move that

.github/workflows/benchmark.yml

@@ -325,7 +325,7 @@ 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 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 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (NVIDIA Training)
@@ -576,7 +576,7 @@ 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 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 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee train_bert.txt
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (AMD MLPerf)
@@ -611,12 +611,12 @@ jobs:
       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 0.9.9 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 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: 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
@@ -692,12 +692,11 @@ jobs:
       run: AMD=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
     - name: Run full CIFAR training w 1 GPU
       run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
-    # TODO: enable
-    # - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-    #   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 ResNet50 training steps (1 gpu)
+      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 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 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 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)
@@ -750,12 +749,12 @@ jobs:
     - name: Test LLAMA-3
       run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
     - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
+      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
     - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
       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 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 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 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)

.github/workflows/test.yml

@@ -329,7 +329,7 @@ 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 extra/onnx.py extra/onnx_parser.py
         python3 -m ruff check examples/mlperf/ --ignore E501
     - name: Lint tinygrad with pylint
       run: python -m pylint tinygrad/
@@ -337,6 +337,7 @@ jobs:
       run: |
         python -m mypy --strict-equality --lineprecision-report .
         cat lineprecision.txt
+        python -m mypy --strict-equality extra/onnx_parser.py
         python -m mypy --strict-equality extra/onnx.py
 
   unittest:
@@ -375,8 +376,8 @@ 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 < 15500 lines
+      run: MAX_LINE_COUNT=15500 python sz.py
 
   fuzzing:
     name: Fuzzing
@@ -542,8 +543,8 @@ 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 handcode_opt
+        run: PYTHONPATH=. MODEL=resnet GPU=1 DEBUG=1 BS=4 HALF=0 python3 examples/handcode_opt.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -626,7 +627,7 @@ jobs:
     - name: Test LLVM=1 DEVECTORIZE=0 for model
       run: PYTHONPATH="." LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
     - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+      run: CPU=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
 
   testwebgpu:
     name: Linux (WebGPU)
@@ -870,28 +871,28 @@ jobs:
       run: WEBGPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
 
   osxremote:
-   name: MacOS (remote metal)
-   runs-on: macos-15
-   timeout-minutes: 10
-   env:
-     REMOTE: 1
-     REMOTEDEV: METAL
-   steps:
-     - name: Checkout Code
-       uses: actions/checkout@v4
-     - name: Setup Environment
-       uses: ./.github/actions/setup-tinygrad
-       with:
-         key: macos-remote
-         deps: testing_minimal
-     - name: Check Device.DEFAULT and print some source
-       run: |
-         python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-         python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
-         DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-     - name: Run REMOTE=1 Test
-       run: |
-         python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
+    name: MacOS (remote metal)
+    runs-on: macos-15
+    timeout-minutes: 10
+    env:
+      REMOTE: 1
+      REMOTEDEV: METAL
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: macos-remote
+          deps: testing_minimal
+      - name: Check Device.DEFAULT and print some source
+        run: |
+          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
+          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
+      - name: Run REMOTE=1 Test
+        run: |
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
 
   amdremote:
     name: Linux (remote)
@@ -977,7 +978,6 @@ 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

docs/developer/layout.md

@@ -18,11 +18,11 @@ Group UOps into kernels.
 
 ---
 
-## tinygrad/codegen/opt
+## tinygrad/opt
 
 Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.
 
-::: tinygrad.codegen.opt.get_optimized_ast
+::: tinygrad.opt.get_optimized_ast
     options:
         members: false
         show_labels: false

docs/tinybox.md

@@ -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.
 
-## Building the OS image
+## tinychat
 
-The OS image is built using `ubuntu-image` from <https://github.com/tinygrad/tinyos>.
+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.
 
-After cloning, run `make green` or `make red` to build a tinybox green or tinybox red image respectively.
+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.

examples/beautiful_mnist.py

@@ -1,12 +1,12 @@
 # model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
-from typing import Callable
+from typing import List, 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,
@@ -21,13 +21,14 @@ if __name__ == "__main__":
   X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))
 
   model = Model()
-  opt = (nn.optim.Adam if not getenv("MUON") else nn.optim.Muon)(nn.state.get_parameters(model))
+  opt = nn.optim.Adam(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

examples/handcode_opt.py

@@ -0,0 +1,134 @@
+from extra.models.resnet import ResNet50
+from extra.mcts_search import mcts_search
+from examples.mlperf.helpers import get_mlperf_bert_model
+from tinygrad import Tensor, Device, dtypes, nn
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.uop.ops import Ops, sym_infer
+from tinygrad.device import Compiled
+from tinygrad.opt.search import beam_search, bufs_from_lin
+from tinygrad.helpers import DEBUG, ansilen, getenv, colored, TRACEMETA
+from extra.optimization.helpers import time_linearizer
+from tinygrad.engine.realize import get_program
+
+def get_sched_resnet():
+  mdl = ResNet50()
+  optim = (nn.optim.LARS if getenv("LARS") else nn.optim.SGD)(nn.state.get_parameters(mdl))
+  BS = getenv("BS", 64)
+
+  # run model twice to get only what changes, these are the kernels of the model
+  for _ in range(2):
+    out = mdl(Tensor.empty(BS, 3, 224, 224))
+    targets = [out]
+    if getenv("BACKWARD"):
+      optim.zero_grad()
+      out.sparse_categorical_crossentropy(Tensor.empty(BS, dtype=dtypes.int)).backward()
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
+    print(f"schedule length {len(sched)}")
+  return sched
+
+def get_sched_bert():
+  mdl = get_mlperf_bert_model()
+  optim = nn.optim.LAMB(nn.state.get_parameters(mdl))
+
+  # fake data
+  BS = getenv("BS", 9)
+  input_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
+  segment_ids = Tensor.empty((BS, 512), dtype=dtypes.float32)
+  attention_mask = Tensor.empty((BS, 512), dtype=dtypes.default_float)
+  masked_positions = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  masked_lm_ids = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  masked_lm_weights = Tensor.empty((BS, 76), dtype=dtypes.float32)
+  next_sentence_labels = Tensor.empty((BS, 1), dtype=dtypes.float32)
+
+  # run model twice to get only what changes, these are the kernels of the model
+  for _ in range(2):
+    lm_logits, seq_relationship_logits = mdl(input_ids, attention_mask, masked_positions, segment_ids)
+    targets = [lm_logits, seq_relationship_logits]
+    if getenv("BACKWARD"):
+      optim.zero_grad()
+      loss = mdl.loss(lm_logits, seq_relationship_logits, masked_lm_ids, masked_lm_weights, next_sentence_labels)
+      # ignore grad norm and loss scaler for now
+      loss.backward()
+      targets += [x for x in optim.schedule_step()]
+    sched = Tensor.schedule(*targets)
+    print(f"schedule length {len(sched)}")
+  return sched
+
+if __name__ == "__main__":
+  if getenv("HALF", 1):
+    dtypes.default_float = dtypes.half
+
+  # the device we are optimizing for
+  device: Compiled = Device[Device.DEFAULT]
+  if getenv("BACKWARD"): Tensor.training = True
+  print(f"optimizing for {Device.DEFAULT}")
+
+  sched = globals()[f"get_sched_{getenv('MODEL', 'resnet')}"]()
+  sched = [x for x in sched if x.ast.op is Ops.SINK]
+
+  # focus on one kernel
+  if getenv("KERNEL", -1) >= 0: sched = sched[getenv("KERNEL", -1):getenv("KERNEL", -1)+1]
+
+  # work with the schedule
+  total_tm = 0
+  running_gflops = 0
+  usage = {}
+  for i,si in enumerate(sched):
+    if DEBUG >= 3: print(si.ast)
+
+    rawbufs = bufs_from_lin(Kernel(si.ast))
+
+    # "linearize" the op into uops in different ways
+    lins: list[tuple[Kernel, str]] = []
+
+    # always try hand coded opt
+    lin = Kernel(si.ast, opts=device.renderer)
+    lin.apply_opts(hand_coded_optimizations(lin))
+    lins.append((lin, "HC"))
+
+    # maybe try tensor cores
+    lin = Kernel(si.ast, opts=device.renderer)
+    if lin.apply_tensor_cores():
+      lins.append((lin, "TC"))
+
+    # try a beam search
+    if beam:=getenv("BEAM"):
+      lin = Kernel(si.ast, opts=device.renderer)
+      lin = beam_search(lin, rawbufs, beam, bool(getenv("BEAM_ESTIMATE", 1)))
+      lins.append((lin, "BEAM"))
+
+    # try MCTS
+    if mcts:=getenv("MCTS"):
+      lin = Kernel(si.ast, opts=device.renderer)
+      lin = mcts_search(lin, rawbufs, mcts)
+      lins.append((lin, "MCTS"))
+
+    # benchmark the programs
+    choices = []
+    for lin, nm in lins:
+      tm = time_linearizer(lin, rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
+      ops = (prg:=get_program(lin.get_optimized_ast(), lin.opts)).estimates.ops
+      gflops = sym_infer(ops, {k:k.min for k in lin.ast.variables()})*1e-9/tm
+      choices.append((tm, gflops, lin, prg, nm))
+
+    sorted_choices = sorted(choices, key=lambda x: x[0])
+    if DEBUG >= 1: # print all kernels
+      for tm, gflops, lin, prg, nm in choices:
+        print(f"                 kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS -- {colored(nm, 'green') if lin is sorted_choices[0][2] else nm}")
+
+    tm, gflops, lin, prg, nm = sorted_choices[0]
+    if getenv("SRC"):
+      print(si.ast)
+      print(lin.applied_opts)
+      print(get_program(lin.get_optimized_ast(), lin.opts).src)
+    total_tm += tm
+    running_gflops += gflops * tm
+    if (key := str([str(m) for m in si.metadata])) not in usage: usage[key] = (0, 0)
+    usage[key] = (usage[key][0] + tm, usage[key][1] + 1)
+    print(f"*** {total_tm*1000:7.2f} ms : kernel {i:2d} {lin.name+' '*(37-ansilen(lin.name))} {str(prg.global_size):18s} {str(prg.local_size):12s} takes {tm*1000:7.2f} ms, {gflops:6.0f} GFLOPS {[repr(m) if TRACEMETA >= 2 else str(m) for m in si.metadata]}")
+  print(f"******* total {total_tm*1000:.2f} ms, {running_gflops/total_tm:6.0f} GFLOPS")
+  print("usage:")
+  for k in sorted(usage, key=lambda x: -usage[x][0])[:10]:
+    print(f"{usage[k][0]*1000:.2f} ms: {k} ({usage[k][1]} times)")

examples/mlperf/dataloader.py

@@ -1,4 +1,6 @@
-import os, random, pickle, queue, struct, math, functools, hashlib, time
+import functools
+import hashlib
+import os, random, pickle, queue, struct, math
 from typing import List
 from pathlib import Path
 from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count
@@ -530,21 +532,21 @@ class BinIdxDataset:
 
     start = self.idx.tell()
     end = start + self.count * dtypes.int32.itemsize
-    self.sizes = self.idx_t[start:end].bitcast(dtypes.int32).numpy()
+    self.sizes = self.idx_t[start:end].bitcast(dtypes.int32)
 
     start = end
     end = start + self.count * dtypes.int64.itemsize
-    self.pointers = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
+    self.pointers = self.idx_t[start:end].bitcast(dtypes.int64)
 
     start = end
     end = start + doc_count * dtypes.int64.itemsize
-    self.doc_idx = self.idx_t[start:end].bitcast(dtypes.int64).numpy()
+    self.doc_idx = self.idx_t[start:end].bitcast(dtypes.int64)
 
     # bin file
     self.bin_t = Tensor(base_path.with_name(f"{base_path.name}.bin"))
 
   def _index(self, idx) -> tuple[int, int]:
-    return int(self.pointers[idx]), int(self.sizes[idx])
+    return self.pointers[idx].item(), self.sizes[idx].item()
 
   def get(self, idx, offset:int=0, length:int|None=None):
     ptr, size = self._index(idx)
@@ -564,13 +566,10 @@ class GPTDataset:
     # check for cache
     cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
     cache_path = base_path.with_name(f"{base_path.name}.{cache_hash}.index_cache")
-    print(f"try loading GPTDataset from {cache_path}...")
     if cache_path.exists():
-      print("cache found, loading...")
       with open(cache_path, "rb") as f:
         self.doc_idx, self.sample_idx, self.shuffle_idx = pickle.load(f)
     else:
-      print("cache not found, building index...")
       self.doc_idx = self._build_doc_idx()
       self.sample_idx = self._build_sample_idx()
       self.shuffle_idx = self._build_shuffle_idx()
@@ -629,20 +628,14 @@ class GPTDataset:
 
   # https://github.com/NVIDIA/Megatron-LM/blob/94bd476bd840c2fd4c3ebfc7448c2af220f4832b/megatron/core/datasets/gpt_dataset.py#L558
   def _build_doc_idx(self):
-    print(f"building doc_idx for {self.num_epochs=}, {self.indexed_dataset.count=}")
-    st = time.perf_counter()
-    # doc_idx = np.mgrid[:self.num_epochs, :self.indexed_dataset.count][1]
-    doc_idx = np.arange(self.indexed_dataset.count).reshape(1, -1).repeat(self.num_epochs, axis=0).flatten()
+    doc_idx = np.mgrid[:self.num_epochs, :self.indexed_dataset.count][1]
+    doc_idx = doc_idx.reshape(-1)
     doc_idx = doc_idx.astype(np.int32)
-    at = time.perf_counter()
     if self.shuffle: self.rng.shuffle(doc_idx)
-    print(f"doc_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
     return doc_idx
 
   def _build_sample_idx(self):
-    print(f"building sample_idx for {self.samples=}, {self.seqlen=}, {self.doc_idx.shape[0]=}")
-    sample_idx_max = max(self.doc_idx.shape[0], self.indexed_dataset.sizes.max())
-    sample_idx = np.empty((self.samples + 1, 2), dtype=np.int64 if sample_idx_max > dtypes.int32.max else np.int32)
+    sample_idx = np.empty((self.samples + 1, 2), dtype=np.int32)
 
     sample_idx_idx, doc_idx_idx, doc_offset = 0, 0, 0
     sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
@@ -652,7 +645,7 @@ class GPTDataset:
       remaining_seqlen = self.seqlen + 1
       while remaining_seqlen > 0:
         doc_idx = int(self.doc_idx[doc_idx_idx])
-        doc_len = int(self.indexed_dataset.sizes[doc_idx]) - doc_offset
+        doc_len = self.indexed_dataset.sizes[doc_idx].item() - doc_offset
         remaining_seqlen -= doc_len
         if remaining_seqlen <= 0:
           doc_offset += remaining_seqlen + doc_len - 1
@@ -661,7 +654,7 @@ class GPTDataset:
           if doc_idx_idx == len(self.doc_idx) - 1:
             assert sample_idx_idx == self.samples
             doc_idx = int(self.doc_idx[doc_idx_idx])
-            doc_offset = int(self.indexed_dataset.sizes[doc_idx]) - 1
+            doc_offset = self.indexed_dataset.sizes[doc_idx].item() - 1
             break
           doc_idx_idx += 1
           doc_offset = 0
@@ -672,18 +665,13 @@ class GPTDataset:
     return sample_idx
 
   def _build_shuffle_idx(self):
-    print(f"building shuffle_idx for {self.samples=}")
-    st = time.perf_counter()
     shuffle_idx = np.arange(self.samples, dtype=np.int32)
-    at = time.perf_counter()
     if self.shuffle: self.rng.shuffle(shuffle_idx)
-    print(f"shuffle_idx built in {at - st:.3f}s, shuffled in {time.perf_counter() - at:.3f}s")
     return shuffle_idx
 
 class BlendedGPTDataset:
   def __init__(self, paths:list[Path], weights:list[float], samples:int, seqlen:int, seed:int, shuffle:bool):
-    self.shuffle = shuffle
-    self.rng = np.random.RandomState(seed)
+    self.seed = seed
 
     # normalize weights
     total_weight = sum(weights)
@@ -695,47 +683,10 @@ class BlendedGPTDataset:
 
     self.datasets = [GPTDataset(path, samples_per_blend[i], seqlen, seed + i, shuffle) for i,path in enumerate(paths)]
 
-    # check for cache
-    cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
-    cache_path = paths[0].with_name(f"{paths[0].name}.{cache_hash}.blend_cache")
-    print(f"try loading BlendedGPTDataset from {cache_path}...")
-    if cache_path.exists():
-      print("cache found, loading...")
-      with open(cache_path, "rb") as f:
-        self.dataset_idx, self.dataset_sample_idx = pickle.load(f)
-    else:
-      print("cache not found, building index...")
-      self.dataset_idx, self.dataset_sample_idx = self._build_blend_idx()
-      # save cache
-      with open(cache_path, "wb") as f:
-        pickle.dump((self.dataset_idx, self.dataset_sample_idx), f)
-
   def get(self, idx:int):
-    tokens = self.datasets[self.dataset_idx[idx]][self.dataset_sample_idx[idx]]
+    tokens = self.datasets[0][idx]
     return tokens
 
-  def _build_blend_idx(self):
-    dataset_idx = np.zeros(self.samples, dtype=np.int16)
-    dataset_sample_idx = np.zeros(self.samples, dtype=np.int64)
-
-    unspent_datasets = set(range(len(self.datasets)))
-    dataset_sample_counts = [0] * len(self.datasets)
-
-    for i in tqdm(range(self.samples)):
-      error_argmax, error_max = 0, 0.0
-      for di in unspent_datasets:
-        error = self.weights[di] * max(i, 1) - dataset_sample_counts[di]
-        if error > error_max:
-          error_max = error
-          error_argmax = di
-
-      dataset_idx[i] = error_argmax
-      dataset_sample_idx[i] = dataset_sample_counts[error_argmax]
-
-      dataset_sample_counts[error_argmax] += 1
-
-    return dataset_idx, dataset_sample_idx
-
 def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
   if val:
     dataset = BlendedGPTDataset([
@@ -788,8 +739,8 @@ if __name__ == "__main__":
 
   def load_llama3(val):
     bs = 24
-    samples = 5760 if val else 1_200_000 * 1152
-    seqlen = 8192
+    samples = 5760 if val else 1_200_000
+    seqlen = 512
 
     max_, min_ = 0, math.inf
     for tokens in tqdm(batch_load_llama3(bs, samples, seqlen, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=5760, val=bool(val)), total=samples//bs):

examples/mlperf/model_train.py

@@ -1296,7 +1296,7 @@ def train_llama3():
   SEED               = config["SEED"]                   = getenv("SEED", 5760)
   SEQLEN             = config["SEQLEN"]                 = getenv("SEQLEN", 8192)
   TRAIN_ON_VAL       = config["TRAIN_ON_VAL"]           = getenv("TRAIN_ON_VAL", 0)
-  SAMPLES            = config["SAMPLES"]                = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000 * 1152)
+  SAMPLES            = config["SAMPLES"]                = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000)
 
   # LR=1e-4 TRAIN_ON_VAL=1 DEFAULT_FLOAT=bfloat16 FUSE_ARANGE=1 JITBEAM=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B WARMUP_STEPS=36 DECAY_STEPS=360 SEQLEN=512 PYTHONPATH=. AMD=1 AMD_LLVM=0 MODEL=llama3 python3 examples/mlperf/model_train.py
   # trains to 7
@@ -1323,22 +1323,24 @@ def train_llama3():
     for v in get_parameters(model):
       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)
+  # 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)
 
   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)
@@ -1353,9 +1355,6 @@ 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()
@@ -1392,12 +1391,11 @@ 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:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
+    tqdm.write(f"{loss.item():.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:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
+        f.write(f"{i} {loss.item():.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
 
     if getenv("CKPT") and (i % 200 == 0 or i == 10):
       tqdm.write("saving checkpoint")

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_1xMI300X/dev_beam.sh

@@ -4,8 +4,6 @@ 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_beam.sh

@@ -5,8 +5,6 @@ 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"

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/dev_run.sh

@@ -8,8 +8,6 @@ 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"

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh

@@ -11,8 +11,6 @@ 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"

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_beam.sh

@@ -2,9 +2,9 @@
 
 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_green/dev_run.sh

@@ -2,9 +2,9 @@
 
 export PYTHONPATH="." NV=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh

@@ -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=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_beam.sh

@@ -2,9 +2,9 @@
 
 export PYTHONPATH="." AMD=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_red/dev_run.sh

@@ -2,9 +2,9 @@
 
 export PYTHONPATH="." AMD=1
 export MODEL="bert"
-export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh

@@ -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=90 EVAL_BS=90
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
 
-export IGNORE_OOB=1
+export FUSE_ARANGE=1 FUSE_ARANGE_UINT=0
 
 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

examples/olmoe.py

@@ -1,7 +1,8 @@
 # https://arxiv.org/pdf/2409.02060
-import time, functools
+import time
 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
@@ -16,7 +17,7 @@ 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).softmax(-1)
+    g = self.gate(x).float().softmax(-1)
 
     g = g.squeeze() # (BS, length, num_experts) -> (num_experts,)
     probs, sel = g.topk(self.activated_experts)
@@ -24,7 +25,7 @@ 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 * probs.reshape(self.activated_experts, 1, 1)).sum(axis=0)
+    return (x_down.float() * 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

examples/yolov3.py

@@ -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.minimum(b1_x2, b2_x2)
-  inter_rect_y2 = np.minimum(b1_y2, b2_y2)
+  inter_rect_x2 = np.maximum(b1_x2, b2_x2)
+  inter_rect_y2 = np.maximum(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].astype(np.float32))
+          bn_biases = Tensor(weights[ptr:ptr + num_bn_biases])
           ptr += num_bn_biases
-          bn_weights = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
+          bn_weights = Tensor(weights[ptr:ptr+num_bn_biases])
           ptr += num_bn_biases
-          bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
+          bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases])
           ptr += num_bn_biases
-          bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
+          bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases])
           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].astype(np.float32))
+          conv_biases = Tensor(weights[ptr: ptr+num_biases])
           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].astype(np.float32))
+        conv_weights = Tensor(weights[ptr:ptr+num_weights])
         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://github.com/shadiakiki1986/yolov3.weights/releases/download/3.0.1/yolov3.weights')
+  model.load_weights('https://pjreddie.com/media/files/yolov3.weights')
   if len(sys.argv) > 1:
     url = sys.argv[1]
   else:

extra/assembly/assembly.py

@@ -1,5 +1,5 @@
 from typing import Tuple, List, NamedTuple, Any, Dict, Optional, Union, DefaultDict, cast
-from tinygrad.codegen.opt.kernel import Ops, MemOp, UOp
+from tinygrad.opt.kernel import Ops, MemOp, UOp
 from tinygrad.uop.ops import BinaryOps, UnaryOps
 from tinygrad.dtype import DType, dtypes
 from tinygrad.helpers import DEBUG

extra/assembly/assembly_arm64.py

@@ -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.codegen.opt.kernel import Ops, UOp
+from tinygrad.opt.kernel import Ops, UOp
 from tinygrad.helpers import CI
 from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage
 

extra/assembly/assembly_ptx.py

@@ -1,7 +1,7 @@
 from typing import List
 import struct
 from tinygrad.codegen.assembly import uops_to_asmstyle, AssemblyLanguage
-from tinygrad.codegen.opt.kernel import Ops, UOp
+from tinygrad.opt.kernel import Ops, UOp
 from tinygrad import dtypes
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
 from tinygrad.runtime.ops_cuda import arch

extra/assembly/assembly_rdna.py

@@ -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.codegen.opt.kernel import Ops
+from tinygrad.opt.kernel import Ops
 from tinygrad.uop.ops import BinaryOps, UnaryOps, TernaryOps
 from tinygrad.runtime.ops_gpu import ROCM_LLVM_PATH
 

extra/backends/triton.py

@@ -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.codegen.opt.kernel import  UOp, Ops
+from tinygrad.opt.kernel import  UOp, Ops
 from triton.compiler import compile as triton_compile
 import linecache
 import math

extra/gemm/amd_uop_matmul.py

@@ -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.codegen.opt.kernel import axis_colors
-from tinygrad.codegen.opt.swizzler import merge_views, view_left
+from tinygrad.opt.kernel import axis_colors
 
 def to_colored(full_shape, axis_types): return '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])
 

extra/gemm/max_matmul.py

@@ -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.codegen.opt.kernel import Kernel, KernelOptError
+from tinygrad.opt.kernel import Kernel, KernelOptError
 from tinygrad.uop.ops import UOp, Ops, BinaryOps, UnaryOps, TernaryOps, KernelInfo
-from tinygrad.codegen.opt.search import Opt, OptOps
+from tinygrad.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

extra/gemm/simple_matmul.py

@@ -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.codegen.opt.kernel import OptOps
+from tinygrad.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

extra/gemm/tinygrad_nv_matmul.py

@@ -1,6 +1,6 @@
 from tinygrad import Tensor, dtypes, Device
 from tinygrad.helpers import getenv, DEBUG
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from dataclasses import replace
 

extra/gemm/tvm_gemm.py

@@ -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.codegen.opt.kernel import Kernel
+from tinygrad.opt.kernel import Kernel
 from tinygrad.device import CompilerOptions
 lin = Kernel(sched[-1].ast, CompilerOptions(has_local=False, supports_float4=False))
 lin.to_program()

extra/hcqfuzz/tests/bert.py

@@ -8,6 +8,7 @@ bert_train_params = {
   "BS": 96,
   "EVAL_BS": 96,
   "FUSE_ARANGE": 1,
+  "FUSE_ARANGE_UINT": 0,
   "BASEDIR": "/raid/datasets/wiki",
 }
 

extra/mcts_search.py

@@ -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.codegen.opt.kernel import Kernel
+from tinygrad.opt.kernel import Kernel
 from tinygrad.device import Buffer, Device, CompileError
-from tinygrad.codegen.opt.search import _ensure_buffer_alloc, get_kernel_actions, _time_program
+from tinygrad.opt.search import _ensure_buffer_alloc, get_kernel_actions, _time_program
 from tinygrad.engine.realize import get_program
 
 class MCTSNode:

extra/models/llama.py

@@ -181,11 +181,13 @@ 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)
-    freqs_cis = self.freqs_cis.cast(h.dtype)[:, start_pos:start_pos+seqlen, :, :, :]
+
+    self.freqs_cis = self.freqs_cis.cast(h.dtype).contiguous()
+    freqs_cis = self.freqs_cis[:, 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))
+    logits = self.output(self.norm(h)).float()
     if math.isnan(temperature): return logits
 
     return sample(logits[:, -1, :].flatten(), temperature, top_k, top_p, alpha_f, alpha_p)

extra/onnx.py

@@ -1,47 +1,89 @@
-from typing import Any, Sequence, cast, Literal, NamedTuple, Generator
-import dataclasses, functools, io, math, types, warnings, pathlib, sys, os, struct, enum
-from io import BufferedReader
-from tinygrad.nn.state import TensorIO
+# mypy: disable-error-code="misc, list-item, assignment, operator, index, arg-type"
+from types import SimpleNamespace
+from typing import Any, Sequence, cast, Literal, Callable, get_args, NamedTuple
+import dataclasses, functools, io, math, types, warnings, pathlib, sys, enum
 from tinygrad.tensor import Tensor, _broadcast_shape, ReductionStr
-from tinygrad.helpers import getenv, DEBUG, all_same, prod, flatten, make_tuple, argsort, is_numpy_ndarray, get_single_element, polyN
+from tinygrad.helpers import getenv, DEBUG, all_same, prod, flatten, make_tuple, argsort, is_numpy_ndarray, get_single_element
 from tinygrad.dtype import DType, ConstType, dtypes, _from_np_dtype
 from tinygrad.device import is_dtype_supported, Device
+from extra.onnx_parser import onnx_load
 
-# ***** protobuf definitions ******
-class WireType(enum.IntEnum):
-  """
-  Protocol Buffer wire types for decoding fields.
-  Reference: https://github.com/protocolbuffers/protobuf/blob/main/python/google/protobuf/internal/wire_format.py#L24-L29
-  """
-  VARINT = 0; FIXED64 = 1; LENGTH_DELIMITED = 2; START_GROUP = 3; END_GROUP = 4; FIXED32 = 5 # noqa: E702
+# https://github.com/onnx/onnx/blob/rel-1.17.0/onnx/onnx.proto3#L500-L544
+data_types: dict[int, DType] = {
+  1:dtypes.float32, 2:dtypes.uint8, 3:dtypes.int8, 4:dtypes.uint16, 5:dtypes.int16, 6:dtypes.int32, 7:dtypes.int64,
+  9:dtypes.bool, 10:dtypes.float16, 11:dtypes.double, 12:dtypes.uint32, 13:dtypes.uint64, 16:dtypes.bfloat16,
+}
 
-class AttributeType(enum.IntEnum):
-  """
-  ONNX attribute type identifiers.
-  Reference: https://github.com/onnx/onnx/blob/rel-1.18.0/onnx/onnx.proto3#L128-L145
-  """
-  FLOAT = 1; INT = 2; STRING = 3; TENSOR = 4; FLOATS = 6; INTS = 7; STRINGS = 8 # noqa: E702
+# https://github.com/onnx/onnx/blob/rel-1.17.0/onnx/onnx.proto3#L128-L145
+attribute_types: dict[int, Callable] = {
+  1: lambda a: float(a.f),
+  2: lambda a: int(a.i),
+  3: lambda a: a.s.data().tobytes().decode("utf8") if isinstance(a.s, Tensor) else a.s.decode("utf8"),
+  4: lambda a: buffer_parse(a.t),
+  6: lambda a: tuple(float(x) for x in a.floats),
+  7: lambda a: tuple(int(x) for x in a.ints),
+  8: lambda a: tuple(x.data().tobytes().decode("utf8") for x in a.strings)
+}
 
-  def to_field_name(self) -> str: return {1: "f", 2: "i", 3: "s", 4: "t", 6: "floats", 7: "ints", 8: "strings"}[self.value]
+# ***** protobuf parsing ******
+from onnx import AttributeProto, TensorProto, TypeProto
 
-class OnnxDataType(enum.IntEnum):
-  """
-  ONNX tensor data type identifiers.
-  Reference: https://github.com/onnx/onnx/blob/rel-1.18.0/onnx/onnx.proto3#L500-L544
-  """
-  FLOAT = 1; UINT8 = 2; INT8 = 3; UINT16 = 4; INT16 = 5; INT32 = 6; INT64 = 7; BOOL = 9; FLOAT16 = 10; DOUBLE = 11; UINT32 = 12 # noqa: E702
-  UINT64 = 13; BFLOAT16 = 16 # noqa: E702
+def has_field(onnx_type: TypeProto|SimpleNamespace, field):
+  if isinstance(onnx_type, TypeProto): return onnx_type.HasField(field)
+  return hasattr(onnx_type, field)
 
-  def to_dtype(self) -> DType: return dtypes.fields()[self.name.lower()]
+def dtype_parse(onnx_dtype: int, fallback_context: str | None = None) -> DType:
+  if onnx_dtype not in data_types: raise NotImplementedError(f"onnx dtype id {onnx_dtype} is not supported")
+  if is_dtype_supported(dtype := data_types[onnx_dtype]): return dtype
+  # if fallback_context is provided, we can fall back to a default dtype
+  if fallback_context is not None:
+    default_dtype = dtypes.default_int if dtypes.is_int(dtype) else dtypes.default_float
+    warnings.warn(f"dtype {dtype} on {Device.DEFAULT} from {fallback_context} is not supported, falling back to {default_dtype}")
+    assert is_dtype_supported(default_dtype), f"dtype {default_dtype} must be supported on {Device.DEFAULT}"
+    return default_dtype
+  raise RuntimeError(f"dtype {dtype} on device {Device.DEFAULT} is not supported")
 
-def dtype_fallback(dtype: DType, fallback_context: str) -> DType:
-  if is_dtype_supported(dtype): return dtype
-  default_dtype = dtypes.default_int if dtypes.is_int(dtype) else dtypes.default_float
-  warnings.warn(f"dtype {dtype} on {Device.DEFAULT} from {fallback_context} is not supported, falling back to {default_dtype}")
-  assert is_dtype_supported(default_dtype), f"dtype {default_dtype} must be supported on {Device.DEFAULT}"
-  return default_dtype
+def attribute_parse(onnx_attribute: AttributeProto):
+  if onnx_attribute.type not in attribute_types: raise NotImplementedError(f"attribute type {onnx_attribute.type} is not supported")
+  return attribute_types[onnx_attribute.type](onnx_attribute)
+
+def buffer_parse(onnx_tensor: TensorProto) -> Tensor:
+  if onnx_tensor.string_data: raise NotImplementedError("Parsing for buffer with string data is not implemented.")
+  to_dtype, true_dtype = dtype_parse(onnx_tensor.data_type, "buffer parse"), data_types[onnx_tensor.data_type]
+  shape = tuple(onnx_tensor.dims)
+  keys = ['float_data', 'int32_data', 'int64_data', 'double_data', 'uint64_data', "raw_data"]
+  data = next((val for k in keys if (val := getattr(onnx_tensor, k)) is not None), None)
+  if data is None: raise RuntimeError("empty buffer")
+  if not isinstance(data, Tensor): return Tensor(data, dtype=to_dtype).reshape(shape)
+  assert data.dtype is dtypes.uint8, data.dtype
+  data = data.bitcast(true_dtype).reshape(shape)
+  data = data.to(Device.DEFAULT) if true_dtype is to_dtype else data.to("cpu").cast(to_dtype).to(Device.DEFAULT)
+  if shape == ():
+    if data.dtype is dtypes.float16 and sys.version_info < (3, 12): data = data.cast(dtypes.float32)
+    return Tensor(data.item(), dtype=to_dtype).reshape(shape)
+  return data
+
+def type_parse(onnx_type: TypeProto):
+  elem_type = onnx_type
+  if has_field(elem_type, "map_type") or has_field(elem_type, "sparse_tensor_type") or has_field(elem_type, "opaque_type"):
+    raise NotImplementedError("parsing for map_type, sparse_tensor_type and opaque_type are not implemented")
+  if is_optional := has_field(elem_type, "optional_type"): elem_type = elem_type.optional_type.elem_type
+  if is_sequence := has_field(elem_type, "sequence_type"): elem_type = elem_type.sequence_type.elem_type
+  if has_field(elem_type, "tensor_type"):
+    shape = tuple(getattr(d, "dim_param", None) or getattr(d, "dim_value") for d in elem_type.tensor_type.shape.dim) \
+      if has_field(elem_type.tensor_type, "shape") else None # test_identity_sequence_cpu
+    dtype = data_types[elem_type.tensor_type.elem_type]
+    return OnnxValue(shape, dtype, is_optional, is_sequence)
+  raise RuntimeError(f"TypeProto was not parsed properly: {onnx_type=}")
+
+# ***** onnx spec *****
+@dataclasses.dataclass(frozen=True)
+class OnnxValue:
+  shape: tuple[str|int, ...]
+  dtype: DType
+  is_optional: bool
+  is_sequence: bool
 
-# ***** onnx spec definitions *****
 class Domain(enum.Enum):
   ONNX = "ai.onnx"
   ONNX_ML = "ai.onnx.ml"
@@ -55,312 +97,15 @@ class OpSetId(NamedTuple):
   domain: Domain
   version: int
 
-@dataclasses.dataclass(frozen=True)
-class OnnxValue:
-  shape: tuple[str|int, ...]
-  dtype: DType
-  is_optional: bool
-  is_sequence: bool
-
 @dataclasses.dataclass(frozen=True)
 class OnnxNode:
+  num: int
   op: str
   opset_id: OpSetId
   inputs: tuple[str, ...]
   outputs: tuple[str, ...]
   opts: dict[str, Any]
 
-# ***** protobuf parsing ******
-class PBBufferedReader(BufferedReader):
-  def __init__(self, tensor: Tensor):
-    assert tensor.dtype == dtypes.uint8, tensor
-    super().__init__(TensorIO(tensor))
-    self.len = tensor.nbytes()
-
-  def decode_varint(self) -> int:
-    """Reference: https://protobuf.dev/programming-guides/encoding/#varints"""
-    result = 0
-    shift = 0
-    while True:
-      data = self.read(1)
-      if data == b"": raise EOFError("decode_varint EOF")
-      result |= (data[0] & 0x7F) << shift
-      if not (data[0] & 0x80): return result
-      shift += 7
-      if shift >= 70: raise ValueError("Varint too long")
-
-  def read_delimited(self, use_tensor=False):
-    str_len = self.decode_varint()
-    if not use_tensor: return self.read(str_len)
-    raw = self.raw
-    assert isinstance(raw, TensorIO)
-    res = raw._tensor[self.tell():(self.tell()+str_len)]
-    self.seek(str_len, os.SEEK_CUR)
-    return res
-  def read_string(self) -> str: return self.read_delimited().decode("utf-8")
-  def read_bytes(self) -> Tensor: return self.read_delimited(use_tensor=True)
-  def read_float(self) -> float: return struct.unpack("<f", self.read(4))[0]
-  def read_packed_floats(self) -> Tensor: return self.read_delimited(use_tensor=True)
-  def read_int64(self) -> int:
-    val = self.decode_varint()
-    return val - 2**64 if val & (1 << 63) else val
-  def read_packed_int64s(self) -> list[int]:
-    total_bytes_len = self.decode_varint()
-    old_pos = self.tell()
-    values = []
-    # need copy here because packed ints are varint
-    while self.tell() < total_bytes_len + old_pos: values.append(self.read_int64())
-    return values
-
-  def skip_field(self, wire_type: WireType) -> None:
-    """Skip a field based on its wire type."""
-    match wire_type:
-      case WireType.VARINT: self.decode_varint()
-      case WireType.FIXED64: self.seek(8, os.SEEK_CUR)
-      case WireType.FIXED32: self.seek(4, os.SEEK_CUR)
-      case WireType.LENGTH_DELIMITED: self.seek(self.decode_varint(), os.SEEK_CUR)
-      case _: raise ValueError(f"Unknown wire type: {wire_type}")
-
-class OnnxPBParser:
-  """
-  ONNX protobuf parser.
-  Reference: https://github.com/onnx/onnx/blob/main/onnx/onnx.proto3
-  """
-  def __init__(self, inp: Tensor|str|pathlib.Path, load_external_data: bool=True):
-    self.file_path: pathlib.Path|None = None
-    self.load_external_data = load_external_data
-    if not isinstance(inp, Tensor):
-      self.file_path = pathlib.Path(inp)
-      self.tensor = Tensor(self.file_path)
-    else: self.tensor = inp
-    self.reader = PBBufferedReader(self.tensor)
-
-  def parse(self) -> dict:
-    """Parses the ONNX model into a nested dictionary. """
-    return self._parse_ModelProto()
-
-  def _parse_message(self, end_pos: int) -> Generator[tuple[int, WireType], None, None]:
-    while self.reader.tell() < end_pos:
-      tag = self.reader.decode_varint()
-      yield tag >> 3, WireType(tag & 0x07)
-
-  def _decode_end_pos(self) -> int:
-    str_len = self.reader.decode_varint()
-    start_pos = self.reader.tell()
-    return start_pos + str_len
-
-  def _parse_ModelProto(self) -> dict:
-    """Entry point for parsing the ONNX model."""
-    obj: dict[str, Any] = {"opset_import": []}
-    for fid, wire_type in self._parse_message(self.reader.len):
-      match fid:
-        case 4: obj["domain"] = self.reader.read_string()
-        case 5: obj["model_version"] = self.reader.read_int64()
-        case 7: obj["graph"] = self._parse_GraphProto()
-        case 8: obj["opset_import"].append(self._parse_OperatorSetIdProto())
-        case _: self.reader.skip_field(wire_type)
-
-    # update opset version
-    opset_imports = {Domain.from_onnx(x.get('domain')):x.get('version', 1) for x in obj["opset_import"]}
-    for n in obj["graph"]["node"]:
-      n_ = n["parsed_node"]
-      n["parsed_node"] = OnnxNode(n_.op, OpSetId(n_.opset_id.domain, opset_imports.get(n_.opset_id.domain, 1)), n_.inputs, n_.outputs, n_.opts)
-    return obj
-
-  def _parse_GraphProto(self) -> dict:
-    obj: dict[str, Any] = {"node": [], "initializer": [], "input": [], "output": []}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["node"].append(self._parse_NodeProto())
-        case 2: obj["name"] = self.reader.read_string()
-        case 5: obj["initializer"].append(self._parse_TensorProto())
-        case 11: obj["input"].append(self._parse_ValueInfoProto())
-        case 12: obj["output"].append(self._parse_ValueInfoProto())
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_NodeProto(self) -> dict:
-    obj: dict[str, Any] = {"input": [], "output": [], "attribute": [], "domain": None}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["input"].append(self.reader.read_string())
-        case 2: obj["output"].append(self.reader.read_string())
-        case 3: obj["name"] = self.reader.read_string()
-        case 4: obj["op_type"] = self.reader.read_string()
-        case 5: obj["attribute"].append(self._parse_AttributeProto())
-        case 6: obj["doc_string"] = self.reader.read_string()
-        case 7: obj["domain"] = self.reader.read_string()
-        case _: self.reader.skip_field(wire_type)
-
-    # parse node
-    attributes = {attr_dict["name"]: attr_dict[AttributeType(attr_dict["type"]).to_field_name()] for attr_dict in obj["attribute"]}
-    opset_id = OpSetId(Domain.from_onnx(obj.get('domain')), 1)  # default version, to be updated later in _parse_ModelProto
-    obj["parsed_node"] = OnnxNode(obj["op_type"], opset_id, tuple(obj["input"]), tuple(obj["output"]), attributes)
-    return obj
-
-  def _parse_TensorProto(self) -> dict:
-    obj: dict[str, Any] = {"dims": []}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["dims"].append(self.reader.read_int64())
-        case 2: obj["data_type"] = self.reader.read_int64()
-        case 4: obj["float_data"] = self.reader.read_packed_floats()
-        case 5: obj["int32_data"] = self.reader.read_packed_int64s()
-        case 7: obj["int64_data"] = self.reader.read_packed_int64s()
-        case 8: obj["name"] = self.reader.read_string()
-        case 9: obj["raw_data"] = self.reader.read_bytes()
-        case 10: obj["double_data"] = self.reader.read_packed_floats()
-        case 11: obj["uint64_data"] = self.reader.read_packed_int64s()
-        case 13: obj.setdefault("external_data", []).append(self._parse_StringStringEntryProto())
-        case 14: obj["data_location"] = self.reader.read_int64()
-        case _: self.reader.skip_field(wire_type)
-
-    # load external data
-    if self.load_external_data and obj.get("data_location", 0) == 1:
-      if "external_data" not in obj: raise ValueError("no external_data")
-      location, length, offset = None, None, 0
-      for kv in obj["external_data"]:
-        if kv["key"] == "location": location = kv["value"]
-        elif kv["key"] == "offset": offset = int(kv["value"])
-        elif kv["key"] == "length": length = int(kv["value"])
-      if location is None: raise ValueError("no location in external_data")
-
-      if self.file_path is None:
-        if isinstance(self.tensor.device, str) and self.tensor.device.startswith("DISK:"):
-          self.file_path = pathlib.Path(self.tensor.device[5:])
-        else: raise Exception("onnx external_data needs the origin file path, try passing onnx file path to onnx_load")
-      ext_path = self.file_path.parent.joinpath(location)
-      if not ext_path.exists(): raise Exception(f"external location not exists: {ext_path}")
-
-      ext_tensor = Tensor(ext_path)
-      obj["raw_data"] = ext_tensor[offset:offset+length] if length is not None else ext_tensor[offset:]
-      obj["data_location"] = 0
-
-    # parse tensor
-    to_dtype = dtype_fallback(true_dtype := OnnxDataType(obj['data_type']).to_dtype(), "buffer parse")
-    shape = tuple(obj['dims'])
-    present_fields = [field for field in ['float_data', 'int32_data', 'int64_data', 'double_data', 'uint64_data', 'raw_data'] if field in obj]
-    assert len(present_fields) == 1, f"only 1 data field is allowed from {obj=}"
-    data = obj[present_fields[0]]
-    if not isinstance(data, Tensor):
-      obj["parsed_tensor"] = Tensor(data, dtype=to_dtype).reshape(shape)
-      return obj
-    assert isinstance(data, Tensor) and data.dtype == dtypes.uint8, data
-    data = data.bitcast(true_dtype).reshape(shape)
-    data = data.to(Device.DEFAULT) if true_dtype is to_dtype else data.to("cpu").cast(to_dtype).to(Device.DEFAULT)
-    # const folding
-    if shape == ():
-      if data.dtype == dtypes.float16 and sys.version_info < (3, 12): data = data.cast(dtypes.float32)
-      data = Tensor(data.item(), dtype=to_dtype).reshape(shape)
-    obj["parsed_tensor"] = data
-    return obj
-
-  def _parse_AttributeProto(self) -> dict:
-    obj: dict[str, Any] = {"floats": [], "ints": [], "strings": []}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["name"] = self.reader.read_string()
-        case 2: obj["f"] = self.reader.read_float()
-        case 3: obj["i"] = self.reader.read_int64()
-        case 4: obj["s"] = self.reader.read_bytes().data().tobytes().decode("utf8")
-        case 5: obj["t"] = self._parse_TensorProto()['parsed_tensor']
-        case 7: obj["floats"].append(self.reader.read_float())
-        case 8: obj["ints"].append(self.reader.read_int64())
-        case 9: obj["strings"].append(self.reader.read_bytes().data().tobytes().decode("utf8"))
-        case 20: obj["type"] = self.reader.read_int64()
-        case _: self.reader.skip_field(wire_type)
-    obj["floats"], obj["ints"], obj["strings"] = tuple(obj["floats"]), tuple(obj["ints"]), tuple(obj["strings"])
-    return obj
-
-  def _parse_ValueInfoProto(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["name"] = self.reader.read_string()
-        case 2: obj["type"] = self._parse_TypeProto()
-        case _: self.reader.skip_field(wire_type)
-
-    # parse type
-    if "type" not in obj: return {**obj, "parsed_type": None}
-    type_obj = obj["type"]
-    if is_optional := "optional_type" in type_obj: type_obj = type_obj["optional_type"]["elem_type"]
-    if is_sequence := "sequence_type" in type_obj: type_obj = type_obj["sequence_type"]["elem_type"]
-    assert "tensor_type" in type_obj, type_obj
-    shape_dims = type_obj['tensor_type'].get('shape', {}).get('dim', [])
-    obj['parsed_type'] = OnnxValue(tuple(d.get('dim_param') or d.get('dim_value') for d in shape_dims),
-                                   OnnxDataType(type_obj['tensor_type']['elem_type']).to_dtype(), is_optional, is_sequence)
-    return obj
-
-  def _parse_TypeProto(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["tensor_type"] = self._parse_TypeProtoTensor()
-        case 4: obj["sequence_type"] = self._parse_TypeProtoSequence()
-        case 9: obj["optional_type"] = self._parse_TypeProtoOptional()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_TypeProtoTensor(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["elem_type"] = self.reader.read_int64()
-        case 2: obj["shape"] = self._parse_TensorShapeProto()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_TypeProtoSequence(self) -> dict:
-    obj = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["elem_type"] = self._parse_TypeProto()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_TypeProtoOptional(self) -> dict:
-    obj = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["elem_type"] = self._parse_TypeProto()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_TensorShapeProto(self) -> dict:
-    obj: dict[str, Any] = {"dim": []}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["dim"].append(self._parse_TensorShapeProtoDimension())
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_TensorShapeProtoDimension(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["dim_value"] = self.reader.read_int64()
-        case 2: obj["dim_param"] = self.reader.read_string()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_StringStringEntryProto(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["key"] = self.reader.read_string()
-        case 2: obj["value"] = self.reader.read_string()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
-  def _parse_OperatorSetIdProto(self) -> dict:
-    obj: dict[str, Any] = {}
-    for fid, wire_type in self._parse_message(self._decode_end_pos()):
-      match fid:
-        case 1: obj["domain"] = self.reader.read_string()
-        case 2: obj["version"] = self.reader.read_int64()
-        case _: self.reader.skip_field(wire_type)
-    return obj
-
 # ***** python const *****
 required_input_python_consts: dict[str, tuple[int, ...]] = {
   "Tile": (1,), "Range": (0,1,2), "Expand": (1,), "Reshape": (1,), "Squeeze": (1,), "Unsqueeze": (1,), "Trilu": (1,), "ConstantOfShape": (0,),
@@ -373,7 +118,7 @@ required_input_python_consts: dict[str, tuple[int, ...]] = {
 cache_misses = 0
 @functools.cache
 def _cached_to_python_const(t:Tensor):
-  if t.dtype == dtypes.uint8: return t.data().tobytes()
+  if t.dtype is dtypes.uint8: return t.data().tobytes()
   if 0 in t.shape: return []
   return t.tolist()
 
@@ -398,18 +143,22 @@ class OnnxRunner:
     model_path: The ONNX model, provided as a file path (a string or Path object) or a Tensor.
   """
   def __init__(self, model_path: Tensor | str | pathlib.Path):
-    model = OnnxPBParser(model_path, load_external_data=True).parse()
-    graph = model["graph"]
-    self.is_training = any(n['parsed_node'].opset_id.domain in {Domain.AI_ONNX_TRAINING, Domain.AI_ONNX_PREVIEW_TRAINING} for n in graph["node"])
-    self.graph_values = {"": None, **{i["name"]: i["parsed_tensor"] for i in graph["initializer"]}}
-    self.graph_inputs = {i["name"]: i["parsed_type"] for i in graph["input"] if i["name"] not in self.graph_values}
-    self.graph_outputs = tuple(o["name"] for o in graph["output"])
-    self.graph_nodes = tuple(n["parsed_node"] for n in graph["node"])
-
+    model = onnx_load(model_path)
+    self.is_training = any(n.domain in {Domain.AI_ONNX_TRAINING, Domain.AI_ONNX_PREVIEW_TRAINING} for n in model.graph.node)
     self.old_training = Tensor.training
     Tensor.training = True if self.is_training else False
-
+    self.graph_values = {"": None, **{x.name:buffer_parse(x) for x in model.graph.initializer}}
+    self.graph_inputs = {x.name:type_parse(x.type) for x in model.graph.input if x.name not in self.graph_values}
+    self.graph_outputs = tuple(x.name for x in model.graph.output)
+    opset_imports = {Domain.from_onnx(getattr(x, "domain", "")):x.version for x in model.opset_import}
+    self.graph_nodes = []
+    for num, n in enumerate(model.graph.node):
+      domain = Domain.from_onnx(n.domain)
+      opset_id = OpSetId(domain, opset_imports.get(domain, 1))
+      self.graph_nodes.append(OnnxNode(num, n.op_type, opset_id, tuple(n.input), tuple(n.output), {x.name:attribute_parse(x) for x in n.attribute}))
+    self.graph_nodes = tuple(self.graph_nodes)
     self.variable_dims: dict[str, int] = {}
+
     self.onnx_ops = onnx_ops
 
   def _parse_input(self, name: str, value: Any, spec: OnnxValue):
@@ -444,7 +193,7 @@ class OnnxRunner:
 
   def to(self, device:str|None):
     self.graph_values = {k:v.to(device) if isinstance(v, Tensor) else v for k,v in self.graph_values.items()}
-    self.graph_nodes = tuple(OnnxNode(n.op, n.opset_id, tuple(n.inputs), tuple(n.outputs),
+    self.graph_nodes = tuple(OnnxNode(n.num, n.op, n.opset_id, tuple(n.inputs), tuple(n.outputs),
                                       {k:v.to(device) if isinstance(v, Tensor) else v for k,v in n.opts.items()}) for n in self.graph_nodes)
     return self
 
@@ -453,7 +202,7 @@ class OnnxRunner:
       if name not in inputs: raise RuntimeError(f"Please provide input data for {name}")
       self.graph_values[name] = self._parse_input(name, inputs[name], input_spec)
 
-    for num, node in enumerate(self.graph_nodes):
+    for node in self.graph_nodes:
       inps = [to_python_const(self.graph_values[name], node.op, i) for i,name in enumerate(node.inputs)]
       opts = node.opts
 
@@ -461,7 +210,7 @@ class OnnxRunner:
       if node.op == "Split" and 'num_outputs' not in opts: opts['num_outputs'] = len(node.outputs)
       if node.op == "Gradient": opts['intermediate_tensors'] = self.graph_values
 
-      if debug >= 1: print(f"{num}: op '{node.op}' opt {opts}")
+      if debug >= 1: print(f"{node.num}: op '{node.op}' opt {opts}")
       if debug >= 2 and node.inputs: print("\tinputs:\n" + "\n".join(f"\t\t{x} - {i!r}" for x,i in zip(node.inputs, inps)))
       ret = self._select_op(node.op, node.opset_id)(*inps, **opts)
       ret = ret if isinstance(ret, tuple) else (ret,)
@@ -469,7 +218,7 @@ class OnnxRunner:
 
       self.graph_values.update(dict(zip(node.outputs, ret[:len(node.outputs)], strict=True)))
 
-      if num == limit:
+      if node.num == limit:
         Tensor.training = self.old_training
         return {name:self.graph_values[name] for name in node.outputs}
     Tensor.training = self.old_training
@@ -480,7 +229,7 @@ class OnnxRunner:
 ####################
 def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionType]]:
   # ***** helper functions *****
-  def _resolve_const(x: Sequence[ConstType]|ConstType): return get_single_element(x) if isinstance(x, Sequence) else x
+  def _resolve_const(x: Sequence[ConstType]|ConstType): return x if isinstance(x, get_args(ConstType)) else get_single_element(x)
 
   def _axes(axes, noop_with_empty_axes): return axes or ([] if noop_with_empty_axes else None)
 
@@ -549,7 +298,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     if value_floats is not None: return Tensor(list(value_floats), dtype=dtypes.float32, requires_grad=False)
     if value_int is not None: return Tensor(value_int, dtype=dtypes.int64, requires_grad=False)
     if value_ints is not None: return Tensor(list(value_ints), dtype=dtypes.int64, requires_grad=False)
-    if value_string is not None or value_strings is not None or sparse_value is not None:
+    if value_string is not None or value_strings is not None and sparse_value is not None:
       raise NotImplementedError('Constant OP not implemented for value_string, value_strings and sparse_value')
 
   def Range(start:float|int|list[float|int], limit:float|int|list[float|int], delta:float|int|list[float|int]):
@@ -565,7 +314,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     raise ValueError(f"pixel_format={pixel_format!r} is not supported.")
 
   def EyeLike(x:Tensor, dtype:int|None=None, k:int=0):
-    ret = Tensor.eye(cast(int, min(x.shape)), dtype=dtype_fallback(OnnxDataType(dtype).to_dtype(), "EyeLike op") if dtype is not None else x.dtype)
+    ret = Tensor.eye(cast(int, min(x.shape)), dtype=dtype_parse(dtype, "EyeLike op") if dtype is not None else x.dtype)
     return ret if x.size(0) == x.size(1) else ret.pad(tuple(None if d == ret.size(0) else (k, d-ret.shape[0]-k) for d in x.shape))
 
   def OptionalHasElement(x:Tensor|None=None): return Tensor(x is not None and x.numel() > 0)
@@ -613,11 +362,13 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
   def BitwiseOr(x:Tensor,y:Tensor): return x | y
   def BitwiseXor(x:Tensor,y:Tensor): return x ^ y
   def BitwiseNot(x:Tensor): return ~x
-  def Mod(x:Tensor,y:Tensor,fmod=0): return x - x.div(y, rounding_mode="trunc") * y if fmod else x % y
+  def Mod(x:Tensor,y:Tensor,fmod=0):
+    if fmod: return x - x.div(y, rounding_mode="trunc") * y
+    return x % y
 
   # ***** Casting Ops *****
   # TODO: saturate
-  def Cast(x:Tensor, to:int, saturate:int=1): return x.cast(dtype_fallback(OnnxDataType(to).to_dtype(), "Cast op"))
+  def Cast(x:Tensor, to:int, saturate:int=1): return x.cast(dtype_parse(to, "Cast op"))
   def CastLike(x:Tensor, target_type:Tensor, saturate:int=1): return x.cast(target_type.dtype)
 
   # ***** Reduce Ops *****
@@ -697,14 +448,13 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
   # ***** Processing Ops *****
   def AveragePool(X: Tensor, kernel_shape:list[int], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, count_include_pad:int=0,
                   dilations:list[int]|int=1, pads:list[int]|int=0, strides:list[int]|int=1):
-    pool_pads = _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad)
-    return X.avg_pool2d(tuple(kernel_shape), strides, dilations, pool_pads, ceil_mode=ceil_mode, count_include_pad=count_include_pad)
+    return X.avg_pool2d(kernel_shape, strides, dilations, _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad),
+                        ceil_mode=ceil_mode, count_include_pad=count_include_pad)
 
   def MaxPool(X: Tensor, kernel_shape:list[int], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, dilations:list[int]|int=1, pads:list[int]|int=0,
               storage_order:int=0, strides:list[int]|int=1):
-    pool_pads = _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad)
-    out = X.max_pool2d(tuple(kernel_shape), strides, dilations, pool_pads, ceil_mode=ceil_mode, return_indices=True)
-    ret, idx = cast(tuple[Tensor, Tensor], out)
+    pads = _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad)
+    ret, idx = X.max_pool2d(kernel_shape, strides, dilations, pads, ceil_mode=ceil_mode, return_indices=True)
     return ret, idx.transpose(-2, -1).cast(dtypes.int64) if storage_order else idx.cast(dtypes.int64)
 
   def Conv(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:list[int]|int=1, group:int=1,
@@ -715,22 +465,20 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
   def ConvTranspose(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:list[int]|int=1, group:int=1,
                     kernel_shape:list[int]|None=None, pads:list[int]|None=None, output_shape:list[int]|None=None, output_padding:list[int]|int=0,
                     strides:list[int]|int=1):
-    input_shape_, kernel_shape_ = X.shape[2:], (kernel_shape or W.shape[2:])
-    strides_, dilations_, output_padding_ = (make_tuple(x, len(input_shape_)) for x in (strides, dilations, output_padding))
+    input_shape, kernel_shape = X.shape[2:], (kernel_shape or W.shape[2:])
+    strides, dilations, output_padding = (make_tuple(x, len(input_shape)) for x in (strides, dilations, output_padding))
     if output_shape is not None: # we pad according to output_shape
-      pads = _auto_pad([s_*(i-1) + op_ + ((k_-1)*d_+1) - os for s_,i,op_,k_,d_,os in
-                        zip(strides_, input_shape_, output_padding_, kernel_shape_, dilations_, output_shape)], auto_pad)
+      pads = _auto_pad([s*(i-1) + op + ((k-1)*d+1) - os for s,i,op,k,d,os in
+                        zip(strides, input_shape, output_padding, kernel_shape, dilations, output_shape)], auto_pad)
     if pads is None: # we generate pads
-      output_shape = output_shape or [X.shape[i+2] * strides_[i] for i in range(len(strides_))]
-      pads = [strides_[i]*(input_shape_[i]-1)+output_padding_[i]+((kernel_shape_[i]-1)*dilations_[i]+1)-output_shape[i]
-              for i in range(len(input_shape_))]
-      pads = _auto_pad(pads, auto_pad) if auto_pad != "NOTSET" else [0] * len(input_shape_) * 2
+      output_shape = output_shape or [X.shape[i+2] * strides[i] for i in range(len(strides))]
+      pads = [strides[i]*(input_shape[i]-1)+output_padding[i]+((kernel_shape[i]-1)*dilations[i]+1)-output_shape[i] for i in range(len(input_shape))]
+      pads = _auto_pad(pads, auto_pad) if auto_pad != "NOTSET" else [0] * len(input_shape) * 2
     pads = _onnx_pads_to_tiny_pads(pads)
-    return X.conv_transpose2d(W, B, group, strides_, dilations_, pads, output_padding_)
+    return X.conv_transpose2d(W, B, stride=strides, groups=group, dilation=dilations, padding=pads, output_padding=output_padding)
 
-  def MaxUnpool(xT: Tensor, xI: Tensor, outshape: list[int]|None=None, kernel_shape:list[int]=[], pads:list[int]|int=0, strides:list[int]|int=1):
-    pads_: int | tuple[int, ...] = tuple(pads) if isinstance(pads, list) else pads
-    return Tensor.max_unpool2d(xT, xI, tuple(kernel_shape), strides, 1, pads_, outshape if outshape is None else tuple(outshape))
+  def MaxUnpool(xT: Tensor, xI: Tensor, outshape: list[int]|None=None, kernel_shape:list[int]=None, pads:list[int]|int=0, strides:list[int]|int=1):
+    return Tensor.max_unpool2d(xT, xI, kernel_shape, strides, 1, pads, outshape if outshape is None else tuple(outshape))
 
   def GlobalAveragePool(X:Tensor): return X.mean(axis=tuple(range(2, X.ndim)), keepdim=True)
   def GlobalMaxPool(X:Tensor): return X.max(axis=tuple(range(2, X.ndim)), keepdim=True)
@@ -754,60 +502,53 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     return x.triu(k_) if upper else x.tril(k_)
 
   def Resize(X:Tensor, roi:list[float]|None=None, scales:list[float]|None=None, sizes:list[int]|None=None, antialias:int=0,
-        axes:list[int]|None=None, coordinate_transformation_mode:str='half_pixel', cubic_coeff_a:float=-0.75, exclude_outside:int=0,
-        extrapolation_value:float=0.0, keep_aspect_ratio_policy:str='stretch', mode:str='nearest', nearest_mode:str='round_prefer_floor'):
-    def _apply_transformation(input_sz, output_sz, scale_dim, mode):
-      index = Tensor.arange(output_sz, requires_grad=False, device=X.device)
-      if mode == "half_pixel": return (index + 0.5) / scale_dim - 0.5
-      if mode == "align_corners": return index * (input_sz - 1) / (output_sz - 1) if output_sz != 1 else Tensor.zeros_like(index)
-      if mode == "asymmetric": return index / scale_dim
-      if mode == "pytorch_half_pixel": return ((index + 0.5) / scale_dim - 0.5) if output_sz != 1 else Tensor.zeros_like(index)
-      if mode == "half_pixel_symmetric":
-        output_dim_scaled = input_sz * scale_dim
-        return (input_sz / 2) * (1 - (output_sz / output_dim_scaled)) + (index + 0.5) / scale_dim - 0.5
-      raise ValueError(f"invalid {coordinate_transformation_mode=}")
+            axes:list[int]|None=None, coordinate_transformation_mode:str='half_pixel', cubic_coeff_a:float=-0.75, exclude_outside:int=0,
+            extrapolation_value:float=0.0, keep_aspect_ratio_policy:str='stretch', mode:str='nearest', nearest_mode:str='round_prefer_floor'):
+    def _apply_nearest_mode(index: Tensor, input_dim, mode: str):
+      if mode == "round_prefer_floor": index = (index - 0.5).ceil()
+      elif mode == "round_prefer_ceil": index = (index + 0.5).floor()
+      elif mode in ["floor", "ceil"]: index = getattr(index, mode)()
+      else: raise ValueError(f"invalid {nearest_mode=}")
+      return index.cast(dtypes.int32).clip(0, input_dim-1)
+    def _apply_transformation(index: Tensor, input_dim, scale_dim, mode):
+      # TODO: needs more testing, not confident in this
+      # NOTE: their reference implementation differ from the implementation in their reference docs
+      # https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_resize.py
+      # https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize
+      output_dim = scale_dim * input_dim
+      if mode == "half_pixel": index = (index + 0.5) / scale_dim - 0.5
+      elif mode == "align_corners": index = index * (input_dim - 1) / (output_dim - 1) if output_dim != 1 else Tensor([0])
+      elif mode == "asymmetric": index = index / scale_dim
+      elif mode == "pytorch_half_pixel": index = (index + 0.5) / scale_dim - 0.5 if output_dim != 1 else Tensor([-0.5])
+      elif mode == "half_pixel_symmetric": index = input_dim / 2 * (1 - int(output_dim) / output_dim) + (index + 0.5) / scale_dim - 0.5
+      else: raise NotImplementedError(f"invalid {coordinate_transformation_mode=}")
+      return index.clip(0, input_dim-1)
 
-    if antialias: raise NotImplementedError("antialias is not implemented")
-    axes = axes or list(range(X.ndim))
+    scales, sizes = (None if scales is None else scales[2-(X.ndim-len(scales)):]), (None if sizes is None else sizes[2-(X.ndim-len(sizes)):])
+    # we pre permute the axes and permute back after resize
+    axes, input_shape, = (axes or list(range(X.ndim))), cast(tuple[int, ...], X.shape[2:]),
     perm = [a for a in range(len(X.shape)) if a not in axes] + list(axes)
-    # we pre-permute the axes and permute back after resize
-    # the permute aligns X's axes to scales, sizes, and roi
     X = X.permute(*perm)
 
-    input_shape = cast(tuple[int, ...], X.shape[2:])
-    if scales is not None: assert all(sc==1 for sc in scales[:-len(input_shape)]), "resizing batch_size dim or channel dim not supported"
-    if sizes is not None: assert tuple(sizes[:-2]) == tuple(X.shape[X.ndim-len(sizes):-2]),  "resizing batch_size dim or channel dim not supported"
-
-    scales, sizes = (None if scales is None else scales[-len(input_shape):]), (None if sizes is None else sizes[-len(input_shape):])
     if sizes is not None:
       if keep_aspect_ratio_policy in ["not_larger", "not_smaller"]:
         scale_fxn = min if keep_aspect_ratio_policy == "not_larger" else max
-        scale = scale_fxn(sz / sh for sz,sh in zip(sizes, input_shape))
-        sizes, scales = [int(scale * sh + 0.5) for sh in input_shape], [scale]*len(input_shape)
-      else: scales = [sz / sh for sz, sh in zip(sizes, input_shape)]
+        scales = [scale_fxn([sizes[i] / input_shape[i] for i in range(len(input_shape)) if i+2 in axes])] * 2
+        sizes = [int((scales[0] * input_shape[i]) + 0.5) if i+2 in axes else input_shape[i] for i in range(X.ndim-2)]
+      else:
+        scales = [size / input_shape for size, input_shape in zip(sizes, input_shape)]
     else:
-      assert scales is not None, "either sizes or scales must be provided"
-      sizes = [int(sc * sh) for sc, sh in zip(scales, input_shape)]
-
-    if all(sz == sh for sz, sh in zip(sizes, input_shape)): return X.permute(*argsort(perm)) if perm else X
+      sizes = [int(sc*sh) for sc, sh in zip(scales, input_shape)]
 
+    # NOTE: this transformation makes it so that we can't just call Tensor.interpolate
+    # in Tensor.interpolate, we use indexes without any transformation
     indexes = []
-    for input_sz, output_sz, scale in zip(input_shape, sizes, scales):
-      indexes.append(_apply_transformation(input_sz, output_sz, scale, coordinate_transformation_mode))
-
-    if mode in ["nearest", "linear"]: indexes = [idx.clip(0, sz-1) for idx, sz in zip(indexes, input_shape)]
+    for shape, size, scale in zip(input_shape, sizes, scales):
+      indexes.append(_apply_transformation(Tensor.arange(size), shape, scale, coordinate_transformation_mode))
 
     if mode == "nearest":
-      mode_operations = {
-        "round_prefer_floor": lambda idx: (idx - 0.5).ceil(),
-        "round_prefer_ceil": lambda idx: (idx + 0.5).floor(),
-        "floor": lambda idx: idx.floor(),
-        "ceil": lambda idx: idx.ceil()
-      }
-      if nearest_mode not in mode_operations: raise ValueError(f"invalid {nearest_mode=}")
-      indexes = [mode_operations[nearest_mode](idx).int() for idx in indexes]
+      indexes = [_apply_nearest_mode(index, shape, nearest_mode) for (index, shape) in zip(indexes, input_shape)]
       X = X[(..., *Tensor.meshgrid(*indexes))]
-
     if mode == "linear":
       expand = list(X.shape)
       for i in range(-len(sizes), 0):
@@ -815,50 +556,12 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
         reshape[i] = expand[i] = sizes[i]
         low, high, perc = [y.reshape(reshape).expand(expand) for y in (index.floor().int(), index.ceil().int(), index - index.floor())]
         X = X.gather(i, low).lerp(X.gather(i, high), perc)
-
-    if mode == "cubic":
-      A = cubic_coeff_a
-
-      # Keys weights
-      # see piecewise function in: https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
-      def W0_1(x:Tensor): return polyN(x, [A + 2, -(A + 3), 0, 1])
-      def W1_2(x: Tensor): return polyN(x, [A, -5 * A, 8 * A, -4 * A])
-
-      expand = list(X.shape)
-      for i in range(-len(sizes), 0):
-        input_sz = cast(int, X.shape[i])
-        reshape, index = [1] * X.ndim, indexes[i]
-        reshape[i] = expand[i] = sizes[i]
-
-        p = index.floor().int()
-        ratio = index - p  # in [0, 1]
-
-        # Neighbor indices
-        idx0, idx1, idx2, idx3 = [p + d for d in [-1, 0, 1, 2]]
-        # Weights of distance from index and neighbor indices
-        c0, c1, c2, c3 = W1_2(ratio+1), W0_1(ratio), W0_1(-(ratio-1)), W1_2(-(ratio-2))
-
-        if exclude_outside:
-          c0 = ((idx0 >= 0) & (idx0 < input_sz)).where(c0, 0)
-          c1 = ((idx1 >= 0) & (idx1 < input_sz)).where(c1, 0)
-          c2 = ((idx2 >= 0) & (idx2 < input_sz)).where(c2, 0)
-          c3 = ((idx3 >= 0) & (idx3 < input_sz)).where(c3, 0)
-
-          total = c0 + c1 + c2 + c3
-          c0, c1, c2, c3 = c0 / (total + 1e-9), c1 / (total + 1e-9), c2 / (total + 1e-9), c3 / (total + 1e-9)
-
-        # Reshape and expand
-        expanded_indices = [y.clip(0, input_sz - 1).reshape(reshape).expand(expand) for y in [idx0, idx1, idx2, idx3]]
-        expanded_coeffs = [y.reshape(reshape).expand(expand) for y in [c0, c1, c2, c3]]
-
-        # Gather values and apply coefficients
-        gathered_values = [X.gather(i, idx) for idx in expanded_indices]
-        X = sum(v * c for v, c in zip(gathered_values, expanded_coeffs))
+    if mode == "cubic": raise NotImplementedError("cubic interpolation is not implemented")
     return X.permute(*argsort(perm)) if perm else X
   def Upsample(X, scales, mode): return Resize(X=X, scales=scales, mode=mode)  # deprecated
 
   def TopK(X:Tensor, K:int|list[int], axis:int=-1, largest:int=1, sorted:int=1):  # noqa: A002
-    val, idx = X.topk(_resolve_const(K), axis, bool(largest), bool(sorted))
+    val, idx = X.topk(_resolve_const(K), axis, largest, sorted)
     return val, idx.cast(dtypes.int64)
 
   # ***** Neural Network Ops *****
@@ -880,7 +583,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     x = x.reshape(x.shape[0], num_groups, -1).layernorm(eps=epsilon).reshape(x.shape)
     return x * scale.reshape(1, -1, *[1] * (x.ndim-2)) + bias.reshape(1, -1, *[1] * (x.ndim-2))
   def InstanceNormalization(x:Tensor, scale:Tensor, bias:Tensor, epsilon:float=1e-05):
-    return GroupNormalization(x, scale, bias, num_groups=cast(int, x.shape[1]), epsilon=epsilon)
+    return GroupNormalization(x, scale, bias, num_groups=x.shape[1], epsilon=epsilon)
   def LayerNormalization(x:Tensor, scale:Tensor, bias:Tensor, axis:int=-1, epsilon:float=1e-05, stash_type:int=1):
     assert stash_type == 1, "only float32 is supported"
     axes = tuple(i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim))
@@ -976,7 +679,6 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     q, k, v = qkv.split(qkv_hidden_sizes, dim=2)
 
     batch_size, seq_len, _ = x.shape
-    assert num_heads is not None, "num_heads must be provided"
     q_head_size, k_head_size, v_head_size = (sz // num_heads for sz in qkv_hidden_sizes)
     q, k, v = (x.reshape(batch_size, seq_len, num_heads, hsz).transpose(1, 2) for x, hsz in zip((q, k, v), (q_head_size, k_head_size, v_head_size)))
 
@@ -990,7 +692,6 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
     if mask_index is not None:
       assert 4 >= mask_index.ndim >= 1, f"{mask_index.ndim=}"
-      assert isinstance(batch_size, int), f"{batch_size=}"
       if mask_index.ndim != 1: mask = mask_index.bool()
       else:
         if mask_index.shape[0] == batch_size:
@@ -1031,7 +732,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
       K = K.repeat((1, _q_heads // _kv_heads, 1, 1))
       V = V.repeat((1, _q_heads // _kv_heads, 1, 1))
 
-    effective_scale = scale if scale is not None else 1.0 / (cast(int, Q.shape[-1]) ** 0.5)
+    effective_scale = scale if scale is not None else 1.0 / (Q.shape[-1] ** 0.5)
     scores = (Q @ K.transpose(-1, -2)) * effective_scale
     qk_matmul_return_val = scores
 
@@ -1069,12 +770,12 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
       assert num_heads is not None, "num_heads must be provided for 3D input"
       X = X.reshape(*X.shape[:-1], num_heads, X.shape[-1] // num_heads)
 
-    head_size = cast(int, X.shape[-1])
+    head_size = X.shape[-1]
     rot_dim = rotary_embedding_dim or head_size
     x_rotate, x_pass = X[..., :rot_dim], X[..., rot_dim:]
 
-    cos = cos_cache[position_ids] if position_ids is not None else cos_cache[:head_size]
-    sin = sin_cache[position_ids] if position_ids is not None else sin_cache[:head_size]
+    cos = cos_cache[position_ids] if position_ids is not None else cos_cache[:X.shape[1]]
+    sin = sin_cache[position_ids] if position_ids is not None else sin_cache[:X.shape[1]]
     cos = cos[..., :rot_dim//2].unsqueeze(2)
     sin = sin[..., :rot_dim//2].unsqueeze(2)
 
@@ -1132,8 +833,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
   def ScatterElements(x: Tensor, indices: Tensor, updates: Tensor, axis=0, reduction:Literal["none", "add", "mul", "min", "max"]="none"):
     indices = (indices < 0).where(x.shape[axis], 0) + indices
     if reduction == "none": return x.scatter(axis, indices, updates)
-    reduction_ = cast(Literal["sum", "prod", "amin", "amax"], {"add": "sum", "mul": "prod", "min": "amin", "max": "amax"}[reduction])
-    return x.scatter_reduce(axis, indices, updates, reduction_)
+    return x.scatter_reduce(axis, indices, updates, {"add": "sum", "mul": "prod", "min": "amin", "max": "amax"}.get(reduction))
   def GatherElements(x:Tensor, indices:Tensor, axis:int):
     indices = (indices < 0).where(x.shape[axis], 0) + indices
     return x.gather(axis, indices)
@@ -1142,14 +842,14 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     if axis is None:
       inp = inp.flatten()
       axis = 0
-    axis = inp._resolve_dim(axis)
+    if axis < 0: axis += inp.ndim
     con = Tensor([i for i,cond in enumerate(condition) if cond]) # compress in python
     return inp[tuple(con if i == axis else slice(None) for i in range(inp.ndim))]
 
   # ***** Quantization Ops *****
   def QuantizeLinear(x:Tensor, y_scale:Tensor, y_zero_point:Tensor|int=0, axis:int=1, block_size:int=0, output_dtype:int=0, saturate=1):
     if isinstance(y_zero_point, Tensor): out_dtype = y_zero_point.dtype
-    elif output_dtype != 0: out_dtype = dtype_fallback(OnnxDataType(output_dtype).to_dtype(), "QuantizeLinear op")
+    elif output_dtype != 0: out_dtype = dtype_parse(output_dtype, "QuantizeLinear op")
     else: out_dtype = dtypes.uint8
     y_scale, y_zero_point = _prepare_quantize(x, y_scale, y_zero_point, axis, block_size)
     if out_dtype == dtypes.uchar:
@@ -1171,12 +871,12 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     x_scale, x_zero_point = _prepare_quantize(x, x_scale, x_zero_point, axis, block_size)
     return ((x.int() - x_zero_point) * x_scale).cast(x_scale.dtype)
 
-  def QLinearConv(x:Tensor, x_scale:Tensor, x_zero_point:Tensor, w:Tensor, w_scale:Tensor, w_zero_point:Tensor, y_scale:Tensor,
-                  y_zero_point:Tensor, B:Tensor|None=None, **opts):
+  def QLinearConv(x:Tensor, x_scale:Tensor, x_zero_point:Tensor|int, w:Tensor, w_scale:Tensor, w_zero_point:Tensor|int, y_scale:Tensor,
+                  y_zero_point: Tensor|int, B:Tensor|None=None, **opts):
     return _qlinearop_quantized(Conv, [x,w], [x_zero_point,w_zero_point], [x_scale,w_scale], y_scale, y_zero_point, **{"B":B, **opts})
 
-  def QLinearMatMul(a:Tensor, a_scale:Tensor, a_zero_point:Tensor, b:Tensor, b_scale:Tensor, b_zero_point:Tensor, y_scale:Tensor,
-                    y_zero_point:Tensor) -> Tensor:
+  def QLinearMatMul(a:Tensor, a_scale:Tensor, a_zero_point:Tensor|int, b:Tensor, b_scale:Tensor, b_zero_point:Tensor|int, y_scale:Tensor,
+                    y_zero_point:Tensor|int) -> Tensor:
     return _qlinearop_quantized(Tensor.matmul, [a,b], [a_zero_point,b_zero_point], [a_scale,b_scale], y_scale, y_zero_point)
 
   def QLinearAdd(a:Tensor, a_scale:Tensor, a_zero_point:Tensor, b:Tensor, b_scale:Tensor, b_zero_point:Tensor, c_scale:Tensor, c_zero_point:Tensor):
@@ -1189,10 +889,10 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     assert channels_last == 0, "TODO NHWC"
     return _qlinearop_float(GlobalAveragePool, [X], [x_zero_point], [x_scale], y_scale, y_zero_point)
 
-  def ConvInteger(x: Tensor, w: Tensor, x_zero_point:Tensor = Tensor(0), w_zero_point:Tensor = Tensor(0), B: Tensor | None = None, **opts) -> Tensor:
+  def ConvInteger(x: Tensor, w: Tensor, x_zero_point: Tensor | int = 0, w_zero_point: Tensor | int = 0, B: Tensor | None = None, **opts) -> Tensor:
     return _op_integer(Conv, [x,w], [x_zero_point,w_zero_point], **{"B":B, **opts})
 
-  def MatMulInteger(A: Tensor, B: Tensor, a_zero_point: Tensor = Tensor(0), b_zero_point: Tensor = Tensor(0)) -> Tensor:
+  def MatMulInteger(A: Tensor, B: Tensor, a_zero_point: Tensor | int = 0, b_zero_point: Tensor | int = 0) -> Tensor:
     return _op_integer(Tensor.matmul, [A,B], [a_zero_point,b_zero_point])
 
   # ***** Training Ops *****

extra/onnx_parser.py

@@ -0,0 +1,207 @@
+# https://github.com/onnx/onnx/blob/main/onnx/onnx.proto3
+
+import os, pathlib, struct
+from io import BufferedReader
+from types import SimpleNamespace
+from tinygrad.nn.state import TensorIO
+from tinygrad.tensor import Tensor
+
+# Protobuf Wire Types
+WIRETYPE_VARINT = 0; WIRETYPE_FIXED64 = 1; WIRETYPE_LENGTH_DELIMITED = 2; WIRETYPE_START_GROUP = 3; WIRETYPE_END_GROUP = 4; WIRETYPE_FIXED32 = 5 # noqa: E702
+
+# TensorProto.DataType
+class TensorDataType:
+  UNDEFINED = 0; FLOAT = 1; UINT8 = 2; INT8 = 3; UINT16 = 4; INT16 = 5; INT32 = 6; INT64 = 7 # noqa: E702
+  STRING = 8; BOOL = 9; FLOAT16 = 10; DOUBLE = 11; UINT32 = 12; UINT64 = 13; COMPLEX64 = 14; COMPLEX128 = 15; BFLOAT16 = 16 # noqa: E702
+
+# AttributeProto.AttributeType
+class AttributeType:
+  UNDEFINED = 0; FLOAT = 1; INT = 2; STRING = 3; TENSOR = 4; GRAPH = 5; SPARSE_TENSOR = 11; TYPE_PROTO = 13; FLOATS = 6; INTS = 7 # noqa: E702
+  STRINGS = 8; TENSORS = 9; GRAPHS = 10; SPARSE_TENSORS = 12; TYPE_PROTOS = 14 # noqa: E702
+
+class PBType: FLOAT = 1; INT = 2; STRING = 3; FLOATS = 4; INTS = 5; STRINGS = 6; BYTES = 7; SUB = 8 # noqa: E702
+
+PB_INFOS: dict[str, dict] = {
+  "OperatorSetIdProto": {1: ("domain", PBType.STRING), 2: ("version", PBType.INT)},
+  "StringStringEntryProto": {1: ("key", PBType.STRING), 2: ("value", PBType.STRING)},
+  "TensorProto": {1: ("dims", PBType.INT, True), 2: ("data_type", PBType.INT), 4: ("float_data", PBType.FLOATS),
+    13: ("external_data", PBType.SUB, True, "StringStringEntryProto"), 14: ("data_location", PBType.INT),
+    5: ("int32_data", PBType.INTS), 7: ("int64_data", PBType.INTS), 8: ("name", PBType.STRING), 9: ("raw_data", PBType.BYTES),
+    10: ("double_data", PBType.FLOATS), 11: ("uint64_data", PBType.INTS)},
+  "TensorShapeProtoDimension": {1: ("dim_value", PBType.INT), 2: ("dim_param", PBType.STRING)},
+  "TensorShapeProto": {1: ("dim", PBType.SUB, True, "TensorShapeProtoDimension")},
+  "ModelProto": {1: ("ir_version", PBType.INT), 5: ("model_version", PBType.INT),
+    2: ("producer_name", PBType.STRING), 3: ("producer_version", PBType.STRING), 4: ("domain", PBType.STRING), 6: ("doc_string", PBType.STRING),
+    7: ("graph", PBType.SUB, False, ("GraphProto", lambda: {"node": [], "initializer": [], "input": [], "output": [], "value_info": []})),
+    8: ("opset_import",PBType.SUB, True, "OperatorSetIdProto")},
+  "GraphProto": {2: ("name", PBType.STRING), 10: ("doc_string", PBType.STRING),
+    1: ("node", PBType.SUB, True, ("NodeProto", lambda: {"input": [], "output": [], "attribute": [], "domain": None})),
+    5: ("initializer", PBType.SUB, True, ("TensorProto", lambda: {"dims": [], "float_data": None, "int32_data": None, "string_data": None,
+                                                                  "int64_data": None, "double_data": None, "uint64_data": None, "raw_data": None})),
+    11: ("input", PBType.SUB, True, "ValueInfoProto"), 12: ("output", PBType.SUB, True, "ValueInfoProto")},
+  "NodeProto": { 1: ("input", PBType.STRING, True), 2: ("output", PBType.STRING, True), 3: ("name", PBType.STRING),
+    4: ("op_type", PBType.STRING), 6: ("doc_string", PBType.STRING), 7: ("domain", PBType.STRING),
+    5: ("attribute", PBType.SUB, True, ("AttributeProto", lambda: {"floats": [], "ints": [], "strings": []}))},
+  "AttributeProto": {1: ("name", PBType.STRING), 20: ("type", PBType.INT), 3: ("i", PBType.INT), 8: ("ints", PBType.INT, True),
+    2: ("f", PBType.FLOAT), 7: ("floats", PBType.FLOAT, True), 4: ("s", PBType.BYTES), 9: ("strings", PBType.BYTES, True),
+    5:("t", PBType.SUB, False, ("TensorProto", lambda: {"dims": [], "float_data": None, "int32_data": None, "string_data": None, "int64_data": None,
+                                                        "double_data": None, "uint64_data": None, "raw_data": None}))},
+  "ValueInfoProto": {1: ("name", PBType.STRING), 2: ("type", PBType.SUB, False, "TypeProto"), 3: ("doc_string", PBType.STRING)},
+  "TypeProto": {1: ("tensor_type", PBType.SUB, False, "TypeProtoTensor"), 4: ("sequence_type", PBType.SUB, False, "TypeProtoSequence"),
+    9: ("optional_type", PBType.SUB, False, "TypeProtoOptional"), 6: ("denotation", PBType.STRING)},
+  "TypeProtoSequence": {1: ("elem_type", PBType.SUB, False, "TypeProto")},
+  "TypeProtoOptional": {1: ("elem_type", PBType.SUB, False, "TypeProto")},
+  "TypeProtoTensor": {1: ("elem_type", PBType.INT), 2: ("shape", PBType.SUB, False, ("TensorShapeProto", lambda: {"dim": []}))},
+}
+
+def onnx_load(fn: Tensor|str|pathlib.Path, load_external_data: bool=True):
+  parser = OnnxParser(fn, load_external_data)
+  onnx_model = parser.parse()
+  model = dict_to_namespace(onnx_model)
+  return model
+
+def gen_result(obj: dict, key_name, val, repeated: bool):
+  if repeated: obj.setdefault(key_name, []).append(val)
+  else: obj[key_name] = val
+
+def dict_to_namespace(d):
+  if isinstance(d, dict): return SimpleNamespace(**{k: dict_to_namespace(v) for k, v in d.items()})
+  elif isinstance(d, list): return [dict_to_namespace(i) for i in d]
+  return d
+
+class OnnxParser:
+  def __init__(self, inp: Tensor|str|pathlib.Path, load_external_data: bool=True):
+    self.file_path: pathlib.Path|None = None
+    self.load_external_data = load_external_data
+    if not isinstance(inp, Tensor):
+      self.file_path = pathlib.Path(inp)
+      self.tensor = Tensor(self.file_path)
+    else: self.tensor = inp
+    self.attr_func_dict = { PBType.BYTES: self._handle_bytes, PBType.SUB: self._handle_sub_message, PBType.FLOATS: self._handle_packed_floats,
+      PBType.INT: self._handle_int64, PBType.INTS: self._handle_packed_int64s, PBType.STRING: self._handle_string, PBType.FLOAT: self._handle_float}
+    self.registered_handles = {}
+    for pb_name in PB_INFOS:
+      res = {}
+      for fid, config in PB_INFOS[pb_name].items():
+        parser_fn, repeated = None, False
+        if len(config) == 2: name, attr = config
+        elif len(config) == 3: name, attr, repeated = config
+        elif len(config) == 4: name, attr, repeated, parser_fn = config
+        handler_fn = self.attr_func_dict[attr]
+        def _wrapper_handler(obj, reader, wt, h=handler_fn, n=name, p=parser_fn, r=repeated): return h(obj, n, reader, wt, parser_func=p, repeated=r)
+        res[fid] = _wrapper_handler
+      self.registered_handles[pb_name] = res
+
+  def parse(self):
+    reader = BufferedReader(TensorIO(self.tensor))
+    return self._parse_message(reader, "ModelProto", lambda: {"opset_import": [], "domain": None, "graph": None})
+
+  def decode_varint(self, reader: BufferedReader) -> int:
+    result = 0
+    shift = 0
+    while True:
+      data = reader.read(1)
+      if data == b"": raise EOFError("decode_varint EOF")
+      result |= (data[0] & 0x7F) << shift
+      if not (data[0] & 0x80): return result
+      shift += 7
+      if shift >= 70: raise ValueError("Varint too long")
+
+  def skip_field_value(self, reader: BufferedReader, wire_type):
+    if wire_type == WIRETYPE_VARINT: self.decode_varint(reader)
+    elif wire_type == WIRETYPE_FIXED64: reader.seek(8, os.SEEK_CUR)
+    elif wire_type == WIRETYPE_FIXED32: reader.seek(4, os.SEEK_CUR)
+    elif wire_type == WIRETYPE_LENGTH_DELIMITED: reader.seek(self.decode_varint(reader), os.SEEK_CUR)
+    else: raise ValueError(f"Unknown wire type: {wire_type}")
+
+  def _parse_message(self, reader, message_field_handlers_name, initial_obj_factory=lambda: {}):
+    message_field_handlers = self.registered_handles[message_field_handlers_name]
+    obj = initial_obj_factory()
+    while True:
+      try:
+        tag_val = self.decode_varint(reader)
+        field_number = tag_val >> 3
+        wire_type = tag_val & 0x07
+        if handler := message_field_handlers.get(field_number):
+          handler(obj, reader, wire_type)
+        else: self.skip_field_value(reader, wire_type)
+      except EOFError: break
+    if message_field_handlers_name == "TensorProto" and self.load_external_data and obj.get("data_location", 0) == 1: self._parse_external_data(obj)
+    return obj
+
+  def _handle_delimited(self, reader:BufferedReader, use_tensor=False) -> Tensor|bytes:
+    str_len = self.decode_varint(reader)
+    if not use_tensor: return reader.read(str_len)
+    raw = reader.raw
+    assert isinstance(raw, TensorIO)
+    res = raw._tensor[reader.tell():(reader.tell()+str_len)]
+    reader.seek(str_len, os.SEEK_CUR)
+    return res
+
+  def _handle_string(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_LENGTH_DELIMITED: raise ValueError(f"Expected length-delimited for string field '{key_name}'")
+    value = self._handle_delimited(reader)
+    assert isinstance(value, bytes)
+    gen_result(obj, key_name, value.decode("utf-8"), repeated)
+
+  def _handle_bytes(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_LENGTH_DELIMITED: raise ValueError(f"Expected length-delimited for bytes field '{key_name}'")
+    value = self._handle_delimited(reader, use_tensor=True)
+    gen_result(obj, key_name, value, repeated)
+
+  def _handle_int64(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_VARINT: raise ValueError(f"Expected varint for int64 field '{key_name}'")
+    val = self.decode_varint(reader)
+    gen_result(obj, key_name, val - 2**64 if val & (1 << 63) else val, repeated)
+
+  def _handle_float(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_FIXED32: raise ValueError(f"Expected fixed32 for float field '{key_name}'")
+    val, = struct.unpack("<f", reader.read(4))
+    gen_result(obj, key_name, val, repeated)
+
+  def _handle_packed_int64s(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_LENGTH_DELIMITED: raise ValueError("Packed int64s expected length_delimited")
+    total_bytes_len = self.decode_varint(reader)
+    old_pos = reader.tell()
+    values = []
+    while reader.tell() < total_bytes_len + old_pos:
+      val = self.decode_varint(reader) # need copy here because packed ints are varint
+      values.append(val - 2**64 if val & (1 << 63) else val)
+    obj[key_name] = values
+
+  def _handle_packed_floats(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_LENGTH_DELIMITED: raise ValueError("Packed floats expected length_delimited")
+    value = self._handle_delimited(reader, use_tensor=True)
+    obj[key_name] = value
+
+  def _handle_sub_message(self, obj, key_name, reader, wire_type, parser_func=None, repeated=False):
+    if wire_type != WIRETYPE_LENGTH_DELIMITED: raise ValueError(f"Expected length-delimited for sub-message field '{key_name}'")
+    value = self._handle_delimited(reader, use_tensor=True)
+    assert isinstance(value, Tensor)
+    if isinstance(parser_func, str): sub_obj = self._parse_message(BufferedReader(TensorIO(value)), parser_func)
+    elif isinstance(parser_func, tuple): sub_obj = self._parse_message(BufferedReader(TensorIO(value)), parser_func[0], parser_func[1])
+    else: sub_obj = parser_func(BufferedReader(TensorIO(value)))
+    gen_result(obj, key_name, sub_obj, repeated)
+
+  def _parse_external_data(self, obj):
+    if "external_data" not in obj: raise ValueError("no external_data")
+    location = None
+    length = None
+    offset = 0
+    for kv in obj["external_data"]:
+      if kv["key"] == "location": location = kv["value"]
+      if kv["key"] == "offset": offset = int(kv["value"])
+      if kv["key"] == "length": length = int(kv["value"])
+    if location is None: raise ValueError("no location in external_data")
+    if self.file_path is None:
+      # get onnx file path from Tensor
+      if isinstance(self.tensor.device, str) and self.tensor.device.startswith("DISK:"):
+        self.file_path = pathlib.Path(self.tensor.device[5:])
+        if not (ext_path := self.file_path.parent.joinpath(location)).exists():
+          raise Exception(f"external location not exists: {ext_path}, may caused by symbolic link, try passing onnx file path to onnx_load")
+      else: raise Exception("onnx external_data need the origin file path, try passing onnx file path to onnx_load")
+    ext_path = self.file_path.parent.joinpath(location)
+    if not ext_path.exists(): raise Exception(f"external location not exists: {ext_path}")
+    ext_tensor = Tensor(ext_path)
+    obj["raw_data"] = ext_tensor[offset:offset+length] if length is not None else ext_tensor[offset:]
+    obj["data_location"] = 0

extra/optimization/extract_policynet.py

@@ -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.codegen.opt.search import actions
+from tinygrad.opt.search import actions
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, lin_to_feats, assert_same_lin
-from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.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.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Opt, OptOps
 
 INNER = 256
 class PolicyNet:

extra/optimization/extract_sa_pairs.py

@@ -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.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Opt, OptOps
 
 # more stuff
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.search import actions
+from tinygrad.opt.kernel import Kernel
+from tinygrad.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

extra/optimization/get_action_space.py

@@ -1,8 +1,8 @@
 import random
 from extra.optimization.helpers import load_worlds, ast_str_to_lin
-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.opt.search import actions
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.heuristic import hand_coded_optimizations
 from tinygrad.helpers import tqdm
 
 tactions = set()

extra/optimization/helpers.py

@@ -1,6 +1,6 @@
 # stuff needed to unpack a kernel
 from tinygrad import Variable
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.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.codegen.opt.kernel import Kernel
+from tinygrad.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.codegen.opt.search import _ensure_buffer_alloc, _time_program
+from tinygrad.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

extra/optimization/pretrain_valuenet.py

@@ -1,4 +1,4 @@
-from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.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.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Opt, OptOps
 
 from extra.optimization.helpers import lin_to_feats, MAX_DIMS
 

extra/optimization/rl.py

@@ -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.codegen.opt.search import actions, bufs_from_lin, get_kernel_actions
+from tinygrad.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

extra/optimization/run_qnet.py

@@ -1,6 +1,6 @@
 from typing import List, Tuple
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.search import get_kernel_actions, actions
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.search import get_kernel_actions, actions
 
 _net = None
 def beam_q_estimate(beam:List[Tuple[Kernel, float]]) -> List[Tuple[Kernel, float]]:

extra/optimization/search.py

@@ -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.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.search import beam_search, bufs_from_lin
 
 
 if __name__ == '__main__':

extra/optimization/test_net.py

@@ -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.codegen.opt.search import bufs_from_lin, actions, get_kernel_actions
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.opt.search import bufs_from_lin, actions, get_kernel_actions
+from tinygrad.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

extra/optimization/test_time_linearizer.py

@@ -1,5 +1,5 @@
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
-from tinygrad.codegen.opt.search import bufs_from_lin, get_kernel_actions
+from tinygrad.opt.search import bufs_from_lin, get_kernel_actions
 
 if __name__ == "__main__":
   ast_strs = load_worlds()

extra/perfetto/to_perfetto.py

@@ -1,6 +1,6 @@
 import sys, pickle, decimal, json
 from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent
-from tinygrad.helpers import tqdm, temp, ProfileEvent, ProfileRangeEvent, TracingKey
+from tinygrad.helpers import tqdm, temp, ProfileEvent, ProfileRangeEvent
 
 devices:dict[str, tuple[decimal.Decimal, decimal.Decimal, int]] = {}
 def prep_ts(device:str, ts:decimal.Decimal, is_copy): return int(decimal.Decimal(ts) + devices[device][is_copy])
@@ -11,14 +11,12 @@ def dev_ev_to_perfetto_json(ev:ProfileDeviceEvent):
           {"name": "thread_name", "ph": "M", "pid": dev_to_pid(ev.device)['pid'], "tid": 0, "args": {"name": "COMPUTE"}},
           {"name": "thread_name", "ph": "M", "pid": dev_to_pid(ev.device)['pid'], "tid": 1, "args": {"name": "COPY"}}]
 def range_ev_to_perfetto_json(ev:ProfileRangeEvent):
-  name = ev.name.display_name if isinstance(ev.name, TracingKey) else ev.name
-  return [{"name": name, "ph": "X", "ts": prep_ts(ev.device, ev.st, ev.is_copy), "dur": float(ev.en-ev.st), **dev_to_pid(ev.device, ev.is_copy)}]
+  return [{"name": ev.name, "ph": "X", "ts": prep_ts(ev.device, ev.st, ev.is_copy), "dur": float(ev.en-ev.st), **dev_to_pid(ev.device, ev.is_copy)}]
 def graph_ev_to_perfetto_json(ev:ProfileGraphEvent, reccnt):
   ret = []
   for i,e in enumerate(ev.ents):
     st, en = ev.sigs[e.st_id], ev.sigs[e.en_id]
-    name = e.name.display_name if isinstance(e.name, TracingKey) else e.name
-    ret += [{"name": name, "ph": "X", "ts": prep_ts(e.device, st, e.is_copy), "dur": float(en-st), **dev_to_pid(e.device, e.is_copy)}]
+    ret += [{"name": e.name, "ph": "X", "ts": prep_ts(e.device, st, e.is_copy), "dur": float(en-st), **dev_to_pid(e.device, e.is_copy)}]
     for dep in ev.deps[i]:
       d = ev.ents[dep]
       ret += [{"ph": "s", **dev_to_pid(d.device, d.is_copy), "id": reccnt+len(ret), "ts": prep_ts(d.device, ev.sigs[d.en_id], d.is_copy), "bp": "e"}]
@@ -26,8 +24,6 @@ def graph_ev_to_perfetto_json(ev:ProfileGraphEvent, reccnt):
   return ret
 def to_perfetto(profile:list[ProfileEvent]):
   # Start json with devices.
-  profile += [ProfileDeviceEvent("TINY")]
-
   prof_json = [x for ev in profile if isinstance(ev, ProfileDeviceEvent) for x in dev_ev_to_perfetto_json(ev)]
   for ev in tqdm(profile, desc="preparing profile"):
     if isinstance(ev, ProfileRangeEvent): prof_json += range_ev_to_perfetto_json(ev)

extra/replay_pkl.py

@@ -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.codegen.opt.kernel import Kernel, Opt, OptOps
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.opt.heuristic import hand_coded_optimizations
 import numpy as np
 
 def move_jit_captured_to_dev(captured, device="DSP"):

extra/torch_backend/backend.py

@@ -223,18 +223,15 @@ 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):
-  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))))
+  return wrap(Tensor.arange(0, end, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
 
 @torch.library.impl("aten::arange.start", "privateuseone")
 def arange_start(start, end, dtype=None, device=None, pin_memory=None):
-  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))))
+  return wrap(Tensor.arange(start, end, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
 
 @torch.library.impl("aten::arange.start_step", "privateuseone")
 def arange_start_step(start, end, step, dtype=None, device=None, pin_memory=None):
-  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))))
+  return wrap(Tensor.arange(start, end, step, dtype=_from_torch_dtype(dtype or torch.get_default_dtype())))
 
 @torch.library.impl("aten::convolution_overrideable", "privateuseone")
 def convolution_overrideable(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups):
@@ -371,7 +368,6 @@ 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,

extra/torch_backend/test.py

@@ -135,7 +135,7 @@ class TestTorchBackend(unittest.TestCase):
     print(c.cpu())
 
   def test_maxpool2d_backward(self):
-    x = torch.arange(3*3, dtype=torch.float32, device=device).reshape(1, 1, 3, 3).requires_grad_(True)
+    x = torch.arange(3*3, 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]])
 
@@ -203,12 +203,6 @@ 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

extra/torch_muon.py

@@ -1,75 +0,0 @@
-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

setup.py

@@ -26,7 +26,7 @@ setup(name='tinygrad',
       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.codegen.opt',
+                  'tinygrad.runtime.support.am', 'tinygrad.runtime.graph', 'tinygrad.shape', 'tinygrad.uop', 'tinygrad.opt',
                   'tinygrad.runtime.support.nv', 'tinygrad.apps'],
       package_data = {'tinygrad': ['py.typed'], 'tinygrad.viz': ['index.html', 'assets/**/*', 'js/*']},
       classifiers=[

test/external/external_benchmark_hcopt.py

@@ -1,7 +1,7 @@
 import random
 from tinygrad.helpers import getenv
-from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.opt.search import beam_search, bufs_from_lin
+from tinygrad.opt.heuristic import hand_coded_optimizations
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
 
 def optimize_kernel(k):

test/external/external_benchmark_schedule.py

@@ -1,8 +1,12 @@
+from typing import List
 from extra.models.resnet import ResNet50
-from tinygrad import Tensor, nn, Device
-from tinygrad.helpers import Profiling, Timing, getenv
+from tinygrad import Tensor, nn
+from tinygrad.helpers import Profiling, Timing, getenv, BEAM, NOOPT, DEBUG, Context, ansilen
 from tinygrad.uop.ops import Ops
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.heuristic import hand_coded_optimizations
 from tinygrad.codegen import get_rewrites_for_renderer, apply_rewrites, rewrites_for_linearizer
+from tinygrad.opt.search import beam_search, bufs_from_lin
 from tinygrad.uop.spec import type_verify
 
 if __name__ == "__main__":
@@ -27,13 +31,26 @@ if __name__ == "__main__":
       if not SCHEDULE_ONLY:
         asts = list({x.ast.key:x.ast for x in sched if x.ast.op is Ops.SINK}.values())
         if (restrict_kernel := getenv("RESTRICT_KERNEL", -1)) != -1: asts = asts[restrict_kernel:restrict_kernel+1]
+        kernels: List[Kernel] = []
+        with Timing(f"***** model opts({len(asts):2d}) in  "):
+          with Profiling(PROFILE >= 3):
+            for ast in asts:
+              k = Kernel(ast)
+              if BEAM:
+                with Context(DEBUG=max(2, DEBUG.value)): k = beam_search(k, bufs_from_lin(k), BEAM.value)
+              elif NOOPT: pass
+              else: k.apply_opts(hand_coded_optimizations(k))
+              kernels.append(k)
+
+        with Timing("***** model prep in      "):
+          kernels = [(k, k.get_optimized_ast(), get_rewrites_for_renderer(k.opts, linearizer=False)) for k in kernels]
 
-        rewrites = get_rewrites_for_renderer(Device.default.renderer, linearizer=False)
         with Profiling(PROFILE, fn="/tmp/rewrite.prof"):
           with Timing("***** model rewrite in   "):
             rewritten_uops = []
-            for u in asts:
-              rewritten_uops.append(apply_rewrites(u, rewrites))
+            for i,(k,u,rewrites) in enumerate(kernels):
+              with Timing(f"rewrite {i:2d} {k.name}{' '*(50-ansilen(k.name))}", enabled=getenv("VERBOSE", 0)):
+                rewritten_uops.append(apply_rewrites(u, rewrites))
 
         if LINEARIZE:
           with Timing("***** model linearize in "):

test/external/external_benchmark_sdxl_softmax.py

@@ -8,7 +8,7 @@ if __name__ == "__main__":
   GlobalCounters.reset()
   t.softmax(-1, dtype="half", _single_kernel=True).realize()
 
-  from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+  from tinygrad.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())

test/external/external_debug_metal_sd_conv.py

@@ -1,8 +1,8 @@
 # ruff: noqa: E501
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.dtype import dtypes
 from tinygrad.engine.realize import CompiledRunner, get_program
-from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.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

test/external/external_test_hcq_fuzz_failures.py

@@ -4,10 +4,10 @@ os.environ["VALIDATE_HCQ"]="1"
 
 import unittest, random
 import numpy as np
-from tinygrad.codegen.opt.kernel import Kernel, KernelOptError
+from tinygrad.opt.kernel import Kernel, KernelOptError
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.codegen.opt.search import Opt, OptOps
+from tinygrad.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
 

test/external/external_test_hip_compile.py

@@ -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.codegen.opt.kernel import Kernel
+from tinygrad.opt.kernel import Kernel
 
 class TestHIPCompileSpeed(unittest.TestCase):
   @unittest.skipIf(Device.DEFAULT != "HIP", "only run on HIP")

test/external/external_test_nv.py

@@ -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.codegen.opt.search import Opt, OptOps
+from tinygrad.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.codegen.opt.kernel import Kernel
+from tinygrad.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

test/external/external_test_onnx_backend.py

@@ -53,7 +53,6 @@ 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
@@ -66,10 +65,6 @@ 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):
@@ -170,6 +165,10 @@ 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
@@ -179,8 +178,6 @@ 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
 

test/external/external_test_onnx_ops.py

@@ -75,49 +75,6 @@ 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)
@@ -294,6 +251,7 @@ 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]:

test/external/external_test_onnx_runner.py

@@ -3,7 +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 OnnxDataType
+from extra.onnx import data_types
 from tinygrad.frontend.onnx import OnnxRunner
 from hypothesis import given, strategies as st
 
@@ -86,8 +86,8 @@ class TestOnnxRunner(unittest.TestCase):
       output = runner({'inp': Tensor([1])})['output']
       np.testing.assert_equal(output.numpy(), weights + 1)
 
-all_dtypes = list(OnnxDataType)
-device_supported_dtypes = {odt for odt in OnnxDataType if is_dtype_supported(odt.to_dtype())}
+all_dtypes = list(data_types.keys())
+device_supported_dtypes = {odt for odt, dtype in data_types.items() if is_dtype_supported(dtype)}
 
 class TestOnnxRunnerDtypes(unittest.TestCase):
   """
@@ -95,7 +95,7 @@ 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 = OnnxDataType(onnx_dtype).to_dtype()
+    true_dtype = data_types[onnx_dtype]
     # inputs always preserve their true dtype.
     if is_input:
       return true_dtype

test/external/external_test_train_gpt2.py

@@ -2,11 +2,11 @@
 import unittest
 
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.codegen.opt.search import Opt, OptOps
+from tinygrad.opt.search import Opt, OptOps
 from tinygrad.dtype import dtypes
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import View
-from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.opt.kernel import Kernel
 
 from test.external.fuzz_linearizer import run_linearizer
 

test/external/fuzz_fast_idiv.py

@@ -1,26 +1,33 @@
 import random
-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
+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
 random.seed(42)
 
-powers_of_two = [2**i for i in range(64)]
+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)
+
 if __name__ == "__main__":
-  for i in range(10_000):
-    if i % 1000 == 0:
-      print(f"Progress: {i}")
+  x = Int('x')
+  for _ in range(10_000):
     dt = random.choice(dtypes.ints)
-    u = UOp.variable('x', random.randint(dt.min, 0), random.randint(1, dt.max), dtype=dt)
+    u = UOp(Ops.DEFINE_VAR, dt, arg=('x', 0, random.randint(1, dtypes.max(dt))), src=())
     d = random.randint(1, max(1, u.arg[2]))
-    if d in powers_of_two: continue
-    expr = fast_idiv(None, u, d)
+
+    expr = fast_idiv(Device[Device.DEFAULT].renderer, u, d)
     if expr is None: continue
-
-    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=}"
+    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}"

test/external/fuzz_linearizer.py

@@ -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.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.opt.kernel import Kernel
+from tinygrad.opt.kernel import Opt, OptOps
+from tinygrad.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

test/external/process_replay/process_replay.py

@@ -1,7 +1,6 @@
 #!/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))
@@ -12,9 +11,7 @@ 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))
@@ -50,13 +47,9 @@ 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|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 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 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))])

test/external/speed_beam_v_hcopt.py

@@ -1,7 +1,7 @@
 from tinygrad import Device
 from tinygrad.helpers import getenv, DEBUG, BEAM
-from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.opt.search import beam_search, bufs_from_lin
+from tinygrad.opt.heuristic import hand_coded_optimizations
 from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
 
 if __name__ == "__main__":

test/external/speed_compare_amd_am.py

@@ -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.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.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

test/external/speed_compare_cuda_nv.py

@@ -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.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.opt.heuristic import hand_coded_optimizations
+from tinygrad.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

test/external/speed_compare_cuda_ptx.py

@@ -1,10 +1,10 @@
 import itertools
 from tinygrad import Device
 from tinygrad.engine.realize import CompiledRunner, get_program
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.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.codegen.opt.search import bufs_from_lin
+from tinygrad.opt.search import bufs_from_lin
 from tinygrad.runtime.ops_cuda import PTXCompiler, PTXRenderer, CUDACompiler
 
 if __name__ == "__main__":

test/external/verify_kernel.py

@@ -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.codegen.opt.kernel import Kernel
+from tinygrad.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
 

test/test_arange.py

@@ -3,11 +3,10 @@ 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.codegen.opt.kernel import Opt, OptOps, Kernel, KernelOptError
+from tinygrad.opt.kernel import Opt, OptOps, Kernel, KernelOptError
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
-from tinygrad.codegen.opt.search import get_kernel_actions
+from tinygrad.opt.search import get_kernel_actions
 from tinygrad.uop.ops import Ops
-from tinygrad.codegen import apply_rewrites, rewrites_for_views
 
 class TestArange(unittest.TestCase):
   def _get_flops(self, N, opts=None):
@@ -15,7 +14,10 @@ class TestArange(unittest.TestCase):
     tt = Tensor.arange(N)
     sched = tt.schedule()
     self.assertEqual(len(sched), 1)
-    p = get_program(sched[-1].ast, opts=opts)
+    k = Kernel(sched[-1].ast)
+    if opts is not None:
+      for o in opts: k.apply_opt(o)
+    p = get_program(k.get_optimized_ast(), k.opts)
     print(p.name)
     #print(p.src)
     ExecItem(CompiledRunner(p), [tt.uop.buffer]).run()
@@ -50,11 +52,11 @@ class TestArange(unittest.TestCase):
     def test_complexity_w_local_and_padto(self): return self.test_complexity([Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.PADTO, axis=1, arg=32)])
 
   def test_all_opts(self, opts=None, exclude=None):
-    k = Kernel(apply_rewrites(Tensor.arange(256).schedule()[-1].ast, rewrites_for_views))
+    k = Kernel(Tensor.arange(256).schedule()[-1].ast)
     if opts is not None:
       for o in opts: k.apply_opt(o)
     all_opts_256 = [kk.applied_opts for kk in get_kernel_actions(k, include_0=False).values()]
-    k = Kernel(apply_rewrites(Tensor.arange(2560).schedule()[-1].ast, rewrites_for_views))
+    k = Kernel(Tensor.arange(2560).schedule()[-1].ast)
     if opts is not None:
       for o in opts: k.apply_opt(o)
     all_opts_2560 = [kk.applied_opts for kk in get_kernel_actions(k, include_0=False).values()]

test/test_const_folding.py

@@ -139,9 +139,10 @@ class TestBitcastConstFolding(unittest.TestCase):
 class TestIndexingConstFolding(unittest.TestCase):
   def test_scalar_index(self):
     t = Tensor.arange(16).float().reshape(1,1,4,4).realize()
-    _check_ast_count(1, t[:,:,Tensor(1),:])
-    _check_ast_count(1, t[:,:,Tensor(1)+2,:])
-    _check_ast_count(1, t[:,:,Tensor(1),Tensor(0)])
+    # TODO: fold these
+    _check_ast_count(2, t[:,:,Tensor(1),:])
+    _check_ast_count(2, t[:,:,Tensor(1)+2,:])
+    _check_ast_count(2, t[:,:,Tensor(1),Tensor(0)])
 
   @unittest.expectedFailure
   def test_const_tensor_index(self):
@@ -290,12 +291,17 @@ 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

test/test_disassembly.py

@@ -1,21 +0,0 @@
-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()

test/test_edgecases.py

@@ -62,6 +62,7 @@ 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.
@@ -218,6 +219,7 @@ 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)
@@ -256,11 +258,12 @@ 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, atol=1e-7)
+    np.testing.assert_allclose(out, torch_out)
 
   @unittest.skip("this is flaky")
   @unittest.expectedFailure

test/test_hcq.py

@@ -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.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
 from tinygrad import Variable
 
 MOCKGPU = getenv("MOCKGPU")
@@ -163,8 +163,10 @@ class TestHCQ(unittest.TestCase):
     a = Tensor.randint((3, 3, 3), dtype=dtypes.int, device=Device.DEFAULT).realize()
     b = a + 1
     si = b.schedule()[-1]
+    k = Kernel(si.ast, opts=TestHCQ.d0.renderer)
+    for i in range(3): k.apply_opt(Opt(op=OptOps.LOCAL, axis=0, arg=3))
 
-    runner = CompiledRunner(get_program(si.ast, TestHCQ.d0.renderer, opts=[Opt(op=OptOps.LOCAL, axis=0, arg=3) for _ in range(3)]))
+    runner = CompiledRunner(get_program(k.get_optimized_ast(), k.opts))
 
     zb = Buffer(Device.DEFAULT, 3 * 3 * 3, dtypes.int, options=BufferSpec(cpu_access=True, nolru=True)).ensure_allocated()
     zt = Buffer(Device.DEFAULT, 3 * 3 * 3, dtypes.int, options=BufferSpec(cpu_access=True, nolru=True)).ensure_allocated()
@@ -336,7 +338,7 @@ class TestHCQ(unittest.TestCase):
     et = float(sig_en.timestamp - sig_st.timestamp)
 
     print(f"exec kernel time: {et:.2f} us")
-    assert 0.1 <= et <= (100000 if MOCKGPU or Device.DEFAULT in {"CPU", "LLVM"} else 100)
+    assert 0.1 <= et <= (15000 if MOCKGPU or Device.DEFAULT in {"CPU", "LLVM"} else 100)
 
   def test_speed_copy_bandwidth(self):
     if TestHCQ.d0.hw_copy_queue_t is None: self.skipTest("device does not support copy queue")

test/test_jit.py

@@ -718,6 +718,8 @@ 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)
@@ -792,7 +794,6 @@ class TestJitGraphSplit(unittest.TestCase):
 
   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")
@@ -817,6 +818,7 @@ 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):

test/test_linalg.py

@@ -62,15 +62,5 @@ 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()

test/test_linearizer.py

@@ -2,7 +2,8 @@ import numpy as np
 import unittest
 from dataclasses import replace
 
-from tinygrad.codegen.opt.kernel import Opt, OptOps, KernelOptError, Kernel, AxisType
+from tinygrad.opt.kernel import Opt, OptOps, KernelOptError, Kernel, AxisType
+from tinygrad.codegen import rewrites_for_views, apply_rewrites
 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
@@ -10,12 +11,9 @@ 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.codegen.opt.heuristic import hand_coded_optimizations
+from tinygrad.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
-
-def push_views(ast): return apply_rewrites(ast, rewrites_for_views)
 
 def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
   if isinstance(r, Tensor): r = [r]
@@ -25,7 +23,7 @@ def helper_realized_ast(r:Tensor|list[Tensor]) -> tuple[UOp, list[Buffer]]:
   # now all input buffers in s[-1] should be realized
   # create fresh buffers for the outputs
   bufs = [Buffer((x).device, x.size, x.dtype).allocate() if i < len(s[-1].ast.src) else x for i,x in enumerate(s[-1].bufs)]
-  return push_views(s[-1].ast), bufs
+  return apply_rewrites(s[-1].ast, rewrites_for_views), bufs
 
 def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0, use_tensor_cores:int=1):
   a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in)
@@ -124,7 +122,7 @@ class TestLinearizer(unittest.TestCase):
     with Context(FUSE_ARANGE=1):
       sink = dataset[idxs].contiguous().kernelize().uop.base.src[1].arg.ast
     real_index = dataset.numpy()[idxs.numpy()].reshape(4, 256, 1, 1)
-    helper_linearizer_ast(push_views(sink), [dataset, idxs], wanna_output=[real_index])
+    helper_linearizer_ast(sink, [dataset, idxs], wanna_output=[real_index])
 
   def test_two_nested_range(self):
     a = Tensor.randn(2, ).realize()
@@ -217,7 +215,9 @@ class TestLinearizer(unittest.TestCase):
     # these are of size 3 to avoid float4 coalesce
     r = a[:-1] + a[1:]
 
-    uops = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0)]).uops
+    k = Kernel(r.schedule()[-1].ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=0))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
     num_loads = len([uop for uop in uops if uop.op is Ops.LOAD])
     assert num_loads <= 4, "more load uops than needed"
     assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?"
@@ -228,7 +228,9 @@ class TestLinearizer(unittest.TestCase):
     a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
     r = a.expand([2]) + b.expand([2])
 
-    uops = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0)]).uops
+    k = Kernel(r.schedule()[-1].ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=0))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
     num_ops = len([uop for uop in uops if uop.op in GroupOp.ALU])
     assert num_ops <= 1, "more alu uops than needed"
 
@@ -237,7 +239,10 @@ class TestLinearizer(unittest.TestCase):
     x, w = Tensor.randn((1,1,3)).realize(), Tensor.randn((1,1,2)).realize()
     r = Tensor.conv2d(x,w,padding=1).relu()
 
-    uops = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
+    k = Kernel(r.schedule()[-1].ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=0))
+    k.apply_opt(Opt(op=OptOps.UNROLL, axis=0, arg=0))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
     accs = [u for u in uops if u.op is Ops.DEFINE_REG]
     stores = [u for u in uops if u.op is Ops.STORE]
     assert len(accs) == 0  # it's removed now
@@ -249,7 +254,9 @@ class TestLinearizer(unittest.TestCase):
   @unittest.skipUnless(Device.DEFAULT == "CPU", "test only for CPU")
   def test_upcast_with_locals_cpu(self):
     out = Tensor.ones(64,64).contiguous() @ Tensor.ones(64,64).contiguous()
-    prg = get_program(out.schedule()[-1].ast, opts=[Opt(OptOps.LOCAL, axis=0, arg=4)]).uops
+    k = Kernel(out.schedule()[-1].ast)
+    k.apply_opt(Opt(OptOps.LOCAL, axis=0, arg=4))
+    prg = get_program(k.get_optimized_ast(), k.opts)
     self.assertEqual(len(prg.src.split("for")), 5)
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
@@ -259,8 +266,10 @@ class TestLinearizer(unittest.TestCase):
   def test_upcast_with_locals(self):
     x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
     r = (x@y).relu()
+    realized_ast = r.schedule()[-1].ast
     opts_to_apply = [Opt(op=OptOps.GROUP, axis=0, arg=8), Opt(op=OptOps.LOCAL, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]
-    program = get_program(r.schedule()[-1].ast, opts=opts_to_apply)
+    realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
+    program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
 
     stores = [u for u in program.uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
 
@@ -274,7 +283,10 @@ class TestLinearizer(unittest.TestCase):
   def test_zero_fold(self):
     a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize()
     r = Tensor.stack(a, b)
-    uops = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0)]).uops
+
+    k = Kernel(r.schedule()[-1].ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=0))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
     num_ops = len([uop for uop in uops if uop.op in GroupOp.ALU])
     assert num_ops == 0, "more alu uops than needed"
 
@@ -284,14 +296,16 @@ class TestLinearizer(unittest.TestCase):
       if is_dtype_supported(tensor_dtype) and is_dtype_supported(acc_dtype):
         a = Tensor([1, 2, 3], dtype=tensor_dtype).sum()
         realized_ast = a.schedule()[-1].ast
-        program = get_program(realized_ast, opts=[])
+        realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple()))
+        program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
         local = [uop for uop in program.uops if uop.op is Ops.DEFINE_REG]
         assert local[0].dtype.base == acc_dtype
 
   def test_arg_acc_dtype(self):
     def helper_arg_acc_dtype(c: Tensor, expected_dtype:DType):
       realized_ast = c.schedule()[-1].ast
-      program = get_program(realized_ast, opts=[])
+      realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple()))
+      program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
       local = [uop for uop in program.uops if uop.op is Ops.DEFINE_REG]
       self.assertEqual(local[0].dtype.base, expected_dtype)
 
@@ -328,7 +342,7 @@ class TestLinearizer(unittest.TestCase):
       a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in)
       r = a.matmul(b, dtype=tc.dtype_out)
       sched = r.schedule()
-      realized_ast = push_views(sched[-1].ast)
+      realized_ast = sched[-1].ast
       kernel = Kernel(realized_ast)
       kernel.apply_tensor_cores(1, axis=0, tc_select=-1, tc_opt=2)
       prg = get_program(kernel.get_optimized_ast(), kernel.opts)
@@ -410,7 +424,7 @@ 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.codegen.opt.search import get_kernel_actions
+      from tinygrad.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])
@@ -749,7 +763,11 @@ class TestFloat4(unittest.TestCase):
     c = a + b
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=4))
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=2))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
+
     assert TestFloat4.count_float4(uops) == (4, 2)
 
   @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
@@ -760,7 +778,10 @@ class TestFloat4(unittest.TestCase):
       c = a + b
 
       s = c.schedule()[0]
-      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]).uops
+      k = Kernel(s.ast)
+      k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=4))
+      k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=shift))
+      return get_program(k.get_optimized_ast(), k.opts).uops
 
     sizes = [12, 8, 16]
     shifts = [3, 2, 4]
@@ -790,7 +811,10 @@ class TestFloat4(unittest.TestCase):
     c = a + b
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=1, arg=4))
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=1, arg=2))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
 
     assert TestFloat4.count_float4(uops) == (0, 2)
 
@@ -802,7 +826,10 @@ class TestFloat4(unittest.TestCase):
       c = a + b
 
       s = c.schedule()[0]
-      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]).uops
+      k = Kernel(s.ast)
+      k.shift_to(1, 4, AxisType.UPCAST)  # manual trigger float4 dim
+      k.shift_to(1, shift, AxisType.UPCAST, insert_at=k.shape_len-1)
+      return get_program(k.get_optimized_ast(), k.opts).uops
 
     sizes = [13, 9, 17]
     shifts = [3, 2, 4]
@@ -820,7 +847,9 @@ class TestFloat4(unittest.TestCase):
     # float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UNROLL, axis=0, arg=4)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UNROLL, axis=0, arg=4))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
 
     assert TestFloat4.count_float4(uops) == (0, 0)
 
@@ -834,7 +863,10 @@ class TestFloat4(unittest.TestCase):
     # UPDATE: now we do this fusion
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=0))
+    k.apply_opt(Opt(op=OptOps.UNROLL, axis=0, arg=0))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
 
     assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
 
@@ -847,7 +879,9 @@ class TestFloat4(unittest.TestCase):
     # since the top axis is not contiguous.
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=4))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
 
     assert TestFloat4.count_float4(uops) == (0, 1)
 
@@ -859,7 +893,9 @@ class TestFloat4(unittest.TestCase):
     # should float4 b but not a
 
     s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
+    k = Kernel(s.ast)
+    k.apply_opt(Opt(op=OptOps.UPCAST, axis=0, arg=4))
+    uops = get_program(k.get_optimized_ast(), k.opts).uops
 
     assert TestFloat4.count_float4(uops) == (1, 1)
 
@@ -948,7 +984,7 @@ class TestHandCodedOpts(unittest.TestCase):
     layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.empty(6, 20))
 
     s = layer_2.schedule()[-1]
-    k = Kernel(push_views(s.ast))
+    k = Kernel(s.ast)
     k.apply_opts(hand_coded_optimizations(k))
     assert len(k.bufs) == 6  # make sure all ops are done in one kernel
     # masked upcast should upcast masked axis of size 7
@@ -961,7 +997,7 @@ class TestHandCodedOpts(unittest.TestCase):
     monster = Tensor.stack(*[Tensor.stack(*[Tensor.empty(16) for _ in range(6)]) for _ in range(6)])
 
     s = monster.schedule()[-1]
-    k = Kernel(push_views(s.ast))
+    k = Kernel(s.ast)
     k.apply_opts(hand_coded_optimizations(k))
     assert len(k.bufs) == 37  # make sure all ops are done in one kernel
     # should upcast the two Tensor.stacks
@@ -977,7 +1013,7 @@ class TestHandCodedOpts(unittest.TestCase):
       wino_schedule = out.schedule()
       # collect upcasts of tile transform kernels
       for i, si in enumerate(wino_schedule):
-        k = Kernel(push_views(si.ast))
+        k = Kernel(si.ast)
         k.apply_opts(hand_coded_optimizations(k))
         if k.reduceop is not None: continue  # not a tile transform kernel (there is a gemm reduce kernel)
         if len(k.bufs) < 22: continue  # not a tile transform kernel (there's a permute kernel at the end)
@@ -989,7 +1025,7 @@ class TestHandCodedOpts(unittest.TestCase):
 
       backward_schedule = Tensor.schedule(x.grad, w.grad)
       for si in backward_schedule:
-        k = Kernel(push_views(si.ast))
+        k = Kernel(si.ast)
         k.apply_opts(hand_coded_optimizations(k))
         if len(k.bufs) < 20: continue  # not a tile transform kernel
         # heuristic number to make sure that at least some upcasts but not too many upcasts are being done

test/test_linearizer_dumb.py

@@ -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.codegen.opt.search import Opt, OptOps
-from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.opt.search import Opt, OptOps
+from tinygrad.opt.kernel import Kernel
 from tinygrad.engine.realize import get_program
 
 class TestLinearizerDumb(unittest.TestCase):

test/test_linearizer_overflows.py

@@ -1,8 +1,8 @@
 # ruff: noqa: E501
 import unittest
 from tinygrad import dtypes
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.search import Opt, OptOps, bufs_from_lin
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.search import Opt, OptOps, bufs_from_lin
 from extra.optimization.helpers import time_linearizer
 
 # stuff needed to unpack a kernel

test/test_nn.py

@@ -401,7 +401,7 @@ class TestNN(unittest.TestCase):
       torch_z = torch_layer(torch_x)
       np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)
 
-  def test_embedding_one_kernel(self, ops=612000, kcount=2):
+  def test_embedding_one_kernel(self, ops=41410, kcount=3):
     GlobalCounters.reset()
     layer = Embedding(20, 30)
     layer.weight = Tensor.zeros_like(layer.weight).contiguous()
@@ -409,7 +409,7 @@ class TestNN(unittest.TestCase):
                 [12, 19, 8, 1]])
     result = layer(a)
     schedule = result.schedule()
-    self.assertEqual(len([item for item in schedule if item.ast.op is Ops.SINK]), kcount, "first run realizes weight and embedding")
+    self.assertEqual(kcount, len([item for item in schedule if item.ast.op is Ops.SINK]), "first run realizes weight and embedding")
     run_schedule(schedule)
 
     b = Tensor([[1, 2, 3],
@@ -440,13 +440,6 @@ 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)
 

test/test_ops.py

@@ -699,14 +699,6 @@ 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)
@@ -965,9 +957,8 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
     helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=3), lambda t: Tensor.softplus(t, beta=3), grad_atol=1e-6)
     helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=1/3), lambda t: Tensor.softplus(t, beta=1/3), grad_atol=1e-6)
-    helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=3, threshold=0.5),
-                              lambda t: Tensor.softplus(t, beta=3, threshold=0.5), grad_atol=1e-6)
-    helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6, low=300, high=400)
+    # # TODO: support threshold and enable this
+    # helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6, low=300, high=400)
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6, low=-400, high=-300)
     helper_test_op([()], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
 
@@ -1101,9 +1092,6 @@ 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)
@@ -2750,10 +2738,10 @@ 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,-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[[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[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()
@@ -2776,14 +2764,10 @@ 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[[1.1]]
-    with self.assertRaises(IndexError): a[Tensor([True, False])]
-    with self.assertRaises(IndexError): a[[True, False]]
+    with self.assertRaises(IndexError): a[Tensor([True, True])]
     # 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

test/test_opt_gemm.py

@@ -2,7 +2,7 @@ import numpy as np
 import unittest
 from tinygrad import Tensor
 from tinygrad.helpers import get_single_element
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 
 class TestOptGemm(unittest.TestCase):
@@ -17,7 +17,9 @@ class TestOptGemm(unittest.TestCase):
     t = self.a.T @ self.b.T
     # TODO: this should be a generic test helper
     si = get_single_element(t.schedule())
-    run = CompiledRunner(get_program(si.ast, opts=opts))
+    k = Kernel(si.ast)
+    k.apply_opts(opts)
+    run = CompiledRunner(get_program(k.get_optimized_ast(), k.opts))
     ExecItem(run, si.bufs).run()
     test = si.bufs[0].numpy().reshape(self.res.shape)
     np.testing.assert_allclose(self.res, test, atol=1e-4)

test/test_optim.py

@@ -2,10 +2,9 @@ import numpy as np
 import torch
 import unittest
 from tinygrad import Tensor, Device, dtypes
-from tinygrad.nn.optim import Adam, SGD, AdamW, Muon
+from tinygrad.nn.optim import Adam, SGD, AdamW
 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)
@@ -58,12 +57,9 @@ 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)
@@ -87,28 +83,6 @@ 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)

test/test_quantize_onnx.py

@@ -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.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.opt.kernel import Kernel, Opt, OptOps
 from tinygrad.engine.realize import CompiledRunner, ExecItem, lower_schedule_item, get_program
-from tinygrad.codegen.opt.search import bufs_from_lin
+from tinygrad.opt.search import bufs_from_lin
 from tinygrad.shape.shapetracker import ShapeTracker, View # noqa: F401 # pylint: disable=unused-import
 
 N = 512
@@ -40,7 +40,10 @@ def create_gemm_model(model_path:str, batch_size=N, in_size=N, out_size=N, bias=
 
 def sexec(out:Tensor, opts:list[Opt], replace_src=None, run_count=3):
   si = out.schedule()[-1]
-  prg = get_program(si.ast, opts=opts)
+  k = Kernel(si.ast, opts=Device[Device.DEFAULT].renderer)
+  #opts = [Opt(op=OptOps.UPCAST, axis=0, arg=128)] #, Opt(op=OptOps.UNROLL, axis=0, arg=4)]
+  k.apply_opts(opts)
+  prg = get_program(k.get_optimized_ast(), k.opts)
   if replace_src is not None:
     old_name = prg.src.split("__attribute__((noinline)) void ")[1].split("(")[0]
     prg = replace(prg, src=replace_src + "/* DSP boilerplate */" + prg.src.split("/* DSP boilerplate */")[1].replace(old_name, "fxn"))
@@ -294,7 +297,10 @@ class TestDSPCache(unittest.TestCase):
             x41,)),)),)),))""")
     opts = [Opt(op=OptOps.UNROLL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=32), Opt(op=OptOps.UPCAST, axis=0, arg=4)]
     with Context(DEVECTORIZE=0, QUANTIZE=1):
-      prg = get_program(ast, opts=opts)
+      k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
+      k.apply_opts(opts)
+      prg = get_program(k.get_optimized_ast(), k.opts)
+      #print(prg.src)
 
     new_src = """
 typedef int int32 __attribute__((aligned(128),vector_size(128)));
@@ -356,7 +362,7 @@ __attribute__((noinline)) void r_196_32_4_24_8(unsigned char* restrict __attribu
     prg = replace(prg, src=new_src+prg.src.split("/* DSP boilerplate */ ")[1])
     rt = CompiledRunner(prg)
     #Device.default.compiler.disassemble(rt.lib)
-    ei = ExecItem(rt, bufs_from_lin(Kernel(ast)))
+    ei = ExecItem(rt, bufs_from_lin(k))
     tm = ei.run(wait=True)
     print(f"final time {tm*1e6:.2f} us")
 

test/test_schedule.py

@@ -6,7 +6,7 @@ import unittest
 import numpy as np
 import functools
 from typing import cast
-from hypothesis import assume, given, settings, strategies as strat
+from hypothesis import assume, given, strategies as strat
 
 from tinygrad import nn, dtypes, Device, Tensor
 from tinygrad.device import is_dtype_supported
@@ -16,7 +16,7 @@ from tinygrad.uop.ops import PatternMatcher, UOp, Ops, GroupOp, UPat, graph_rewr
 from tinygrad.uop.symbolic import symbolic_simple
 from tinygrad.helpers import CI, DEBUG, FUSE_ARANGE, SPLIT_REDUCEOP, GlobalCounters, Context, getenv, all_same, temp
 from tinygrad.schedule.kernelize import merge_views, get_kernelize_map, Kernel
-from tinygrad.engine.schedule import create_schedule_with_vars
+from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars
 from tinygrad.engine.realize import CompiledRunner, run_schedule, lower_schedule
 
 class KernelCountException(Exception): pass
@@ -70,7 +70,7 @@ def _test_conv2d(allowed:int, dtype:DType=dtypes.float, **kwargs):
 def schedule_graph_rewrite(big_sink:UOp): return get_kernelize_map(big_sink)[big_sink]
 
 class TestSchedule(unittest.TestCase):
-  def test_arange_avgpool2d(self, kcount=1):
+  def test_arange_avgpool2d(self, kcount=2):
     x = Tensor.arange(25).reshape(1,1,5,5).cast(dtypes.float32)
     t = x.avg_pool2d(padding=1)
     sched = t.schedule()
@@ -151,7 +151,6 @@ class TestSchedule(unittest.TestCase):
       self.assertEqual(root.item(), sum(range(N)))
 
   @given(strat.sampled_from(range(2,4)), strat.sampled_from(range(2,4)), strat.sampled_from(range(0,4)), strat.sampled_from(range(0,4)))
-  @settings(deadline=None)
   def test_indexing_scalars(self, x, y, a, b):
     assume(a<x and b<y)
     X = Tensor.randn(x, y).realize()
@@ -1028,14 +1027,14 @@ class TestSchedule(unittest.TestCase):
     Tensor.manual_seed(0)
     x = Tensor.randn(4, 32).realize()
     out = x.argmin(-1)
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3))
     np.testing.assert_equal(out.numpy(), x.numpy().argmin(axis=-1))
 
   def test_argmax_multireduce_fusion(self):
     Tensor.manual_seed(0)
     x = Tensor.randn(4, 32).realize()
     out = x.argmax(-1)
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3))
     np.testing.assert_equal(out.numpy(), x.numpy().argmax(axis=-1))
 
   def test_scaled_dot_product_attention_multireduce_fusion(self):
@@ -1354,7 +1353,8 @@ class TestSchedule(unittest.TestCase):
     r = a.sum(0) + 6
     b = r.sum(0) * 4
     c = r.sum(1) * 2
-    check_schedule([b, c], 3)
+    schedule = check_schedule([b, c], 3)
+    self.assertIs(store_val(schedule[0]).op, Ops.ADD)
 
   def test_multireduce_simple_chase(self):
     Tensor.manual_seed(0)
@@ -1376,7 +1376,8 @@ class TestSchedule(unittest.TestCase):
     r = a.sum(2) + b
     d = r.T * 4
     e = r * d
-    check_schedule([d, e], 3)
+    schedule = check_schedule([d, e], 3)
+    self.assertIs(store_val(schedule[0]).op, Ops.ADD)
 
   def test_multireduce_push_permute_chase(self):
     Tensor.manual_seed(0)
@@ -1386,6 +1387,7 @@ class TestSchedule(unittest.TestCase):
     d = r.T * 4
     e = r * (d + a).sum(2)
     schedule = check_schedule([d, e], 3) # make sure it doesn't fuse
+    self.assertIs(store_val(schedule[0]).op, Ops.ADD)
     run_schedule(schedule)
     np.testing.assert_allclose(d.numpy(), (a.numpy().sum(2) + b.numpy()).T * 4, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(e.numpy(), (a.numpy().sum(2) + b.numpy()) * (d.numpy() + a.numpy()).sum(2), atol=1e-4, rtol=1e-4)
@@ -1396,7 +1398,8 @@ class TestSchedule(unittest.TestCase):
     c = Tensor.empty(16, )
     r = a.sum(1) + c
     d = r[:4] * b
-    check_schedule(d, 2)
+    schedule = check_schedule(d, 2)
+    self.assertIs(store_val(schedule[0]).op, Ops.ADD)
 
   def test_multireduce_push_shrink_chase(self):
     Tensor.manual_seed(0)
@@ -1408,13 +1411,15 @@ class TestSchedule(unittest.TestCase):
     out = r[:4] * b + d.sum(1)[:4]
     # schedule = check_schedule(out, 2)
     schedule = check_schedule(out, 3)
+    self.assertIs(store_val(schedule[0]).op, Ops.ADD)
     run_schedule(schedule)
     np.testing.assert_allclose(out.numpy(), (a.numpy().sum(1) + c.numpy())[:4] * b.numpy() + d.numpy().sum(1)[:4], atol=1e-4, rtol=1e-4)
 
   def test_midreduce_nochase(self):
     a = Tensor.empty(16, 16)
     b = (a.sum(0) + a.max(1)) + 2
-    check_schedule(b, 2)
+    schedule = check_schedule(b, 2)
+    self.assertIs(store_val(schedule[0]).op, Ops.REDUCE_AXIS)
 
   def test_multireduce_midreduce_nochase(self):
     Tensor.manual_seed(0)
@@ -1422,6 +1427,7 @@ class TestSchedule(unittest.TestCase):
     b = (a.sum(0)+a.max(0) + a.max(1)+a.sum(1)) + 2
     # schedule = check_schedule(b, 2)
     schedule = check_schedule(b, 4)
+    self.assertIs(store_val(schedule[0]).op, Ops.REDUCE_AXIS)
     run_schedule(schedule)
     np.testing.assert_allclose(b.numpy(), a.numpy().sum(0)+a.numpy().max(0) + a.numpy().max(1)+a.numpy().sum(1)+2, atol=1e-4, rtol=1e-4)
 
@@ -1613,7 +1619,7 @@ class TestSchedule(unittest.TestCase):
     Tensor.manual_seed(0)
     x = Tensor.randn(10, 20).realize()
     out = x.argmax(1)
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 3)) # TODO: push a reduceop through a reshape
 
   def test_conv2d(self): _test_conv2d(7)
   def test_conv2d_fused(self): _test_conv2d(5, FUSE_CONV_BW=1)
@@ -1727,8 +1733,7 @@ class TestIndexing(unittest.TestCase):
       s = Tensor.schedule(*lst)
       lowered = [x[1] for x in lower_schedule(s.copy())]
       kernels = [ei for ei in list(lowered) if isinstance(ei.prg, CompiledRunner)]
-      if FUSE_ARANGE and len(kernels) != cnt:
-        raise KernelCountException(f"{len(kernels)} != {cnt}")
+      if FUSE_ARANGE: self.assertEqual(len(kernels), cnt)
       for ei in lowered: ei.run(do_update_stats=True)
     return s
 
@@ -1741,26 +1746,26 @@ 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, 1)
-    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, 5)
+    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, -1]]
-    self.check_schedule(xt, 1)
-    np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0, -1]])
+    xt = X[[0]]
+    self.check_schedule(xt, 2)
+    np.testing.assert_equal(xt.numpy(), (np.arange(10)+1)[[0]])
 
   def test_advanced_indexing_alt(self):
     X = Tensor.arange(6).reshape(3, 2)+1
     xt = X[[Tensor([2]), Tensor([1])]]
-    self.check_schedule(xt, 3)
+    self.check_schedule(xt, 6)
     np.testing.assert_equal(xt.numpy(), 6)
 
   def test_advanced_simple_indexing_combined(self):
     X = Tensor.arange(16).reshape(4, 4)
-    xt = X[1:2, [-1, 2]]
-    self.check_schedule(xt, 1)
+    xt = X[1:2, [1, 2]]
+    self.check_schedule(xt, 4)
 
   def test_push_through_reshape(self):
     Tensor.manual_seed(0)
@@ -1897,6 +1902,8 @@ class TestIndexing(unittest.TestCase):
     a = Tensor.arange(4).reshape(2, 2, 1).expand(2, 2, 2).contiguous().to("CPU")
     sched = self.check_schedule(a, 2) # NOTE: there is a contiguous between REDUCE_AXIS and COPY
     self.assertIs(sched[2].ast.op, Ops.COPY)
+    self.assertIs(store_val(sched[1]).op, Ops.LOAD)
+    self.assertIs(store_val(sched[0]).op, Ops.ADD)
     np.testing.assert_equal(a.numpy(), [[[0, 0], [1, 1]], [[2, 2], [3, 3]]])
 
   @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
@@ -1980,6 +1987,24 @@ class TestIndexing(unittest.TestCase):
     self.assertEqual(new_uop.st, ShapeTracker.from_shape((4,)).reshape((4, 1)))
     self.assertEqual(swizzle_cnt(new_uop), 0)
 
+  def test_no_rewrite_elementwise(self):
+    a = Tensor.empty(32, 32)
+    b = Tensor.empty(32, 32)
+    sink = (a+b).schedule()[0].ast
+    self.assertEqual(swizzle_cnt(sink), 0)
+
+  def test_simple_store_reshape(self):
+    a = Tensor.empty(32, 32).sum(axis=1)+Tensor.empty(1,32)
+    ast = a.schedule()[0].ast
+    self.assertEqual(ast.shape, (32, 1))
+    self.assertEqual(a.uop.shape, (1, 32))
+
+  def test_no_reshape_reduceop(self):
+    a = Tensor.empty(32, 32).sum(axis=(1,)).contiguous()
+    ast = a.schedule()[0].ast
+    self.assertEqual(ast.shape, (32, 1))
+    self.assertEqual(a.uop.shape, (32,))
+
 def swizzle_cnt(u:UOp) -> int:
   return len([x for x in u.toposort() if x.op is Ops.VIEW and len(x.src) != 0 and x.src[0].op not in {Ops.BUFFER, Ops.DEFINE_GLOBAL, Ops.ASSIGN}])
 
@@ -2067,6 +2092,7 @@ class TestSwizzle(unittest.TestCase):
     np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)
 
   # kernels can only have 1 or n in each dim
+  @unittest.expectedFailure
   def test_dont_parallelize_different_n(self):
     Tensor.manual_seed(0)
     x = Tensor.randn(4, 2, 2).realize()
@@ -2082,6 +2108,7 @@ class TestSwizzle(unittest.TestCase):
     run_schedule(check_schedule(t, 3))
     np.testing.assert_equal(t.numpy(), [[0.5, 0.5], [0.5, 0.5], [0., 0.]])
 
+def store_val(si:ScheduleItem): return si.ast.src[0].src[1]
 zero_pm = UPat(Ops.CONST, arg=0)
 class TestView(unittest.TestCase):
   def test_all_masked_out(self):
@@ -2090,6 +2117,7 @@ class TestView(unittest.TestCase):
     # all masked out, degrades to const 0
     b = a.pad(((0, 10), None))[10:]
     sched = check_schedule(b.contiguous(), 1)
+    assert zero_pm.match(store_val(sched[-1]), {})
     run_schedule(sched)
     np.testing.assert_equal(b.numpy(), 0)
 
@@ -2100,6 +2128,7 @@ class TestView(unittest.TestCase):
     assert b.shape == (10, 10)
     sched = check_schedule(b.contiguous(), 1)
     self.assertEqual(sched[-1].ast.full_shape, (10, 10))
+    assert zero_pm.match(store_val(sched[-1]), {})
     run_schedule(sched)
     np.testing.assert_equal(b.numpy(), 0)
 
@@ -2114,6 +2143,8 @@ class TestView(unittest.TestCase):
     b = a.pad(((0, 5), None))[5:]
     assert b.shape == (10, 10)
     sched = check_schedule(b.contiguous(), 1)
+    self.assertEqual(store_val(sched[-1]).op, Ops.LOAD)
+    self.assertEqual(store_val(sched[-1]).st_arg, b.uop.st)
     run_schedule(sched)
     np.testing.assert_allclose(b.numpy(), np.pad(a.numpy(), ((0, 5), (0, 0)))[5:])
 
@@ -2229,6 +2260,24 @@ class TestConst(unittest.TestCase):
     sched = a.schedule()
     self.assertEqual(len(sched), 1)
 
+  def test_const_ast(self):
+    a = Tensor.ones((4,)).pad((1, 1)).contiguous()
+    sched = a.schedule()
+    print(sched[0].ast)
+    const_ast_pattern = UPat(Ops.SINK, src=(UPat.store(UPat(), UPat.where(UPat(Ops.VALID), UPat.cvar("x"), UPat(Ops.CONST, arg=0))),))
+    self.assertEqual(len(const_ast_pattern.match(sched[0].ast, {})), 1)
+    run_schedule(sched)
+    self.assertListEqual(a.tolist(), [0, 1, 1, 1, 1, 0])
+
+  def test_unmasked_const_ast(self):
+    a = Tensor.ones((4,)).contiguous()
+    sched = a.schedule()
+    print(sched[0].ast)
+    const_ast_pattern = UPat(Ops.SINK, src=(UPat.store(UPat(), UPat(Ops.CONST)),))
+    self.assertEqual(len(const_ast_pattern.match(sched[0].ast, {})), 1)
+    run_schedule(sched)
+    self.assertListEqual(a.tolist(), [1, 1, 1, 1])
+
   # ** part 2: scheduler behavior when const folding happens later
 
   def test_const_folding_no_realize(self):

test/test_search.py

@@ -1,8 +1,8 @@
 import unittest
 
-from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel
+from tinygrad.opt.kernel import Opt, OptOps, Kernel
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.codegen.opt.search import bufs_from_lin, actions, beam_search
+from tinygrad.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.codegen.opt.search import get_kernel_actions
+    from tinygrad.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.codegen.opt.search import get_kernel_actions
+    from tinygrad.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.codegen.opt.search import get_kernel_actions
+    from tinygrad.opt.search import get_kernel_actions
     a = Tensor.rand(16, 16)
     b = Tensor.rand(16, 16)
     realized_ast, _ = helper_realized_ast(a @ b)
@@ -131,11 +131,10 @@ class TestBEAM(unittest.TestCase):
     assert tm
 
   def test_beam_unnamed_kernels(self):
-    from test.test_linearizer import push_views
     a = Tensor.rand(100)
     b = Tensor.rand(100)
     si = (a+b).schedule()[-1]
-    lin = Kernel(push_views(si.ast))
+    lin = Kernel(si.ast)
     bufs = bufs_from_lin(lin)
     # TODO: beam should have better instrumentation so we don't have to check this indirect thing
     kcount = len(Kernel.kernel_cnt)

test/test_softmax_fusion.py

@@ -111,12 +111,6 @@ class TestFuse(unittest.TestCase):
     with Context(NOOPT=1):
       self._test_fuse(Tensor.scaled_dot_product_attention, q, k, v, atol=1e-5)
 
-  def test_mismatch_reduce(self):
-    a = Tensor.ones(16, 10).contiguous().realize()
-    b = Tensor.ones(16, 20).contiguous().realize()
-    c = (a.sum(axis=1) + b.sum(axis=1)).fuse()
-    self.assertListEqual(c.tolist(), [30]*16)
-
 class TestSoftmaxFusion(unittest.TestCase):
   @classmethod
   def setUpClass(cls):

test/test_stunning.py

@@ -25,7 +25,7 @@ class TestStunning(unittest.TestCase):
     nv = a[12].cat(a[76]).tolist()
 
     vi = Variable('i', 0, a.shape[0]-1)
-    with self.assertRaisesRegex(AssertionError, "bind mismatch on"):
+    with self.assertRaisesRegex(AssertionError, "different values for the same key"):
       wv = a[vi.bind(12)].cat(a[vi.bind(76)]).tolist()
       self.assertListEqual(nv, wv)
 

test/test_uops.py

@@ -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.codegen.opt.kernel import Opt, OptOps
+from tinygrad.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)
 
@@ -415,17 +415,6 @@ 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):
@@ -519,6 +508,19 @@ class TestShapeSpec(unittest.TestCase):
     a = Tensor.ones((4, 4)).uop
     self.assertEqual(a.st, ShapeTracker.from_shape(()).reshape((1,1)).expand((4,4)))
 
+  def test_padded_const(self):
+    a = Tensor.ones((1, 1)).pad(((1, 1), (1, 1)))
+    ast = a.contiguous().schedule()[0].ast
+    valid_pattern = UPat(Ops.WHERE, src=(UPat(Ops.VALID), UPat.cvar(), UPat.cvar()))
+    valid_ternary = [x for x in ast.toposort() if valid_pattern.match(x, {})][0]
+    # the WHERE outputs a contiguous (3, 3)
+    self.assertEqual(valid_ternary.st, ShapeTracker.from_shape((3, 3)))
+    valid, x, y = valid_ternary.src
+    # very notably, only the first source is padded
+    self.assertIsNotNone(valid.st.views[-1].mask)
+    assert x.st.views[-1].mask is y.st.views[-1].mask is None
+    assert all(s.shape == (3, 3) for s in valid_ternary.src)
+
   # NOTE: CONST ShapeTracker comes from its source
   def test_scalar_const(self):
     a = Tensor(0).uop

test/test_uops_stats.py

@@ -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.codegen.opt.kernel import Opt, OptOps, KernelOptError
+from tinygrad.opt.kernel import Kernel, Opt, OptOps, KernelOptError
 from tinygrad.device import Device
 
 def flops_mem(uops, ignore_indexing=False):
@@ -173,60 +173,76 @@ class TestStatsOptimized(unittest.TestCase):
     self.assertEqual(p.estimates.mem, 3*N*N*4) # 3 NxN mats with floats
 
   def test_gemm(self):
-    p = get_program(self.ast_gemm, opts=[])
+    k = Kernel(self.ast_gemm)
+    p = get_program(k.get_optimized_ast(), k.opts)
     self.check_gemm(p)
     self.assertEqual(p.estimates.lds, 2*N*N*N*4 + 4*N*N)
 
   def test_gemm_tc_unroll(self):
-    try:
-      p = get_program(self.ast_gemm, opts=[Opt(OptOps.TC, 0, (-1, 0, 1)), Opt(OptOps.UNROLL, 0, 2)])
-    except KernelOptError:
-      raise unittest.SkipTest("no tensor cores")
+    k = Kernel(self.ast_gemm)
+    if not k.apply_tensor_cores(): self.skipTest("no tensor cores")
+    k.apply_opt(Opt(OptOps.UNROLL, 0, 2))
+    p = get_program(k.get_optimized_ast(), k.opts)
     print(p.src)
     self.check_gemm(p)
 
   # this is a good lesson about why UPCASTing is a good idea
 
   def test_gemm_one_upcasted(self):
-    p = get_program(self.ast_gemm, opts=[Opt(OptOps.UPCAST, 0, 4)])
+    k = Kernel(self.ast_gemm)
+    k.apply_opt(Opt(OptOps.UPCAST, 0, 4))
+    p = get_program(k.get_optimized_ast(), k.opts)
     self.check_gemm(p)
     self.assertEqual(p.estimates.lds, N*N*N*4 + N*N*N*4//4 + 4*N*N)
 
   def test_gemm_upcasted(self):
-    p = get_program(self.ast_gemm, opts=[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)])
+    k = Kernel(self.ast_gemm)
+    k.apply_opt(Opt(OptOps.UPCAST, 0, 4))
+    k.apply_opt(Opt(OptOps.UPCAST, 1, 4))
+    k.apply_opt(Opt(OptOps.UNROLL, 0, 4))
+    p = get_program(k.get_optimized_ast(), k.opts)
     self.check_gemm(p)
     self.assertEqual(p.estimates.lds, 2*N*N*N*4//4 + 4*N*N)
 
   def test_gemm_upcasted_locals(self):
+    k = Kernel(self.ast_gemm)
+    k.apply_opt(Opt(OptOps.UPCAST, 0, 4))
+    k.apply_opt(Opt(OptOps.UPCAST, 1, 4))
     try:
-      p = get_program(self.ast_gemm, opts=[Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4),
-                                           Opt(OptOps.LOCAL, 0, 4),  Opt(OptOps.LOCAL, 1, 4)])
+      k.apply_opt(Opt(OptOps.LOCAL, 0, 5))
+      k.apply_opt(Opt(OptOps.LOCAL, 1, 5))
     except KernelOptError:
       raise unittest.SkipTest("no locals")
+    p = get_program(k.get_optimized_ast(), k.opts)
     self.check_gemm(p)
     self.assertEqual(p.estimates.lds, 2*N*N*N*4//4 + 4*N*N)
 
   def test_gemm_group(self):
+    k = Kernel(self.ast_gemm)
     try:
-      p = get_program(self.ast_gemm, opts=[Opt(OptOps.GROUP, 0, 4)])
+      k.apply_opt(Opt(OptOps.GROUP, 0, 4))
     except KernelOptError:
       raise unittest.SkipTest("no locals")
     SZ = N*N*4
+    p = get_program(k.get_optimized_ast(), k.opts)
     # NOTE: these are sort of wrong. they aren't honoring the IF statement
     self.check_gemm(p, extra_flops=SZ*4)
     self.assertEqual(p.estimates.lds, 2*N*N*N*4 + SZ*4 + (SZ*4 + 4*N*N)*4)
 
   def test_reduce(self):
-    p = get_program(self.ast_reduce, opts=[])
+    k = Kernel(self.ast_reduce)
+    p = get_program(k.get_optimized_ast(), k.opts)
     print(p.name, p.estimates.ops, p.estimates.mem, p.estimates.lds)
     self.assertEqual(p.estimates.ops, N*N)
     self.assertEqual(p.estimates.mem, N*N*4 + 4)
 
   def test_reduce_group(self):
+    k = Kernel(self.ast_reduce)
     try:
-      p = get_program(self.ast_reduce, opts=[Opt(OptOps.GROUP, 0, 50)])
+      k.apply_opt(Opt(OptOps.GROUP, 0, 50))
     except KernelOptError:
       raise unittest.SkipTest("no locals")
+    p = get_program(k.get_optimized_ast(), k.opts)
     # NOTE: these are wrong, they don't respect the if statement
     print(p.name, p.estimates.ops, p.estimates.mem, p.estimates.lds)
 

test/test_winograd.py

@@ -1,7 +1,10 @@
 import unittest
 import numpy as np
 from tinygrad import Tensor, GlobalCounters, dtypes, Context, nn
-from tinygrad.helpers import CI, Profiling, WINO, getenv
+from tinygrad.uop.ops import Ops
+from tinygrad.helpers import Timing, CI, Profiling, WINO, DEBUG, getenv
+from tinygrad.opt.kernel import Kernel
+from tinygrad.opt.heuristic import hand_coded_optimizations
 
 class TestWinogradClose(unittest.TestCase):
   def test_close(self):
@@ -25,6 +28,30 @@ class TestWinograd(unittest.TestCase):
   def tearDown(self):
     WINO.value = self.old
 
+  def test_speed(self):
+    x = Tensor.empty(1,4,9,9)
+    w = Tensor.empty(4,4,3,3)
+
+    with Timing("running conv: "):
+      out = Tensor.conv2d(x, w)
+
+    with Timing("scheduling: "):
+      sched = out.schedule()
+
+    for i,s in enumerate(sched):
+      if s.ast.op is not Ops.SINK: continue
+      ops = s.ast.toposort()
+      with Timing(f"linearize {i} with {len(ops):4d} ops: "):
+        l = Kernel(s.ast)
+        l.apply_opts(hand_coded_optimizations(l))
+      assert len(l.sts) <= 256  # just the current value to prevent regression
+      if DEBUG >= 2: print(f"{len(l.sts):4d} shapetrackers with max {max(len(x.views) for x in l.sts)} views")
+      for st in l.sts:
+        assert len(st.views) <= 2, "too many views in winograd"
+        if DEBUG >= 3:
+          print(f"{len(st.views):3d} views")
+          for v in st.views: print(v)
+
   def test_profile(self):
     x,w = Tensor.rand(1,4,9,9).realize(), Tensor.rand(4,4,3,3).realize()
     with Profiling(enabled=not CI, sort='time'):

test/unit/test_conv_shapetracker.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+import unittest
+from tinygrad.uop.ops import Ops
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Conv2d
+from tinygrad.shape.shapetracker import ShapeTracker, View
+from tinygrad.helpers import prod
+from test.unit.test_shapetracker import shapetracker_getitem
+
+class TestConvShapetracker(unittest.TestCase):
+  def test_conv_3x3_one_view(self):
+    conv = Conv2d(16, 32, (3, 3))
+    # first run to init the weights, they are scheduled.
+    conv(Tensor.empty(1, 16, 10, 10)).schedule()
+    # run it again to get the kernels
+    sched = [si for si in conv(Tensor.empty(1, 16, 10, 10)).schedule() if si.ast.op is Ops.SINK]
+    assert len(sched) == 1, f"conv should only have one kernel, getting {len(sched)}"
+    for st in [x.st_arg for x in sched[0].ast.toposort() if x.op is Ops.LOAD]:
+      assert len(st.views) == 1
+
+  def test_conv_2x2_backward_one_view(self):
+    X = Tensor.rand(1, 1, 3, 3, requires_grad=True)
+    conv = Conv2d(1, 1, (2, 2), bias=False)
+    conv(X).mean().backward()
+    si = X.grad.schedule()[-1]
+    print(si)
+    ldb = [x for x in si.ast.toposort() if x.op is Ops.LOAD][0]
+    st: ShapeTracker = ldb.st_arg.simplify()
+    print(si.bufs[1].size)
+    self.assertEqual(si.bufs[1].size, st.real_size())
+    for v in st.views: print(v)
+
+    # same st
+    test_st = ShapeTracker((
+      View(shape=(1, 1, 2, 4, 2, 4), strides=(0, 0, 2, 8, 1, 4), offset=0, mask=((0, 1), (0, 1), (0, 2), (0, 2), (0, 2), (0, 2)), contiguous=False),
+      View(shape=(1, 1, 1, 1, 3, 3, 3, 3), strides=(0, 0, 0, 0, 24, 8, 3, 1), offset=0,
+           mask=((0, 1), (0, 1), (0, 1), (0, 1), (0, 2), (0, 3), (0, 2), (0, 3)), contiguous=False)))
+    #test_st = ShapeTracker((
+    #  View(shape=(2,4), strides=(1,4), offset=0, mask=None, contiguous=False),
+    #)).simplify()
+      #View(shape=(1, 1, 2, 4, 2, 4), strides=(0, 0, 2, 8, 1, 4), offset=0, mask=((0, 1), (0, 1), (0, 2), (0, 2), (0, 2), (0, 2)), contiguous=False),
+      #View(shape=(1, 1, 1, 1, 3, 3, 3, 3), strides=(0, 0, 0, 0, 24, 8, 3, 1), offset=0,
+      #     mask=((0, 1), (0, 1), (0, 1), (0, 1), (0, 2), (0, 3), (0, 2), (0, 3)), contiguous=False))).simplify()
+    print("*** new ***")
+    for v in test_st.views: print(v)
+    for i in range(prod(st.shape)):
+      i1, i2 = shapetracker_getitem(st, i), shapetracker_getitem(test_st, i)
+      print(i, i1, i2, si.bufs[1].size, i1==i2)
+      #self.assertEqual(i1, i2)
+
+    with self.assertRaises(AssertionError):
+      assert len(st.views) <= 2
+
+if __name__ == '__main__':
+  unittest.main()

test/unit/test_graph_rewrite.py

@@ -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.CONST)),
-      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
+      (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)),
     ])
     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.CONST)),
-      (UPat(Ops.CONST, name="x"), lambda x: x.replace(op=Ops.DEFINE_VAR)),
+      (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)),
     ])
     with self.assertRaises(RuntimeError):
       graph_rewrite(a, pm, bottom_up=True)

test/unit/test_linearizer_rewrite.py

@@ -1,7 +1,7 @@
 import unittest
 from tinygrad import Tensor, Context, Device
 from tinygrad.engine.realize import get_program
-from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.opt.kernel import Opt, OptOps
 from tinygrad.uop.ops import KernelInfo
 
 class TestLinearizerRewrite(unittest.TestCase):

test/unit/test_llm_tokenizer.py

@@ -1,8 +1,7 @@
-import unittest, base64, functools, sys
+import unittest, base64, functools
 from tinygrad.apps.llm import SimpleTokenizer, get_llama_re
 from tinygrad.helpers import fetch
 
-@unittest.skipIf(sys.platform == 'win32', "fetch race condition on Windows")
 class TestLLMTokenizer(unittest.TestCase):
   @functools.cached_property
   def basic_tok(self): return SimpleTokenizer(".*", { b"a": 0, b"b": 1, b"c": 2, b"ab": 3, b"bc": 4 }, { "<x>": 5, "<y>": 6, "<z>": 7 })

test/unit/test_rewrite_not_ready.py

@@ -1,110 +0,0 @@
-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()

test/unit/test_search.py

@@ -1,11 +1,10 @@
 import unittest
 from tinygrad import Tensor, Device
-from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.opt.kernel import Kernel
 from tinygrad.device import Buffer
-from tinygrad.codegen.opt.search import get_test_global_size, bufs_from_lin
+from tinygrad.opt.search import get_test_global_size, bufs_from_lin
 from tinygrad.helpers import GlobalCounters
 from extra.optimization.helpers import time_linearizer
-from test.test_linearizer import push_views
 
 class TestSearchUtil(unittest.TestCase):
   def test_get_test_global_size(self):
@@ -26,7 +25,7 @@ class TestSearchUtil(unittest.TestCase):
     a = Tensor.randn(4, 4).realize()
     b = a+a[0]
     si = b.schedule()[0]
-    rawbufs = bufs_from_lin(Kernel(push_views(si.ast)))
+    rawbufs = bufs_from_lin(Kernel(si.ast))
     assert len(rawbufs) == 2
     assert all(r is not None for r in rawbufs)
     assert all(isinstance(r, Buffer) for r in rawbufs)
@@ -39,13 +38,13 @@ class TestTimeLinearizer(unittest.TestCase):
     si = (a+1).schedule()[0]
     # create fresh empty buffers
     rawbufs = [Buffer(b.device, b.size, b.dtype).allocate() for b in si.bufs]
-    tm = time_linearizer(Kernel(push_views(si.ast)), rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
+    tm = time_linearizer(Kernel(si.ast), rawbufs, allow_test_size=False, cnt=10, disable_cache=True)
     assert tm > 0 and tm != float('inf')
 
   # Ensure that the kernel count is not incremented by time_linearizer when clearing l2
   def test_kernel_count(self):
     ast = Tensor.zeros(16).contiguous().kernelize().uop.src[1].arg.ast
-    lin = Kernel(push_views(ast))
+    lin = Kernel(ast)
     bufs = bufs_from_lin(lin)
 
     kernel_count = GlobalCounters.kernel_count

test/unit/test_transcendental_helpers.py

@@ -2,8 +2,8 @@ import unittest, math
 import numpy as np
 from tinygrad import dtypes
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.uop.decompositions import TRANSCENDENTAL_SUPPORTED_DTYPES, payne_hanek_reduction, cody_waite_reduction
-from tinygrad.uop.decompositions import frexp, rintk, xpow, xexp2, xlog2, trig_poly, pow2if
+from tinygrad.uop.transcendental import TRANSCENDENTAL_SUPPORTED_DTYPES, payne_hanek_reduction, cody_waite_reduction
+from tinygrad.uop.transcendental import frexp, rintk, xpow, xexp2, xlog2, trig_poly, pow2if
 from test.helpers import eval_uop
 
 class TestTranscendentalFunctions(unittest.TestCase):

test/unit/test_uop_spec.py

@@ -34,6 +34,17 @@ class TestUOpSpec(unittest.TestCase):
     store = UOp(Ops.STORE, dtypes.void, (buf_0.view(ShapeTracker.from_shape((32, 1))), a+b))
     helper_test_verify_ast(store)
 
+  def test_exactly_one_full_shape(self):
+    dtype = dtypes.int
+    bufs = [UOp(Ops.DEFINE_GLOBAL, dtype.ptr(), (), i) for i in range(6)]
+    a = UOp(Ops.LOAD, dtype, (bufs[2].view(ShapeTracker.from_shape((32, 1))),))
+    b = UOp(Ops.LOAD, dtype, (bufs[3].view(ShapeTracker.from_shape((32, 1))),))
+    st0 = UOp.store(bufs[0].view(ShapeTracker.from_shape((32, 1))), a+b)
+    a = UOp(Ops.LOAD, dtype, (bufs[4].view(ShapeTracker.from_shape((32, 32))),))
+    b = UOp(Ops.LOAD, dtype, (bufs[5].view(ShapeTracker.from_shape((32, 32))),))
+    st1 = UOp.store(bufs[1].view(ShapeTracker.from_shape((32, 32))), a+b)
+    with self.assertRaises(InvalidASTException): helper_test_verify_ast(st0, st1)
+
   def test_no_implicit_broadcasting(self):
     bufs = [UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), i) for i in range(2)]
     a = UOp(Ops.LOAD, dtypes.float, (bufs[1].view(ShapeTracker.from_shape((4, 32))),))
@@ -62,6 +73,14 @@ class TestUOpSpec(unittest.TestCase):
     st = UOp.store(bufs[0].view(ShapeTracker.from_shape((32, 1))), r+a)
     with self.assertRaises(InvalidASTException): helper_test_verify_ast(st)
 
+  def test_buffer_uops_st(self):
+    a = Tensor.randn(4, 4)+2
+    helper_test_verify_ast(ast:=a.schedule()[-1].ast)
+    store_st = [u.st for u in ast.toposort() if u.op is Ops.STORE][0]
+    self.assertEqual(store_st, ShapeTracker.from_shape((4, 4)))
+    const_st = [u.st for u in ast.toposort() if u.op is Ops.CONST][0]
+    self.assertEqual(const_st, ShapeTracker.from_shape((1, 1)).expand((4, 4)))
+
   def test_assert_swizzle(self):
     buf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     a = UOp(Ops.LOAD, dtypes.float, (buf.view(ShapeTracker.from_shape((32, 1))),))