = torch.linalg.cross(a, b)
+    np.testing.assert_equal(cross.cpu().numpy(), np.array([[0, -1, 0], [1, 0, 0]], dtype=np.float32))
+
  def test_scalar_assign(self):
    a = torch.tensor([1, 2, 3], device=device)
    a[1] = 4
@@ -0,0 +1,75 @@
+import torch
+
+#credit to KellerJordan at https://github.com/KellerJordan/Muon/tree/master
+#some changes: classic momentum instead of weighting gradient
+#added ns_steps, ns_params, nesterov as hyperparams
+def zeropower_via_newtonschulz5(G:torch.tensor, steps:int, params:tuple[int, ...]):
+  """
+  Newton-Schulz iteration to compute the zeroth power / orthogonalization of G. We opt to use a
+  quintic iteration whose coefficients are selected to maximize the slope at zero. For the purpose
+  of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
+  zero even beyond the point where the iteration no longer converges all the way to one everywhere
+  on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
+  where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model
+  performance at all relative to UV^T, where USV^T = G is the SVD.
+  """
+  assert G.ndim >= 2 # batched Muon implementation by @scottjmaddox, and put into practice in the record by @YouJiacheng
+
+  a, b, c = params
+  X = G
+  if G.size(-2) > G.size(-1):
+    X = X.mT
+
+  # Ensure spectral norm is at most 1
+  X = X / (X.norm(dim=(-2, -1), keepdim=True) + 1e-7)
+  # Perform the NS iterations
+  for _ in range(steps):
+    A = X @ X.mT
+    B = b * A + c * A @ A # quintic computation strategy adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
+    X = a * X + B @ X
+
+  if G.size(-2) > G.size(-1):
+    X = X.mT
+
+  return X
+
+def muon_update(grad, momentum, beta=0.95, ns_steps=5, ns_params=(3.4445, -4.7750,  2.0315), nesterov=True):
+  if beta:
+    momentum.mul_(beta).add_(grad)
+    update = grad.add(momentum,alpha=beta) if nesterov else momentum
+  else: update = grad
+  if update.ndim == 4: # for the case of conv filters
+    update = update.view(len(update), -1)
+  update = zeropower_via_newtonschulz5(update, steps=ns_steps, params=ns_params)
+  return update
+
+class SingleDeviceMuon(torch.optim.Optimizer):
+  """
+  Muon variant for usage in non-distributed settings.
+  """
+  def __init__(self, params, lr=0.02, weight_decay=0.0, momentum=0.95, ns_steps=5, ns_params=(3.4445, -4.7750,  2.0315), nesterov=True):
+    defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, ns_steps=ns_steps, ns_params=ns_params, nesterov=nesterov)
+    super().__init__(params, defaults)
+
+  @torch.no_grad()
+  def step(self, closure=None):
+
+    loss = None
+    if closure is not None:
+      with torch.enable_grad():
+        loss = closure()
+
+    for group in self.param_groups:
+      for p in group["params"]:
+        if p.grad is None:
+          p.grad = torch.zeros_like(p)  # Force synchronization
+        state = self.state[p]
+        if len(state) == 0:
+          state["momentum_buffer"] = torch.zeros_like(p)
+        update = muon_update(p.grad, state["momentum_buffer"], beta=group["momentum"], ns_steps=group["ns_steps"],
+                             ns_params=group["ns_params"], nesterov=group["nesterov"])
+        p.mul_(1.0 - group["lr"] * group["weight_decay"])
+
+        p.add_(update.reshape(p.shape), alpha=-group["lr"])
+
+    return loss
@@ -24,10 +24,28 @@ setup(name='tinygrad',
      license='MIT',
      long_description=long_description,
      long_description_content_type='text/markdown',
-      packages = ['tinygrad', 'tinygrad.runtime.autogen', 'tinygrad.runtime.autogen.am', 'tinygrad.codegen', 'tinygrad.nn',
-                  'tinygrad.renderer', 'tinygrad.engine', 'tinygrad.viz', 'tinygrad.runtime', 'tinygrad.runtime.support', 'tinygrad.schedule',
-                  'tinygrad.runtime.support.am', 'tinygrad.runtime.graph', 'tinygrad.shape', 'tinygrad.uop', 'tinygrad.opt',
-                  'tinygrad.runtime.support.nv', 'tinygrad.apps'],
+      packages = [
+        'tinygrad',
+        'tinygrad.apps',
+        'tinygrad.codegen',
+        'tinygrad.codegen.opt',
+        'tinygrad.engine',
+        'tinygrad.frontend',
+        'tinygrad.nn',
+        'tinygrad.renderer',
+        'tinygrad.runtime',
+        'tinygrad.runtime.autogen',
+        'tinygrad.runtime.autogen.am',
+        'tinygrad.runtime.autogen.nv',
+        'tinygrad.runtime.graph',
+        'tinygrad.runtime.support',
+        'tinygrad.runtime.support.am',
+        'tinygrad.runtime.support.nv',
+        'tinygrad.schedule',
+        'tinygrad.shape',
+        'tinygrad.uop',
+        'tinygrad.viz',
+      ],
      package_data = {'tinygrad': ['py.typed'], 'tinygrad.viz': ['index.html', 'assets/**/*', 'js/*']},
      classifiers=[
        "Programming Language :: Python :: 3",
@@ -1,7 +1,7 @@
 import random
 from tinygrad.helpers import getenv
-from tinygrad.opt.search import beam_search, bufs_from_lin
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer

 def optimize_kernel(k):
@@ -8,7 +8,7 @@ if __name__ == "__main__":
  GlobalCounters.reset()
  t.softmax(-1, dtype="half", _single_kernel=True).realize()

-  from tinygrad.opt.kernel import Kernel, Opt, OptOps
+  from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
  from tinygrad.helpers import get_single_element
  GlobalCounters.reset()
  si = get_single_element(t.softmax(-1, dtype="half", _single_kernel=True).schedule())
@@ -1,8 +1,8 @@
 # ruff: noqa: E501
-from tinygrad.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.dtype import dtypes
 from tinygrad.engine.realize import CompiledRunner, get_program
-from tinygrad.opt.search import bufs_from_lin
+from tinygrad.codegen.opt.search import bufs_from_lin
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
@@ -4,10 +4,10 @@ os.environ["VALIDATE_HCQ"]="1"

 import unittest, random
 import numpy as np
-from tinygrad.opt.kernel import Kernel, KernelOptError
+from tinygrad.codegen.opt.kernel import Kernel, KernelOptError
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.opt.search import Opt, OptOps
+from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad import Device, dtypes, Tensor
 from test.external.fuzz_linearizer import compare_linearizer, compare_states, get_fuzz_rawbuf_like

@@ -3,7 +3,7 @@ from tinygrad.runtime.support.hip_comgr import compile_hip
 from tinygrad import Tensor
 from tinygrad.device import Device
 from tinygrad.engine.schedule import create_schedule
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel

 class TestHIPCompileSpeed(unittest.TestCase):
  @unittest.skipIf(Device.DEFAULT != "HIP", "only run on HIP")
@@ -2,11 +2,11 @@ import unittest, struct, array, ctypes
 from tinygrad import Device, dtypes, Tensor
 from tinygrad.helpers import to_mv
 from tinygrad.runtime.ops_nv import NVDevice, HWQueue
-from tinygrad.opt.search import Opt, OptOps
+from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad.engine.realize import get_runner, CompiledRunner, get_program
 from test.external.fuzz_linearizer import get_fuzz_rawbufs

-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.uop.ops import LazyOp, Ops, ReduceOps, BufferOps, MemBuffer
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
@@ -53,6 +53,7 @@ backend_test.exclude('test_dynamicquantizelinear_cpu')
 backend_test.exclude('test_dynamicquantizelinear_expanded_cpu')

 # BUG: ORT fails these with numerical error but we match ORT numerically
+# see: https://onnx.ai/backend-scoreboard/onnxruntime_details_stable.html
 # tested in external_test_onnx_ops.py::TestMainOnnxOps.test_qlinearmatmul_2D_int8_float16
 backend_test.exclude('test_qlinearmatmul_2D_int8_float16_cpu')
 # tested in external_test_onnx_ops.py::TestMainOnnxOps.test_qlinearmatmul_3D_int8_float16
@@ -65,6 +66,10 @@ backend_test.exclude('test_qlinearmatmul_3D_int8_float32_cpu')
 backend_test.exclude('test_maxunpool_export_with_output_shape_cpu')
 # tested in external_test_onnx_ops.py::TestMainOnnxOps.test_averagepool_3d_dilations_large_count_include_pad_is_1_ceil_mode_is_True
 backend_test.exclude('test_averagepool_3d_dilations_large_count_include_pad_is_1_ceil_mode_is_True_cpu')
+# tested in external_test_onnx_ops.py::TestMainOnnxOps.test_resize_downsample_scales_linear_align_corners
+backend_test.exclude('test_resize_downsample_scales_linear_align_corners_cpu')
+# tested in external_test_onnx_ops.py::TestMainOnnxOps.test_resize_downsample_scales_cubic_align_corners
+backend_test.exclude('test_resize_downsample_scales_cubic_align_corners_cpu')

 # about different dtypes
 if not is_dtype_supported(dtypes.float64):
@@ -165,10 +170,6 @@ backend_test.exclude('test_deform_conv_*')
 backend_test.exclude('test_lppool_*')
 backend_test.exclude('test_scan_*')
 backend_test.exclude('test_split_to_sequence_*')
-backend_test.exclude('test_resize_downsample_scales_cubic_*') # unsure how to implement cubic
-backend_test.exclude('test_resize_downsample_sizes_cubic_*') # unsure how to implement cubic
-backend_test.exclude('test_resize_upsample_scales_cubic_*') # unsure how to implement cubic
-backend_test.exclude('test_resize_upsample_sizes_cubic_*') # unsure how to implement cubic
 backend_test.exclude('test_ai_onnx_ml_tree_ensemble_*') # https://github.com/onnx/onnx/blob/main/onnx/reference/ops/aionnxml/op_tree_ensemble.py#L121

 # rest of the failing tests
@@ -178,6 +179,8 @@ backend_test.exclude('test_resize_tf_crop_and_resize_axes_3_2_cpu') # tf_crop_an
 backend_test.exclude('test_resize_tf_crop_and_resize_extrapolation_value_cpu') # tf_crop_and_resize value not implemented
 backend_test.exclude('test_resize_downsample_scales_linear_antialias_cpu') # antialias not implemented
 backend_test.exclude('test_resize_downsample_sizes_linear_antialias_cpu') # antialias not implemented
+backend_test.exclude('test_resize_downsample_scales_cubic_antialias_cpu') # antialias not implemented
+backend_test.exclude('test_resize_downsample_sizes_cubic_antialias_cpu') # antialias not implemented
 backend_test.exclude('test_ai_onnx_ml_label_encoder_tensor_value_only_mapping_cpu') # bad data type string
 backend_test.exclude('test_ai_onnx_ml_label_encoder_tensor_mapping_cpu') # bad data type string

@@ -75,6 +75,49 @@ class TestMainOnnxOps(TestOnnxOps):
    outputs = ["y"]
    self.helper_test_single_op("Gather", inputs, attributes, outputs)

+  # NOTE: resize OP is sensitive to numerical errors
+  def _test_resize_scales(self, scale_values, **kwargs):
+    for sc in scale_values:
+      for ct_mode in ["half_pixel", "align_corners", "asymmetric", "pytorch_half_pixel", "half_pixel_symmetric"]:
+        with self.subTest(coordinate_transformation_mode=ct_mode, scale=sc, **kwargs):
+          X = np.array([[[[1, 2, 3, 4],
+                          [5, 6, 7, 8],
+                          [9,10,11,12]]]], dtype=np.float32)
+          scales = np.array([1.0, 1.0, sc, sc], dtype=np.float32)
+          inputs = {"X": X, "roi": np.array([], dtype=np.float32), "scales": scales}
+          attributes = {"coordinate_transformation_mode": ct_mode, **kwargs}
+          outputs = ["out"]
+          self.helper_test_single_op("Resize", inputs, attributes, outputs)
+
+  def test_resize_linear_mode(self):
+    self._test_resize_scales([0.01, 0.25, 0.5, 0.51, 0.6, 1.0, 1.5, 2.0, 3.5, 20.0], mode="linear")
+
+  def test_resize_nearest_mode(self):
+    # excluded 3.5 because some values divide into slight numerical differences, which when rounded gives wrong results
+    self._test_resize_scales([0.01, 0.25, 0.5, 0.51, 0.6, 1.0, 1.5, 2.0, 20.0], mode="nearest")
+
+  def test_resize_cubic_mode(self):
+    self._test_resize_scales([0.01, 0.25, 0.5, 0.51, 0.6, 1.0, 1.5, 2.0, 3.5, 20.0], mode="cubic", exclude_outside=1)
+    self._test_resize_scales([0.01, 0.25, 0.5, 0.51, 0.6, 1.0, 1.5, 2.0, 3.5, 20.0], mode="cubic", exclude_outside=0)
+
+  def test_resize_downsample_scales_linear_align_corners(self):
+    # https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-131
+    X = np.array([[[[1, 2, 3, 4], [5, 6, 7, 8]]]], dtype=np.float32)
+    scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
+    inputs = {"X": X, "roi": np.array([], dtype=np.float32), "scales": scales}
+    attributes = {"mode": "linear", "coordinate_transformation_mode": "align_corners"}
+    outputs = ["out"]
+    self.helper_test_single_op("Resize", inputs, attributes, outputs)
+
+  def test_resize_downsample_scales_cubic_align_corners(self):
+    # https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-131
+    X = np.array([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]]], dtype=np.float32)
+    scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
+    inputs = {"X": X, "roi": np.array([], dtype=np.float32), "scales": scales}
+    attributes = {"mode": "cubic", "coordinate_transformation_mode": "align_corners"}
+    outputs = ["out"]
+    self.helper_test_single_op("Resize", inputs, attributes, outputs)
+
  def test_maxunpool_export_with_output_shape(self):
    # https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-91
    xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32)
@@ -251,7 +294,6 @@ class TestTrainingOnnxOps(TestOnnxOps):
        outputs = ["X_out", "V_out"]
        self._validate_training("Momentum", onnx_fxn, inputs, attributes, outputs)

-  @unittest.expectedFailure  # TODO: regression from removing StrEnum in Domain
  def test_adam_t_greater_than_zero(self):
    from onnx.backend.test.case.node.adam import apply_adam
    for t in [1, 3, 100]:
@@ -3,8 +3,7 @@ import numpy as np
 from tinygrad import dtypes, Tensor
 from tinygrad.uop.ops import Ops
 from tinygrad.device import is_dtype_supported
-from extra.onnx import data_types
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.frontend.onnx import OnnxRunner, OnnxDataType
 from hypothesis import given, strategies as st

 # copied from test_const_folding.py
@@ -86,8 +85,8 @@ class TestOnnxRunner(unittest.TestCase):
      output = runner({'inp': Tensor([1])})['output']
      np.testing.assert_equal(output.numpy(), weights + 1)

-all_dtypes = list(data_types.keys())
-device_supported_dtypes = {odt for odt, dtype in data_types.items() if is_dtype_supported(dtype)}
+all_dtypes = list(OnnxDataType)
+device_supported_dtypes = {odt for odt in OnnxDataType if is_dtype_supported(odt.to_dtype())}

 class TestOnnxRunnerDtypes(unittest.TestCase):
  """
@@ -95,7 +94,7 @@ class TestOnnxRunnerDtypes(unittest.TestCase):
  External tensors (inputs) preserve their original dtype - user must ensure compatibility with device.
  """
  def _get_expected_dtype(self, onnx_dtype: int, is_input: bool):
-    true_dtype = data_types[onnx_dtype]
+    true_dtype = OnnxDataType(onnx_dtype).to_dtype()
    # inputs always preserve their true dtype.
    if is_input:
      return true_dtype
@@ -2,11 +2,11 @@
 import unittest

 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.opt.search import Opt, OptOps
+from tinygrad.codegen.opt.search import Opt, OptOps
 from tinygrad.dtype import dtypes
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel

 from test.external.fuzz_linearizer import run_linearizer

@@ -1,33 +1,26 @@
 import random
-from z3 import Int, Solver, sat
-from tinygrad import dtypes, Device
-from tinygrad.uop.ops import UOp, Ops, UPat, graph_rewrite, PatternMatcher
-from tinygrad.codegen.optional import fast_idiv
+import z3
+from tinygrad import dtypes
+from tinygrad.uop.spec import z3_renderer, z3_cdiv
+from tinygrad.uop.ops import UOp, graph_rewrite
+from tinygrad.uop.decompositions import fast_idiv
 random.seed(42)

-z3_renderer = PatternMatcher([
-  (UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
-  # Because fast_idiv only works for non-negative integers we can emulate machine arithmetic with modulo operations.
-  (UPat(Ops.SHR, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(({x.src[0].arg}/(2**{x.src[1].arg}))%{dtypes.max(x.dtype)+1})")),
-  (UPat(Ops.MUL, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(({x.src[0].arg}*{x.src[1].arg})%{dtypes.max(x.dtype)+1})")),
-  (UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),
-  (UPat(Ops.CAST, src=UPat(Ops.NOOP), name="x"), lambda x:  UOp(Ops.NOOP, arg=f"{x.src[0].arg}")),
-])
-
-def render(self) -> str:
-  ret = graph_rewrite(self.simplify(), z3_renderer)
-  return ret.arg if ret.op is Ops.NOOP else str(ret)
-
+powers_of_two = [2**i for i in range(64)]
 if __name__ == "__main__":
-  x = Int('x')
-  for _ in range(10_000):
+  for i in range(10_000):
+    if i % 1000 == 0:
+      print(f"Progress: {i}")
    dt = random.choice(dtypes.ints)
-    u = UOp(Ops.DEFINE_VAR, dt, arg=('x', 0, random.randint(1, dtypes.max(dt))), src=())
+    u = UOp.variable('x', random.randint(dt.min, 0), random.randint(1, dt.max), dtype=dt)
    d = random.randint(1, max(1, u.arg[2]))
-
-    expr = fast_idiv(Device[Device.DEFAULT].renderer, u, d)
+    if d in powers_of_two: continue
+    expr = fast_idiv(None, u, d)
    if expr is None: continue
-    solver = Solver()
-    solver.add(x>=u.arg[1], x<=u.arg[2])
-    if solver.check(eval(render(expr)) != x/d) == sat:
-      assert False, f"Failed: {render(expr)} != x//{d} at x={solver.model()[x]}\nx={u}\nd={d}"
+
+    solver = z3.Solver()
+    z3_sink = graph_rewrite(expr.sink(u), z3_renderer, ctx=(solver, {}))
+    z3_expr, x = z3_sink.src[0].arg, z3_sink.src[1].arg
+
+    if solver.check(z3_expr != z3_cdiv(x, d)) == z3.sat:
+      assert False, f"Failed: {expr.render()} != x//{d} at x={solver.model()}\nx={u}\nd={d}\n{z3_expr=}\n{x/d=}"
@@ -21,9 +21,9 @@ if os.getenv("VALIDATE_HCQ", 0) != 0:

 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.kernel import Opt, OptOps
-from tinygrad.opt.search import get_kernel_actions, bufs_from_lin
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.search import get_kernel_actions, bufs_from_lin
 from tinygrad.engine.realize import CompiledRunner
 from tinygrad.helpers import getenv, from_mv, prod, colored, Context, DEBUG, Timing
 from tinygrad.uop.ops import UOp, Ops
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 # compare kernels created by HEAD against master
 import os, multiprocessing, logging, pickle, sqlite3, difflib, warnings, itertools, functools, base64, codecs
+from dataclasses import replace
 from typing import Callable, Any

 ASSERT_DIFF = int((flag:="[pr]") in os.getenv("COMMIT_MESSAGE", flag) or flag in os.getenv("PR_TITLE", flag))
@@ -11,7 +12,9 @@ try:
  from tinygrad.renderer import Renderer, ProgramSpec
  from tinygrad.engine.realize import get_program
  from tinygrad.uop.ops import UOp, Ops, KernelInfo
+  from tinygrad.codegen.opt.kernel import Opt
  from tinygrad.helpers import VERSION, Context, ContextVar, colored, db_connection, getenv, tqdm
+  from tinygrad.device import Device
 except ImportError as e:
  print(repr(e))
  exit(int(ASSERT_DIFF))
@@ -47,9 +50,13 @@ def replay_kernelize(ret:dict[UOp, UOp], big_sink:UOp) -> tuple[str, str, tuple[
    return "\n".join([f"{len(asts)} kernels", *asts])
  return to_str(new_sink), to_str(ret[big_sink]), (big_sink,)

-def replay_get_program(p:ProgramSpec, ast:UOp, renderer:Renderer) -> tuple[str, str, tuple[Any, ...]]:
-  input_ast = ast.replace(arg=KernelInfo(opts_to_apply=p.applied_opts, name=p.name)) if ast.arg is None else ast
-  p2 = get_program(input_ast, renderer)
+def replay_get_program(p:ProgramSpec, ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None) -> tuple[str, str, tuple[Any, ...]]:
+  # NOTE: this always uses the opts_to_apply path
+  sink_arg = ast.arg or KernelInfo(opts_to_apply=p.applied_opts)
+  input_ast = ast.replace(arg=replace(sink_arg, name=p.name))
+  # if no renderer was provided, open the device to get it
+  if renderer is None: renderer = Device[p.device].renderer
+  p2 = get_program(input_ast, renderer=renderer)
  def to_str(ret:ProgramSpec) -> str:
    # PYTHON renderer pickles UOps, first unpickle and decode here
    if p.device.startswith("PYTHON"): return "\n".join([str(x) for x in pickle.loads(base64.b64decode(ret.src))])
@@ -1,7 +1,7 @@
 from tinygrad import Device
 from tinygrad.helpers import getenv, DEBUG, BEAM
-from tinygrad.opt.search import beam_search, bufs_from_lin
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer

 if __name__ == "__main__":
@@ -2,8 +2,8 @@ from tinygrad import Device, dtypes
 from tinygrad.helpers import getenv, colorize_float, DEBUG
 from extra.optimization.helpers import load_worlds, ast_str_to_lin
 from test.external.fuzz_linearizer import get_fuzz_rawbufs
-from tinygrad.opt.heuristic import hand_coded_optimizations
-from tinygrad.opt.search import bufs_from_lin
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import bufs_from_lin
 from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.runtime.ops_amd import AMDDevice
@@ -2,8 +2,8 @@ from tinygrad import Device, dtypes
 from tinygrad.helpers import getenv, colorize_float
 from extra.optimization.helpers import load_worlds, ast_str_to_lin
 from test.external.fuzz_linearizer import get_fuzz_rawbufs
-from tinygrad.opt.heuristic import hand_coded_optimizations
-from tinygrad.opt.search import bufs_from_lin
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.search import bufs_from_lin
 from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.tensor import _to_np_dtype
 import numpy as np
@@ -1,10 +1,10 @@
 import itertools
 from tinygrad import Device
 from tinygrad.engine.realize import CompiledRunner, get_program
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from tinygrad.helpers import getenv, colorize_float
 from extra.optimization.helpers import load_worlds, ast_str_to_lin
-from tinygrad.opt.search import bufs_from_lin
+from tinygrad.codegen.opt.search import bufs_from_lin
 from tinygrad.runtime.ops_cuda import PTXCompiler, PTXRenderer, CUDACompiler

 if __name__ == "__main__":
@@ -3,7 +3,7 @@ from collections import defaultdict
 from extra.optimization.helpers import kern_str_to_lin, time_linearizer
 from test.external.fuzz_linearizer import compare_linearizer
 from tinygrad.helpers import colored
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.kernel import Kernel

 # Use this with the LOGKERNS options to verify that all executed kernels are valid and evaluate to the same ground truth results

@@ -87,16 +87,19 @@ class AMDDriver(VirtDriver):
        functools.partial(TextFileDesc, text=gpu_props.format(drm_render_minor=gpu_id))),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0',
               functools.partial(DirFileDesc, child_names=[str(am.GC_HWID), str(am.SDMA0_HWID), str(am.NBIF_HWID)])),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/major', functools.partial(TextFileDesc, text='11')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/minor', functools.partial(TextFileDesc, text='0')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.GC_HWID}/0/base_addr',
               functools.partial(TextFileDesc, text='0x00001260\n0x0000A000\n0x0001C000\n0x02402C00')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/major', functools.partial(TextFileDesc, text='6')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/minor', functools.partial(TextFileDesc, text='0')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.SDMA0_HWID}/0/base_addr',
               functools.partial(TextFileDesc, text='0x00001260\n0x0000A000\n0x0001C000\n0x02402C00')),
+      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}', functools.partial(DirFileDesc, child_names=['0'])),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/major', functools.partial(TextFileDesc, text='4')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/minor', functools.partial(TextFileDesc, text='3')),
      VirtFile(f'/sys/class/drm/renderD{gpu_id}/device/ip_discovery/die/0/{am.NBIF_HWID}/0/revision', functools.partial(TextFileDesc, text='0')),
@@ -9,7 +9,15 @@ except ModuleNotFoundError:
  raise unittest.SkipTest("onnx not installed, skipping onnx test")
 from tinygrad.frontend.onnx import OnnxRunner
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import CI, fetch, temp
+from tinygrad.device import Device
+from tinygrad.helpers import CI, fetch, temp, Context
+
+try:
+  from extra.onnx_helpers import validate
+  from extra.huggingface_onnx.huggingface_manager import DOWNLOADS_DIR, snapshot_download_with_retry
+  HUGGINGFACE_AVAILABLE = True
+except ModuleNotFoundError:
+  HUGGINGFACE_AVAILABLE = False

 def run_onnx_torch(onnx_model, inputs):
  import torch
@@ -137,5 +145,36 @@ class TestOnnxModel(unittest.TestCase):
    print(cls, _LABELS[cls])
    assert "car" in _LABELS[cls] or _LABELS[cls] == "convertible"

+@unittest.skipUnless(HUGGINGFACE_AVAILABLE and Device.DEFAULT == "METAL", "only run on METAL")
+class TestHuggingFaceOnnxModels(unittest.TestCase):
+  @classmethod
+  def setUpClass(cls):
+    cls._ctx = Context(MAX_BUFFER_SIZE=0)
+    cls._ctx.__enter__()
+
+  @classmethod
+  def tearDownClass(cls):
+    cls._ctx.__exit__()
+
+  def _validate(self, repo_id, model_file, custom_inputs, rtol=1e-4, atol=1e-4):
+    onnx_model_path = snapshot_download_with_retry(
+      repo_id=repo_id,
+      allow_patterns=["*.onnx", "*.onnx_data"],
+      cache_dir=str(DOWNLOADS_DIR)
+    )
+    onnx_model_path = onnx_model_path / model_file
+    file_size = onnx_model_path.stat().st_size
+    print(f"Validating model: {repo_id}/{model_file} ({file_size/1e6:.2f}M)")
+    validate(onnx_model_path, custom_inputs, rtol=rtol, atol=atol)
+
+  def test_xlm_roberta_large(self):
+    repo_id = "FacebookAI/xlm-roberta-large"
+    model_file = "onnx/model.onnx"
+    custom_inputs = {
+      "input_ids": np.random.randint(0, 250002, (1, 11), dtype=np.int64),
+      "attention_mask": np.ones((1, 11), dtype=np.int64),
+    }
+    self._validate(repo_id, model_file, custom_inputs)
+
 if __name__ == "__main__":
  unittest.main()
@@ -3,9 +3,9 @@ import numpy as np
 from tinygrad import Tensor, GlobalCounters, dtypes, nn, Device, Variable
 from tinygrad.helpers import CI, Context, getenv
 from tinygrad.engine.realize import run_schedule
-from tinygrad.opt.kernel import Opt, OptOps, Kernel, KernelOptError
+from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel, KernelOptError
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
-from tinygrad.opt.search import get_kernel_actions
+from tinygrad.codegen.opt.search import get_kernel_actions
 from tinygrad.uop.ops import Ops
 from tinygrad.codegen import apply_rewrites, rewrites_for_views

@@ -143,13 +143,12 @@ class TestIndexingConstFolding(unittest.TestCase):
    _check_ast_count(1, t[:,:,Tensor(1)+2,:])
    _check_ast_count(1, t[:,:,Tensor(1),Tensor(0)])

-  @unittest.expectedFailure
  def test_const_tensor_index(self):
-    # TODO: implement const tensor folded indexing
+    # TODO: these can be 0, implement const tensor folded indexing
    t = Tensor.arange(16).float().reshape(1,1,4,4).realize()
-    _check_ast_count(0, t[:,:,Tensor.ones(2,1),:])
-    _check_ast_count(0, t[:,:,Tensor.ones(1,2)+2,:])
-    _check_ast_count(0, t[:,:,Tensor.ones(1,1),Tensor.zeros(2,1,2)])
+    _check_ast_count(1, t[:,:,Tensor.ones(2,1,dtype=dtypes.int),:])
+    _check_ast_count(1, t[:,:,Tensor.ones(1,2,dtype=dtypes.int)+2,:])
+    _check_ast_count(1, t[:,:,Tensor.ones(1,1,dtype=dtypes.int),Tensor.zeros(2,1,2,dtype=dtypes.int)])

 class TestMovedConstFolding(unittest.TestCase):
  def test_add_shrunk_zero(self):
@@ -290,17 +289,12 @@ class TestMultiConstFolding(unittest.TestCase):
    np.testing.assert_equal((t + zero).numpy(), np.arange(16))
    np.testing.assert_equal((t * zero).numpy(), [0] * 16)
    np.testing.assert_equal((t * one).numpy(), np.arange(16))
-
-  def test_multi_todo_pow(self):
-    ds = tuple(f"{Device.DEFAULT}:{i}" for i in range(4))
-    t = Tensor.arange(16).float().to(ds).realize()
-    zero = Tensor.zeros(16).to(ds).realize()
-    one = Tensor.ones(16).to(ds).realize()
-
-    # TODO: fix pow folding
    _check_ast_count(0, t ** zero)
    _check_ast_count(0, t ** one)
    _check_ast_count(0, one ** t)
+    np.testing.assert_equal((t ** zero).numpy(), [1] * 16)
+    np.testing.assert_equal((t ** one).numpy(), np.arange(16))
+    np.testing.assert_equal((one ** t).numpy(), [1] * 16)

 class TestTautologicalCompare(unittest.TestCase):
  # without const folding, these would have triggered -Wtautological-compare in clang
@@ -0,0 +1,21 @@
+import unittest, io
+from tinygrad import Tensor, dtypes
+from contextlib import redirect_stdout
+from tinygrad.device import Device
+from tinygrad.helpers import OSX
+from tinygrad.engine.realize import get_program
+
+class TestDisassembly(unittest.TestCase):
+  # TODO: fails on llvm. llvm.LLVMGetHostCPUName() returns "generic"
+  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and OSX, "m series cpus support fp16 arithmetic")
+  def test_float16_alu(self):
+    c = Tensor([1], dtype=dtypes.float16) + Tensor([1], dtype=dtypes.float16)
+    s = c.schedule()[-1]
+    p = get_program(s.ast, Device[Device.DEFAULT].renderer)
+    lib = Device[Device.DEFAULT].compiler.compile(p.src)
+    out = io.StringIO()
+    with redirect_stdout(out): Device[Device.DEFAULT].compiler.disassemble(lib)
+    assert "fcvt" not in out.getvalue()
+
+if __name__ == "__main__":
+  unittest.main()
@@ -62,7 +62,6 @@ class TestNaNEdgeCases(unittest.TestCase):
 class TestEmptyTensorEdgeCases(unittest.TestCase):
  # we don't need more of these

-  @unittest.expectedFailure
  def test_sort_empty(self):
    # Sorting an empty tensor works in PyTorch and should return empty
    # values and indices. tinygrad raises an error instead.
@@ -219,7 +218,6 @@ class TestAssignIssues(unittest.TestCase):
    t.shrink(((1, 3), (1, 3))).assign(Tensor.ones(2, 2))
    np.testing.assert_allclose(t.numpy(), torch_tensor.numpy())

-  @unittest.expectedFailure
  def test_assign_broadcast(self):
    # broadcasting during assign should behave like PyTorch
    torch_tensor = torch.zeros(3, 5)
@@ -258,12 +256,11 @@ class TestEdgeCases(unittest.TestCase):
    out = Tensor(arr).pad((1, -1, 1, -1), mode='circular')
    np.testing.assert_equal(out.numpy(), torch_out.numpy())

-  @unittest.expectedFailure
  def test_arange_float_step(self):
    # float steps should match PyTorch exactly
    torch_out = torch.arange(0, 2, 0.3).numpy()
    out = Tensor.arange(0, 2, 0.3).numpy()
-    np.testing.assert_allclose(out, torch_out)
+    np.testing.assert_allclose(out, torch_out, atol=1e-7)

  @unittest.skip("this is flaky")
  @unittest.expectedFailure
@@ -6,7 +6,7 @@ from tinygrad.runtime.support.hcq import HCQCompiled, HCQBuffer
 from tinygrad.runtime.autogen import libc
 from tinygrad.runtime.support.system import PCIIfaceBase
 from tinygrad.engine.realize import get_runner, CompiledRunner, get_program
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps
 from tinygrad import Variable

 MOCKGPU = getenv("MOCKGPU")
@@ -718,8 +718,6 @@ class TestJitGraphSplit(unittest.TestCase):
  def ji_xfer(self): return {"type": "xfer"}

  def test_jit_split_simple(self):
-    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")
-
    @TinyJit
    def f(inp):
      op0 = self.compute(Device.DEFAULT, inp)
@@ -792,9 +790,9 @@ class TestJitGraphSplit(unittest.TestCase):
      multigraph=[self.ji_graph(5)],
      hcqgraph=[self.ji_graph(5)])

-  @unittest.skip("flaky")
  def test_jit_multidev_xfer(self):
    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
+    if Device.DEFAULT == "METAL" or REAL_DEV == "METAL": raise unittest.SkipTest("Metal is flaky, with multidevice (same as metal llama 4gpu?)")

    try: Device[f"{Device.DEFAULT}:1"]
    except Exception: raise unittest.SkipTest("no multidevice")
@@ -819,7 +817,6 @@ class TestJitGraphSplit(unittest.TestCase):
  @unittest.skipIf(getenv("MOCKGPU"), "MockGPU does not support parallel copies")
  def test_jit_multidev_copy(self):
    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
-    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")

    @TinyJit
    def f(inp):
@@ -62,5 +62,15 @@ class TestLinAlg(unittest.TestCase):
      orthogonality_helper(Q)
      reconstruction_helper([Q,R],a)

+  def test_newton_schulz(self):
+    coefficients = [(2, -1.5, 0.5), (2.0, -1.4, 0.2, 0.2)]#these params map to the sign function
+    sizes = [(2,2), (3,2), (2,3), (2,2,2)]
+    for coefs in coefficients:
+      for size in sizes:
+        a = Tensor.randn(size)
+        b = Tensor.newton_schulz(a, steps=20, params=coefs, eps=0.0)
+        # ns(A) = U @ Vt -> (U @ Vt) @ (U @ Vt)t = I
+        orthogonality_helper(b if size[-1] > size[-2] else b.transpose(-2, -1), tolerance=1e-1)
+
 if __name__ == "__main__":
  unittest.main()
@@ -2,7 +2,7 @@ import numpy as np
 import unittest
 from dataclasses import replace

-from tinygrad.opt.kernel import Opt, OptOps, KernelOptError, Kernel, AxisType
+from tinygrad.codegen.opt.kernel import Opt, OptOps, KernelOptError, Kernel, AxisType
 from tinygrad.codegen.gpudims import get_grouped_dims
 from tinygrad.uop.ops import UOp, Ops, GroupOp, KernelInfo
 from tinygrad.device import Device, Buffer, is_dtype_supported
@@ -10,7 +10,7 @@ from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
 from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
-from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
 from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM
 from tinygrad.dtype import DType, dtypes, AddrSpace
 from tinygrad.codegen import apply_rewrites, rewrites_for_views
@@ -410,7 +410,7 @@ class TestLinearizer(unittest.TestCase):
        np.testing.assert_allclose(result, golden_result, atol=0.1, rtol=0.2)

      # check that get_kernel_actions produces all 9 options
-      from tinygrad.opt.search import get_kernel_actions
+      from tinygrad.codegen.opt.search import get_kernel_actions
      tc_actions = [k for i, k in get_kernel_actions(Kernel(realized_ast), False).items() if k.applied_opts[0].op == OptOps.TC]

      available_tc = len([x for x in Device[Device.DEFAULT].renderer.tensor_cores if x.dtype_in == tc.dtype_in and x.dtype_out == tc.dtype_out])
@@ -8,8 +8,8 @@ from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.helpers import getenv
 from tinygrad.shape.shapetracker import ShapeTracker, View
-from tinygrad.opt.search import Opt, OptOps
-from tinygrad.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.engine.realize import get_program

 class TestLinearizerDumb(unittest.TestCase):
@@ -1,8 +1,8 @@
 # ruff: noqa: E501
 import unittest
 from tinygrad import dtypes
-from tinygrad.opt.kernel import Kernel
-from tinygrad.opt.search import Opt, OptOps, bufs_from_lin
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.search import Opt, OptOps, bufs_from_lin
 from extra.optimization.helpers import time_linearizer

 # stuff needed to unpack a kernel
@@ -440,6 +440,13 @@ class TestNN(unittest.TestCase):
      result = layer(a)
      self.assertEqual(result.shape, shp + (embed_size,))

+  def test_embedding_regression(self):
+    # used to fail bounds check
+    with Context(FUSE_ARANGE=1):
+      embedding = Embedding(100, 1024)
+      input_ids = Tensor.empty(16, 16)
+      embedding(input_ids).realize()
+
  def test_load_state_dict(self):
    layer = Conv2d(3, 5, kernel_size=3)

@@ -393,7 +393,7 @@ class TestOps(unittest.TestCase):
  def test_trunc(self):
    helper_test_op([()], lambda x: x.trunc(), forward_only=True)
    helper_test_op([(45,35)], lambda x: x.trunc(), forward_only=True)
-    helper_test_op(None, lambda x: x.trunc(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501]], forward_only=True)
+    helper_test_op(None, lambda x: x.trunc(), vals=[[1.499, 1.5, 1.501, 1.0, 2.1, 0.0, -5.0, -2.499, -2.5, -2.501, 1e12, -1e12]], forward_only=True)
  def test_floor(self):
    helper_test_op([()], lambda x: x.floor(), forward_only=True)
    helper_test_op([(45,35)], lambda x: x.floor(), forward_only=True)
@@ -699,6 +699,14 @@ class TestOps(unittest.TestCase):
    helper_test_op(None, lambda x: x**-0.3, vals=[[0.0]])
    helper_test_op(None, lambda x: x**-1.0, vals=[[-1.0, 0.0, 1.0]])

+  def test_int_pow_const_int(self):
+    helper_test_op(None, lambda x: x**0, vals=[[-2,0,2]], forward_only=True, atol=0)
+    helper_test_op(None, lambda x: x**1, vals=[[-2,0,2]], forward_only=True, atol=0)
+    helper_test_op(None, lambda x: x**2, vals=[[-2,0,2]], forward_only=True, atol=0)
+    helper_test_op(None, lambda x: x**7, vals=[[11,12,13]], forward_only=True, atol=0)
+    helper_test_op(None, lambda x: x**29, vals=[[-2,0,2]], forward_only=True, atol=0)
+    self.helper_test_exception(None, lambda x: x**-2, vals=[[-2,0,2]], forward_only=True, expected=RuntimeError)
+
  @unittest.skip("not supported")
  def test_pow_int(self):
    def _test(base, exponent): helper_test_op(None, lambda x,y: x**y, vals=[base, exponent], forward_only=True)
@@ -1093,6 +1101,9 @@ class TestOps(unittest.TestCase):
    helper_test_op(None, lambda x: x.type(torch.int32).argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[True, False]])

  def test_sort(self):
+    for shape in [(0,), (0,5), (1,), (1,5)]:
+      helper_test_op([shape], lambda x: x.sort(0).values, lambda x: x.sort(0)[0], forward_only=True)
+      helper_test_op([shape], lambda x: x.sort(0).indices.type(torch.int32), lambda x: x.sort(0)[1], forward_only=True)
    for dim in [-1, 0, 1]:
      for descending in [True, False]:
        helper_test_op([(8,8,6)], lambda x: x.sort(dim, descending).values, lambda x: x.sort(dim, descending)[0], forward_only=True)
@@ -2739,10 +2750,10 @@ class TestOps(unittest.TestCase):
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[0],b,c,d,:], lambda x: x[[0],j,k,o,:])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[[0]]],b,c,d,[[1]]], lambda x: x[[[[0]]],j,k,o,[[1]]])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[1,0],b,c,d,:], lambda x: x[[1,0],j,k,o,:])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[[1,0,-1],b,c,d,:], lambda x: x[[1,0,-1],j,k,o,:])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[1,2,3],...], lambda x: x[i,j,k,[1,2,3],...])
    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,b,c,[[1],[2],[3]],...], lambda x: x[i,j,k,[[1],[2],[3]],...])
-    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[2,1,0],e], lambda x: x[i,[2,1,0],k,[2,1,0],p])
+    helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,[2,1,0],c,[-2,1,0],e], lambda x: x[i,[2,1,0],k,[-2,1,0],p])

  def test_slice_fancy_indexing_tuple_indices(self):
    a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
@@ -2765,10 +2776,14 @@ class TestOps(unittest.TestCase):
    a = Tensor.ones(10,11,12)
    # tensors used as indices must be int tensors
    with self.assertRaises(IndexError): a[Tensor(1.1)]
-    with self.assertRaises(IndexError): a[Tensor([True, True])]
+    with self.assertRaises(IndexError): a[[1.1]]
+    with self.assertRaises(IndexError): a[Tensor([True, False])]
+    with self.assertRaises(IndexError): a[[True, False]]
    # shape mismatch, cannot broadcast. either exception is okay
    with self.assertRaises((IndexError, ValueError)): a[Tensor.randint(3,1,1,1), Tensor.randint(1,4,1,1), Tensor.randint(2,4,4,1)]
    with self.assertRaises((IndexError, ValueError)): a[Tensor.randint(3,1,1,1), Tensor.randint(1,4,1,1,1)]
+    # this is fine
+    helper_test_op([(5, 6)], lambda x: x[[True, False, 2]])

  def test_gather(self):
    # indices cannot have gradient
@@ -2,7 +2,7 @@ import numpy as np
 import unittest
 from tinygrad import Tensor
 from tinygrad.helpers import get_single_element
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program

 class TestOptGemm(unittest.TestCase):
@@ -2,9 +2,10 @@ import numpy as np
 import torch
 import unittest
 from tinygrad import Tensor, Device, dtypes
-from tinygrad.nn.optim import Adam, SGD, AdamW
+from tinygrad.nn.optim import Adam, SGD, AdamW, Muon
 from tinygrad.helpers import CI
 from tinygrad.device import is_dtype_supported
+from extra.torch_muon import SingleDeviceMuon as TorchMuon

 np.random.seed(1337)
 x_init = np.random.randn(1,4).astype(np.float32)
@@ -57,9 +58,12 @@ class TestOptim(unittest.TestCase):
  def _test_sgd(self, steps, opts, atol, rtol): self._test_optim(SGD, torch.optim.SGD, steps, opts, atol, rtol)
  def _test_adam(self, steps, opts, atol, rtol): self._test_optim(Adam, torch.optim.Adam, steps, opts, atol, rtol)
  def _test_adamw(self, steps, opts, atol, rtol): self._test_optim(AdamW, torch.optim.AdamW, steps, opts, atol, rtol)
+  #TODO: use torch.muon when it comes out
+  def _test_muon(self, steps, opts, atol, rtol): self._test_optim(Muon, TorchMuon, steps, opts, atol, rtol)

  def test_multistep_sgd_high_lr_teeny(self): self._test_sgd(2, {'lr': 1.1, 'teeny': True}, 1e-6, 1e-5)
  def test_multistep_adam_high_lr_teeny(self): self._test_adam(2, {'lr': 1.1, 'teeny': True}, 2e-4, 5e-4)
+  def test_multistep_muon_high_lr_teeny(self): self._test_muon(2, {'lr': 1.1, 'teeny': True}, 2e-4, 5e-4)

  def test_sgd(self): self._test_sgd(1, {'lr': 0.001}, 1e-6, 0)
  def test_sgd_high_lr(self): self._test_sgd(1, {'lr': 10}, 1e-6, 1e-5)
@@ -83,6 +87,28 @@ class TestOptim(unittest.TestCase):
  def test_multistep_sgd_high_lr_nesterov_momentum_wd(self):
    self._test_sgd(10, {'lr': 9, 'momentum': 0.9, 'nesterov': True, 'weight_decay': 0.1}, 1e-5, 3e-4)

+  def test_muon(self): self._test_muon(1, {'lr': 0.001}, 1e-6, 0)
+  def test_muon_high_lr(self): self._test_muon(1, {'lr': 10}, 1e-6, 3e-4)
+  def test_muon_wd(self): self._test_muon(1, {'lr': 0.001, 'weight_decay': 0.01}, 1e-6, 0)
+  def test_muon_high_lr_wd(self): self._test_muon(1, {'lr': 10, 'weight_decay': 0.01}, 1e-6, 3e-4)
+
+  # NOTE: momentum set to 0.95 by default, nesterov set to True by default
+  def test_multistep_muon_momentum_wd(self): self._test_muon(10, {'lr': 0.001, 'weight_decay': 0.01}, 1e-5, 0)
+  # ns defaults are numerically unstable, but it is tolerable in real training (see nsteps/nparam tests)
+  def test_multistep_muon_high_lr_momentum_wd(self): self._test_muon(10, {'lr': 10, 'weight_decay': 0.01}, 1e-1, 3e-4)
+  def test_multistep_muon_no_nesterov_momentum(self): self._test_muon(10, {'lr': 0.001, 'nesterov': False}, 1e-5, 0)
+  def test_multistep_muon_high_lr_no_nesterov_momentum(self): self._test_muon(10, {'lr': 10, 'nesterov': False}, 0.5e-1, 1e-1)
+
+  def test_muon_ns_steps(self): self._test_muon(1, {'lr': 0.001, 'ns_steps': 3}, 1e-6, 0)
+  def test_muon_high_lr_ns_steps(self): self._test_muon(1, {'lr': 10, 'ns_steps': 3}, 1e-5, 3e-4)
+  def test_muon_ns_params(self): self._test_muon(1, {'lr': 0.001,'ns_params': (2.0,-1.5,0.5)}, 1e-6, 0)
+  def test_muon_high_lr_ns_params(self): self._test_muon(1, {'lr': 10,'ns_params': (2.0,-1.5,0.5)}, 1e-5, 3e-4)
+
+  def test_muon_momentum_wd_ns_steps_ns_params(self):
+    self._test_muon(10, {'lr': 0.001, 'momentum': 0.90, 'weight_decay': 0.01, 'ns_steps': 3, 'ns_params': (2.0,-1.5,0.5)}, 1e-5, 0)
+  def test_multistep_muon_high_lr_momentum_wd_ns_steps_ns_params(self):
+    self._test_muon(10, {'lr': 10, 'momentum': 0.90, 'weight_decay': 0.01, 'ns_steps': 3, 'ns_params': (2.0,-1.5,0.5)}, 1e-5, 3e-4)
+
  def test_adam(self): self._test_adam(1, {'lr': 0.001}, 1e-5, 0)
  def test_adam_high_lr(self): self._test_adam(1, {'lr': 10}, 1e-4, 1e-4)
  def test_adamw(self): self._test_adamw(1, {'lr': 0.001}, 1e-5, 0)
@@ -4,9 +4,9 @@ import unittest
 from dataclasses import replace
 from tinygrad import Tensor, Context, Device, dtypes
 from tinygrad.uop.ops import Ops, UOp # noqa: F401 # pylint: disable=unused-import
-from tinygrad.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, lower_schedule_item, get_program
-from tinygrad.opt.search import bufs_from_lin
+from tinygrad.codegen.opt.search import bufs_from_lin
 from tinygrad.shape.shapetracker import ShapeTracker, View # noqa: F401 # pylint: disable=unused-import

 N = 512
@@ -1741,15 +1741,15 @@ class TestIndexing(unittest.TestCase):

  def test_simple_indexing_alt(self):
    X = Tensor.arange(16).reshape(4, 4)
-    xt = X[[1, 2], [1, 2]]
-    self.check_schedule(xt, 3)
-    np.testing.assert_equal(xt.numpy(), (np.arange(16).reshape(4, 4))[[1, 2], [1, 2]])
+    xt = X[[1, 2], [-1, 2]]
+    self.check_schedule(xt, 1)
+    np.testing.assert_equal(xt.numpy(), (np.arange(16).reshape(4, 4))[[1, 2], [-1, 2]])

  def test_advanced_indexing(self):
    X = Tensor.arange(10)+1
-    xt = X[[0]]
-    self.check_schedule(xt, 2)
-    np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0]])
+    xt = X[[0, -1]]
+    self.check_schedule(xt, 1)
+    np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0, -1]])

  def test_advanced_indexing_alt(self):
    X = Tensor.arange(6).reshape(3, 2)+1
@@ -1759,8 +1759,8 @@ class TestIndexing(unittest.TestCase):

  def test_advanced_simple_indexing_combined(self):
    X = Tensor.arange(16).reshape(4, 4)
-    xt = X[1:2, [1, 2]]
-    self.check_schedule(xt, 2)
+    xt = X[1:2, [-1, 2]]
+    self.check_schedule(xt, 1)

  def test_push_through_reshape(self):
    Tensor.manual_seed(0)
@@ -1,8 +1,8 @@
 import unittest

-from tinygrad.opt.kernel import Opt, OptOps, Kernel
+from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.opt.search import bufs_from_lin, actions, beam_search
+from tinygrad.codegen.opt.search import bufs_from_lin, actions, beam_search
 from tinygrad.device import Device
 from tinygrad.tensor import Tensor
 from tinygrad.dtype import dtypes
@@ -36,7 +36,7 @@ class TestBEAM(unittest.TestCase):

  def test_get_kernel_actions_dedup(self):
    from test.test_linearizer import helper_realized_ast
-    from tinygrad.opt.search import get_kernel_actions
+    from tinygrad.codegen.opt.search import get_kernel_actions
    a = Tensor.empty(4, 3)
    b = Tensor.empty(3)
    realized_ast, _ = helper_realized_ast(a @ b)
@@ -61,7 +61,7 @@ class TestBEAM(unittest.TestCase):
  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
  def test_search_over_shape(self):
    from test.test_linearizer import helper_realized_ast
-    from tinygrad.opt.search import get_kernel_actions
+    from tinygrad.codegen.opt.search import get_kernel_actions

    dtype_pairs = [(tc.dtype_in, tc.dtype_out) for tc in Device[Device.DEFAULT].renderer.tensor_cores]
    multi_shape_dtype_pairs = [dts for dts in dtype_pairs if dtype_pairs.count(dts) > 1]
@@ -78,7 +78,7 @@ class TestBEAM(unittest.TestCase):

  def test_get_kernel_actions_preserves_actions_state(self):
    from test.test_linearizer import helper_realized_ast
-    from tinygrad.opt.search import get_kernel_actions
+    from tinygrad.codegen.opt.search import get_kernel_actions
    a = Tensor.rand(16, 16)
    b = Tensor.rand(16, 16)
    realized_ast, _ = helper_realized_ast(a @ b)
@@ -117,6 +117,14 @@ class TestFuse(unittest.TestCase):
    c = (a.sum(axis=1) + b.sum(axis=1)).fuse()
    self.assertListEqual(c.tolist(), [30]*16)

+  @unittest.skipUnless(Device.DEFAULT == "METAL", "METAL TC")
+  def test_fuse_and_tc_opt(self):
+    A = Tensor.randn(8, 8).realize()
+    B = Tensor.randn(8, 8).realize()
+    C = Tensor.ones(1, 8, 8).pad(((1,1), None, None),).sum(0)
+    out = (C + (A @ B)).fuse()
+    out.realize()
+
 class TestSoftmaxFusion(unittest.TestCase):
  @classmethod
  def setUpClass(cls):
@@ -106,6 +106,24 @@ class TestTiny(unittest.TestCase):
    probs = Tensor.rand(1, 1, 28, 28).sequential(layers).tolist()
    self.assertEqual(len(probs[0]), 10)

+  # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
+  @unittest.skipIf(IMAGE>0 or (CI and Device.DEFAULT == "DSP"), "failing because of make things that can't be images not images")
+  def test_mnist_backward(self):
+    # NOTE: we don't have the whole model here for speed
+    layers = [
+      nn.Conv2d(1, 32, 5), Tensor.relu,
+      nn.Conv2d(32, 32, 5), Tensor.relu]
+
+    # replace random weights with ones
+    # TODO: there's a bug here where it's tying two of the biases together. we need UNIQUE const
+    #Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
+    for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))
+
+    # realize gradients
+    for x in nn.state.get_parameters(layers): x.requires_grad_()
+    Tensor.empty(4, 1, 28, 28).sequential(layers).sum().backward()
+    Tensor.realize(*[x.grad for x in nn.state.get_parameters(layers) if x.grad is not None])
+
  # *** image ***

  @unittest.skipIf(Device.DEFAULT != "GPU", "image only supported on GPU")
@@ -14,7 +14,7 @@ from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.symbolic import sym
 from tinygrad.device import is_dtype_supported
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps

 def to_uops_list(u:list[UOp], opts=None, skip_check=False) -> list[UOp]: return full_rewrite(UOp.sink(*u), opts)

@@ -415,6 +415,17 @@ class TestAssembly(unittest.TestCase):
    uops = program.uops
    self.assertEqual(len([x.op for x in uops if x.op is Ops.MULACC]), 4)

+  def test_use_cmpeq(self):
+    g = UOp(Ops.DEFINE_GLOBAL, dtypes.uint32.ptr(), (), 0)
+    c = UOp(Ops.CONST, dtypes.uint, (), 7)
+    l = UOp(Ops.LOAD, dtypes.uint, (g.index(c),))
+    comp = l.ne(c).ne(True)
+    uops = to_uops_list([comp], opts=Device[Device.DEFAULT].renderer)
+    Device[Device.DEFAULT].renderer.render(uops)
+    ops = [x.op for x in uops]
+    self.assertIn(Ops.CMPEQ, ops)
+    self.assertNotIn(Ops.CMPNE, ops)
+
 class TestUOpMethod(unittest.TestCase):
  @unittest.skip("uops lt no longer ordered")
  def test_compare_alu_same_src_different_arg(self):
@@ -6,7 +6,7 @@ from tinygrad.renderer import Estimates
 from tinygrad.codegen import full_rewrite
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.dtype import dtypes
-from tinygrad.opt.kernel import Opt, OptOps, KernelOptError
+from tinygrad.codegen.opt.kernel import Opt, OptOps, KernelOptError
 from tinygrad.device import Device

 def flops_mem(uops, ignore_indexing=False):
@@ -53,5 +53,13 @@ class TestCastConvenienceMethod(unittest.TestCase):
      self.assertEqual(t.float().dtype, dtypes.float)
      self.assertEqual(t.double().dtype, dtypes.double)

+class TestDtypeTolist(unittest.TestCase):
+  def test_bfloat16(self):
+    self.assertEqual(Tensor([-60000, 1.5, 3.1, 60000], device="PYTHON", dtype=dtypes.bfloat16).tolist(), [-59904.0, 1.5, 3.09375, 59904.0])
+    # 448
+    self.assertEqual(Tensor([-30000, 1.5, 3.1, 30000], device="PYTHON", dtype=dtypes.fp8e4m3).tolist(), [-448.0, 1.5, 3.0, 448.0])
+    # 57344
+    self.assertEqual(Tensor([-30000, 1.5, 3.1, 30000], device="PYTHON", dtype=dtypes.fp8e5m2).tolist(), [-28672.0, 1.5, 3.0, 28672.0])
+
 if __name__ == "__main__":
  unittest.main()
@@ -303,8 +303,8 @@ class TestRecurse(unittest.TestCase):
  def test_inf_loop(self):
    a = UOp.variable('a', 0, 10)
    pm = PatternMatcher([
-      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.DEFINE_REG)),
-      (UPat(Ops.DEFINE_REG, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
+      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.CONST)),
+      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm)
@@ -312,8 +312,8 @@ class TestRecurse(unittest.TestCase):
  def test_inf_loop_bottom_up(self):
    a = UOp.variable('a', 0, 10)
    pm = PatternMatcher([
-      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.DEFINE_REG)),
-      (UPat(Ops.DEFINE_REG, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
+      (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.CONST)),
+      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
    ])
    with self.assertRaises(RuntimeError):
      graph_rewrite(a, pm, bottom_up=True)
@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor, Context, Device
 from tinygrad.engine.realize import get_program
-from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.codegen.opt.kernel import Opt, OptOps
 from tinygrad.uop.ops import KernelInfo

 class TestLinearizerRewrite(unittest.TestCase):
@@ -0,0 +1,110 @@
+import unittest
+from dataclasses import dataclass, field
+from tinygrad.uop.ops import PatternMatcher, UOp, graph_rewrite, Ops, UPat, GroupOp, RewriteNotReady
+
+# we could insert CHILDREN node
+
+@dataclass
+class ChildrenContext:
+  children: dict[UOp, list[UOp]]|None = None
+
+# this is a generic child labeller
+def extract_children(ctx:ChildrenContext, x:UOp):
+  if ctx.children is not None: return
+  ctx.children = {k:list(v.keys()) for k,v in x.get_children_map().items() if len(v) > 1}
+
+def mark_children(ctx:ChildrenContext, x:UOp):
+  new_srcs = [(UOp(Ops.CHILD, s.dtype, src=(s,), arg=(ctx.children[s].index(x), len(ctx.children[s]))) if s in ctx.children else s) for s in x.src]
+  return x.replace(src=tuple(new_srcs))
+
+pm_children = PatternMatcher([
+  (UPat(Ops.SINK, name="x"), extract_children),
+  (UPat(GroupOp.All-{Ops.CHILD}, name="x"), mark_children),
+])
+
+@dataclass
+class TestContext:
+  seen_children: dict[UOp, set[int]] = field(default_factory=dict)
+  ready_children: dict[UOp, set[int]] = field(default_factory=dict)
+  seen_consts:int = 0
+  saved_seen_consts:int = 0
+  exp2_visit_count:int = 0
+
+# this is a generic pattern
+def visit_child(ctx:ChildrenContext, x:UOp):
+  if x.src[0] not in ctx.seen_children:
+    ctx.seen_children[x.src[0]] = set()
+    ctx.ready_children[x.src[0]] = set()
+  ctx.seen_children[x.src[0]].add(x.arg[0])
+  if len(ctx.seen_children[x.src[0]]) != x.arg[1]:
+    print(f"visit CHILD {x.arg} bottom up -- not ready {ctx.seen_children[x.src[0]]}")
+    raise RewriteNotReady
+  print(f"visit CHILD {x.arg} bottom up -- READY {ctx.seen_children[x.src[0]]}")
+  ctx.ready_children[x.src[0]].add(x.arg[0])
+
+pm_child_visitor = PatternMatcher([
+  (UPat(Ops.CHILD, name="x"), visit_child),
+])
+
+# this is for the test
+def see_const(ctx:ChildrenContext, c:UOp): ctx.seen_consts += c.arg
+def see_exp2(ctx:ChildrenContext): ctx.exp2_visit_count += 1
+def save_seen_consts(ctx:ChildrenContext, x:UOp): ctx.saved_seen_consts = ctx.seen_consts
+pm_consts = PatternMatcher([
+  (UPat(Ops.DEFINE_VAR, name="x"), save_seen_consts),
+  (UPat()+UPat.cvar("c"), see_const),
+  (UPat(Ops.EXP2), see_exp2),
+])
+
+class TestChildrenRewrite(unittest.TestCase):
+  def test_not_ready_double_simple(self):
+    global_a = UOp.variable("a", 0, 10).exp2()
+    inter = (global_a+global_a).exp2()
+    global_sink = (inter+inter).sink()
+
+    sink = graph_rewrite(global_sink, pm_children, ctx=ChildrenContext(), bottom_up=True)
+    ctx = TestContext()
+    graph_rewrite(sink, pm_consts, ctx=ctx, bottom_up=True)
+    self.assertEqual(ctx.exp2_visit_count, 2)
+
+  def test_not_ready_double(self):
+    global_a = UOp.variable("a", 0, 10).exp2()
+    inter = ((global_a+1000)+(global_a+100)).exp2()
+    global_sink = ((inter+10)+(inter+1)).sink()
+
+    sink = graph_rewrite(global_sink, pm_children, ctx=ChildrenContext(), bottom_up=True)
+    print("test_not_ready_double")
+    ctx = TestContext()
+    graph_rewrite(sink, pm_child_visitor+pm_consts, ctx=ctx, bottom_up=True)
+    self.assertEqual(ctx.exp2_visit_count, 2)
+    self.assertEqual(ctx.seen_consts, ctx.saved_seen_consts)
+    self.assertEqual(ctx.seen_consts, 1111)
+
+  def test_in_srcs_twice(self):
+    global_a = UOp.variable("a", 0, 10).exp2()
+    global_sink = (global_a+global_a).sink()
+
+    ctx = TestContext()
+    graph_rewrite(global_sink, pm_consts, ctx=ctx, bottom_up=True)
+    self.assertEqual(ctx.exp2_visit_count, 1)
+
+  def test_not_ready(self):
+    global_a = UOp.variable("a", 0, 10).exp2()
+    global_sink = ((global_a+2)+(global_a+3)).sink()
+
+    # without children and not ready, we don't see both adds before the DEFINE_VAR
+    ctx = TestContext()
+    graph_rewrite(global_sink, pm_consts, ctx=ctx, bottom_up=True)
+    self.assertNotEqual(ctx.seen_consts, ctx.saved_seen_consts)
+    self.assertEqual(ctx.exp2_visit_count, 1)
+
+    # with children and not ready we do
+    sink = graph_rewrite(global_sink, pm_children, ctx=ChildrenContext(), bottom_up=True)
+    ctx = TestContext()
+    graph_rewrite(sink, pm_child_visitor+pm_consts, ctx=ctx, bottom_up=True)
+    self.assertEqual(ctx.seen_consts, ctx.saved_seen_consts)
+    self.assertEqual(ctx.exp2_visit_count, 1)
+    self.assertSetEqual(list(ctx.ready_children.values())[0], {0,1})
+
+if __name__ == '__main__':
+  unittest.main()
@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor
 from tinygrad.uop.ops import PatternMatcher, Ops, UPat, graph_rewrite, RewriteContext, UOp
-from tinygrad.schedule.kernelize import sym, merge_views
+from tinygrad.schedule.kernelize import kernelize_sym, merge_views

 class TestRewriteTrackedChildren(unittest.TestCase):
  @unittest.skip("track_children no longer supported")
@@ -57,7 +57,7 @@ class TestRewriteTrackedChildren(unittest.TestCase):
    extra = PatternMatcher([(UPat(Ops.REDUCE_AXIS, name="r"), print_children)])
    a = Tensor.empty(3, 3)
    r = (a+0).sum()
-    graph_rewrite(r.uop, merge_views+sym+extra, track_children=True)
+    graph_rewrite(r.uop, merge_views+kernelize_sym+extra, track_children=True)

 if __name__ == '__main__':
  unittest.main()
--- a/Show More
+++ b/Show More