gate MULTIOUTPUT

* new linearizer with early endrange

* cleanups

* second stage removal

* not store

* do that later

* end cleanup

* fix globals

* end

* multi end

* fix ends earlier

* work

* do_merge_ends

* mini change

* range_gate

* fix cpu

* test fixups

* ranges on index

* not for ptx

* delete linearizer

* remove more junk

* delete that test

* we insert endif

* all ends

.github/actions/setup-tinygrad/action.yml

@@ -302,4 +302,4 @@ runs:
     - name: Install mesa (macOS)
       if: inputs.mesa == 'true' && runner.os == 'macOS'
       shell: bash
-      run: brew install sirhcm/tinymesa/tinymesa
+      run: brew install sirhcm/tinymesa/tinymesa_cpu

.github/workflows/benchmark.yml

@@ -131,7 +131,7 @@ jobs:
     - name: UsbGPU copy speeds
       run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB python3.11 test/external/external_test_usb_asm24.py TestDevCopySpeeds
     #- name: UsbGPU openpilot test
-    #  run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB NOLOCALS=0 IMAGE=0 GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+    #  run: sudo -E PYTHONPATH=. AMD=1 AMD_IFACE=USB GRAPH_ONE_KERNEL=1 python3.11 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
     - uses: actions/upload-artifact@v4
       with:
         name: Speed (Mac)
@@ -625,16 +625,12 @@ jobs:
       run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
     - name: benchmark openpilot 0.9.9 dmonitoring
       run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
-    - name: openpilot compile3 0.9.9 driving_vision
-      run: PYTHONPATH="." ASSERT_MIN_STEP_TIME=18 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="." ASSERT_MIN_STEP_TIME=7 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="." ASSERT_MIN_STEP_TIME=12 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
-    - name: openpilot compile3 Space Lab policy + vision
-      run: |
-        PYTHONPATH="." ASSERT_MIN_STEP_TIME=4 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
-        PYTHONPATH="." ASSERT_MIN_STEP_TIME=26 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/c824f68646a3b94f117f01c70dc8316fb466e05fbd42ccdba440b8a8dc86914b
+    - name: openpilot compile3 0.10.1 driving_vision
+      run: BENCHMARK_LOG=openpilot_0_10_1_vision PYTHONPATH="." ASSERT_MIN_STEP_TIME=25 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+    - name: openpilot compile3 0.10.1 driving_policy
+      run: BENCHMARK_LOG=openpilot_0_10_1_policy PYTHONPATH="." ASSERT_MIN_STEP_TIME=7 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/refs/heads/master/selfdrive/modeld/models/driving_policy.onnx
+    - name: openpilot compile3 0.10.1 dmonitoring
+      run: BENCHMARK_LOG=openpilot_0_10_1_dmonitoring PYTHONPATH="." ASSERT_MIN_STEP_TIME=12 DEV=QCOM FLOAT16=1 IMAGE=2 NOLOCALS=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/refs/heads/master/selfdrive/modeld/models/dmonitoring_model.onnx
     - name: benchmark MobileNetV2 on DSP
       run: |
         # generate quantized weights
@@ -642,7 +638,7 @@ jobs:
         ln -s /data/home/tiny/tinygrad/testsig-*.so .
         PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
         # benchmark on DSP with NOOPT=1, the devectorizer has issues
-        PYTHONPATH=. CC=clang-19 DSP=1 DONT_REALIZE_EXPAND=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+        PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
     - name: Run process replay tests
       run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
     - uses: actions/upload-artifact@v4

.github/workflows/test.yml

@@ -374,15 +374,13 @@ jobs:
           llvm: 'true'
       - name: Test openpilot model kernel count and gate usage
         run: |
-          ALLOWED_KERNEL_COUNT=190 ALLOWED_READ_IMAGE=2081 ALLOWED_GATED_READ_IMAGE=28 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
-      - name: Test openpilot alt model correctness (float32)
-        run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
-      - name: Test openpilot fastvits model correctness (float32)
-        run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
-      # - name: Test openpilot simple_plan vision model correctness (float32)
-      #   run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
-      - name: Test openpilot LLVM compile
-        run: CPU=1 CPU_LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
+          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1452 ALLOWED_GATED_READ_IMAGE=122 FLOAT16=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+      - name: Test openpilot CL compile fp16
+        run: FLOAT16=1 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+      - name: Test openpilot CL compile fp32 (test correctness)
+        run: DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/haraschax/filedump/raw/refs/heads/master/driving_vision_fp32.onnx
+      - name: Test openpilot LLVM compile fp16
+        run: FLOAT16=1 CPU=1 CPU_LLVM=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 

autogen_stubs.sh

@@ -279,9 +279,9 @@ generate_llvm() {
     --clang-args="$(llvm-config-14 --cflags)" \
     -o "$BASE/llvm.py"
 
-  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support, tinygrad.helpers as helpers\g" "$BASE/llvm.py"
+  sed -i "s\import ctypes\import ctypes, tinygrad.runtime.support.llvm as llvm_support\g" "$BASE/llvm.py"
   sed -i "s\FIXME_STUB\llvm\g" "$BASE/llvm.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH, ctypes.RTLD_GLOBAL if helpers.OSX else ctypes.DEFAULT_MODE)\g" "$BASE/llvm.py"
+  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(llvm_support.LLVM_PATH)\g" "$BASE/llvm.py"
 
   fixup "$BASE/llvm.py"
 }
@@ -520,17 +520,17 @@ generate_mesa() {
   LVP_NIR_OPTIONS=$(./extra/mesa/lvp_nir_options.sh $MESA_SRC)
 
   fixup $BASE/mesa.py
-  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "f'{brew_prefix()}/lib/libtinymesa_cpu.dylib'"
+  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "brew_path('tinymesa_cpu')" "brew_path('tinymesa')"
   echo "lvp_nir_options = gzip.decompress(base64.b64decode('$LVP_NIR_OPTIONS'))" >> $BASE/mesa.py
   cat <<EOF | sed -i "/import ctypes.*/r /dev/stdin" $BASE/mesa.py
-def brew_prefix():
-  try: return subprocess.check_output(['brew', '--prefix', 'tinymesa']).decode().strip()
-  except Exception: return ''
+def brew_path(nm):
+  try: return f"{subprocess.check_output(['brew', '--prefix', nm]).decode().strip()}/lib/lib{nm}.dylib"
+  except Exception: return 'failed'
 EOF
-  sed -i "/in_dll/s/.*/try: &\nexcept AttributeError: pass/" $BASE/mesa.py
+  sed -i "/in_dll/s/.*/try: &\nexcept (AttributeError, ValueError): pass/" $BASE/mesa.py
   sed -i "s/import ctypes/import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers/" $BASE/mesa.py
   sed -i "s/ctypes.CDLL('.\+')/(dll := _try_dlopen_tinymesa_cpu())/" $BASE/mesa.py
-  echo "def __getattr__(nm): raise AttributeError() if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
+  echo "def __getattr__(nm): raise AttributeError('LLVMpipe requires tinymesa_cpu' if 'tinymesa_cpu' not in dll._name else f'attribute {nm} not found') if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
   sed -i "s/ctypes.glsl_base_type/glsl_base_type/" $BASE/mesa.py
   # bitfield bug in clang2py
   sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py

examples/gpt2.py

@@ -232,7 +232,7 @@ if __name__ == "__main__":
   gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith("gpt2_gguf_") else GPT2.build(args.model_size)
 
   if args.benchmark != -1:
-    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
+    gpt2.model(Tensor.randint(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
   else:
     texts = gpt2.generate(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)
     if not args.noshow:

examples/openpilot/compile3.py

@@ -1,9 +1,5 @@
 import os, sys, pickle, time, re
 import numpy as np
-if "FLOAT16" not in os.environ: os.environ["FLOAT16"] = "1"
-if "IMAGE" not in os.environ: os.environ["IMAGE"] = "2"
-if "NOLOCALS" not in os.environ: os.environ["NOLOCALS"] = "1"
-if "JIT_BATCH_SIZE" not in os.environ: os.environ["JIT_BATCH_SIZE"] = "0"
 
 from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters, Device, dtypes
 from tinygrad.helpers import DEBUG, getenv
@@ -21,11 +17,14 @@ def compile(onnx_file):
 
   input_shapes = {name: spec.shape for name, spec in run_onnx.graph_inputs.items()}
   input_types = {name: spec.dtype for name, spec in run_onnx.graph_inputs.items()}
+
   # Float inputs and outputs to tinyjits for openpilot are always float32
+  # TODO this seems dumb
   input_types = {k:(dtypes.float32 if v is dtypes.float16 else v) for k,v in input_types.items()}
   Tensor.manual_seed(100)
-  new_inputs = {k:Tensor.randn(*shp, dtype=input_types[k]).mul(8).realize() for k,shp in sorted(input_shapes.items())}
-  new_inputs_numpy = {k:v.numpy() for k,v in new_inputs.items()}
+  inputs = {k:Tensor(Tensor.randn(*shp, dtype=input_types[k]).mul(8).realize().numpy(), device='NPY') for k,shp in sorted(input_shapes.items())}
+  if not getenv("NPY_IMG"):
+    inputs = {k:Tensor(v.numpy(), device=Device.DEFAULT).realize() if 'img' in k else v for k,v in inputs.items()}
   print("created tensors")
 
   run_onnx_jit = TinyJit(lambda **kwargs:
@@ -33,8 +32,6 @@ def compile(onnx_file):
   for i in range(3):
     GlobalCounters.reset()
     print(f"run {i}")
-    inputs = {**{k:v.clone() for k,v in new_inputs.items() if 'img' in k},
-              **{k:Tensor(v, device="NPY").realize() for k,v in new_inputs_numpy.items() if 'img' not in k}}
     with Context(DEBUG=max(DEBUG.value, 2 if i == 2 else 1)):
       ret = run_onnx_jit(**inputs).numpy()
     # copy i == 1 so use of JITBEAM is okay
@@ -69,14 +66,9 @@ def compile(onnx_file):
   print(f"mdl size is {mdl_sz/1e6:.2f}M")
   print(f"pkl size is {pkl_sz/1e6:.2f}M")
   print("**** compile done ****")
-  return test_val
+  return inputs, test_val
 
-def test_vs_compile(run, new_inputs, test_val=None):
-  new_inputs_numpy = {k:v.numpy() for k,v in new_inputs.items()}
-
-  # create fake "from_blob" tensors for the inputs, and wrapped NPY tensors for the numpy inputs (these have the same underlying memory)
-  inputs = {**{k:v for k,v in new_inputs.items() if 'img' in k},
-            **{k:Tensor(v, device="NPY").realize() for k,v in new_inputs_numpy.items() if 'img' not in k}}
+def test_vs_compile(run, inputs, test_val=None):
 
   # run 20 times
   step_times = []
@@ -93,68 +85,57 @@ def test_vs_compile(run, new_inputs, test_val=None):
     min_time = min(step_times)
     assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
 
-  print(out, val.shape, val.dtype)
   if test_val is not None: np.testing.assert_equal(test_val, val)
   print("**** test done ****")
 
   # test that changing the numpy changes the model outputs
-  if any([x.device == 'NPY' for x in inputs.values()]):
-    for v in new_inputs_numpy.values(): v *= 2
-    out = run(**inputs)
-    changed_val = out.numpy()
-    np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, val, changed_val)
+  inputs_2x = {k: Tensor(v.numpy()*2, device=v.device) for k,v in inputs.items()}
+  out = run(**inputs_2x)
+  changed_val = out.numpy()
+  np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, val, changed_val)
   return val
 
-def test_vs_onnx(new_inputs, test_val, onnx_file, ort=False):
-  new_inputs_numpy = {k:v.numpy() for k,v in new_inputs.items()}
+def test_vs_onnx(new_inputs, test_val, onnx_file, tol):
+  import onnxruntime as ort
+  
+  onnx_inputs = {k:v.numpy() for k,v in new_inputs.items()}
   onnx_model = onnx.load(onnx_file)
 
-  timings = []
-  if ort:
-    # test with onnxruntime
-    import onnxruntime as ort
-    onnx_session = ort.InferenceSession(onnx_file)
-    for _ in range(1 if test_val is not None else 5):
-      st = time.perf_counter()
-      onnx_output = onnx_session.run([onnx_model.graph.output[0].name], {k:v.astype(np.float16) for k,v in new_inputs_numpy.items()})
-      timings.append(time.perf_counter() - st)
-    new_torch_out = onnx_output[0]
-  else:
-    # test with torch
-    import torch
-    from onnx2torch import convert
-    inputs = {k.name:new_inputs_numpy[k.name] for k in onnx_model.graph.input}
-    torch_model = convert(onnx_model).float()
-    with torch.no_grad():
-      for _ in range(1 if test_val is not None else 5):
-        st = time.perf_counter()
-        torch_out = torch_model(*[torch.tensor(x) for x in inputs.values()])
-        timings.append(time.perf_counter() - st)
-      new_torch_out = torch_out.numpy()
+  ORT_TO_NP_DTYPES: dict[str, np.dtype] = {
+    'tensor(float)': np.dtype('float32'),
+    'tensor(float16)': np.dtype('float16'),
+    'tensor(uint8)': np.dtype('uint8'),
+  }
 
-  if test_val is not None:
-    np.testing.assert_allclose(new_torch_out.reshape(test_val.shape), test_val, atol=1e-4, rtol=1e-2)
-    print("test vs onnx passed")
+  timings = []
+  onnx_session = ort.InferenceSession(onnx_file)
+  onnx_types = {x.name: ORT_TO_NP_DTYPES[x.type] for x in onnx_session.get_inputs()}
+  onnx_inputs = {k:onnx_inputs[k].astype(onnx_types[k]) for k in onnx_inputs}
+
+  for _ in range(1 if test_val is not None else 5):
+    st = time.perf_counter()
+    onnx_output = onnx_session.run([onnx_model.graph.output[0].name], onnx_inputs)
+    timings.append(time.perf_counter() - st)
+
+  np.testing.assert_allclose(onnx_output[0].reshape(test_val.shape), test_val, atol=tol, rtol=tol)
+  print("test vs onnx passed")
   return timings
 
+def bench(run, inputs):
+  from extra.bench_log import WallTimeEvent, BenchEvent
+  for _ in range(10):
+    with WallTimeEvent(BenchEvent.STEP):
+      run(**inputs).numpy()
+
 if __name__ == "__main__":
   onnx_file = fetch(OPENPILOT_MODEL)
-  test_val = compile(onnx_file) if not getenv("RUN") else None
+  inputs, outputs = compile(onnx_file)
 
   with open(OUTPUT, "rb") as f: pickle_loaded = pickle.load(f)
 
-  # same randomness as compile
-  Tensor.manual_seed(100)
-  new_inputs = {nm:Tensor.randn(*st.shape, dtype=dtype).mul(8).realize() for nm, (st, _, dtype, _) in
-                sorted(zip(pickle_loaded.captured.expected_names, pickle_loaded.captured.expected_st_vars_dtype_device))}
-
-  test_val = test_vs_compile(pickle_loaded, new_inputs, test_val)
-  if getenv("BENCHMARK"):
-    for be in ["torch", "ort"]:
-      try:
-        timings = test_vs_onnx(new_inputs, None, onnx_file, be=="ort")
-        print(f"timing {be}: {min(timings)*1000:.2f} ms")
-      except Exception as e:
-        print(f"{be} fail with {e}")
-  if not getenv("FLOAT16"): test_vs_onnx(new_inputs, test_val, onnx_file, getenv("ORT"))
+  test_vs_compile(pickle_loaded, inputs, outputs)
+  if not getenv("FLOAT16"):
+    test_vs_onnx(inputs, outputs, onnx_file, 1e-4)
 
+  if getenv("BENCHMARK_LOG", ""):
+    bench(pickle_loaded, inputs)

examples/sdv2.py

@@ -99,6 +99,7 @@ if __name__ == "__main__":
   parser.add_argument('--timing',      action='store_true', help="Print timing per step")
   parser.add_argument('--noshow',      action='store_true', help="Don't show the image")
   parser.add_argument('--fp16',        action='store_true', help="Cast the weights to float16")
+  parser.add_argument('--fakeweights', action='store_true', help="Skip loading checkpoints and use fake weights")
   args = parser.parse_args()
 
   N = 1
@@ -112,19 +113,22 @@ if __name__ == "__main__":
 
   model = StableDiffusionV2(**params)
 
-  default_weights_url = 'https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors'
-  weights_fn = args.weights_fn
-  if not weights_fn:
-    weights_url = args.weights_url if args.weights_url else default_weights_url
-    weights_fn  = fetch(weights_url, os.path.basename(str(weights_url)))
-
   with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    load_state_dict(model, safe_load(weights_fn), strict=False)
+    if not args.fakeweights:
+      default_weights_url = 'https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors'
+      weights_fn = args.weights_fn
+      if not weights_fn:
+        weights_url = args.weights_url if args.weights_url else default_weights_url
+        weights_fn  = fetch(weights_url, os.path.basename(str(weights_url)))
+
+        load_state_dict(model, safe_load(weights_fn), strict=False)
 
     if args.fp16:
       for k,v in get_state_dict(model).items():
         if k.startswith("model"):
-          v.replace(v.cast(dtypes.float16).realize())
+          v.replace(v.cast(dtypes.float16))
+
+    Tensor.realize(*get_state_dict(model).values())
 
   c  = { "crossattn": model.cond_stage_model(args.prompt) }
   uc = { "crossattn": model.cond_stage_model("") }

examples/stable_diffusion.py

@@ -263,14 +263,16 @@ if __name__ == "__main__":
   parser.add_argument('--timing', action='store_true', help="Print timing per step")
   parser.add_argument('--seed', type=int, help="Set the random latent seed")
   parser.add_argument('--guidance', type=float, default=7.5, help="Prompt strength")
+  parser.add_argument('--fakeweights', action='store_true', help="Skip loading checkpoints and use fake weights")
   args = parser.parse_args()
 
   model = StableDiffusion()
 
   # load in weights
   with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    model_bin = fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt')
-    load_state_dict(model, torch_load(model_bin)['state_dict'], verbose=False, strict=False, realize=False)
+    if not args.fakeweights:
+      model_bin = fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt')
+      load_state_dict(model, torch_load(model_bin)['state_dict'], verbose=False, strict=False, realize=False)
 
     if args.fp16:
       for k,v in get_state_dict(model).items():

examples/test_onnx_imagenet.py

@@ -19,8 +19,8 @@ from tinygrad.helpers import fetch, getenv
 
 # QUANT=1 python3 examples/test_onnx_imagenet.py
 #   https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx
-# DONT_REALIZE_EXPAND=1 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
-# VIZ=1 DONT_REALIZE_EXPAND=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx
+# python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+# VIZ=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx
 
 def imagenet_dataloader(cnt=0):
   input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)

extra/mmapeak/mmapeak.py

@@ -7,31 +7,34 @@ import os
 NUM_WORKGROUPS = 96
 WAVE_SIZE = 32
 NUM_WAVES = 2
-FLOPS_PER_MATMUL =  16*16*16*2
-INTERNAL_LOOP  = 1_000_000
-INSTRUCTIONS_PER_LOOP = 1_000
+FLOPS_PER_MATMUL = 16*16*16*2
+INTERNAL_LOOP = 1_000_00
+INSTRUCTIONS_PER_LOOP = 200
+DIRECTIVE = ".amdhsa_wavefront_size32 1"
 
 assemblyTemplate = (pathlib.Path(__file__).parent / "template.s").read_text()
 
-def launchBenchmark(instruction, vgprIndices, dense = True):
-  if dense:
+def launchBenchmark(instruction, vgprIndices, dense=True, accum=False, extra=""):
+  if accum:
+    instructions = "{} a[0:{}], v[{}:{}], v[{}:{}], 1{}\n".format(instruction, vgprIndices[0],
+                                                                vgprIndices[1], vgprIndices[2],
+                                                                vgprIndices[1], vgprIndices[2], extra)
+  elif dense:
     instructions = "{} v[0:{}], v[{}:{}], v[{}:{}], 1\n".format(instruction, vgprIndices[0],
                                                                 vgprIndices[1], vgprIndices[2],
-                                                                vgprIndices[1], vgprIndices[2]) * INSTRUCTIONS_PER_LOOP
+                                                                vgprIndices[1], vgprIndices[2])
   else:
     instructions = "{} v[0:{}], v[{}:{}], v[{}:{}], v{}\n".format(instruction, vgprIndices[0],
-                                                                vgprIndices[1], vgprIndices[2],
-                                                                vgprIndices[3], vgprIndices[4],
-                                                                vgprIndices[5]) * INSTRUCTIONS_PER_LOOP
-  src = assemblyTemplate.replace("INSTRUCTION", instructions)
+                                                                  vgprIndices[1], vgprIndices[2],
+                                                                  vgprIndices[3], vgprIndices[4],
+                                                                  vgprIndices[5])
+  src = assemblyTemplate.replace("INTERNAL_LOOP", str(INTERNAL_LOOP)).replace("INSTRUCTION", instructions*INSTRUCTIONS_PER_LOOP)
+  src = src.replace("DIRECTIVE", DIRECTIVE)
   lib = COMPILER.compile(src)
   fxn = AMDProgram(DEV, "matmul", lib)
-  start = time.perf_counter()
-  fxn(global_size=(NUM_WORKGROUPS,1,1), local_size=(WAVE_SIZE*NUM_WAVES,1,1), wait=True) #For some reason the returned time is very small after the first kernel execution
-  end = time.perf_counter()
-  elapsed = end-start
+  elapsed = fxn(global_size=(NUM_WORKGROUPS,1,1), local_size=(WAVE_SIZE*NUM_WAVES,1,1), wait=True)
   FLOPs = FLOPS_PER_MATMUL * NUM_WAVES * NUM_WORKGROUPS * INTERNAL_LOOP * INSTRUCTIONS_PER_LOOP
-  print("{:<29} : {} T(FL)OPS".format(instruction, round(FLOPs/elapsed/10**12, 2)))
+  print(f"{instruction:<29} : {FLOPs/elapsed/10**12:.2f} T(FL)OPS")
 
 if __name__=="__main__":
   DEVICENUM = os.getenv("DEVICENUM", "0")
@@ -40,18 +43,17 @@ if __name__=="__main__":
   except:
     raise RuntimeError("Error while initiating AMD device")
 
-  if (ARCH := DEV.arch) not in ['gfx1100', 'gfx1201']:
-    raise RuntimeError("only gfx1100 and gfx1201 supported")
-  COMPILER = HIPCompiler(ARCH)
-
-  if ARCH == 'gfx1100':
+  COMPILER = HIPCompiler(DEV.arch)
+  if DEV.arch in {'gfx1100', 'gfx1103'}:
+    if DEV.arch == 'gfx1103':
+      NUM_WORKGROUPS = 8
     launchBenchmark("v_wmma_bf16_16x16x16_bf16", (7,8,15))
     launchBenchmark("v_wmma_f16_16x16x16_f16", (7,8,15))
     launchBenchmark("v_wmma_f32_16x16x16_bf16", (7,8,15))
     launchBenchmark("v_wmma_f32_16x16x16_f16", (7,8,15))
     launchBenchmark("v_wmma_i32_16x16x16_iu4", (7,8,9))
     launchBenchmark("v_wmma_i32_16x16x16_iu8", (7,8,11))
-  if ARCH == 'gfx1201':
+  elif DEV.arch == 'gfx1201':
     NUM_WORKGROUPS = 64
     launchBenchmark("v_wmma_bf16_16x16x16_bf16", (3,4,7))
     launchBenchmark("v_wmma_f16_16x16x16_f16", (3,4,7))
@@ -76,4 +78,18 @@ if __name__=="__main__":
     launchBenchmark("v_swmmac_f32_16x16x32_bf8_fp8", (7,8,9,10,13,14), False)
     launchBenchmark("v_swmmac_f32_16x16x32_bf8_bf8", (7,8,9,10,13,14), False)
     FLOPS_PER_MATMUL = 16*16*64*2
-    launchBenchmark("v_swmmac_i32_16x16x64_iu4", (7,8,9,10,13,14), False)
+    launchBenchmark("v_swmmac_i32_16x16x64_iu4", (7,8,9,10,13,14), False)
+  elif DEV.arch == 'gfx950':
+    DIRECTIVE = ".amdhsa_accum_offset 4"
+    NUM_WORKGROUPS = 256
+    WAVE_SIZE = 64
+    NUM_WAVES = 4
+    launchBenchmark("v_mfma_f32_16x16x16_bf16", (3,0,1), accum=True)
+    FLOPS_PER_MATMUL = 16*16*32*2
+    launchBenchmark("v_mfma_f32_16x16x32_bf16", (3,0,3), accum=True)
+    FLOPS_PER_MATMUL = 16*16*128*2
+    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,7), accum=True) # fp8
+    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,5), accum=True, extra=", cbsz:2 blgp:2") # fp6
+    launchBenchmark("v_mfma_f32_16x16x128_f8f6f4", (3,0,3), accum=True, extra=", cbsz:4 blgp:4") # fp4
+  else:
+    raise RuntimeError(f"arch {DEV.arch} not supported.")

extra/mmapeak/template.s

@@ -1,9 +1,9 @@
     .text
     .globl matmul
-    .p2align 8 
+    .p2align 8
     .type matmul,@function
 matmul:
-    s_mov_b32 s1, 1000000
+    s_mov_b32 s1, INTERNAL_LOOP
     s_mov_b32 s2, 0
     inner_loop:
         INSTRUCTION
@@ -17,7 +17,7 @@ matmul:
 .amdhsa_kernel matmul
   .amdhsa_next_free_vgpr .amdgcn.next_free_vgpr
   .amdhsa_next_free_sgpr .amdgcn.next_free_sgpr
-  .amdhsa_wavefront_size32 1
+  DIRECTIVE
 .end_amdhsa_kernel
 
 .amdgpu_metadata
@@ -28,7 +28,7 @@ amdhsa.version:
 amdhsa.kernels:
   - .name: matmul
     .symbol: matmul.kd
-    .kernarg_segment_size:  0
+    .kernarg_segment_size: 0
     .group_segment_fixed_size: 0
     .private_segment_fixed_size: 0
     .kernarg_segment_align: 4
@@ -36,6 +36,5 @@ amdhsa.kernels:
     .sgpr_count: 8
     .vgpr_count: 32
     .max_flat_workgroup_size: 1024
-    .args:
 ...
 .end_amdgpu_metadata

extra/onnx_helpers.py

@@ -3,8 +3,21 @@ from tinygrad.tensor import _to_np_dtype
 from tinygrad.nn.onnx import OnnxRunner, OnnxValue
 import numpy as np
 import onnxruntime as ort
+ort_options = ort.SessionOptions()
+ort_options.log_severity_level = 3
 
 def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
+  """
+  Generate example input tensors based on the provided ONNX graph input specifications.
+
+  NOTE: This is not guaranteed to be reliable. It's a best-effort helper
+  that uses heuristics to guess input shapes and values.
+
+  Example:
+    from tinygrad.nn.onnx import OnnxRunner
+    from extra.onnx_helpers import get_example_inputs
+    inputs = get_example_inputs(OnnxRunner(model_path).graph_inputs)
+  """
   def _get_shape(onnx_shape: tuple[str|int]):
     shape = []
     for onnx_dim in onnx_shape:
@@ -44,11 +57,9 @@ def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
     ret.update({name:value})
   return ret
 
-def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+def _get_tinygrad_and_ort_np_outputs(onnx_file, inputs):
   run_onnx = OnnxRunner(onnx_file)
 
-  ort_options = ort.SessionOptions()
-  ort_options.log_severity_level = 3
   ort_sess = ort.InferenceSession(onnx_file, ort_options, ["CPUExecutionProvider"])
   np_inputs = {k:v.numpy() if isinstance(v, Tensor) else v for k,v in inputs.items()}
   out_names = list(run_onnx.graph_outputs)
@@ -56,9 +67,101 @@ def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
   ort_out = dict(zip(out_names, out_values))
 
   tinygrad_out = run_onnx(inputs)
+  Tensor.realize(*(x for x in tinygrad_out.values() if x is not None))
+  tinygrad_out = {k:v.numpy() if v is not None else None for k,v in tinygrad_out.items()}
+  return tinygrad_out, ort_out
+
+def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+  """
+  Compares the final output tensors of an onnx model run in tinygrad and onnxruntime.
+  """
+  tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(onnx_file, inputs)
 
   assert tinygrad_out.keys() == ort_out.keys()
   for k in tinygrad_out.keys():
     tiny_v, onnx_v = tinygrad_out[k], ort_out[k]
     if tiny_v is None: assert onnx_v is None, f"{k}: {tiny_v=}, {onnx_v=}"
-    else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
+    else: np.testing.assert_allclose(tiny_v, onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
+
+def validate_all_intermediates(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+  """
+  Compares all intermediate node output of an onnx model run in tinygrad and onnxruntime.
+  """
+  report = generate_node_output_report(onnx_file, inputs)
+  for i, node in enumerate(report):
+    node_name = node["node"]
+    op = node["op"]
+    outputs = node["outputs"]
+    for output in outputs:
+      output_name = output["name"]
+      tinygrad_out = output["tinygrad"]
+      ort_out = output["onnxruntime"]
+      try:
+        if tinygrad_out is None: assert ort_out is None, f"None outputs are not equal {tinygrad_out=} {ort_out=}"
+        else: np.testing.assert_allclose(tinygrad_out, ort_out, rtol=rtol, atol=atol)
+        print(f"Validated {i}: {op=} {node_name=} {output_name=}")
+      except AssertionError as e:
+        print(f"FAILED {i}: {op=} {node_name=} {output_name=}")
+        print(str(e).strip() + "\n")
+
+def generate_node_output_report(onnx_file, inputs):
+  """
+  Build a report of all ONNX node outputs from tinygrad and onnxruntime
+
+  Returns:
+    A list of dictionaries, where each entry corresponds to one
+    node in the ONNX graph. The structure is as follows:
+    [
+      {
+        "node": str,  # The name of the ONNX node.
+        "op": str,    # The operation type of the ONNX node.
+        "outputs": [
+          {
+            "name": str,                       # The name of the output tensor.
+            "tinygrad": np.ndarray | None,     # The output value from tinygrad.
+            "onnxruntime": np.ndarray | None,  # The output value from onnxruntime.
+          },
+          ...
+        ]
+      },
+      ...
+    ]
+  """
+  import onnx_graphsurgeon as gs
+  import onnx
+  import tempfile
+
+  # rewrite the model to output all the node outputs
+  # `infer_shapes` here tries to fill the shapes and dtypes of intermediate values which graphsurgeon requires when assigning them as outputs
+  inferred_model = onnx.shape_inference.infer_shapes(onnx.load(onnx_file))
+  model = gs.import_onnx(inferred_model)
+  model_nodes = model.nodes
+  node_outputs = [n.outputs for n in model.nodes]
+  model.outputs = [
+      each_output for outputs in node_outputs for each_output in outputs
+      if not (each_output.dtype is None and each_output.shape is None)  # output with None dtype and None shape is likely a `None` value
+  ]
+  rewritten_model = gs.export_onnx(model)
+
+  # TODO: remove this once ORT supports 1.18.0
+  if getattr(rewritten_model, "ir_version", 0) > 10:
+    rewritten_model.ir_version = 10
+
+  with tempfile.NamedTemporaryFile(suffix=".onnx") as f:
+    onnx.save(rewritten_model, f.name)
+    rewritten_model_path = f.name
+    tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(rewritten_model_path, inputs)
+
+  report = []
+  for node in model_nodes:
+    outputs = []
+    for each_output in node.outputs:
+      if each_output.dtype is None and each_output.shape is None:
+        continue
+      name = each_output.name
+      tinygrad_output = tinygrad_out[name]
+      ort_output = ort_out[name]
+      outputs.append({"name": name, "tinygrad": tinygrad_output, "onnxruntime": ort_output})
+    report.append({"node": node.name, "op": node.op, "outputs": outputs})
+
+  return report

extra/qcom_gpu_driver/adreno_pm4.xml

@@ -1,7 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <database xmlns="http://nouveau.freedesktop.org/"
 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
+xsi:schemaLocation="https://gitlab.freedesktop.org/freedreno/ rules-fd.xsd">
+<import file="freedreno_copyright.xml"/>
 <import file="adreno/adreno_common.xml"/>
 
 <enum name="vgt_event_type" varset="chip">
@@ -20,9 +21,9 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="HLSQ_FLUSH" value="7" variants="A3XX-A4XX"/>
 	<value name="VIZQUERY_END" value="8" variants="A2XX"/>
 	<value name="SC_WAIT_WC" value="9" variants="A2XX"/>
-	<value name="WRITE_PRIMITIVE_COUNTS" value="9" variants="A6XX"/>
-	<value name="START_PRIMITIVE_CTRS" value="11" variants="A6XX"/>
-	<value name="STOP_PRIMITIVE_CTRS" value="12" variants="A6XX"/>
+	<value name="WRITE_PRIMITIVE_COUNTS" value="9" variants="A6XX-"/>
+	<value name="START_PRIMITIVE_CTRS" value="11" variants="A6XX-"/>
+	<value name="STOP_PRIMITIVE_CTRS" value="12" variants="A6XX-"/>
 	<!-- Not sure that these 4 events don't have the same meaning as on A5XX+ -->
 	<value name="RST_PIX_CNT" value="13" variants="A2XX-A4XX"/>
 	<value name="RST_VTX_CNT" value="14" variants="A2XX-A4XX"/>
@@ -30,8 +31,8 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="STAT_EVENT" value="16" variants="A2XX-A4XX"/>
 	<value name="CACHE_FLUSH_AND_INV_TS_EVENT" value="20" variants="A2XX-A4XX"/>
 	<doc>
-		If A6XX_RB_SAMPLE_COUNT_CONTROL.copy is true, writes OQ Z passed
-		sample counts to RB_SAMPLE_COUNT_ADDR.  This writes to main
+		If A6XX_RB_SAMPLE_COUNTER_CNTL.copy is true, writes OQ Z passed
+		sample counts to RB_SAMPLE_COUNTER_BASE.  This writes to main
 		memory, skipping UCHE.
 	</doc>
 	<value name="ZPASS_DONE" value="21"/>
@@ -96,6 +97,13 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	</doc>
 	<value name="BLIT" value="30" variants="A5XX-"/>
 
+	<doc>
+	Flip between the primary and secondary LRZ buffers. This is used
+	for concurrent binning, so that BV can write to one buffer while
+	BR reads from the other.
+	</doc>
+	<value name="LRZ_FLIP_BUFFER" value="36" variants="A7XX-"/>
+
 	<doc>
 		Clears based on GRAS_LRZ_CNTL configuration, could clear
 		fast-clear buffer or LRZ direction.
@@ -112,11 +120,12 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="LRZ_FLUSH" value="38" variants="A5XX-"/>
 	<value name="BLIT_OP_FILL_2D" value="39" variants="A5XX-"/>
 	<value name="BLIT_OP_COPY_2D" value="40" variants="A5XX-A6XX"/>
-	<value name="UNK_40" value="40" variants="A7XX"/>
+	<value name="LRZ_CACHE_INVALIDATE" value="40" variants="A7XX-"/>
+	<value name="LRZ_Q_CACHE_INVALIDATE" value="41" variants="A7XX-"/>
 	<value name="BLIT_OP_SCALE_2D" value="42" variants="A5XX-"/>
 	<value name="CONTEXT_DONE_2D" value="43" variants="A5XX-"/>
-	<value name="UNK_2C" value="44" variants="A5XX-"/>
-	<value name="UNK_2D" value="45" variants="A5XX-"/>
+	<value name="VSC_BINNING_START" value="44" variants="A5XX-"/>
+	<value name="VSC_BINNING_END" value="45" variants="A5XX-"/>
 
 	<!-- a6xx events -->
 	<doc>
@@ -129,21 +138,22 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<!-- note, some of these are the same as a6xx, just named differently -->
 
 	<doc> Doesn't seem to do anything </doc>
-	<value name="DUMMY_EVENT" value="1" variants="A7XX"/>
-	<value name="CCU_INVALIDATE_DEPTH" value="24" variants="A7XX"/>
-	<value name="CCU_INVALIDATE_COLOR" value="25" variants="A7XX"/>
-	<value name="CCU_RESOLVE_CLEAN" value="26" variants="A7XX"/>
-	<value name="CCU_FLUSH_DEPTH" value="28" variants="A7XX"/>
-	<value name="CCU_FLUSH_COLOR" value="29" variants="A7XX"/>
-	<value name="CCU_RESOLVE" value="30" variants="A7XX"/>
-	<value name="CCU_END_RESOLVE_GROUP" value="31" variants="A7XX"/>
-	<value name="CCU_CLEAN_DEPTH" value="32" variants="A7XX"/>
-	<value name="CCU_CLEAN_COLOR" value="33" variants="A7XX"/>
-	<value name="CACHE_RESET" value="48" variants="A7XX"/>
-	<value name="CACHE_CLEAN" value="49" variants="A7XX"/>
+	<value name="DUMMY_EVENT" value="1" variants="A7XX-"/>
+	<value name="CCU_INVALIDATE_DEPTH" value="24" variants="A7XX-"/>
+	<value name="CCU_INVALIDATE_COLOR" value="25" variants="A7XX-"/>
+	<value name="CCU_RESOLVE_CLEAN" value="26" variants="A7XX-"/>
+	<value name="CCU_FLUSH_DEPTH" value="28" variants="A7XX-"/>
+	<value name="CCU_FLUSH_COLOR" value="29" variants="A7XX-"/>
+	<value name="CCU_RESOLVE" value="30" variants="A7XX-"/>
+	<value name="CCU_END_RESOLVE_GROUP" value="31" variants="A7XX-"/>
+	<value name="CCU_CLEAN_DEPTH" value="32" variants="A7XX-"/>
+	<value name="CCU_CLEAN_COLOR" value="33" variants="A7XX-"/>
+	<value name="CACHE_RESET" value="48" variants="A7XX-"/>
+	<value name="CACHE_CLEAN" value="49" variants="A7XX-"/>
 	<!-- TODO: deal with name conflicts with other gens -->
-	<value name="CACHE_FLUSH7" value="50" variants="A7XX"/>
-	<value name="CACHE_INVALIDATE7" value="51" variants="A7XX"/>
+	<value name="CACHE_FLUSH7" value="50" variants="A7XX-"/>
+	<value name="CACHE_INVALIDATE7" value="51" variants="A7XX-"/>
+	<value name="DEPTH_BUFFER_FLIP" value="0x3d" variants="A8XX-"/>
 </enum>
 
 <enum name="pc_di_primtype">
@@ -324,7 +334,7 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<doc>fetch state sub-blocks and initiate shader code DMAs</doc>
 	<value name="CP_SET_STATE" value="0x25"/>
 	<doc>load constant into chip and to memory</doc>
-	<value name="CP_SET_CONSTANT" value="0x2d"/>
+	<value name="CP_SET_CONSTANT" value="0x2d" variants="A2XX"/>
 	<doc>load sequencer instruction memory (pointer-based)</doc>
 	<value name="CP_IM_LOAD" value="0x27"/>
 	<doc>load sequencer instruction memory (code embedded in packet)</doc>
@@ -371,7 +381,7 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="CP_LOAD_STATE" value="0x30" variants="A3XX"/>
 	<value name="CP_LOAD_STATE4" value="0x30" variants="A4XX-A5XX"/>
 	<doc>Conditionally load a IB based on a flag, prefetch enabled</doc>
-	<value name="CP_COND_INDIRECT_BUFFER_PFE" value="0x3a"/>
+	<value name="CP_COND_INDIRECT_BUFFER_PFE" value="0x3a" variants="A3XX-A5XX"/>
 	<doc>Conditionally load a IB based on a flag, prefetch disabled</doc>
 	<value name="CP_COND_INDIRECT_BUFFER_PFD" value="0x32" variants="A3XX"/>
 	<doc>Load a buffer with pre-fetch enabled</doc>
@@ -514,7 +524,7 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<!--
 	Seems to set the mode flags which control which CP_SET_DRAW_STATE
 	packets are executed, based on their ENABLE_MASK values
-	
+
 	CP_SET_MODE w/ payload of 0x1 seems to cause CP_SET_DRAW_STATE
 	packets w/ ENABLE_MASK & 0x6 to execute immediately
 	 -->
@@ -537,7 +547,7 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="CP_LOAD_STATE6_GEOM" value="0x32" variants="A6XX-"/>
 	<value name="CP_LOAD_STATE6_FRAG" value="0x34" variants="A6XX-"/>
 	<!--
-	Note: For IBO state (Image/SSBOs) which have shared state across
+	Note: For UAV state (Image/SSBOs) which have shared state across
 	shader stages, for 3d pipeline CP_LOAD_STATE6 is used.  But for
 	compute shaders, CP_LOAD_STATE6_FRAG is used.  Possibly they are
 	interchangable.
@@ -566,20 +576,21 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="IN_PREEMPT" value="0x0f" variants="A6XX-"/>
 
 	<!-- TODO do these exist on A5xx? -->
-	<value name="CP_SCRATCH_WRITE" value="0x4c" variants="A6XX"/>
+	<value name="CP_SCRATCH_WRITE" value="0x4c" variants="A6XX-"/>
 	<value name="CP_REG_TO_MEM_OFFSET_MEM" value="0x74" variants="A6XX-"/>
 	<value name="CP_REG_TO_MEM_OFFSET_REG" value="0x72" variants="A6XX-"/>
 	<value name="CP_WAIT_MEM_GTE" value="0x14" variants="A6XX"/>
 	<value name="CP_WAIT_TWO_REGS" value="0x70" variants="A6XX"/>
 	<value name="CP_MEMCPY" value="0x75" variants="A6XX-"/>
 	<value name="CP_SET_BIN_DATA5_OFFSET" value="0x2e" variants="A6XX-"/>
+	<!-- A750+, set in place of CP_SET_BIN_DATA5_OFFSET but has different values -->
+	<value name="CP_SET_UNK_BIN_DATA" value="0x2d" variants="A7XX-"/>
 	<doc>
                 Write CP_CONTEXT_SWITCH_*_INFO from CP to the following dwords,
                 and forcibly switch to the indicated context.
 	</doc>
 	<value name="CP_CONTEXT_SWITCH" value="0x54" variants="A6XX"/>
-	<!-- Note, kgsl calls this CP_SET_AMBLE: -->
-	<value name="CP_SET_CTXSWITCH_IB" value="0x55" variants="A6XX-"/>
+	<value name="CP_SET_AMBLE" value="0x55" variants="A6XX-"/>
 
 	<!--
 	Seems to always have the payload:
@@ -630,8 +641,7 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 	<value name="CP_BV_BR_COUNT_OPS" value="0x1b" variants="A7XX-"/>
 	<doc> Clears, adds to local, or adds to global timestamp </doc>
 	<value name="CP_MODIFY_TIMESTAMP" value="0x1c" variants="A7XX-"/>
-	<!-- similar to CP_CONTEXT_REG_BUNCH, but discards first two dwords?? -->
-	<value name="CP_CONTEXT_REG_BUNCH2" value="0x5d" variants="A7XX-"/>
+	<value name="CP_NON_CONTEXT_REG_BUNCH" value="0x5d" variants="A7XX-"/>
 	<doc>
 		Write to a scratch memory that is read by CP_REG_TEST with
 		SOURCE_SCRATCH_MEM set. It's not the same scratch as scratch registers.
@@ -648,6 +658,11 @@ xsi:schemaLocation="http://nouveau.freedesktop.org/ rules-ng.xsd">
 
 	<doc>Reset various on-chip state used for synchronization</doc>
 	<value name="CP_RESET_CONTEXT_STATE" value="0x1f" variants="A7XX-"/>
+
+	<doc>Invalidates the "CCHE" introduced on a740</doc>
+	<value name="CP_CCHE_INVALIDATE" value="0x3a" variants="A7XX-"/>
+
+	<value name="CP_SCOPE_CNTL" value="0x6c" variants="A7XX-"/>
 </enum>
 
 
@@ -790,14 +805,14 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<value name="SB6_GS_SHADER" value="0xb"/>
 		<value name="SB6_FS_SHADER" value="0xc"/>
 		<value name="SB6_CS_SHADER" value="0xd"/>
-		<value name="SB6_IBO"       value="0xe"/>
-		<value name="SB6_CS_IBO"    value="0xf"/>
+		<value name="SB6_UAV"       value="0xe"/>
+		<value name="SB6_CS_UAV"    value="0xf"/>
 	</enum>
 	<enum name="a6xx_state_type">
 		<value name="ST6_SHADER" value="0"/>
 		<value name="ST6_CONSTANTS" value="1"/>
 		<value name="ST6_UBO" value="2"/>
-		<value name="ST6_IBO" value="3"/>
+		<value name="ST6_UAV" value="3"/>
 	</enum>
 	<enum name="a6xx_state_src">
 		<value name="SS6_DIRECT" value="0"/>
@@ -903,12 +918,6 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	</reg32>
 
 	<stripe varset="chip" variants="A5XX-">
-		<reg32 offset="4" name="4">
-			<bitfield name="INDX_BASE_LO" low="0" high="31"/>
-		</reg32>
-		<reg32 offset="5" name="5">
-			<bitfield name="INDX_BASE_HI" low="0" high="31"/>
-		</reg32>
 		<reg64 offset="4" name="INDX_BASE" type="address"/>
 		<reg32 offset="6" name="6">
 			<!-- max # of elements in index buffer -->
@@ -1084,8 +1093,10 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 			<bitfield name="BINNING" pos="20" varset="chip" variants="A6XX-" type="boolean"/>
 			<bitfield name="GMEM" pos="21" varset="chip" variants="A6XX-" type="boolean"/>
 			<bitfield name="SYSMEM" pos="22" varset="chip" variants="A6XX-" type="boolean"/>
-			<bitfield name="GROUP_ID" low="24" high="28" type="uint"/>
+			<!-- high bit is 28 until a750: -->
+			<bitfield name="GROUP_ID" low="24" high="29" type="uint"/>
 		</reg32>
+		<reg64 offset="1" name="ADDR" type="address"/>
 		<reg32 offset="1" name="1">
 			<bitfield name="ADDR_LO" low="0" high="31" type="hex"/>
 		</reg32>
@@ -1119,39 +1130,63 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	</reg32>
 </domain>
 
+<enum name="a7xx_abs_mask_mode">
+	<value name="ABS_MASK" value="0x1"/>
+	<value name="NO_ABS_MASK" value="0x0"/>
+</enum>
+
 <domain name="CP_SET_BIN_DATA5" width="32">
 	<reg32 offset="0" name="0">
+		<bitfield name="VSC_MASK" low="0" high="15" type="hex">
+			<doc>
+				A mask of bins, starting at VSC_N, whose
+				visibility is OR'd together. A value of 0 is
+				interpreted as 1 (i.e. just use VSC_N for
+				visbility) for backwards compatibility. Only
+				exists on a7xx.
+			</doc>
+		</bitfield>
 		<!-- equiv to PC_VSTREAM_CONTROL.SIZE on a3xx/a4xx: -->
 		<bitfield name="VSC_SIZE" low="16" high="21" type="uint"/>
 		<!-- equiv to PC_VSTREAM_CONTROL.N on a3xx/a4xx: -->
 		<bitfield name="VSC_N" low="22" high="26" type="uint"/>
+		<bitfield name="ABS_MASK" pos="28" type="a7xx_abs_mask_mode" addvariant="yes">
+			<doc>
+				If this field is 1, VSC_MASK and VSC_N are
+				ignored and instead a new ordinal immediately
+				after specifies the full 32-bit mask of bins
+				to use. The mask is "absolute" instead of
+				relative to VSC_N.
+			</doc>
+		</bitfield>
 	</reg32>
-	<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
-	<reg32 offset="1" name="1">
-		<bitfield name="BIN_DATA_ADDR_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="2" name="2">
-		<bitfield name="BIN_DATA_ADDR_HI" low="0" high="31" type="hex"/>
-	</reg32>
-	<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
-	<reg32 offset="3" name="3">
-		<bitfield name="BIN_SIZE_ADDRESS_LO" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="4" name="4">
-		<bitfield name="BIN_SIZE_ADDRESS_HI" low="0" high="31"/>
-	</reg32>
-	<!-- new on a6xx, where BIN_DATA_ADDR is the DRAW_STRM: -->
-	<reg32 offset="5" name="5">
-		<bitfield name="BIN_PRIM_STRM_LO" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="6" name="6">
-		<bitfield name="BIN_PRIM_STRM_HI" low="0" high="31"/>
-	</reg32>
-	<!--
-		a7xx adds a few more addresses to the end of the pkt
-	 -->
-	<reg64 offset="7" name="7"/>
-	<reg64 offset="9" name="9"/>
+	<stripe varset="a7xx_abs_mask_mode" variants="NO_ABS_MASK">
+		<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
+		<reg64 offset="1" name="BIN_DATA_ADDR" type="address"/>
+		<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
+		<reg64 offset="3" name="BIN_SIZE_ADDR" type="address"/>
+		<!-- new on a6xx, where BIN_DATA_ADDR is the DRAW_STRM: -->
+		<reg64 offset="5" name="BIN_PRIM_STRM" type="address"/>
+		<!--
+			a7xx adds a few more addresses to the end of the pkt
+		 -->
+		<reg64 offset="7" name="7"/>
+		<reg64 offset="9" name="9"/>
+	</stripe>
+	<stripe varset="a7xx_abs_mask_mode" variants="ABS_MASK">
+		<reg32 offset="1" name="ABS_MASK"/>
+		<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
+		<reg64 offset="2" name="BIN_DATA_ADDR" type="address"/>
+		<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
+		<reg64 offset="4" name="BIN_SIZE_ADDR" type="address"/>
+		<!-- new on a6xx, where BIN_DATA_ADDR is the DRAW_STRM: -->
+		<reg64 offset="6" name="BIN_PRIM_STRM" type="address"/>
+		<!--
+			a7xx adds a few more addresses to the end of the pkt
+		 -->
+		<reg64 offset="8" name="8"/>
+		<reg64 offset="10" name="10"/>
+	</stripe>
 </domain>
 
 <domain name="CP_SET_BIN_DATA5_OFFSET" width="32">
@@ -1162,23 +1197,42 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
                 stream is recorded.
 	</doc>
 	<reg32 offset="0" name="0">
+		<bitfield name="VSC_MASK" low="0" high="15" type="hex"/>
 		<!-- equiv to PC_VSTREAM_CONTROL.SIZE on a3xx/a4xx: -->
 		<bitfield name="VSC_SIZE" low="16" high="21" type="uint"/>
 		<!-- equiv to PC_VSTREAM_CONTROL.N on a3xx/a4xx: -->
 		<bitfield name="VSC_N" low="22" high="26" type="uint"/>
+		<bitfield name="ABS_MASK" pos="28" type="a7xx_abs_mask_mode" addvariant="yes"/>
 	</reg32>
-	<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
-	<reg32 offset="1" name="1">
-		<bitfield name="BIN_DATA_OFFSET" low="0" high="31" type="uint"/>
-	</reg32>
-	<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
-	<reg32 offset="2" name="2">
-		<bitfield name="BIN_SIZE_OFFSET" low="0" high="31" type="uint"/>
-	</reg32>
-	<!-- BIN_DATA2_ADDR -> VSC_PIPE[p].DATA2_ADDRESS -->
-	<reg32 offset="3" name="3">
-		<bitfield name="BIN_DATA2_OFFSET" low="0" high="31" type="uint"/>
-	</reg32>
+	<stripe varset="a7xx_abs_mask_mode" variants="NO_ABS_MASK">
+		<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
+		<reg32 offset="1" name="1">
+			<bitfield name="BIN_DATA_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+		<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
+		<reg32 offset="2" name="2">
+			<bitfield name="BIN_SIZE_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+		<!-- BIN_DATA2_ADDR -> VSC_PIPE[p].DATA2_ADDRESS -->
+		<reg32 offset="3" name="3">
+			<bitfield name="BIN_DATA2_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+	</stripe>
+	<stripe varset="a7xx_abs_mask_mode" variants="ABS_MASK">
+		<reg32 offset="1" name="ABS_MASK"/>
+		<!-- BIN_DATA_ADDR -> VSC_PIPE[p].DATA_ADDRESS -->
+		<reg32 offset="2" name="2">
+			<bitfield name="BIN_DATA_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+		<!-- BIN_SIZE_ADDRESS -> VSC_SIZE_ADDRESS + (p * 4)-->
+		<reg32 offset="3" name="3">
+			<bitfield name="BIN_SIZE_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+		<!-- BIN_DATA2_ADDR -> VSC_PIPE[p].DATA2_ADDRESS -->
+		<reg32 offset="4" name="4">
+			<bitfield name="BIN_DATA2_OFFSET" low="0" high="31" type="uint"/>
+		</reg32>
+	</stripe>
 </domain>
 
 <domain name="CP_REG_RMW" width="32">
@@ -1196,6 +1250,9 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	</doc>
 	<reg32 offset="0" name="0">
 		<bitfield name="DST_REG" low="0" high="17" type="hex"/>
+		<bitfield name="DST_SCRATCH" pos="19" type="boolean" varset="chip" variants="A7XX-"/>
+		<!-- skip implied CP_WAIT_FOR_IDLE + CP_WAIT_FOR_ME -->
+		<bitfield name="SKIP_WAIT_FOR_ME" pos="23" type="boolean" varset="chip" variants="A7XX-"/>
 		<bitfield name="ROTATE" low="24" high="28" type="uint"/>
 		<bitfield name="SRC1_ADD" pos="29" type="boolean"/>
 		<bitfield name="SRC1_IS_REG" pos="30" type="boolean"/>
@@ -1209,7 +1266,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	</reg32>
 </domain>
 
-<domain name="CP_REG_TO_MEM" width="32">
+<domain name="CP_REG_TO_MEM" width="32" prefix="chip">
 	<reg32 offset="0" name="0">
 		<bitfield name="REG" low="0" high="17" type="hex"/>
 		<!-- number of registers/dwords copied is max(CNT, 1). -->
@@ -1217,12 +1274,12 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="64B" pos="30" type="boolean"/>
 		<bitfield name="ACCUMULATE" pos="31" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="DEST" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="2" name="2" varset="chip" variants="A5XX-">
-		<bitfield name="DEST_HI" low="0" high="31"/>
-	</reg32>
+	<stripe varset="chip" variants="A2XX-A4XX">
+		<reg32 offset="1" name="DEST" type="address"/>
+	</stripe>
+	<stripe varset="chip" variants="A5XX-">
+		<reg64 offset="1" name="DEST" type="address"/>
+	</stripe>
 </domain>
 
 <domain name="CP_REG_TO_MEM_OFFSET_REG" width="32">
@@ -1238,12 +1295,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="64B" pos="30" type="boolean"/>
 		<bitfield name="ACCUMULATE" pos="31" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="DEST" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="2" name="2" varset="chip" variants="A5XX-">
-		<bitfield name="DEST_HI" low="0" high="31"/>
-	</reg32>
+	<reg64 offset="1" name="DEST" type="waddress"/>
 	<reg32 offset="3" name="3">
 		<bitfield name="OFFSET0" low="0" high="17" type="hex"/>
 		<bitfield name="OFFSET0_SCRATCH" pos="19" type="boolean"/>
@@ -1263,18 +1315,8 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="64B" pos="30" type="boolean"/>
 		<bitfield name="ACCUMULATE" pos="31" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="DEST" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="2" name="2" varset="chip" variants="A5XX-">
-		<bitfield name="DEST_HI" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="3" name="3">
-		<bitfield name="OFFSET_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="4" name="4">
-		<bitfield name="OFFSET_HI" low="0" high="31" type="hex"/>
-	</reg32>
+	<reg64 offset="1" name="DEST" type="waddress"/>
+	<reg64 offset="3" name="OFFSET" type="waddress"/>
 </domain>
 
 <domain name="CP_MEM_TO_REG" width="32">
@@ -1287,12 +1329,12 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<!-- does the same thing as CP_MEM_TO_MEM::UNK31 -->
 		<bitfield name="UNK31" pos="31" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="SRC" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="2" name="2" varset="chip" variants="A5XX-">
-		<bitfield name="SRC_HI" low="0" high="31"/>
-	</reg32>
+	<stripe varset="chip" variants="A2XX-A4XX">
+		<reg32 offset="1" name="SRC" type="address"/>
+	</stripe>
+	<stripe varset="chip" variants="A5XX-">
+		<reg64 offset="1" name="SRC" type="address"/>
+	</stripe>
 </domain>
 
 <domain name="CP_MEM_TO_MEM" width="32">
@@ -1312,6 +1354,10 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<!-- some other kind of wait -->
 		<bitfield name="UNK31" pos="31" type="boolean"/>
 	</reg32>
+	<reg64 offset="1" name="DST" type="waddress"/>
+	<reg64 offset="3" name="SRC_A" type="address"/>
+	<reg64 offset="5" name="SRC_B" type="address"/>
+	<reg64 offset="7" name="SRC_C" type="address"/>
 	<!--
 	followed by sequence of addresses.. the first is the
 	destination and the rest are N src addresses which are
@@ -1346,6 +1392,8 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="SCRATCH" low="20" high="22" type="uint"/>
 		<!-- number of registers/dwords copied is CNT + 1. -->
 		<bitfield name="CNT" low="24" high="26" type="uint"/>
+		<!-- skip implied CP_WAIT_FOR_IDLE + CP_WAIT_FOR_ME -->
+		<bitfield name="SKIP_WAIT_FOR_ME" pos="27" type="boolean" varset="chip" variants="A7XX-"/>
 	</reg32>
 </domain>
 
@@ -1368,12 +1416,12 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 </domain>
 
 <domain name="CP_MEM_WRITE" width="32">
-	<reg32 offset="0" name="0">
-		<bitfield name="ADDR_LO" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="ADDR_HI" low="0" high="31"/>
-	</reg32>
+	<stripe varset="chip" variants="A2XX-A4XX">
+		<reg32 offset="0" name="ADDR" type="address"/>
+	</stripe>
+	<stripe varset="chip" variants="A5XX-">
+		<reg64 offset="0" name="ADDR" type="address"/>
+	</stripe>
 	<!-- followed by the DWORDs to write -->
 </domain>
 
@@ -1425,24 +1473,14 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="POLL" low="4" high="5" type="poll_memory_type"/>
 		<bitfield name="WRITE_MEMORY" pos="8" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="POLL_ADDR_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="2" name="2">
-		<bitfield name="POLL_ADDR_HI" low="0" high="31" type="hex"/>
-	</reg32>
+	<reg64 offset="1" name="POLL_ADDR" type="address"/>
 	<reg32 offset="3" name="3">
 		<bitfield name="REF" low="0" high="31"/>
 	</reg32>
 	<reg32 offset="4" name="4">
 		<bitfield name="MASK" low="0" high="31"/>
 	</reg32>
-	<reg32 offset="5" name="5">
-		<bitfield name="WRITE_ADDR_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="6" name="6">
-		<bitfield name="WRITE_ADDR_HI" low="0" high="31" type="hex"/>
-	</reg32>
+	<reg64 offset="5" name="WRITE_ADDR" type="waddress"/>
 	<reg32 offset="7" name="7">
 		<bitfield name="WRITE_DATA" low="0" high="31"/>
 	</reg32>
@@ -1457,12 +1495,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<!-- Reserved for flags, presumably? Unused in FW -->
 		<bitfield name="RESERVED" low="0" high="31" type="hex"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="POLL_ADDR_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="2" name="2">
-		<bitfield name="POLL_ADDR_HI" low="0" high="31" type="hex"/>
-	</reg32>
+	<reg64 offset="1" name="POLL_ADDR" type="address"/>
 	<reg32 offset="3" name="3">
 		<bitfield name="REF" low="0" high="31"/>
 	</reg32>
@@ -1480,12 +1513,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="POLL" low="4" high="5" type="poll_memory_type"/>
 		<bitfield name="WRITE_MEMORY" pos="8" type="boolean"/>
 	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="POLL_ADDR_LO" low="0" high="31" type="hex"/>
-	</reg32>
-	<reg32 offset="2" name="2">
-		<bitfield name="POLL_ADDR_HI" low="0" high="31" type="hex"/>
-	</reg32>
+	<reg64 offset="1" name="POLL_ADDR" type="address"/>
 	<reg32 offset="3" name="3">
 		<bitfield name="REF" low="0" high="31"/>
 	</reg32>
@@ -1619,12 +1647,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	TODO what is gpuaddr for, seems to be all 0's.. maybe needed for
 	context switch?
 	 -->
-	<reg32 offset="1" name="1">
-		<bitfield name="ADDR_0_LO" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="2" name="2">
-		<bitfield name="ADDR_0_HI" low="0" high="31"/>
-	</reg32>
+	<reg64 offset="1" name="ADDR" type="waddress"/>
 	<reg32 offset="3" name="3">
 		<!-- ??? -->
 	</reg32>
@@ -1653,8 +1676,8 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="WRITE_SAMPLE_COUNT" pos="12" type="boolean"/>
 		<!-- Write sample count at (iova + 16) -->
 		<bitfield name="SAMPLE_COUNT_END_OFFSET" pos="13" type="boolean"/>
-		<!-- *(iova + 8) = *(iova + 16) - *iova -->
-		<bitfield name="WRITE_SAMPLE_COUNT_DIFF" pos="14" type="boolean"/>
+		<!-- *(iova + 8) += *(iova + 16) - *iova -->
+		<bitfield name="WRITE_ACCUM_SAMPLE_COUNT_DIFF" pos="14" type="boolean"/>
 
 		<!-- Next 4 flags are valid to set only when concurrent binning is enabled -->
 		<!-- Increment 16b BV counter. Valid only in BV pipe -->
@@ -1668,15 +1691,11 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<bitfield name="WRITE_DST" pos="24" type="event_write_dst" addvariant="yes"/>
 		<!-- Writes into WRITE_DST from WRITE_SRC. RB_DONE_TS requires WRITE_ENABLED. -->
 		<bitfield name="WRITE_ENABLED" pos="27" type="boolean"/>
+		<bitfield name="IRQ" pos="31" type="boolean"/>
 	</reg32>
 
 	<stripe varset="event_write_dst" variants="EV_DST_RAM">
-		<reg32 offset="1" name="1">
-			<bitfield name="ADDR_0_LO" low="0" high="31"/>
-		</reg32>
-		<reg32 offset="2" name="2">
-			<bitfield name="ADDR_0_HI" low="0" high="31"/>
-		</reg32>
+		<reg64 offset="1" name="1" type="waddress"/>
 		<reg32 offset="3" name="3">
 			<bitfield name="PAYLOAD_0" low="0" high="31"/>
 		</reg32>
@@ -1743,9 +1762,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	<reg32 offset="0" name="0">
 	</reg32>
 	<stripe varset="chip" variants="A4XX">
-		<reg32 offset="1" name="1">
-			<bitfield name="ADDR" low="0" high="31"/>
-		</reg32>
+		<reg32 offset="1" name="ADDR" type="address"/>
 		<reg32 offset="2" name="2">
 			<!-- localsize is value minus one: -->
 			<bitfield name="LOCALSIZEX" low="2" high="11" type="uint"/>
@@ -1754,12 +1771,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		</reg32>
 	</stripe>
 	<stripe varset="chip" variants="A5XX-">
-		<reg32 offset="1" name="1">
-			<bitfield name="ADDR_LO" low="0" high="31"/>
-		</reg32>
-		<reg32 offset="2" name="2">
-			<bitfield name="ADDR_HI" low="0" high="31"/>
-		</reg32>
+		<reg64 offset="1" name="ADDR" type="address"/>
 		<reg32 offset="3" name="3">
 			<!-- localsize is value minus one: -->
 			<bitfield name="LOCALSIZEX" low="2" high="11" type="uint"/>
@@ -1771,40 +1783,88 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 
 <domain name="CP_SET_MARKER" width="32" varset="chip" prefix="chip" variants="A6XX-">
 	<doc>Tell CP the current operation mode, indicates save and restore procedure</doc>
+	<enum name="set_marker_mode">
+		<value value="0" name="SET_RENDER_MODE"/>
+		<!-- IFPC - inter-frame power collapse -->
+		<value value="1" name="SET_IFPC_MODE"/>
+	</enum>
+	<enum name="a6xx_ifpc_mode">
+		<value value="0" name="IFPC_ENABLE"/>
+		<value value="1" name="IFPC_DISABLE"/>
+	</enum>
 	<enum name="a6xx_marker">
-		<value value="1" name="RM6_BYPASS"/>
-		<value value="2" name="RM6_BINNING"/>
-		<value value="4" name="RM6_GMEM"/>
-		<value value="5" name="RM6_ENDVIS"/>
-		<value value="6" name="RM6_RESOLVE"/>
-		<value value="7" name="RM6_YIELD"/>
+		<value value="1" name="RM6_DIRECT_RENDER"/>
+		<value value="2" name="RM6_BIN_VISIBILITY"/>
+		<value value="3" name="RM6_BIN_DIRECT"/>
+		<value value="4" name="RM6_BIN_RENDER_START"/>
+		<value value="5" name="RM6_BIN_END_OF_DRAWS"/>
+		<value value="6" name="RM6_BIN_RESOLVE"/>
+		<value value="7" name="RM6_BIN_RENDER_END"/>
 		<value value="8" name="RM6_COMPUTE"/>
-		<value value="0xc" name="RM6_BLIT2DSCALE"/>  <!-- no-op (at least on current sqe fw) -->
+		<value value="12" name="RM6_BLIT2DSCALE"/>  <!-- no-op (at least on current sqe fw) -->
 
 		<!--
 			These values come from a6xx_set_marker() in the
 			downstream kernel, and they can only be set by the kernel
 		-->
-		<value value="0xd" name="RM6_IB1LIST_START"/>
-		<value value="0xe" name="RM6_IB1LIST_END"/>
-		<!-- IFPC - inter-frame power collapse -->
-		<value value="0x100" name="RM6_IFPC_ENABLE"/>
-		<value value="0x101" name="RM6_IFPC_DISABLE"/>
+		<value value="13" name="RM6_IB1LIST_START"/>
+		<value value="14" name="RM6_IB1LIST_END"/>
+		<value value="15" name="RM7_BIN_VISIBILITY_END"/>
+
+		<!-- new in a8xx: -->
+		<value value="32" name="RM8_DEPTH_PASS_START"/>
+		<value value="33" name="RM8_DEPTH_PASS_END"/>
 	</enum>
-	<reg32 offset="0" name="0">
-		<!--
-			NOTE: blob driver and some versions of freedreno/turnip set
-			b4, which is unused (at least by current sqe fw), but interferes
-			with parsing if we extend the size of the bitfield to include
-			b8 (only sent by kernel mode driver).  Really, the way the
-			parsing works in the firmware, only b0-b3 are considered, but
-			if b8 is set, the low bits are interpreted differently.  To
-			model this, without getting confused by spurious b4, this is
-			described as two overlapping bitfields:
-		 -->
-		<bitfield name="MODE" low="0" high="8" type="a6xx_marker"/>
-		<bitfield name="MARKER" low="0" high="3" type="a6xx_marker"/>
-	</reg32>
+	<stripe varset="chip" variants="A6XX-A7XX">
+		<reg32 offset="0" name="0">
+			<!-- if b8 is set, the low bits are interpreted differently (and b4 ignored) -->
+			<bitfield name="MARKER_MODE" pos="8" type="set_marker_mode" addvariant="yes"/>
+
+
+			<bitfield name="MODE" low="0" high="3" type="a6xx_marker" varset="set_marker_mode" variants="SET_RENDER_MODE"/>
+			<!-- used by preemption to determine if GMEM needs to be saved or not -->
+			<bitfield name="USES_GMEM" pos="4" type="boolean" varset="set_marker_mode" variants="SET_RENDER_MODE"/>
+
+
+			<bitfield name="IFPC_MODE" pos="0" type="a6xx_ifpc_mode" varset="set_marker_mode" variants="SET_IFPC_MODE"/>
+
+
+			<!--
+				CP_SET_MARKER is used with these bits to create a
+				critical section around a workaround for ray tracing.
+				The workaround happens after BVH building, and appears
+				to invalidate the RTU's BVH node cache. It makes sure
+				that only one of BR/BV/LPAC is executing the
+				workaround at a time, and no draws using RT on BV/LPAC
+				are executing while the workaround is executed on BR (or
+				vice versa, that no draws on BV/BR using RT are executed
+				while the workaround executes on LPAC), by
+				hooking subsequent CP_EVENT_WRITE/CP_DRAW_*/CP_EXEC_CS.
+				The blob usage is:
+
+
+				CP_SET_MARKER(RT_WA_START)
+				... workaround here ...
+				CP_SET_MARKER(RT_WA_END)
+				...
+				CP_SET_MARKER(SHADER_USES_RT)
+				CP_DRAW_INDX(...) or CP_EXEC_CS(...)
+			-->
+			<bitfield name="SHADER_USES_RT" pos="9" type="boolean" variants="A7XX-"/>
+			<bitfield name="RT_WA_START" pos="10" type="boolean" variants="A7XX-"/>
+			<bitfield name="RT_WA_END" pos="11" type="boolean" variants="A7XX-"/>
+		</reg32>
+	</stripe>
+	<stripe varset="chip" variants="A8XX-">
+		<reg32 offset="0" name="0">
+			<!-- if b8 is set, the low bits are interpreted differently (and b4 ignored) -->
+			<bitfield name="MARKER_MODE" pos="8" type="set_marker_mode" addvariant="yes"/>
+			<bitfield name="USES_GMEM" pos="7" type="boolean" varset="set_marker_mode" variants="SET_RENDER_MODE"/>
+			<bitfield name="MODE" low="0" high="6" type="a6xx_marker" varset="set_marker_mode" variants="SET_RENDER_MODE"/>
+			<bitfield name="IFPC_MODE" pos="0" type="a6xx_ifpc_mode" varset="set_marker_mode" variants="SET_IFPC_MODE"/>
+			<!-- idk if the RT w/a fields apply to a8xx as well -->
+		</reg32>
+	</stripe>
 </domain>
 
 <domain name="CP_SET_PSEUDO_REG" width="32" varset="chip" prefix="chip" variants="A6XX-">
@@ -1830,9 +1890,9 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 			If concurrent binning is disabled then BR also does binning so it will also
 			write the "real" registers in BR.
 		-->
-		<value value="8" name="DRAW_STRM_ADDRESS"/>
-		<value value="9" name="DRAW_STRM_SIZE_ADDRESS"/>
-		<value value="10" name="PRIM_STRM_ADDRESS"/>
+		<value value="8" name="VSC_PIPE_DATA_DRAW_BASE"/>
+		<value value="9" name="VSC_SIZE_BASE"/>
+		<value value="10" name="VSC_PIPE_DATA_PRIM_BASE"/>
 		<value value="11" name="UNK_STRM_ADDRESS"/>
 		<value value="12" name="UNK_STRM_SIZE_ADDRESS"/>
 
@@ -1933,11 +1993,11 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 			a bitmask of which modes pass the test.
 		-->
 
-		<!-- RM6_BINNING -->
+		<!-- RM6_BIN_VISIBILITY -->
 		<bitfield name="BINNING" pos="25" variants="RENDER_MODE" type="boolean"/>
 		<!-- all others -->
 		<bitfield name="GMEM" pos="26" variants="RENDER_MODE" type="boolean"/>
-		<!-- RM6_BYPASS -->
+		<!-- RM6_DIRECT_RENDER -->
 		<bitfield name="SYSMEM" pos="27" variants="RENDER_MODE" type="boolean"/>
 
 		<bitfield name="BV" pos="25" variants="THREAD_MODE" type="boolean"/>
@@ -2010,54 +2070,45 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	</reg32>
 </domain>
 
-<domain name="CP_SET_CTXSWITCH_IB" width="32">
+<domain name="CP_SET_AMBLE" width="32">
 	<doc>
-                Used by the userspace driver to set various IB's which are
-                executed during context save/restore for handling
-                state that isn't restored by the
-                context switch routine itself.
+		Used by the userspace and kernel drivers to set various IB's
+		which are executed during context save/restore for handling
+		state that isn't restored by the context switch routine itself.
 	</doc>
-	<enum name="ctxswitch_ib">
-		<value name="RESTORE_IB" value="0">
+	<enum name="amble_type">
+		<value name="PREAMBLE_AMBLE_TYPE" value="0">
 			<doc>Executed unconditionally when switching back to the context.</doc>
 		</value>
-		<value name="YIELD_RESTORE_IB" value="1">
+		<value name="BIN_PREAMBLE_AMBLE_TYPE" value="1">
                         <doc>
 				Executed when switching back after switching
 				away during execution of
-				a CP_SET_MARKER packet with RM6_YIELD as the
-				payload *and* the normal save routine was
-				bypassed for a shorter one. I think this is
-				connected to the "skipsaverestore" bit set by
-				the kernel when preempting.
+				a CP_SET_MARKER packet with RM6_BIN_RENDER_END as the
+				payload *and* skipsaverestore is set. This is
+				expected to restore static register values not
+				saved when skipsaverestore is set.
 			</doc>
 		</value>
-		<value name="SAVE_IB" value="2">
+		<value name="POSTAMBLE_AMBLE_TYPE" value="2">
                         <doc>
 				Executed when switching away from the context,
 				except for context switches initiated via
 				CP_YIELD.
                         </doc>
 		</value>
-		<value name="RB_SAVE_IB" value="3">
+		<value name="KMD_AMBLE_TYPE" value="3">
 			<doc>
 				This can only be set by the RB (i.e. the kernel)
 				and executes with protected mode off, but
-				is otherwise similar to SAVE_IB.
-
-				Note, kgsl calls this CP_KMD_AMBLE_TYPE
+				is otherwise similar to POSTAMBLE_AMBLE_TYPE.
 			</doc>
 		</value>
 	</enum>
-	<reg32 offset="0" name="0">
-		<bitfield name="ADDR_LO" low="0" high="31"/>
-	</reg32>
-	<reg32 offset="1" name="1">
-		<bitfield name="ADDR_HI" low="0" high="31"/>
-	</reg32>
+	<reg64 offset="0" name="ADDR" type="address"/>
 	<reg32 offset="2" name="2">
 		<bitfield name="DWORDS" low="0" high="19" type="uint"/>
-		<bitfield name="TYPE" low="20" high="21" type="ctxswitch_ib"/>
+		<bitfield name="TYPE" low="20" high="21" type="amble_type"/>
 	</reg32>
 </domain>
 
@@ -2089,12 +2140,12 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 		<value name="UNK_EVENT_WRITE" value="0x4"/>
 		<doc>
 			Tracks GRAS_LRZ_CNTL::GREATER, GRAS_LRZ_CNTL::DIR, and
-			GRAS_LRZ_DEPTH_VIEW with previous values, and if one of
+			GRAS_LRZ_VIEW_INFO with previous values, and if one of
 			the following is true:
 			- GRAS_LRZ_CNTL::GREATER has changed
 			- GRAS_LRZ_CNTL::DIR has changed, the old value is not
 			  CUR_DIR_GE, and the new value is not CUR_DIR_DISABLED
-			- GRAS_LRZ_DEPTH_VIEW has changed
+			- GRAS_LRZ_VIEW_INFO has changed
 			then it does a LRZ_FLUSH with GRAS_LRZ_CNTL::ENABLE
 			forced to 1.
 			Only exists in a650_sqe.fw.
@@ -2209,7 +2260,7 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 
 <domain name="CP_MEM_TO_SCRATCH_MEM" width="32">
 	<doc>
-		Best guess is that it is a faster way to fetch all the VSC_STATE registers
+		Best guess is that it is a faster way to fetch all the VSC_CHANNEL_VISIBILITY registers
 		and keep them in a local scratch memory instead of fetching every time
 		when skipping IBs.
 	</doc>
@@ -2257,7 +2308,25 @@ opcode: CP_LOAD_STATE4 (30) (4 dwords)
 	<reg32 offset="0" name="0">
 		<bitfield name="CLEAR_ON_CHIP_TS" pos="0" type="boolean"/>
 		<bitfield name="CLEAR_RESOURCE_TABLE" pos="1" type="boolean"/>
-		<bitfield name="CLEAR_GLOBAL_LOCAL_TS" pos="2" type="boolean"/>
+		<bitfield name="CLEAR_BV_BR_COUNTER" pos="2" type="boolean"/>
+		<bitfield name="RESET_GLOBAL_LOCAL_TS" pos="3" type="boolean"/>
+	</reg32>
+</domain>
+
+<domain name="CP_SCOPE_CNTL" width="32">
+	<enum name="cp_scope">
+		<value value="0" name="INTERRUPTS"/>
+	</enum>
+	<reg32 offset="0" name="0">
+		<bitfield name="DISABLE_PREEMPTION" pos="0" type="boolean"/>
+		<bitfield low="28" high="31" name="SCOPE" type="cp_scope"/>
+	</reg32>
+</domain>
+
+<domain name="CP_INDIRECT_BUFFER" width="32" varset="chip" prefix="chip" variants="A5XX-">
+	<reg64 offset="0" name="IB_BASE" type="address"/>
+	<reg32 offset="2" name="2">
+		<bitfield name="IB_SIZE" low="0" high="19"/>
 	</reg32>
 </domain>
 

extra/qcom_gpu_driver/opencl_ioctl.py

@@ -97,7 +97,7 @@ def parse_cmd_buf(dat):
 
         if state_block == SB6_CS_SHADER:
           from extra.disassemblers.adreno import disasm_raw
-          if state_type == ST6_SHADER and IOCTL > 2:
+          if state_type == ST6_SHADER and IOCTL > 3:
             disasm_raw(get_mem(((vals[2] << 32) | vals[1]), num_unit * 128))
           if state_type == ST6_CONSTANTS:
             x = get_mem(((vals[2] << 32) | vals[1]), num_unit*4)
@@ -106,25 +106,30 @@ def parse_cmd_buf(dat):
               print('constants')
               hexdump(x)
           if state_type == ST6_IBO:
-            ibos_bytes = get_mem((vals[2] << 32) | vals[1], num_unit * 16 * 4)
+            if state_src == 0x1:
+              ibos_bytes = get_mem(CAPTURED_STATE['bindless_base'] + ((vals[2] << 32) | vals[1]) * 4, num_unit * 64)
+            else: ibos_bytes = get_mem((vals[2] << 32) | vals[1], num_unit * 16 * 4)
             CAPTURED_STATE['ibos'] = ibos_bytes[:]
             if IOCTL > 1:
               print('texture ibos')
               hexdump(ibos_bytes)
         elif state_block == SB6_CS_TEX:
           if state_type == ST6_SHADER:
-            samplers_bytes = get_mem((vals[2] << 32) | vals[1], num_unit * 4 * 4)
+            if state_src == 0x1:
+              samplers_bytes = get_mem(CAPTURED_STATE['bindless_base'] + ((vals[2] << 32) | vals[1]) * 4, num_unit * 64)
+            else: samplers_bytes = get_mem((vals[2] << 32) | vals[1], num_unit * 4 * 4)
             CAPTURED_STATE['samplers'] = samplers_bytes[:]
             if IOCTL > 1:
               print('texture samplers')
               hexdump(samplers_bytes)
           if state_type == ST6_CONSTANTS:
-            descriptors_bytes = get_mem((vals[2] << 32) | vals[1], 1600)
+            if state_src == 0x1:
+              descriptors_bytes = get_mem(CAPTURED_STATE['bindless_base'] + ((vals[2] << 32) | vals[1]) * 4, num_unit * 64)
+            else: descriptors_bytes = get_mem((vals[2] << 32) | vals[1], 1600)
             CAPTURED_STATE['descriptors'] = descriptors_bytes[:]
             if IOCTL > 1:
               print('texture descriptors')
               hexdump(descriptors_bytes)
-
       elif ops[opcode] == "CP_REG_TO_MEM":
         reg, cnt, b64, accum = vals[0] & 0x3FFFF, (vals[0] >> 18) & 0xFFF, (vals[0] >> 30) & 0x1, (vals[0] >> 31) & 0x1
         dest = vals[1] | (vals[2] << 32)
@@ -152,6 +157,10 @@ def parse_cmd_buf(dat):
         if IOCTL > 0:
           print(f'THREADSIZE-{(vals[0] >> 20)&0x1}\nEARLYPREAMBLE-{(vals[0] >> 23) & 0x1}\nMERGEDREGS-{(vals[0] >> 3) & 0x1}\nTHREADMODE-{vals[0] & 0x1}\nHALFREGFOOTPRINT-{(vals[0] >> 1) & 0x3f}\nFULLREGFOOTPRINT-{(vals[0] >> 7) & 0x3f}\nBRANCHSTACK-{(vals[0] >> 14) & 0x3f}\n')
           print(f'SP_CS_UNKNOWN_A9B1-{vals[1]}\nSP_CS_BRANCH_COND-{vals[2]}\nSP_CS_OBJ_FIRST_EXEC_OFFSET-{vals[3]}\nSP_CS_OBJ_START-{vals[4] | (vals[5] << 32)}\nSP_CS_PVT_MEM_PARAM-{vals[6]}\nSP_CS_PVT_MEM_ADDR-{vals[7] | (vals[8] << 32)}\nSP_CS_PVT_MEM_SIZE-{vals[9]}')
+      if offset == 0xa9e8:
+        CAPTURED_STATE['bindless_base'] = (vals[0] | (vals[1] << 32)) & ~0b11
+        # print(hex(CAPTURED_STATE['bindless_base']))
+        # hexdump(get_mem(CAPTURED_STATE['bindless_base'], 0x200))
       if offset == 0xb180:
         if IOCTL > 0:
           print('border color offset', hex(vals[1] << 32 | vals[0]))
@@ -171,8 +180,8 @@ def ioctl(fd, request, argp):
     name, stype = nrs[nr]
     s = get_struct(argp, stype)
     if IOCTL > 0: print(f"{ret:2d} = {name:40s}", ' '.join(format_struct(s)))
-    if name == "IOCTL_KGSL_GPUOBJ_INFO": pass
-      # mmaped[s.gpuaddr] = mmap.mmap(fd, s.size, offset=s.id*0x1000)
+    if name == "IOCTL_KGSL_GPUOBJ_INFO":
+      mmaped[s.gpuaddr] = mmap.mmap(fd, s.size, offset=s.id*0x1000)
     if name == "IOCTL_KGSL_GPU_COMMAND":
       for i in range(s.numcmds):
         cmd = get_struct(s.cmdlist+ctypes.sizeof(msm_kgsl.struct_kgsl_command_object)*i, msm_kgsl.struct_kgsl_command_object)

extra/usbgpu/tbgpu/installer/Shared/TinyGPUViewModel.swift

@@ -56,7 +56,7 @@ class TinyGPUViewModel: NSObject {
 	}
 #endif
 
-	private let dextIdentifier: String = Bundle.main.bundleIdentifier! + ".Driver"
+	private let dextIdentifier: String = "org.tinygrad.tinygpu.edriver"
 
 	public var dextLoadingState: String {
 		switch state {

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension.xcodeproj/project.pbxproj

@@ -321,7 +321,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_ENTITLEMENTS = macOS/macOS.entitlements;
 				CODE_SIGN_IDENTITY = "-";
-				"CODE_SIGN_IDENTITY[sdk=macosx*]" = "Apple Development";
+				"CODE_SIGN_IDENTITY[sdk=macosx*]" = "-";
 				CODE_SIGN_STYLE = Automatic;
 				COMBINE_HIDPI_IMAGES = YES;
 				CURRENT_PROJECT_VERSION = 1;
@@ -357,7 +357,7 @@
 				CLANG_ENABLE_MODULES = YES;
 				CODE_SIGN_ENTITLEMENTS = macOS/macOS.entitlements;
 				CODE_SIGN_IDENTITY = "-";
-				"CODE_SIGN_IDENTITY[sdk=macosx*]" = "Apple Development";
+				"CODE_SIGN_IDENTITY[sdk=macosx*]" = "-";
 				CODE_SIGN_STYLE = Automatic;
 				COMBINE_HIDPI_IMAGES = YES;
 				CURRENT_PROJECT_VERSION = 1;
@@ -502,7 +502,7 @@
 			buildSettings = {
 				AD_HOC_CODE_SIGNING_ALLOWED = YES;
 				CODE_SIGN_ENTITLEMENTS = TinyGPUDriverExtension/TinyGPUDriver.entitlements;
-				CODE_SIGN_IDENTITY = "Apple Development";
+				CODE_SIGN_IDENTITY = "-";
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
 				DEVELOPMENT_TEAM = 9YG3G8543N;
@@ -530,7 +530,7 @@
 			buildSettings = {
 				AD_HOC_CODE_SIGNING_ALLOWED = YES;
 				CODE_SIGN_ENTITLEMENTS = TinyGPUDriverExtension/TinyGPUDriver.entitlements;
-				CODE_SIGN_IDENTITY = "Apple Development";
+				CODE_SIGN_IDENTITY = "-";
 				CODE_SIGN_STYLE = Automatic;
 				CURRENT_PROJECT_VERSION = 1;
 				DEVELOPMENT_TEAM = 9YG3G8543N;

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/Info.plist

@@ -12,8 +12,8 @@
 			<string>IOUserService</string>
 			<key>IOMatchCategory</key>
 			<string>TinyGPUDriver</string>
-			<key>IOPCIPrimaryMatch</key>
-			<string>0x70001002&amp;0xF000FFFF</string>
+			<key>IOPCIClassMatch</key>
+			<string>0x03000000</string>
 			<key>IOPCITunnelCompatible</key>
 			<true/>
 			<key>IOProviderClass</key>

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.cpp

@@ -87,7 +87,7 @@ kern_return_t TinyGPUDriver::Start_Impl(IOService* in_provider)
 		}
 		off = next;
 	}
-	ivars->pci->Reset(0);
+	ivars->pci->Reset(kIOPCIDeviceResetTypeHotReset);
 #endif
 
 	uint16_t commandRegister;
@@ -221,3 +221,39 @@ error:
 	}
 	return err;
 }
+
+kern_return_t TinyGPUDriver::CfgRead(uint32_t off, uint32_t size, uint32_t* outVal)
+{
+  if (!ivars->pci || !outVal) return kIOReturnNotReady;
+
+  if (size == 1) {
+	uint8_t v8 = 0;
+	ivars->pci->ConfigurationRead8(off, &v8);
+	*outVal = v8;
+  } else if (size == 2) {
+	uint16_t v16 = 0;
+	ivars->pci->ConfigurationRead16(off, &v16);
+	*outVal = v16;
+  } else if (size == 4) {
+	uint32_t v32 = 0;
+	ivars->pci->ConfigurationRead32(off, &v32);
+	*outVal = v32;
+  }
+  return 0;
+}
+
+kern_return_t TinyGPUDriver::CfgWrite(uint32_t off, uint32_t size, uint32_t val)
+{
+  if (!ivars->pci) return kIOReturnNotReady;
+  if (size == 1) ivars->pci->ConfigurationWrite8 (off, (uint8_t)val);
+  else if (size == 2) ivars->pci->ConfigurationWrite16(off, (uint16_t)val);
+  else if (size == 4) ivars->pci->ConfigurationWrite32(off, (uint32_t)val);
+  return 0;
+}
+
+kern_return_t TinyGPUDriver::ResetDevice()
+{
+	if (!ivars->pci) return kIOReturnNotReady;
+	ivars->pci->Reset(kIOPCIDeviceResetTypeFunctionReset);
+	return 0;
+}

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.entitlements

@@ -6,7 +6,11 @@
 	<array>
 		<dict>
 			<key>IOPCIMatch</key>
-			<string>0x70001002&amp;0xF000FFFF</string>
+			<string>0x00001002&amp;0x0000FFFF</string>
+		</dict>
+		<dict>
+			<key>IOPCIMatch</key>
+			<string>0x000010de&amp;0x0000FFFF</string>
 		</dict>
 	</array>
 	<key>com.apple.developer.driverkit.allow-any-userclient-access</key>

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriver.iig

@@ -28,6 +28,11 @@ public:
 
 	kern_return_t MapBar(uint32_t bar, IOMemoryDescriptor** memory) LOCALONLY;
 	kern_return_t CreateDMA(size_t size, TinyGPUCreateDMAResp* dmaDesc) LOCALONLY;
+
+	kern_return_t CfgRead(uint32_t off, uint32_t size, uint32_t* val) LOCALONLY;
+	kern_return_t CfgWrite(uint32_t off, uint32_t size, uint32_t val) LOCALONLY;
+	kern_return_t ResetDevice() LOCALONLY;
+	kern_return_t BarInfo() LOCALONLY;
 };
 
 #endif /* TinyGPUDriver_h */

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriverUserClient.cpp

@@ -62,8 +62,41 @@ kern_return_t TinyGPUDriverUserClient::Stop_Impl(IOService* in_provider)
 	return Stop(in_provider, SUPERDISPATCH);
 }
 
-kern_return_t TinyGPUDriverUserClient::ExternalMethod(uint64_t in_selector, IOUserClientMethodArguments* in_arguments, const IOUserClientMethodDispatch* in_dispatch, OSObject* in_target, void* in_reference)
+kern_return_t TinyGPUDriverUserClient::ExternalMethod(uint64_t selector, IOUserClientMethodArguments* args, const IOUserClientMethodDispatch* in_dispatch, OSObject* in_target, void* in_reference)
 {
+	kern_return_t err = 0;
+
+	os_log(OS_LOG_DEFAULT, "tinygpu: rpc (%llu) in:%d, out:%d", selector, args->scalarInputCount, args->scalarOutputCount);
+
+	if (selector == TinyGPURPC::ReadCfg) {
+		if (args->scalarInputCount != 2 or args->scalarOutputCount < 1) return kIOReturnBadArgument;
+
+		uint32_t off = uint32_t(args->scalarInput[0]);
+		uint32_t size = uint32_t(args->scalarInput[1]);
+
+		uint32_t val = 0;
+		err = ivars->provider->CfgRead(off, size, &val);
+		os_log(OS_LOG_DEFAULT, "tinygpu: read cfg off:%x sz:%d, val:%x", off, size, val);
+
+		if (!err) {
+			args->scalarOutput[0] = val;
+			args->scalarOutputCount = 1;
+		}
+		return err;
+	} else if (selector == TinyGPURPC::WriteCfg) {
+		if (args->scalarInputCount != 3) return kIOReturnBadArgument;
+
+		uint32_t off = uint32_t(args->scalarInput[0]);
+		uint32_t size = uint32_t(args->scalarInput[1]);
+		uint32_t val = uint32_t(args->scalarInput[2]);
+
+		os_log(OS_LOG_DEFAULT, "tinygpu: wr cfg off:%x sz:%d, val:%x", off, size, val);
+		return ivars->provider->CfgWrite(off, size, val);
+	} else if (selector == TinyGPURPC::Reset) {
+		os_log(OS_LOG_DEFAULT, "tinygpu: reset");
+		return ivars->provider->ResetDevice();
+	}
+
 	return kIOReturnUnsupported;
 }
 

extra/usbgpu/tbgpu/installer/TinyGPUDriverExtension/TinyGPUDriverUserClient.iig

@@ -3,6 +3,13 @@
 
 #include <DriverKit/IOUserClient.iig>
 
+enum TinyGPURPC
+{
+	ReadCfg,
+	WriteCfg,
+	Reset
+};
+
 class TinyGPUDriverUserClient : public IOUserClient
 {
 public:

test/external/external_benchmark_bert_softmax.py

@@ -1,4 +1,4 @@
-from tinygrad import Tensor, dtypes, Context, GlobalCounters
+from tinygrad import Tensor, dtypes, GlobalCounters
 dtypes.default_float = dtypes.float16
 from tinygrad.dtype import to_dtype
 from tinygrad.helpers import getenv
@@ -13,6 +13,5 @@ if __name__ == "__main__":
 
   # test single kernel softmax
   GlobalCounters.reset()
-  with Context(DONT_GROUP_REDUCES=1):
-    single_kernel_softmax(t, -1, acc_dtype).realize()
+  single_kernel_softmax(t, -1, acc_dtype).realize()
 

test/external/external_benchmark_schedule.py

@@ -2,7 +2,8 @@ from extra.models.resnet import ResNet50
 from tinygrad import Tensor, nn, Device
 from tinygrad.helpers import Profiling, Timing, getenv
 from tinygrad.uop.ops import Ops
-from tinygrad.codegen import get_rewrites_for_renderer, apply_rewrites, rewrites_for_linearizer
+from tinygrad.codegen import get_rewrites_for_renderer, apply_rewrites
+from tinygrad.codegen.late.control_flow import linearize
 from tinygrad.uop.spec import type_verify
 
 if __name__ == "__main__":
@@ -39,7 +40,7 @@ if __name__ == "__main__":
           with Timing("***** model linearize in "):
             uops_line = []
             for u in rewritten_uops:
-              uops_line.append(apply_rewrites(u, rewrites_for_linearizer))
+              uops_line.append(linearize(u))
           with Timing("***** model verify in    "):
-            for u in uops_line: type_verify(u.arg.lst)
-          print(sum(len(u.arg.lst) for u in uops_line))
+            for u in uops_line: type_verify(u)
+          print(sum(len(u) for u in uops_line))

test/external/external_debug_metal_sd_conv.py

@@ -1,46 +0,0 @@
-# ruff: noqa: E501
-from tinygrad.codegen.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.uop.ops import UOp, Ops
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import View
-
-ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-  UOp(Ops.STORE, dtypes.void, arg=None, src=(
-    UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=0, src=()),
-    UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 1, 1, 1), strides=(81920, 0, 64, 8, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=()),
-    UOp(Ops.ADD, dtypes.half, arg=None, src=(
-      UOp(Ops.ADD, dtypes.half, arg=None, src=(
-        UOp(Ops.CAST, dtypes.half, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
-            UOp(Ops.CAST, dtypes.float, arg=None, src=(
-              UOp(Ops.MUL, dtypes.half, arg=None, src=(
-                UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=1, src=()),
-                  UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 2, 1, 2560, 4, 10, 4, 10), strides=(0, 163840, 0, 64, 0, 8, 0, 1), offset=-9, mask=((0, 1), (0, 2), (0, 1), (0, 2560), (0, 4), (1, 9), (0, 4), (1, 9)), contiguous=False), View(shape=(2, 1, 1280, 8, 8, 2560, 3, 3), strides=(4096000, 0, 0, 40, 1, 1600, 440, 11), offset=0, mask=None, contiguous=False))), src=()),)),
-                UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=2, src=()),
-                  UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 2560, 3, 3), strides=(0, 0, 23040, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),)),
-        UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=3, src=()),
-          x17:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(2, 1, 1280, 8, 8, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-      UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(), arg=4, src=()),
-         x17,)),)),)),))
-opts = [Opt(op=OptOps.UPCAST, axis=3, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=2, arg=0), Opt(op=OptOps.UNROLL, axis=1, arg=0), Opt(op=OptOps.LOCAL, axis=1, arg=8), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.LOCAL, axis=2, arg=2)]
-
-k = Kernel(ast)
-k.apply_opts(opts)
-bufs = bufs_from_lin(k)
-
-prg = CompiledRunner(get_program(k.ast, k.opts, k.applied_opts))
-
-for i in range(10):
-  speed = prg(bufs, var_vals={}, wait=True)
-  print(f"kernel time: {speed*1e3:.2f} ms")
-
-# on M1 Max
-# 11ms before block 9b0859d71780fef5cf3831e317f74e53f2483229
-# 15ms after block cbcc1c20eb09a1342f6581cfbb99632bade982a8

test/external/external_test_onnx_ops.py

@@ -282,11 +282,11 @@ class TestTrainingOnnxOps(TestOnnxOps):
     tiny_out = runner(inps)
     onnx_out = onnx_fxn(**inps, **opts)
     for (nm, t_out), o_out in  zip(tiny_out.items(), onnx_out):
-      np.testing.assert_allclose(t_out.numpy(), o_out, rtol=1e-3, atol=1e-6, err_msg=f"{nm} failed")
+      np.testing.assert_allclose(t_out.numpy(), o_out, rtol=1e-6, atol=1e-6, err_msg=f"{nm} failed")
 
-  def test_adagrad_t_greater_than_zero(self):
+  def test_adagrad_t(self):
     from onnx.backend.test.case.node.adagrad import apply_adagrad
-    for t in [1, 3, 100]:
+    for t in [0, 1, 3, 100]:
       inputs = {
         "r": np.array(0.01, dtype=np.float32),
         "t": np.array(t, dtype=np.int32),
@@ -298,10 +298,10 @@ class TestTrainingOnnxOps(TestOnnxOps):
       outputs = ["X_out", "H_out"]
       self._validate_training("Adagrad", apply_adagrad, inputs, attributes, outputs)
 
-  def test_momentum_t_greater_than_zero(self):
+  def test_momentum(self):
     from onnx.backend.test.case.node.momentum import apply_momentum, apply_nesterov
     for onnx_fxn, mode in ((apply_momentum, "standard"), (apply_nesterov, "nesterov")):
-      for t in [1, 3, 100]:
+      for t in [0, 1, 3, 100]:
         inputs = {
           "r": np.array(0.01, dtype=np.float32),
           "t": np.array(t, dtype=np.int32),
@@ -313,9 +313,9 @@ class TestTrainingOnnxOps(TestOnnxOps):
         outputs = ["X_out", "V_out"]
         self._validate_training("Momentum", onnx_fxn, inputs, attributes, outputs)
 
-  def test_adam_t_greater_than_zero(self):
+  def test_adam(self):
     from onnx.backend.test.case.node.adam import apply_adam
-    for t in [1, 3, 100]:
+    for t in [0, 1, 3, 100]:
       inputs = {
         "r": np.array(0.01, dtype=np.float32),
         "t": np.array(t, dtype=np.int32),
@@ -422,6 +422,7 @@ class TestContribOnnxOps(TestOnnxOps):
         outputs = ["C"]
         self.helper_test_single_op("QLinearAdd", inputs, attributes, outputs, atol=1) # TODO: look into why this is inaccurate
 
+  def test_qlinear_add_round_half_to_even(self):
     with self.subTest(test_case="round_half_to_even"):
       inputs = {
         "A": np.array([1, 1, 1, 1], dtype=np.int8),
@@ -435,7 +436,7 @@ class TestContribOnnxOps(TestOnnxOps):
       }
       attributes = {}
       outputs = ["C"]
-      self.helper_test_single_op("QLinearAdd", inputs, attributes, outputs)
+      self.helper_test_single_op("QLinearAdd", inputs, attributes, outputs, atol=1) # TODO: look into why this is inaccurate
 
   def test_qlinear_mul(self):
     for dtype, zero_point in [(np.uint8, 128), (np.int8, 0)]:

test/external/external_test_train_gpt2.py

@@ -1,55 +0,0 @@
-# ruff: noqa: E501
-import unittest
-
-from tinygrad.uop.ops import UOp, Ops
-from .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 test.external.fuzz_linearizer import run_linearizer
-
-class TestTrainGpt2Kernel(unittest.TestCase):
-  def test_1(self):
-    # kernel 244
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(206045184), arg=0, src=()),
-        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 1), strides=(51511296, 50304, 1, 0), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
-        UOp(Ops.MUL, dtypes.float, arg=None, src=(
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(3145728), arg=1, src=()),
-          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(786432, 768, 0, 1), offset=0, mask=None, contiguous=False),)), src=()),)),
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(38633472), arg=2, src=()),
-          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(0, 0, 768, 1), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),))
-
-    opts = [Opt(op=OptOps.LOCAL, axis=0, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=3), Opt(op=OptOps.LOCAL, axis=0, arg=2)]
-    kernel = Kernel(ast)
-    kernel.apply_opts(opts)
-    run_linearizer(kernel)
-
-  def test_2(self):
-    # kernel 254
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(3145728), arg=0, src=()),
-        UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 1, 768), strides=(786432, 768, 0, 1), offset=0, mask=None, contiguous=True),)), src=()),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (2,)), src=(
-        UOp(Ops.MUL, dtypes.float, arg=None, src=(
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(38633472), arg=1, src=()),
-          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(0, 0, 768, 1), offset=0, mask=None, contiguous=False),)), src=()),)),
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(205852672), arg=2, src=()),
-          UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4, 1024, 50304, 768), strides=(51463168, 50257, 1, 0), offset=0, mask=((0, 4), (0, 1024), (0, 50257), (0, 768)), contiguous=False),)), src=()),)),)),)),)),))
-
-    opts = [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=3, arg=4)]
-    kernel = Kernel(ast)
-    kernel.apply_opts(opts)
-    run_linearizer(kernel)
-
-if __name__ == "__main__":
-  unittest.main()

test/external/speed_v_theoretical.py

@@ -91,11 +91,11 @@ class TestKernelSpeed(unittest.TestCase):
 
   # theoretical is nv_tflops=165, amd_tflops=123
   def test_gemm_4096(self): self._test_matmul(4096, nv_tflops=115, amd_tflops=65)
-  def test_gemm_8192(self): self._test_matmul(8192, nv_tflops=125, amd_tflops=60)
+  def test_gemm_8192(self): self._test_matmul(8192, nv_tflops=115, amd_tflops=60)
 
   # theoretical is nv_gbs=1008, amd_gbs=960
   def test_gemv_16384_4096(self): self._test_matmul(16384, 4096, 1, nv_gbs=840, amd_gbs=750)
-  def test_gemv_4096_16384(self): self._test_matmul(4096, 16384, 1, nv_gbs=830, amd_gbs=750)
+  def test_gemv_4096_16384(self): self._test_matmul(4096, 16384, 1, nv_gbs=820, amd_gbs=750)
 
 if __name__ == '__main__':
   unittest.main()

test/test_const_folding.py

@@ -24,7 +24,6 @@ class TestUnaryOpsConstFolding(unittest.TestCase):
     _check_ast_count(0, Tensor.ones(4).cast(dtypes.int16))
     _check_ast_count(0, Tensor.full(4, fill_value=-1).cast(dtypes.uint16))
 
-  @unittest.expectedFailure  # no two level fold
   def test_neg_folding(self):
     _check_ast_count(0, Tensor([1, 2, 3]).mul(-1).neg())
     _check_ast_count(0, Tensor([1, 2, 3]).neg().mul(-1))
@@ -83,10 +82,8 @@ class TestBinaryOpsConstFolding(unittest.TestCase):
   def test_div_tensor_one(self):
     _check_ast_count(0, Tensor([1.0, 2, 3, 4]) / Tensor.ones(4))
 
-  @unittest.expectedFailure  # TODO: fix
   def test_idiv_literal_one(self):
     _check_ast_count(0, Tensor([1, 2, 3, 4]) // 1)
-  @unittest.expectedFailure  # TODO: fix
   def test_idiv_tensor_one(self):
     _check_ast_count(0, Tensor([1, 2, 3, 4]) // Tensor.ones(4, dtype=dtypes.int32))
 

test/test_linearizer.py

@@ -41,7 +41,7 @@ class TestLinearizer(unittest.TestCase):
   def _test_no_nested_ranges(self, lins, skip=None):
     for l in lins:
       range_in_acc = flatten([[x for x in u.src if x.op is Ops.RANGE] for u in l.uops if u.op is Ops.DEFINE_REG])
-      ranges = [u.op for u in l.uops if (u.op is Ops.RANGE and u in range_in_acc) or (u.op is Ops.ENDRANGE and u.src[0] in range_in_acc)]
+      ranges = [u.op for u in l.uops if (u.op is Ops.RANGE and u in range_in_acc) or (u.op is Ops.END and u.src[0] in range_in_acc)]
       for i,u in enumerate(ranges):
         if skip and i in skip: continue
         assert ranges[i-1] != u, f"multireduce nested the ranges! {ranges[i-1], {u}}"
@@ -205,7 +205,7 @@ class TestLinearizer(unittest.TestCase):
     # the uops graph is DEFINE_REG -> 4x STORE 0.0 -> RANGE -> 4x ALU -> 4x STORE -> ENDRANGE
     uops = get_program(ast, opts=opt).uops
     begin_range = [i for i, x in enumerate(uops) if x.op is Ops.RANGE][-1]
-    end_range = [i for i, x in enumerate(uops) if x.op is Ops.ENDRANGE][0]
+    end_range = [i for i, x in enumerate(uops) if x.op is Ops.END][0]
     for i,u in enumerate(uops): print(i, u.op, [uops.index(s) for s in u.src], u.arg, u.dtype)
     for u in uops:
       if u.op is Ops.STORE and isinstance(dt:=u.src[0].dtype, PtrDType) and dt.addrspace is AddrSpace.REG:
@@ -214,8 +214,8 @@ class TestLinearizer(unittest.TestCase):
         else:
           assert u.src[1].op in GroupOp.ALU
           assert begin_range < uops.index(u) < end_range
-      # children of STORE are placed after ENDRANGE
-      if any(x.op is Ops.STORE and x.src[1].op in GroupOp.ALU for x in u.src):
+      # children of END are placed after ENDRANGE
+      if any(x.op is Ops.END and x.src[1].op in GroupOp.ALU for x in u.src):
         assert end_range < uops.index(u)
 
   def test_grouped_dims(self):
@@ -400,7 +400,7 @@ class TestLinearizer(unittest.TestCase):
     # # check the children's vins
     # TODO: src ALU are not the same, should it?
     # assert barrier.src == tuple(local_stores)
-    assert len([u for u in uops if u.op is Ops.IF and u.src[-1] == barrier]) == 1
+    assert len([u for u in uops if u.op is Ops.IF and u.src[1] == barrier]) == 1
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")

test/test_ops.py

@@ -2602,6 +2602,7 @@ class TestOps(unittest.TestCase):
       lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(111,28)),
       lambda x: Tensor.avg_pool2d(x, kernel_size=(111,28)), rtol=1e-5)
 
+  @unittest.skipIf(Device.DEFAULT == "AMD" and CI, "remu failure?")
   def test_avg_pool3d_failure(self):
     with Context(NOOPT=0):
       helper_test_op([(1,1,16,16,16)],

test/test_profiler.py

@@ -92,7 +92,9 @@ class TestProfiler(unittest.TestCase):
     #  assert evs[i].st > evs[i-1].en, "timestamp not aranged"
 
   def test_profile_multidev(self):
-    d1 = Device[f"{Device.DEFAULT}:1"]
+    try: d1 = Device[f"{Device.DEFAULT}:1"]
+    except Exception as e: self.skipTest(f"second device not available {e}")
+
     buf1 = Buffer(Device.DEFAULT, 2, dtypes.float, options=BufferSpec(nolru=True)).ensure_allocated()
     buf2 = Buffer(f"{Device.DEFAULT}:1", 2, dtypes.float, options=BufferSpec(nolru=True)).ensure_allocated()
 
@@ -109,7 +111,8 @@ class TestProfiler(unittest.TestCase):
       assert evs[0].is_copy, "kernel should be copy"
 
   def test_profile_multidev_transfer(self):
-    d1 = Device[f"{Device.DEFAULT}:1"]
+    try: d1 = Device[f"{Device.DEFAULT}:1"]
+    except Exception as e: self.skipTest(f"second device not available {e}")
 
     buf1 = Tensor.randn(10, 10, device=f"{Device.DEFAULT}:0").realize()
     with helper_collect_profile(TestProfiler.d0, d1) as profile:
@@ -122,7 +125,8 @@ class TestProfiler(unittest.TestCase):
 
   @unittest.skipIf(Device.DEFAULT in "METAL" or (MOCKGPU and Device.DEFAULT == "AMD"), "AMD mockgpu does not support queue wait interrupts")
   def test_profile_graph(self):
-    d1 = Device[f"{Device.DEFAULT}:1"]
+    try: d1 = Device[f"{Device.DEFAULT}:1"]
+    except Exception as e: self.skipTest(f"second device not available {e}")
 
     def f(a):
       x = (a + 1).realize()
@@ -145,7 +149,9 @@ class TestProfiler(unittest.TestCase):
   @unittest.skipIf(CI or not issubclass(type(Device[Device.DEFAULT]), HCQCompiled), "skip CI")
   def test_dev_jitter_matrix(self):
     dev_cnt = 6
-    devs = [Device[f"{Device.DEFAULT}:{i}"] for i in range(dev_cnt)]
+    try: devs = [Device[f"{Device.DEFAULT}:{i}"] for i in range(dev_cnt)]
+    except Exception as e: self.skipTest(f"multiple devices not available {e}")
+
     for dev in devs: dev.synchronize()
     for dev in devs: dev._at_profile_finalize()
 
@@ -219,7 +225,7 @@ class TestProfiler(unittest.TestCase):
     exec_points = [e for e in profile if isinstance(e, ProfilePointEvent) and e.name == "exec"]
     range_events = [e for e in profile if isinstance(e, ProfileRangeEvent) and not e.is_copy]
     self.assertEqual(len(exec_points), len(range_events), 2)
-    self.assertEqual(len(dedup(e.key for e in exec_points)), 1)
+    self.assertEqual(len(dedup(e.arg['name'] for e in exec_points)), 1)
     self.assertEqual(len(dedup(e.arg['metadata'] for e in exec_points)), 1)
 
 if __name__ == "__main__":

test/test_quantize_onnx.py

@@ -72,7 +72,7 @@ class TestQuantizeOnnxCPU(unittest.TestCase):
     out_file = get_quantized_model(sz)
     run_onnx = OnnxRunner(out_file)
     inp = Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32))
-    with Context(DONT_REALIZE_EXPAND=1, QUANTIZE=1):
+    with Context(QUANTIZE=1):
       sched = run_onnx({"input":inp})["output"].schedule()
       ei = lower_schedule_item(sched[-2])
       daccs = [u for u in ei.prg.p.uops if u.op is Ops.DEFINE_REG]
@@ -86,8 +86,7 @@ class TestQuantizeOnnx(unittest.TestCase):
     # divide is ~1500-2000 without reduce_range, 750-900 with it
     out_file = get_quantized_model(sz)
     run_onnx_jit, _ = load_onnx_model(out_file)
-    with Context(DONT_REALIZE_EXPAND=1):
-      run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)))
+    run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)))
 
   def test_prequant_conv2d_1x1(self):
     X = Tensor(np.random.uniform(0, 255, size=(1, 32, 128, 128)).astype(np.uint8))
@@ -109,11 +108,10 @@ class TestQuantizeOnnx(unittest.TestCase):
     N = 512
     X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(xi))
     W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(wi))
-    with Context(DONT_REALIZE_EXPAND=1):
-      # this divide is interesting and forces the accumulator to actually be an int
-      out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8")
-      opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
-      sexec(out, opts)
+    # this divide is interesting and forces the accumulator to actually be an int
+    out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8")
+    opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
+    sexec(out, opts)
 
   def test_prequant_gemm_handcode(self):
     src = """typedef int int128 __attribute__((aligned(512),vector_size(512)));
@@ -203,14 +201,12 @@ class TestQuantizeOnnx(unittest.TestCase):
   def test_prequant_gemm_intacc(self, xi=np.uint8, wi=np.uint8, replace_src=None, N=512, clip=True, opts=None):
     X = Tensor(m1:=(np.random.uniform(0, 255, size=(N,N)).astype(xi))).realize()
     W = Tensor(m2:=(np.random.uniform(0, 255, size=(N,N)).astype(wi))).realize()
-    # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant
     tg_dtype = dtypes.int8 if xi == np.int8 else dtypes.uint8
-    with Context(DONT_REALIZE_EXPAND=1):
-      out = (X.int().matmul(W.int())//1000)
-      if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
-      out = out.cast(tg_dtype)
-      opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
-      sexec(out, opts, replace_src, run_count=1)
+    out = (X.int().matmul(W.int())//1000)
+    if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
+    out = out.cast(tg_dtype)
+    opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
+    sexec(out, opts, replace_src, run_count=1)
     tout = out.numpy()
     mout = ((m1.astype(np.int32) @ m2.astype(np.int32)) // 1000)
     if clip: mout = mout.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
@@ -225,7 +221,6 @@ class TestQuantizeOnnx(unittest.TestCase):
 
   def test_prequant_gemv(self):
     N = 2048
-    # ugh, it's so broken with those casts. need DONT_REALIZE_EXPAND=1 python3 test/test_quantize_onnx.py TestQuantizeOnnx.test_prequant
     X = Tensor(np.random.uniform(0, 255, size=(1,N)).astype(np.uint8)).realize()
     W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)).realize()
     #out = X.cast(dtypes.int) @ W.cast(dtypes.int)

test/test_rangeify.py

@@ -1,7 +1,9 @@
 import unittest
-from tinygrad import Tensor, nn
-from tinygrad.helpers import Context, GlobalCounters
+from tinygrad import Tensor, nn, Device
+from tinygrad.helpers import Context, GlobalCounters, CI, getenv
 from tinygrad.uop.ops import graph_rewrite, PatternMatcher, UPat, Ops
+from tinygrad.renderer.ptx import PTXRenderer
+from tinygrad.renderer.nir import NIRRenderer
 
 class TestRangeifyAssign(unittest.TestCase):
   def test_assign_permuted(self):
@@ -17,8 +19,89 @@ class TestRangeifyAssign(unittest.TestCase):
     self.assertListEqual(lst, lst3)
     self.assertListEqual(lst2, B.permute(1, 0).tolist())
 
+class TestRangeifyEdgeCase(unittest.TestCase):
+  def test_matmul_relu_cat(self):
+    a = Tensor.ones(100, 512).contiguous().realize()
+    c = Tensor.ones(1, 512).contiguous().realize()
+    cm = Tensor.ones(512, 512)
+    c = c @ cm
+    c = c.relu()
+
+    res = Tensor.cat(a, c, dim=0)
+    self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
+
+if getenv("BIG") > 2:
+  # llama 8B (8192)
+  BS, HEADS, SEQLEN, EMB = 4, 32, 8192, 128
+elif getenv("BIG") > 1:
+  # llama 8B
+  BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
+elif getenv("BIG") > 0:
+  # bigger
+  BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
+else:
+  BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
+
+@unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (NIRRenderer, PTXRenderer)), "broken in LVP and PTX")
+class TestPcontig(unittest.TestCase):
+  def test_flash_attention_bw(self):
+    def fa_bw():
+      Tensor.manual_seed(1337)
+      with Context(DEBUG=0):
+        q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize().requires_grad_() for _ in range(3)]
+        attn_output = nn.Linear(HEADS*EMB, HEADS*EMB, bias=False)
+        attn_output.weight.requires_grad_().realize()
+        target = Tensor.rand(BS, SEQLEN, HEADS*EMB).contiguous().realize()
+
+      GlobalCounters.reset()
+      attn = q.scaled_dot_product_attention(k, v).contiguous().contiguous_backward()
+      attn = attn.transpose(1, 2).reshape(BS, SEQLEN, -1)
+      out = attn_output(attn)
+      loss = (out - target).square().mean()
+      loss.backward()
+      #ret = [out, Tensor.stack(q.grad, k.grad, v.grad)]
+      ret = [out, q.grad, k.grad, v.grad]
+      Tensor.realize(*ret)
+      return ret
+
+    with Context(PCONTIG=2, DEBUG=2):
+      grads = fa_bw()
+      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
+
+    with Context(DEBUG=2):
+      cmp_grads = fa_bw()
+      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
+
+    with Context(DEBUG=0):
+      mses = [((x-y)**2).sum().item() for x,y in zip(grads, cmp_grads)]
+    mse = sum(mses)
+    print(f"mse: {mse}")
+    self.assertLessEqual(mse, 1e-6)
+
+  def test_flash_attention(self):
+    def fa():
+      Tensor.manual_seed(1337)
+      with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
+      GlobalCounters.reset()
+      return q.scaled_dot_product_attention(k, v).realize()
+
+    with Context(PCONTIG=2, DEBUG=2):
+      ret = fa()
+      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
+    with Context(DEBUG=2):
+      cmp = fa()
+      print(f"{GlobalCounters.global_ops/1e9:.2f} GFLOPS")
+    with Context(DEBUG=0):
+      mse = ((cmp-ret)**2).sum().item()
+    print(f"mse: {mse}")
+    self.assertLessEqual(mse, 1e-6)
+
+
+# *** non CI rangeify tests below this line ***
+
 N = 256
 
+@unittest.skipIf(CI, "useless in CI, doesn't test anything")
 class TestRangeifyOpt(unittest.TestCase):
   def test_randperm(self):
     Tensor.randperm(10000).realize()
@@ -54,6 +137,7 @@ class TestRangeifyOpt(unittest.TestCase):
     A = Tensor.empty(8,8,8,8).permute(1,0,3,2).flatten()
     A.sum().realize()
 
+@unittest.skipIf(CI, "useless in CI, doesn't test anything")
 class TestRangeify(unittest.TestCase):
   def test_groupnorm(self):
     # ranges 1 and 3 are merging
@@ -200,33 +284,6 @@ class TestRangeify(unittest.TestCase):
     out = blk._feed_forward(x)
     out.realize()
 
-  @unittest.skip("RANGEIFY=0 does nothing")
-  def test_flash_attention(self):
-    BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
-
-    # bigger
-    #BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
-
-    # llama 8B
-    #BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
-
-    def fa():
-      Tensor.manual_seed(1337)
-      with Context(DEBUG=0): q,k,v = [Tensor.rand(BS, HEADS, SEQLEN, EMB).contiguous().realize() for _ in range(3)]
-      return q.scaled_dot_product_attention(k, v).realize()
-
-    with Context(DEBUG=4):
-      GlobalCounters.reset()
-      ret = fa()
-    with Context(RANGEIFY=0):
-      with Context(DEBUG=2):
-        GlobalCounters.reset()
-        cmp = fa()
-      with Context(DEBUG=0):
-        mse = ((cmp-ret)**2).sum().item()
-    print(f"mse: {mse}")
-    self.assertLessEqual(mse, 1e-6)
-
 # contiguous + reduce can support ranges?
 
 @unittest.skip("pm_rangeify no longer exists. test this in a different way")
@@ -280,16 +337,5 @@ class TestRangeifyPM(unittest.TestCase):
     b = self.base.pad(((0,1),(0,0))).pad(((0,0),(0,1)))
     self.assert_same(a, b)
 
-class TestRangeifyEdgeCase(unittest.TestCase):
-  def test_matmul_relu_cat(self):
-    a = Tensor.ones(100, 512).contiguous().realize()
-    c = Tensor.ones(1, 512).contiguous().realize()
-    cm = Tensor.ones(512, 512)
-    c = c @ cm
-    c = c.relu()
-
-    res = Tensor.cat(a, c, dim=0)
-    self.assertEqual(res.numpy()[-1, :16].tolist(), [512] * 16)
-
 if __name__ == '__main__':
   unittest.main()

test/test_schedule.py

@@ -2,9 +2,8 @@
 # schedule confirms the right things are capable of fusing
 # NOTE: this has overlap with external_test_opt.py
 
-import unittest
+import unittest, functools
 import numpy as np
-import functools
 from typing import cast
 from hypothesis import assume, given, settings, strategies as strat
 
@@ -31,7 +30,6 @@ def check_schedule(t:Tensor|list[Tensor]|UOp, allowed:int, to_prerealize:list[Te
   # test lowering all the ScheduleItems to ExecItems
   kernel_cnt = len([si for si,ei in lower_schedule(sched.copy()) if isinstance(ei.prg, CompiledRunner) or not filter_sink])
   if kernel_cnt != allowed:
-    return sched # allow different kernel count, TODO: fix the asserts
     print(f"SCHEDULE ISSUE, expecting {allowed} got {len(sched)}")
     if DEBUG >= 3:
       for i,s in enumerate(sched):
@@ -117,8 +115,7 @@ class TestSchedule(unittest.TestCase):
     c = a+b
     with self.assertRaisesRegex(RuntimeError, "all buffers must be on the same device"): check_schedule(c, 2)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.half) and getenv("CAST_AFTER_EXPAND"), "need half and CAST_AFTER_EXPAND=1")
-  @unittest.skip("CAST_AFTER_EXPAND is not supported")
+  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
   def test_expand_buffer_before_cast(self):
     a = Tensor.randn(4, 2, 1).realize().permute((1, 0, 2))
     b = a.cast(dtypes.half).expand((2, 4, 4))+2
@@ -128,7 +125,7 @@ class TestSchedule(unittest.TestCase):
   def test_indexing_scalars_simple(self):
     X = Tensor.randn(2, 2).realize()
     xt = X[Tensor(1)][Tensor(0)]
-    run_schedule(check_schedule(xt, 2))
+    run_schedule(check_schedule(xt, 1))
     np.testing.assert_equal(xt.numpy(), X.numpy()[1][0])
 
   @unittest.skipIf(CI and Device.DEFAULT == "NV", "crashes on NV CI")
@@ -148,31 +145,30 @@ class TestSchedule(unittest.TestCase):
     assume(a<x and b<y)
     X = Tensor.randn(x, y).realize()
     xt = X[Tensor(a)][Tensor(b)]
-    run_schedule(check_schedule(xt, 2))
+    run_schedule(check_schedule(xt, 1))
     np.testing.assert_equal(xt.numpy(), X.numpy()[a][b])
 
   def test_push_pads_elementwise(self):
     x = Tensor.full((4,4), 2.).contiguous().realize()
     y = Tensor.full((4,4), 4.).contiguous().realize()
     z = (x.reciprocal()*y).pad((None, (0,1),)).sum()
-    run_schedule(check_schedule(z, 2))
+    run_schedule(check_schedule(z, 1))
     self.assertEqual(z.item(), 32)
 
   def test_push_pads_contiguous(self):
     x = Tensor.full((4,1), 2.).contiguous()
     y = Tensor.full((4,4), 4.).contiguous()
     z = (x.reciprocal().expand(4,4)*y).pad((None, (0,1),)).sum()
-    run_schedule(check_schedule(z, 2, [x,y]))
+    run_schedule(check_schedule(z, 1, [x,y]))
     self.assertEqual(z.item(), 32)
 
   def test_rand(self):
     x = Tensor.rand(32)
-    check_schedule(x, 4, [Tensor._device_rng_counters[x.device]])
+    check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
 
   def test_rand_recompute_arange(self):
     x = Tensor.rand(32)
-    with Context(DONT_GROUP_REDUCES=1):
-      check_schedule(x, 3, [Tensor._device_rng_counters[x.device]])
+    check_schedule(x, 1, [Tensor._device_rng_counters[x.device]])
 
   def test_empty_is_not_realized(self):
     a = Tensor.empty(10)
@@ -189,10 +185,7 @@ class TestSchedule(unittest.TestCase):
 
   def test_simplify_padded_const(self):
     a = Tensor.empty(1022).cummax(axis=0)
-    check_schedule(a, 5)
-    # TODO: what is this testing?
-    #ast = sched[0].ast
-    #self.assertLessEqual(len([u for u in ast.toposort() if u.op is Ops.WHERE]), 6)
+    check_schedule(a, 3)
 
   def test_basic_binop_fusion(self):
     a = Tensor.empty(10)
@@ -265,18 +258,17 @@ class TestSchedule(unittest.TestCase):
     c = a.sum(axis=0) + b
     check_schedule(c, 1)
 
-  # not pushing permutes through reduces
   def test_reduce_permute_binop_fusion(self):
     a = Tensor.empty(10,10,10)
     b = Tensor.empty(10,10,1)
     c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
-    check_schedule(c, 2)
+    check_schedule(c, 1)
 
   def test_allow_push_permutes(self):
     a = Tensor.randn(10,10,10).realize()
     b = Tensor.randn(10,10,1).realize()
     c = a.sum(axis=0, keepdim=True).permute(2,1,0) + b
-    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(c, 1))
+    run_schedule(check_schedule(c, 1))
     np.testing.assert_allclose(c.numpy(), np.sum(a.numpy(), axis=0, keepdims=True).transpose(2,1,0)+b.numpy())
 
   def test_binop_early_reshape_reduce_fusion(self):
@@ -341,7 +333,7 @@ class TestSchedule(unittest.TestCase):
     r1 = (x - r0).sum(axis=0).div(2)
     out0 = r0 + y
     out1 = r1 + y
-    schedule = check_schedule([out0, out1], 2)
+    schedule = check_schedule([out0, out1], 3)
     reduceops = [x for si in schedule for x in si.ast.toposort() if x.op in {Ops.REDUCE_AXIS, Ops.REDUCE}]
     self.assertEqual(len(reduceops), 2) # why is RANGEIFY different?
 
@@ -374,7 +366,7 @@ class TestSchedule(unittest.TestCase):
     b = Tensor.full((4,), 2.).contiguous()
     first = a.assign(b)
     second = a.assign(b)
-    check_schedule([first, second], 1)
+    check_schedule([first, second], 2) # TODO: 1?
 
   # NOTE: this is causing "LAZYCACHE=1 incorrectly reuses contiguous const" #4562
   # should contiguous dedup?
@@ -454,7 +446,7 @@ class TestSchedule(unittest.TestCase):
   @unittest.skipUnless(is_dtype_supported(dtypes.ulong), "Needs ulong")
   def test_fold_conv_batchnorm_optim(self):
     # this is too high
-    for optim, cnt in [(nn.optim.Adam, 30), (nn.optim.SGD, 11)]:
+    for optim, cnt in [(nn.optim.Adam, 30), (nn.optim.SGD, 13)]:
       with self.subTest(optim=optim.__name__):
         with Tensor.train():
           img = Tensor.ones(1,3,4,4)
@@ -475,7 +467,7 @@ class TestSchedule(unittest.TestCase):
       fw = bn(x).contiguous_backward().relu().contiguous()
       fw.sum().backward()
       # TODO: this is too many
-      check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 10)
+      check_schedule([x.grad, bn.weight.grad, bn.bias.grad, fw], 9)
 
   def test_fold_conv_relu(self):
     c1 = nn.Conv2d(3,16,3)
@@ -518,9 +510,8 @@ class TestSchedule(unittest.TestCase):
     img = Tensor.empty(64,64)
     x = (img.sum(0) + img.sum(1))
     out = x.relu()
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
-  #@unittest.skip("failing in old lazy")
   def test_push_permute_through_reshape(self):
     a = Tensor.empty(16,16)
     b = Tensor.empty(16,16)
@@ -554,7 +545,7 @@ class TestSchedule(unittest.TestCase):
     c = a+b
     d = a.reshape(10,1)+b.reshape(10,1)
     out = c.sum() + d.sum()
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
   def test_children_dont_push(self):
     a = Tensor.empty(10, 10, 1)
@@ -562,7 +553,7 @@ class TestSchedule(unittest.TestCase):
     d = (a+b).expand(10, 10, 10)
     e = (a+b).permute(2,1,0)
     f = d+e
-    check_schedule(f, 2)
+    check_schedule(f, 1)
 
   # failing in new lazy
   @unittest.skip("always fusing elementwise")
@@ -601,13 +592,13 @@ class TestSchedule(unittest.TestCase):
     e = c[0] * d
     check_schedule(e, 1)
 
-  def test_expand_nofuse(self):
+  def test_expand_fuse(self):
     a = Tensor.empty(1, 16)
     b = Tensor.empty(1, 16)
     c = a * b
     d = Tensor.empty(8192, 16)
     e = c * d
-    check_schedule(e, 2)
+    check_schedule(e, 1)
 
   # this is the failing case in openpilot...it's very simple like this
   def test_image_conv_fusion(self):
@@ -625,7 +616,7 @@ class TestSchedule(unittest.TestCase):
 
     # NOOP, 3 convs, contiguous
     #check_schedule(x, 5)
-    check_schedule(x, 8)
+    check_schedule(x, 7)
 
   def test_image_conv_fusion_minimal(self):
     b1 = Tensor.empty(16)
@@ -808,13 +799,13 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.empty(32, 32, 32)
     y = Tensor.empty(32, 32)
     out = x.sum(axis=2).T+y
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
   def test_two_elus_sum(self):
     x = Tensor.empty(32, 32)
     y = Tensor.empty(32, 32)
     out = x.sum(1).relu().elu() + y.sum(1).relu().elu()
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
   @unittest.skipUnless(SPLIT_REDUCEOP, "Testing split reducop requires SPLIT_REDUCEOP")
   def test_preserve_multistage_reduce(self):
@@ -827,7 +818,7 @@ class TestSchedule(unittest.TestCase):
   def test_multistage_reduce(self):
     x = Tensor.empty(32, 32, 32)
     out = x.sum(2).relu().sum(1)
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
   def test_multistage_reduce_fork(self):
     x = Tensor.empty(32, 32, 32)
@@ -843,7 +834,7 @@ class TestSchedule(unittest.TestCase):
     z = y.matmul(x).sum()
     z.backward()
     out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), np.ones((64,64)))
 
   def test_example_matmul_contig(self):
@@ -852,7 +843,7 @@ class TestSchedule(unittest.TestCase):
     z = y.matmul(x).sum()
     z.backward()
     out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), np.ones((64,64)))
 
   def test_example_matmul_same(self):
@@ -860,7 +851,7 @@ class TestSchedule(unittest.TestCase):
     z = x.matmul(x).sum()
     z.backward()
     out = x.grad.contiguous()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     # NOTE: the gradient flows twice
     np.testing.assert_allclose(out.numpy(), 2*np.ones((64,64)))
 
@@ -883,8 +874,7 @@ class TestSchedule(unittest.TestCase):
     x = x.sum(1)
     x = x[:16]
     out = x + y
-    # NOTE: this could be 1 kernel if we mask the store?
-    check_schedule(out, 2)
+    check_schedule(out, 1)
 
   def test_multireduce_shrink(self):
     Tensor.manual_seed(0)
@@ -896,8 +886,7 @@ class TestSchedule(unittest.TestCase):
     b_out = b.sum(1)
     b_out = b_out[:16]
     out = a_out + b_out + c
-    # run_schedule(check_schedule(out, 2))  # TODO: this should be 1 (can we make it 1 with the new linearizer?)
-    run_schedule(check_schedule(out, 3))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), a.numpy().sum(axis=1)[:16] + b.numpy().sum(axis=1)[:16] + c.numpy(), atol=1e-4, rtol=1e-4)
 
   # broken due to const folding and two contiguous are different kernels
@@ -914,7 +903,7 @@ class TestSchedule(unittest.TestCase):
     out0 = a.sum() + 2
     out1 = a.sum() + 4
     out2 = out0 * out1
-    run_schedule(check_schedule([out0, out1, out2], 1))
+    run_schedule(check_schedule([out0, out1, out2], 3)) # TODO: 1?
     np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-6)
     np.testing.assert_allclose(out1.numpy(), out1_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
     np.testing.assert_allclose(out2.numpy(), out0_np*out1_np, atol=1e-4, rtol=1e-6)
@@ -925,7 +914,7 @@ class TestSchedule(unittest.TestCase):
     out0 = a.sum().exp2()
     # out1 has two paths to a.sum()
     out1 = a.sum() + out0
-    run_schedule(check_schedule([out0, out1], 1))
+    run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
     np.testing.assert_allclose(out0.numpy(), out0_np:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+out0_np, atol=1e-4, rtol=1e-6)
 
@@ -938,7 +927,7 @@ class TestSchedule(unittest.TestCase):
     out2 = b.sum().exp2()
     out3 = b.sum() + out2
     # run_schedule(check_schedule([out0, out1, out2, out3], 1))
-    run_schedule(check_schedule([out0, out1, out2, out3], 6))
+    run_schedule(check_schedule([out0, out1, out2, out3], 4))
     np.testing.assert_allclose(out0.numpy(), np_out0:=np.exp2(a.numpy().sum()), atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(out1.numpy(), np_out1:=a.numpy().sum()+np_out0, atol=1e-4, rtol=1e-4)
     np_b = (a.numpy() + np_out0 + np_out1)
@@ -953,7 +942,7 @@ class TestSchedule(unittest.TestCase):
     out0 = a.sum() + b.sum() + 2
     out1 = a.sum() + b.sum() + 4
     # run_schedule(check_schedule([out0, out1], 1))
-    run_schedule(check_schedule([out0, out1], 4))
+    run_schedule(check_schedule([out0, out1], 2))
     np.testing.assert_allclose(out0.numpy(), a.numpy().sum()+b.numpy().sum()+2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(out1.numpy(), a.numpy().sum()+b.numpy().sum()+4, atol=1e-4, rtol=1e-4)
 
@@ -980,7 +969,7 @@ class TestSchedule(unittest.TestCase):
     out1 = b.max() + out0*2
     out2 = a.sum() + out1
     # run_schedule(check_schedule([out0, out1, out2], 1))
-    run_schedule(check_schedule([out0, out1, out2], 4))
+    run_schedule(check_schedule([out0, out1, out2], 3))
     np.testing.assert_allclose(out0.numpy(), out0_np:=a.numpy().sum()+4, atol=1e-4, rtol=1e-6)
     np.testing.assert_allclose(out1.numpy(), out1_np:=b.numpy().max() + out0_np*2, atol=1e-4, rtol=1e-6)
     np.testing.assert_allclose(out2.numpy(), a.numpy().sum() + out1_np, atol=1e-4, rtol=1e-6)
@@ -1017,7 +1006,7 @@ class TestSchedule(unittest.TestCase):
     b = Tensor.empty(10,)
     c = a.sum() + b[0]
     d = a.sum() + 2
-    check_schedule([c, d], 1)
+    check_schedule([c, d], 2) # TODO: 1?
 
   def test_reduce_multiple_paths_midshrink(self):
     a = Tensor.empty(4, 4)
@@ -1046,7 +1035,7 @@ class TestSchedule(unittest.TestCase):
     k = Tensor.randn(32,8,16,8).realize()
     v = Tensor.randn(32,8,16,8).realize()
     out = Tensor.scaled_dot_product_attention(q,k,v)
-    run_schedule(check_schedule(out, 5))
+    run_schedule(check_schedule(out, 4))
     if getenv("CHECK", 1):
       import torch
       compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
@@ -1054,7 +1043,7 @@ class TestSchedule(unittest.TestCase):
 
     with Context(FUSE_ATTENTION=1):
       out = Tensor.scaled_dot_product_attention(q,k,v)
-      run_schedule(check_schedule(out, 1))
+      run_schedule(check_schedule(out, 4)) # TODO: should be 1?
       if getenv("CHECK", 1):
         import torch
         compare = torch.nn.functional.scaled_dot_product_attention(torch.tensor(q.numpy()),torch.tensor(k.numpy()),torch.tensor(v.numpy()))
@@ -1067,7 +1056,7 @@ class TestSchedule(unittest.TestCase):
     c = Tensor.randn(4, 32).realize()
     out = (c * a.sum(-1, keepdim=True)).sum(-1) + (b * a.sum(-1, keepdim=True)).sum(-1) # a.sum has >1 children but should still fuse
     # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 3))
+    run_schedule(check_schedule(out, 2))
     np.testing.assert_allclose(out.numpy(), \
       (c.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1) + (b.numpy()*a.numpy().sum(axis=-1,keepdims=True)).sum(-1), atol=1e-4, rtol=1e-4)
 
@@ -1112,8 +1101,7 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.randn(4, 32).realize()
     y = Tensor.randn(4, 32).realize()
     out = y.sum(axis=-1) + x.sum(axis=-1)
-    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), y.numpy().sum(axis=-1) + x.numpy().sum(axis=-1), atol=1e-4, rtol=1e-4)
 
   def test_multireduce_fusion_sequential(self):
@@ -1130,7 +1118,7 @@ class TestSchedule(unittest.TestCase):
     y = Tensor.randn(4, 32).realize()
     out = x.std(-1) + y.std(-1)
     # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 4))
+    run_schedule(check_schedule(out, 3))
     np.testing.assert_allclose(out.numpy(), x.numpy().std(axis=-1, ddof=1) + y.numpy().std(axis=-1, ddof=1), atol=1e-4, rtol=1e-4)
 
   def test_multireduce_diffops_sequential(self):
@@ -1146,8 +1134,7 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.randn(4, 32).realize()
     y = Tensor.randn(4, 32).realize()
     out = x.sum(-1) + y.max(-1)
-    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), x.numpy().sum(axis=-1) + y.numpy().max(axis=-1), atol=1e-4, rtol=1e-4)
 
   def test_multireduce_fusion_sequential_and_parallel(self):
@@ -1159,7 +1146,7 @@ class TestSchedule(unittest.TestCase):
     np_mu = (x.numpy() - x.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True) + \
       (y.numpy() - y.numpy().max(axis=-1, keepdims=True)).mean(axis=-1, keepdims=True)
     # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 6))
+    run_schedule(check_schedule(out, 5))
     np.testing.assert_allclose(out[0].numpy(), np.sqrt(np.square(x.numpy() - np_mu).sum(-1)/x.shape[-1]), atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(out[1].numpy(), np.sqrt(np.square(y.numpy() - np_mu).sum(-1)/y.shape[-1]), atol=1e-4, rtol=1e-4)
 
@@ -1168,8 +1155,7 @@ class TestSchedule(unittest.TestCase):
     a,b = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
     c,d = Tensor.randn(4, 64).realize(), Tensor.rand(64,8).realize()
     out = a@b + c@d
-    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), a.numpy()@b.numpy() + c.numpy()@d.numpy(), atol=1e-4, rtol=1e-4)
 
   def test_softmax_fusion(self):
@@ -1180,17 +1166,15 @@ class TestSchedule(unittest.TestCase):
     expected = (x_exp:=np.exp(x.numpy()-x.numpy().max(-1, keepdims=True)))/x_exp.sum(-1, keepdims=True)
     np.testing.assert_allclose(out.numpy(), expected, atol=1e-4, rtol=1e-4)
 
-  # TODO: rangeify stores the output in float32
   @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
-  @unittest.expectedFailure
   def test_softmax_upcast(self):
     # input half, softmax in float
     Tensor.manual_seed(0)
     x = Tensor.randn(4, 12, 64, 64, dtype=dtypes.half).realize()
     out = x.softmax(dtype=dtypes.float)
     sched = out.schedule()
-    self.assertEqual(len(sched), 2)
-    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)
+    self.assertEqual(len(sched), 3)
+    self.assertEqual(sched[0].bufs[0].dtype, dtypes.float)
 
     # input float, softmax in float
     Tensor.manual_seed(0)
@@ -1222,12 +1206,12 @@ class TestSchedule(unittest.TestCase):
   def test_scaled_dot_product_attention_fusion(self):
     x, y, z, m = (Tensor.empty(32, 8, 16, 16) for _ in range(4))
     out = Tensor.scaled_dot_product_attention(x, y, z, attn_mask=m)
-    check_schedule(out, 5)
+    check_schedule(out, 4)
 
   def test_scaled_dot_product_attention_causal_fusion(self):
     x, y, z = (Tensor.empty(32, 8, 16, 16) for _ in range(3))
     out = Tensor.scaled_dot_product_attention(x, y, z, is_causal=True)
-    check_schedule(out, 5)
+    check_schedule(out, 4)
 
   def test_adam_step_fusion(self):
     with Tensor.train():
@@ -1257,7 +1241,7 @@ class TestSchedule(unittest.TestCase):
       opt = nn.optim.Adam(nn.state.get_parameters([c1, c2]), lr=1e-4)
       opt.zero_grad()
       c2(c1(img).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 20)
+      check_schedule(opt.schedule_step(), 18)
 
   def test_sgd_conv_fuse(self):
     with Tensor.train():
@@ -1267,7 +1251,7 @@ class TestSchedule(unittest.TestCase):
       opt = nn.optim.SGD(nn.state.get_parameters(c1))
       opt.zero_grad()
       c1(img).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 3)
+      check_schedule(opt.schedule_step(), 5) # TODO: 3?
 
   def test_sgd_2convs_fuse(self):
     with Tensor.train():
@@ -1303,7 +1287,7 @@ class TestSchedule(unittest.TestCase):
       opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
       opt.zero_grad()
       c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 17)
+      check_schedule(opt.schedule_step(), 15)
 
   def test_sgd_4convs_fuse_conv_bw(self):
     with Tensor.train():
@@ -1316,50 +1300,7 @@ class TestSchedule(unittest.TestCase):
       opt = nn.optim.SGD(nn.state.get_parameters([c1, c2, c3, c4]))
       opt.zero_grad()
       c4(c3(c2(c1(img).relu()).relu()).relu()).relu().sum().backward()
-      check_schedule(opt.schedule_step(), 14)
-
-  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
-  @unittest.expectedFailure
-  def test_prefer_half_buffer(self):
-    x = Tensor.ones(4).contiguous().realize()
-    # y = Tensor.ones(4).contiguous().realize()
-    z = Tensor.ones(4, 4).contiguous().realize()
-
-    # should not create extra kernel if output will be realized anyways
-    dummy = x.sum().half().float()
-    check_schedule(dummy, 1)
-    dummy = x.sum().half().float().contiguous() + 1
-    check_schedule(dummy, 2)
-
-    # shared between two outputs
-    shared = x.sum().half().float()
-    a = shared * 2
-    b = shared * 3
-    sched = check_schedule([a, b], 3)
-    # store reduceop in half
-    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)
-    # fuse cast with the child kernel
-    self.assertEqual(sched[1].bufs[0].dtype, dtypes.float)
-    self.assertEqual(sched[2].bufs[0].dtype, dtypes.float)
-
-    # reduce
-    a = z.sum(axis=0).half().float().sum(axis=0)
-    sched = check_schedule(a, 2)
-    self.assertEqual(sched[0].bufs[0].dtype, dtypes.half)
-    self.assertEqual(sched[1].bufs[0].dtype, dtypes.float)
-
-    # expand
-    # expand will realize just after the .float(), so requires change to realize-before-expand
-    # normal = (x.sum().half().float().reshape(1) * y).sum()
-    # sched = check_schedule(normal, 2)
-    # for si in sched[:-1]: assert all(out.dtype == dtypes.half for out in si.outputs[:-1])
-
-    # parallel reduce
-    # a = x.sum().half().float() * y.sum().half().float()
-    # b = a + 1
-    # c = a + 2
-    # sched = check_schedule([b, c], 4)
-    # doesn't store either in half because it doesn't chase
+      check_schedule(opt.schedule_step(), 15)
 
   def test_reduce_simple_chase(self):
     a = Tensor.empty(4, 4, 4)
@@ -1408,7 +1349,7 @@ class TestSchedule(unittest.TestCase):
     c = Tensor.empty(16, )
     r = a.sum(1) + c
     d = r[:4] * b
-    check_schedule(d, 2)
+    check_schedule(d, 1)
 
   def test_multireduce_push_shrink_chase(self):
     Tensor.manual_seed(0)
@@ -1418,22 +1359,20 @@ class TestSchedule(unittest.TestCase):
     d = Tensor.randn(16, 16).realize()
     r = a.sum(1) + c
     out = r[:4] * b + d.sum(1)[:4]
-    # schedule = check_schedule(out, 2)
-    schedule = check_schedule(out, 3)
+    schedule = check_schedule(out, 1)
     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)
+    check_schedule(b, 1)
 
   def test_multireduce_midreduce_nochase(self):
     Tensor.manual_seed(0)
     a = Tensor.randn(16, 16).realize()
     b = (a.sum(0)+a.max(0) + a.max(1)+a.sum(1)) + 2
-    # schedule = check_schedule(b, 2)
-    schedule = check_schedule(b, 4)
+    schedule = check_schedule(b, 1)
     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)
 
@@ -1445,7 +1384,7 @@ class TestSchedule(unittest.TestCase):
     c = a.sum() + 2
     d = (a.sum() - b.sum()) * 4
     # run_schedule(check_schedule([c, d], 1))
-    run_schedule(check_schedule([c, d], 3))
+    run_schedule(check_schedule([c, d], 2))
     np.testing.assert_allclose(c.numpy(), a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(d.numpy(), (a.numpy().sum() - b.numpy().sum()) * 4, atol=1e-4, rtol=1e-4)
 
@@ -1471,7 +1410,7 @@ class TestSchedule(unittest.TestCase):
     e = c * d
     f = b.sum() - e
     # run_schedule(check_schedule([c, d, e, f], 1))
-    run_schedule(check_schedule([c, d, e, f], 2))
+    run_schedule(check_schedule([c, d, e, f], 4))
     np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
@@ -1486,7 +1425,7 @@ class TestSchedule(unittest.TestCase):
     e = c * d
     f = (b - d).sum() - e
     # run_schedule(check_schedule([c, d, e, f], 1))
-    run_schedule(check_schedule([c, d, e, f], 5))
+    run_schedule(check_schedule([c, d, e, f], 4))
     np.testing.assert_allclose(c.numpy(), c_np:=a.numpy().sum()+2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(d.numpy(), d_np:=a.numpy().sum()*2, atol=1e-4, rtol=1e-4)
     np.testing.assert_allclose(e.numpy(), e_np:=c_np*d_np, atol=1e-4, rtol=1e-4)
@@ -1505,8 +1444,7 @@ class TestSchedule(unittest.TestCase):
     a = Tensor.randn(3, 4, 5).realize()
     b = Tensor.randn(3, 4, 5).realize()
     out = (a.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum(keepdim=True)+b.pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()).contiguous()
-    # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), np.pad(a.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(keepdims=True) + \
                                                    np.pad(b.numpy(), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-4, rtol=1e-4)
 
@@ -1514,7 +1452,7 @@ class TestSchedule(unittest.TestCase):
     Tensor.manual_seed(0)
     a = Tensor.rand(3, 4, 5).realize()
     out = a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_allclose(out.numpy(), np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=1e-5, rtol=1e-6)
 
   def test_multireduce_pad_reduce_unsafe(self):
@@ -1523,7 +1461,7 @@ class TestSchedule(unittest.TestCase):
     b = Tensor.randn(3, 4, 5).abs().realize()
     out = (a.log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum()+b).abs().log2().pad(((0, 1), (0, 1), (0, 1)), value=1.0).sum().contiguous()
     # run_schedule(check_schedule(out, 1))
-    run_schedule(check_schedule(out, 4))
+    run_schedule(check_schedule(out, 2))
     np.testing.assert_allclose(out.numpy(), np.pad(np.log2(np.abs(np.pad(np.log2(a.numpy()), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum() + \
                                                    b.numpy())), ((0, 1), (0, 1), (0, 1)), constant_values=1.0).sum(), atol=3e-4, rtol=1e-5)
 
@@ -1537,7 +1475,7 @@ class TestSchedule(unittest.TestCase):
   def test_shrink_pad_unsafe(self):
     a = Tensor.ones((3, )).contiguous().realize()
     out = a.exp2().shrink(((0, 1),)).pad(((0, 1),)).contiguous()
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     np.testing.assert_equal(out.numpy(), [2, 0])
 
   def test_base_change_shrink_pad(self):
@@ -1545,7 +1483,7 @@ class TestSchedule(unittest.TestCase):
     b = a.exp2()
     c = b[:-1, :-1]
     d = c.pad(((0, 1), (0, 1))) * 2
-    run_schedule(check_schedule(d, 2))
+    run_schedule(check_schedule(d, 1))
     np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:-1, :-1], ((0, 1), (0, 1)))*2)
 
   def test_base_change_expand_pad(self):
@@ -1553,14 +1491,14 @@ class TestSchedule(unittest.TestCase):
     b = a.exp2()
     c = b[:, None, :]
     d = c.pad(((0, 0), (1, 1), (0, 0))) * 2
-    run_schedule(check_schedule(d, 2))
+    run_schedule(check_schedule(d, 1))
     np.testing.assert_equal(d.numpy(), np.pad(np.exp2(a.numpy())[:, None, :], ((0, 0), (1, 1), (0, 0)))*2)
 
   def test_fuse_arange_pad_replicate_mode(self):
     x = Tensor.empty(3,3,3,3, requires_grad=True)
     y = x.pad((-1,2,2,-1), mode="replicate")
     dx = y.sum().gradient(x)[0]
-    sched = check_schedule(dx, 3)
+    sched = check_schedule(dx, 1)
     run_schedule(sched)
     np.testing.assert_allclose(dx.numpy(), [[[[0.,3.,9.],[0,1.,3.],[0.,0.,0.]]]*3]*3)
 
@@ -1570,7 +1508,7 @@ class TestSchedule(unittest.TestCase):
     a = Tensor.ones(4, 4).contiguous().realize()
     b = a.cast(dtypes.half).expand(2, 4, 4)
     c = b.cast(dtypes.int).expand(2, 2, 4, 4)
-    run_schedule(check_schedule(c, 2))
+    run_schedule(check_schedule(c, 1))
     np.testing.assert_equal(c.numpy(), np.ones(((2, 2, 4, 4)), dtype=np.int32))
 
   def test_base_change_pad_expand(self):
@@ -1578,7 +1516,7 @@ class TestSchedule(unittest.TestCase):
     b = Tensor.full((4, 4), 2.).contiguous().realize()
     c = (a + b).pad(((1, 1), (1, 1)))
     d = c.cast(dtypes.int).expand((2, 6, 6)) * 4
-    run_schedule(check_schedule(d, 2))
+    run_schedule(check_schedule(d, 1))
     c_np = np.pad((np.full((4, 4), 2., dtype=np.float32) + np.full((4, 4), 1., dtype=np.float32)), ((1, 1), (1, 1)), constant_values=0.0)
     np.testing.assert_equal(d.numpy(), np.broadcast_to(c_np.astype(np.half), (2, *c_np.shape)) * 4)
 
@@ -1677,7 +1615,7 @@ class TestSchedule(unittest.TestCase):
     self._test_fusion([(4, 4), (1, 4)], lambda a,b:a.sum(1).reshape(b.shape)+b, 1)
 
   def test_late_fusion_post_permute(self):
-    self._test_fusion([(4, 6, 4), (4, 4, 1)], lambda a,b:a.sum(1, keepdim=True).permute((2, 0, 1))+b, 2)
+    self._test_fusion([(4, 6, 4), (4, 4, 1)], lambda a,b:a.sum(1, keepdim=True).permute((2, 0, 1))+b, 1)
 
   def test_late_fusion_double_transpose(self):
     self._test_fusion([(32, 16, 1)],
@@ -1715,6 +1653,7 @@ class TestSchedule(unittest.TestCase):
     self.assertListEqual(realized_const_view.tolist(), [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
 
   @given(strat.sampled_from(dtypes.all), strat.sampled_from(dtypes.all))
+  @unittest.skip("kernel count depends on input")
   def test_cast_padded_const(self, dt1, dt2):
     assume(is_dtype_supported(dt1) and is_dtype_supported(dt2))
     a = Tensor(1, dtype=dt1).reshape(1, 1).pad(((1, 1), None))
@@ -1728,7 +1667,7 @@ class TestSchedule(unittest.TestCase):
     X = Tensor.randn(10, 10).realize()
     idxs = Tensor([0, 2]).realize()
     xt = X[idxs]
-    run_schedule(check_schedule(xt, 2))
+    run_schedule(check_schedule(xt, 1))
     np.testing.assert_equal(xt.numpy(), X.numpy()[idxs.numpy()])
 
   def test_simple_indexing_alt(self):
@@ -1746,7 +1685,7 @@ class TestSchedule(unittest.TestCase):
   def test_advanced_indexing_alt(self):
     X = Tensor.arange(6).reshape(3, 2)+1
     xt = X[[Tensor([2]), Tensor([1])]]
-    run_schedule(check_schedule(xt, 3))
+    run_schedule(check_schedule(xt, 1))
     np.testing.assert_equal(xt.numpy(), 6)
 
   def test_advanced_simple_indexing_combined(self):
@@ -1794,7 +1733,7 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.full((2,2), 16)
     y = x.idiv(Tensor.linspace(2, 8, steps=4, dtype=dtypes.int).reshape(2,2)).pad(((1,1), (1,1)))
     out = y.sum(axis=1)
-    run_schedule(check_schedule(out, 2))
+    run_schedule(check_schedule(out, 1))
     self.assertListEqual(out.tolist(), [0, 12, 4, 0])
 
   def test_arange_transposed_descendants(self):
@@ -1827,7 +1766,7 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.randn(5, 2).realize()
     a = Tensor.arange(10).contiguous()
     out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 3))
+    run_schedule(check_schedule(out, 2))
     np.testing.assert_allclose(out.numpy(), (x.numpy()+np.arange(10)[2]).sum(), atol=1e-5, rtol=1e-6)
 
   def test_arange_index_child(self):
@@ -1843,7 +1782,7 @@ class TestSchedule(unittest.TestCase):
     x = Tensor.randn(5, 2).realize()
     a = (Tensor.arange(10)+1).contiguous()
     out = (x + a[2]).sum()
-    run_schedule(check_schedule(out, 3))
+    run_schedule(check_schedule(out, 2))
     np.testing.assert_allclose(out.numpy(), (x.numpy()+(np.arange(10)+1)[2]).sum(), atol=1e-5, rtol=1e-6)
 
   @unittest.skip("BUFFER_VIEW no longer supported on non-disk devices")
@@ -1858,10 +1797,10 @@ class TestSchedule(unittest.TestCase):
     from extra.models.llama import precompute_freqs_cis
     args = {"dim":32 if CI else 128, "end":2048 if CI else 8192, "theta":10000}
     fused = precompute_freqs_cis(**args)
-    run_schedule(check_schedule(fused, 3))
+    run_schedule(check_schedule(fused, 1))
     if getenv("CHECK", 1):
       ref = precompute_freqs_cis(**args)
-      run_schedule(check_schedule(ref, 3))
+      run_schedule(check_schedule(ref, 1))
       np.testing.assert_equal(fused.numpy(), ref.numpy())
 
   def test_fuse_assign_contiguous(self):
@@ -1903,7 +1842,7 @@ class TestSchedule(unittest.TestCase):
     X = Tensor([[0, 2, 3], [1, 2, 3]]).realize()
     Y = Tensor([1, 2]).realize()
     loss = X.sparse_categorical_crossentropy(Y)
-    run_schedule(check_schedule(loss, 4))
+    run_schedule(check_schedule(loss, 3))
     np.testing.assert_allclose(loss.item(), 0.878309, atol=1e-5, rtol=1e-6)
 
   def test_const_folding_alt(self):
@@ -1924,7 +1863,7 @@ class TestSchedule(unittest.TestCase):
     yt = Tensor.randn(BS, 10).realize()
     with Context(SPLIT_REDUCEOP=0):
       loss = yt.sparse_categorical_crossentropy(Y_train[samples])
-      run_schedule(check_schedule(loss, 6))
+      run_schedule(check_schedule(loss, 5))
       loss_fused = loss.numpy()
     loss_ref = torch.nn.CrossEntropyLoss()(torch.tensor(yt.numpy()), torch.tensor(Y_train.numpy())[torch.tensor(samples.numpy())])
     np.testing.assert_allclose(loss_fused, loss_ref.numpy(), atol=1e-6, rtol=1e-6)
@@ -1934,7 +1873,7 @@ class TestSchedule(unittest.TestCase):
     r = (X+Tensor.arange(16).reshape(4, 4)).sum()
     out0 = r+2
     out1 = r+3
-    run_schedule(check_schedule([out0, out1], 1))
+    run_schedule(check_schedule([out0, out1], 2)) # TODO: 1?
     r_ref = (X.numpy()+np.arange(16).reshape(4, 4)).sum()
     np.testing.assert_allclose(out0.numpy(), r_ref+2, rtol=2e-7)
     np.testing.assert_allclose(out1.numpy(), r_ref+3, rtol=2e-7)
@@ -1976,8 +1915,7 @@ class TestSwizzle(unittest.TestCase):
       a = Tensor.randint(32, 32).realize()
     r = (a+a).sum(1).sum(0)
     # double reduce collapses to a single reduce
-    with Context(DONT_GROUP_REDUCES=1):
-      run_schedule(check_schedule(r, 1))
+    run_schedule(check_schedule(r, 1))
     self.assertEqual(r.numpy(), (a.numpy()+a.numpy()).sum(1).sum(0))
 
   def test_single_swizzle(self):
@@ -1997,33 +1935,29 @@ class TestSwizzle(unittest.TestCase):
       b = Tensor.randint(4,).realize()
     # parallel reduce!
     add = a.sum(0)+b.sum(0)
-    with Context(DONT_GROUP_REDUCES=1):
-      run_schedule(check_schedule(add, 1))
+    run_schedule(check_schedule(add, 1))
     self.assertEqual(add.numpy(), a.numpy().sum(0)+b.numpy().sum(0))
 
-  @unittest.skip("TODO: how do we express the norm")
   def test_softmax_one_kernel(self):
     Tensor.manual_seed(0)
     with Context(DEBUG=0, TRACK_MATCH_STATS=0):
       a = Tensor.randn(32, 32).realize()
     t = a.softmax()
-    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1):
-      check_schedule(t, 1)
+    check_schedule(t, 3) # TODO: 1?
 
   def test_argmax_one_kernel(self):
     Tensor.manual_seed(0)
     with Context(DEBUG=0, TRACK_MATCH_STATS=0):
       a = Tensor.randn(10, 20).realize()
     t = a.argmax(0)
-    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): t.realize()
+    check_schedule(t, 2) # TODO: 1?
 
   def test_swizzle_reduceop(self):
     Tensor.manual_seed(0)
     x = Tensor.randn(4,4).realize()
     y = Tensor.randn(4,4,4).realize()
     out = x.reshape(4,4,1).expand(4,4,4).sum(axis=(1,))+y
-    with Context(DONT_REALIZE_EXPAND=1, DONT_GROUP_REDUCES=1):
-      run_schedule(check_schedule(out, 1))
+    run_schedule(check_schedule(out, 2)) # TODO: 1?
     np.testing.assert_allclose(out.numpy(), np.tile(x.numpy().reshape(4,4,1), (1,1,4)).sum(axis=1)+y.numpy())
 
   def test_permute_rewrite(self):
@@ -2031,7 +1965,7 @@ class TestSwizzle(unittest.TestCase):
     y = Tensor.randn(4, 1, 16).realize()
     z = Tensor.randn(4, 4, 1).realize()
     t = (x*y).sum(axis=(0, 2)).reshape(1, 4, 1).permute(0, 2, 1)+z
-    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): run_schedule(check_schedule(t, 1))
+    run_schedule(check_schedule(t, 2)) # TODO: 1?
     t_np = (x.numpy()*y.numpy()).sum(axis=(0, 2)).reshape(1, 4, 1).transpose(0, 2, 1)+z.numpy()
     np.testing.assert_allclose(t.numpy(), t_np, atol=1e-6, rtol=1e-3)
 
@@ -2042,14 +1976,14 @@ class TestSwizzle(unittest.TestCase):
     a_reduce = a.sum(axis=(2,), keepdim=True).sum(axis=(1,))
     b_reduce = b.sum(axis=(0,))
     t = a_reduce+b_reduce
-    with Context(DONT_GROUP_REDUCES=1, DONT_REALIZE_EXPAND=1): run_schedule(check_schedule(t, 1))
+    run_schedule(check_schedule(t, 1))
 
   def test_parallel_reduce_possible(self):
     Tensor.manual_seed(0)
     x = Tensor.randn(4, 2, 2).realize()
     y = Tensor.randn(4, 2, 2).realize()
     t = x.sum(axis=1)+y.sum(axis=1)
-    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(t, 1))
+    run_schedule(check_schedule(t, 1))
     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
@@ -2058,7 +1992,7 @@ class TestSwizzle(unittest.TestCase):
     x = Tensor.randn(4, 2, 2).realize()
     y = Tensor.randn(4, 3, 2).realize()
     t = x.sum(axis=1)+y.sum(axis=1)
-    with Context(DONT_GROUP_REDUCES=1): run_schedule(check_schedule(t, 1))
+    run_schedule(check_schedule(t, 1))
     np.testing.assert_allclose(t.numpy(), x.numpy().sum(axis=1)+y.numpy().sum(axis=1), atol=1e-6, rtol=1e-3)
 
   def test_unsafe_pad(self):
@@ -2151,7 +2085,7 @@ class TestCopyFolding(unittest.TestCase):
     a = Tensor.arange(3).realize()
     zeros = Tensor.zeros(3).realize()
     b = (a*zeros).to("CPU")
-    run_schedule(check_schedule(b, 0, filter_sink=False))
+    run_schedule(check_schedule(b, 2, filter_sink=False)) # TODO: 0?
     self.assertListEqual(b.tolist(), [0, 0, 0])
     self.assertEqual(b.device, "CPU")
 
@@ -2171,12 +2105,12 @@ class TestCopyFolding(unittest.TestCase):
   def test_copy_to_same_device(self):
     a = Tensor.empty(4).uop
     b = a.copy_to_device(a.device)
-    check_schedule(b, 0, filter_sink=False)
+    check_schedule(b, 1, filter_sink=False) # TODO: 0?
 
   def test_copy_to_same_device_alt(self):
     a = Tensor.empty(4, 4).uop
     b = a.copy_to_device(a.device)
-    check_schedule(b, 0, filter_sink=False)
+    check_schedule(b, 1, filter_sink=False) # TODO: 0?
 
   def test_copy_to_same_device_sched(self):
     a = Tensor.ones(4).contiguous().realize().uop.as_buf()
@@ -2191,13 +2125,11 @@ class TestCopyFolding(unittest.TestCase):
     a = Tensor.empty(4)
     check_schedule(a.clone(), 1, filter_sink=False)
 
-  # NOTE: moving copy before view might change this
   def test_shrink_copy(self):
     a = Tensor.arange(4)
     view = a.shrink(((0, 2),))
     b = view.clone()
-    # NOTE: this was sort of a bug making this 2
-    run_schedule(check_schedule(b, 2, filter_sink=False))
+    run_schedule(check_schedule(b, 1, filter_sink=False))
     self.assertEqual(b.uop.base.buffer.size, 2)
     self.assertEqual(b.uop.size, 2)
     self.assertListEqual(b.tolist(), [0, 1])
@@ -2206,7 +2138,7 @@ class TestCopyFolding(unittest.TestCase):
     a = Tensor.arange(2)
     view = a.reshape(2, 1).expand(2, 2)
     b = view.clone()
-    run_schedule(check_schedule(b, 2, filter_sink=False))
+    run_schedule(check_schedule(b, 1, filter_sink=False))
     self.assertEqual(b.uop.base.buffer.size, 4)
     self.assertEqual(b.uop.size, 4)
     self.assertListEqual(b.tolist(), [[0, 0], [1, 1]])
@@ -2329,7 +2261,7 @@ class TestContiguous(unittest.TestCase):
   def test_double_contiguous_realizes_once(self):
     a = Tensor.empty(4, 1)
     b = a.expand((4, 4)).contiguous().contiguous()
-    check_schedule(b, 1)
+    check_schedule(b, 2) # TODO: should be 1?
 
   def test_view_does_not_realize(self):
     a = Tensor.empty(4)
@@ -2465,10 +2397,6 @@ class TestUOpBecome(unittest.TestCase):
     c = (a.reshape(1, 1, 4, 4)+0).shrink(((0, 1), (0, 1), (0, 3), (0, 3)))+0
     check_schedule([b, c], 0)
     assert all_same([x.uop.base.realized for x in [a,b,c]])
-    # these movement ops result in the same ShapeTracker
-    assert b.uop.st == c.uop.st
-    assert b.uop is c.uop
-    assert UPat(Ops.VIEW, src=(UPat(Ops.BUFFER),)).match(c.uop, {})
 
   def test_setitem_becomes_subbuffer(self):
     a = Tensor.full((4,), 2.).contiguous().realize()
@@ -2486,5 +2414,16 @@ class TestUOpBecome(unittest.TestCase):
     b.shrink(((0,4),)).assign(a_view).realize()
     self.assertListEqual(b.tolist(), [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
 
+class TestScheduleMultioutput(unittest.TestCase):
+  def test_simplest_multioutput(self):
+    with Context(MULTIOUTPUT=1):
+      a = Tensor.ones(256, 256).contiguous().realize()
+      r = a.sum(axis=1)
+      b = r+1
+      c = r+2
+      run_schedule(check_schedule([b, c], 1))
+      np.testing.assert_allclose(b.numpy(), 257)
+      np.testing.assert_allclose(c.numpy(), 258)
+
 if __name__ == '__main__':
   unittest.main(verbosity=2)

test/test_softmax_fusion.py

@@ -165,8 +165,7 @@ class TestSoftmaxFusion(unittest.TestCase):
       sout.realize()
 
     print("*** single kernel softmax ***")
-    # NOTE: DONT_GROUP_REDUCES is required here
-    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2), DONT_GROUP_REDUCES=1):
+    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2)):
       out = single_kernel_softmax(self.test)
       out.realize()
 
@@ -186,7 +185,6 @@ class TestSoftmaxFusion(unittest.TestCase):
 
     np.testing.assert_allclose(sout.numpy(), out.numpy(), atol=3e-7)
 
-  @unittest.skip("recursion error no longer raised")
   def test_softmax_bw(self):
     print("*** softmax bw ***")
     self.test.requires_grad_()
@@ -197,14 +195,11 @@ class TestSoftmaxFusion(unittest.TestCase):
     self.test.grad = None
 
     print("*** single kernel softmax bw ***")
-    # NOTE: DONT_GROUP_REDUCES is required here
-    # TODO: fix RecursionError with DONT_GROUP_REDUCES
-    with self.assertRaises(RecursionError):
-      with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2), DONT_GROUP_REDUCES=1):
-        single_kernel_softmax(self.test).sum().backward()
-        g = self.test.grad.realize()
+    with Context(NOOPT=1, DEBUG=max(DEBUG.value, 2)):
+      single_kernel_softmax(self.test).sum().backward()
+      g = self.test.grad.realize()
 
-      np.testing.assert_allclose(sg.numpy(), g.numpy(), atol=1e-7)
+    np.testing.assert_allclose(sg.numpy(), g.numpy(), atol=1e-7)
 
 if __name__ == '__main__':
   unittest.main()

test/test_symbolic_ops.py

@@ -287,7 +287,6 @@ class TestSymbolicOps(unittest.TestCase):
         symbolic = symbolic_result[:].numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=0)
 
-  @unittest.expectedFailure
   def test_conv2d_ceildiv_edge_case(self):
     v = Variable('v', 11, 50_000)
     val = 39601
@@ -295,9 +294,10 @@ class TestSymbolicOps(unittest.TestCase):
     weight = Tensor.randn(256, 22, 12)
 
     result = x.conv2d(weight=weight, groups=1, stride=6, dilation=1, padding=(3, 3))
-    var_val = {v: val}
+    var_val = {v.expr: val}
     shape = tuple(sym_infer(s, var_val) for s in result.shape)
-    self.assertEqual(shape, (1, 256, 6600))  # TODO: fails if ceildiv is incorrect
+    with self.assertRaises(AssertionError):
+      self.assertEqual(shape, (1, 256, 6600))  # TODO: fails if ceildiv is incorrect
     # TODO: test output is correct
 
 if __name__ == '__main__':

test/test_tensor.py

@@ -810,6 +810,13 @@ class TestTensorMetadata(unittest.TestCase):
     self.assertEqual(len(si.metadata), 1)
     self.assertEqual(si.metadata[0].name, "relu")
 
+  def test_assign(self):
+    x = Tensor.empty(10, 10).realize()
+    x.assign(Tensor.ones(10, 10).contiguous())
+    si = x.schedule()[-1]
+    self.assertEqual(len(si.metadata), 1)
+    self.assertEqual(si.metadata[0].name, "assign")
+
   def test_complex(self):
     x = Tensor.rand(3, requires_grad=True)
     y = Tensor.rand(3, requires_grad=True)

test/test_tensor_uop.py

@@ -3,7 +3,7 @@ import numpy as np
 import unittest
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.engine.realize import run_schedule
-from tinygrad.uop.ops import Ops, UOp, UPat
+from tinygrad.uop.ops import UOp
 from tinygrad.helpers import SPLIT_REDUCEOP
 
 class TestTensorUOp(unittest.TestCase):
@@ -93,7 +93,6 @@ class TestTensorUOp(unittest.TestCase):
     out.realize()
     self.assertEqual(out.tolist(), Tensor.zeros(4, 8).tolist())
 
-reduce_kernel = UPat(Ops.SINK, src=(UPat(Ops.STORE, allow_any_len=True, src=(UPat(), UPat((Ops.REDUCE_AXIS, Ops.REDUCE))))))
 @unittest.skipUnless(SPLIT_REDUCEOP, "only for SPLIT_REDUCEOP")
 class TestReduceOp(unittest.TestCase):
   def test_no_split_reduce_kernel(self):
@@ -101,23 +100,18 @@ class TestReduceOp(unittest.TestCase):
     a = a.sum()
     sched = a.schedule()
     assert len(sched) == 1
-    assert reduce_kernel.match(sched[0].ast, {})
 
   def test_split_reduce_kernel_dim0(self):
     a = Tensor.rand(256, 255).realize()
     a = a.sum()
     sched = a.schedule()
     assert len(sched) == 2
-    for s in sched:
-      assert reduce_kernel.match(s.ast, {})
 
   def test_split_reduce_kernel_dim1(self):
     a = Tensor.rand(255, 256).realize()
     a = a.sum()
     sched = a.schedule()
     assert len(sched) == 2
-    for s in sched:
-      assert reduce_kernel.match(s.ast, {})
 
 if __name__ == "__main__":
   unittest.main()

test/test_uop_graph.py

@@ -665,19 +665,6 @@ class TestUOpGraph(unittest.TestCase):
     bad_gate = UOp.const(dtypes.int, 1)
     with self.assertRaises(AssertionError): to_uops_list([UOp(Ops.STORE, dtypes.void, (glbl0, idx, UOp.const(dtypes.int, 42), bad_gate))])
 
-  def test_switched_range_order(self):
-    glbl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
-    cf = UOp.const(dtypes.float, 0.0)
-    r1 = UOp.range(2, 0)
-    r2 = UOp.range(2, 1)
-    alu = UOp(Ops.MUL, dtypes.int, (r2, r1))
-    store = UOp(Ops.STORE, dtypes.void, (glbl.index(alu), cf))
-    uops = to_uops_list([store])
-    ranges = [x for x in uops if x.op is Ops.RANGE]
-    endranges = [x for x in uops if x.op is Ops.ENDRANGE]
-    # ranges are closed in the right order
-    self.assertEqual(endranges[-1].src[0], ranges[0])
-
 @track_rewrites()
 def expander_rewrite(sink): return graph_rewrite(sink, sym + expander)
 
@@ -845,8 +832,6 @@ class TestIFUOps(unittest.TestCase):
     if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
     self.assertEqual(len(if_uops), 1)
     self.assertEqual(if_uops[0].src[0], gate)
-    for st in sink.src:
-      self.assertEqual(len(st.src), 2)
 
   def test_expand_ifs_one_gate(self):
     gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
@@ -863,8 +848,6 @@ class TestIFUOps(unittest.TestCase):
     if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
     self.assertEqual(len(if_uops), 1)
     self.assertEqual(if_uops[0].src[0], gate)
-    for st in sink.src:
-      self.assertEqual(len(st.src), 2)
 
   # this will be fixed with the merge gated stores bounty
   @unittest.expectedFailure
@@ -879,8 +862,6 @@ class TestIFUOps(unittest.TestCase):
     if_uops = [u for u in sink.toposort() if u.op is Ops.IF]
     self.assertEqual(len(if_uops), 1)
     self.assertEqual(if_uops[0].src[0], gate)
-    for st in sink.src:
-      self.assertEqual(len(st.src), 2)
 
 class TestUOpTags(unittest.TestCase):
   def test_inc_by_one(self):

test/unit/test_block_reorder.py

@@ -1,76 +0,0 @@
-import unittest, random
-from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import print_uops, UOp, Ops
-from tinygrad.codegen.late.linearize import block_reorder
-from tinygrad.renderer.cstyle import OpenCLRenderer
-
-def is_toposorted(lst:list[UOp]):
-  seen = set()
-  for u in lst:
-    if any(p not in seen for p in u.src): return False
-    seen.add(u)
-  return True
-
-class TestBlockReorder(unittest.TestCase):
-  def _test_randomize(self, golden:list[UOp]):
-    # test random order is always same
-    for _ in range(50):
-      # shuffle and form a valid toposort
-      lst = golden[:]
-      random.shuffle(lst)
-      topolst = []
-      for u in lst:
-        for p in u.toposort():
-          if p not in topolst: topolst.append(p)
-      assert is_toposorted(topolst)
-
-      for x,y in zip(golden, this_order:=block_reorder(topolst)):
-        if x is not y:
-          print_uops(golden)
-          print_uops(this_order)
-        self.assertIs(x, y)
-
-  def _test_render(self, golden:list[UOp]):
-    return OpenCLRenderer().render(golden)
-
-  def test_loads(self):
-    a = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=0)
-    b = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=1)
-    c = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=2)
-    v1 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx0")
-    v2 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx1")
-    v1 = v1*27
-    v2 = v2*4
-    loads = [
-      a.index(v1).load(dtype=dtypes.float),
-      a.index(v1+1).load(dtype=dtypes.float),
-      a.index(v1+2).load(dtype=dtypes.float),
-      a.index(v1+3).load(dtype=dtypes.float),
-      b.index(v2).load(dtype=dtypes.float),
-      b.index(v2+1).load(dtype=dtypes.float),
-      b.index(v2+2).load(dtype=dtypes.float),
-      b.index(v2+3).load(dtype=dtypes.float)]
-    #random.shuffle(loads)
-    sink = c.store(sum(loads)).sink()
-
-    # determine golden order
-    golden = block_reorder(list(sink.toposort()))
-
-    # render for test
-    print(self._test_render(golden))
-    #print_uops(golden)
-
-    # assert the loads are in this order
-    self.assertListEqual([g.src[0].src[1].render() for g in golden if g.op is Ops.LOAD],
-                         ['(gidx1*4)', '((gidx1*4)+1)', '((gidx1*4)+2)', '((gidx1*4)+3)',
-                          '(gidx0*27)', '((gidx0*27)+1)', '((gidx0*27)+2)', '((gidx0*27)+3)'])
-
-    # assert math is after loads
-    first_math = [i for i,g in enumerate(golden) if g.op is Ops.ADD and g.dtype == dtypes.float][0]
-    assert not any(x.op is Ops.LOAD for x in golden[first_math:])
-
-    # confirm the sort is stable
-    self._test_randomize(golden)
-
-if __name__ == '__main__':
-  unittest.main()

test/unit/test_disk_tensor.py

@@ -433,17 +433,15 @@ class TestDiskTensorMovement(unittest.TestCase):
     t = Tensor(self.fn)
     self.assertListEqual(t[16:18].tolist(), [16,17])
 
-  # TODO: fix this! at least assert on it
-  @unittest.expectedFailure
   def test_slice_read_cat(self):
     t = Tensor(self.fn)
-    self.assertListEqual(Tensor.cat(t[16:18], t[20:22]).tolist(), [16,17,20,21])
+    with self.assertRaises(AssertionError):
+      self.assertListEqual(Tensor.cat(t[16:18], t[20:22]).tolist(), [16,17,20,21])
 
-  # TODO: fix this! at least assert on it
-  @unittest.expectedFailure
   def test_slice_sum(self):
     t = Tensor(self.fn)
-    self.assertListEqual((t[16:18]+t[20:22]).tolist(), [16+20,17+21])
+    with self.assertRaises(AssertionError):
+      self.assertListEqual((t[16:18]+t[20:22]).tolist(), [16+20,17+21])
 
 if __name__ == "__main__":
   unittest.main()

test/unit/test_kernelize.py

@@ -20,8 +20,8 @@ class TestKernelize(unittest.TestCase):
     self.assertEqual(len([s for s in a0.uop.toposort() if s.op is Ops.KERNEL]), 2)
     self.assertIs(a1.uop.base.op, Ops.REDUCE_AXIS)
     # input Tensor and user contiguous kernelize
-    self.assertIs(a0.uop.base.op, Ops.ASSIGN)
-    self.assertIs(a.uop.base.op, Ops.ASSIGN)
+    self.assertIs(a0.uop.base.op, Ops.AFTER)
+    self.assertIs(a.uop.base.op, Ops.AFTER)
 
   def test_two_reduce_w_add(self):
     a = Tensor.ones(16,16).contiguous()
@@ -31,7 +31,7 @@ class TestKernelize(unittest.TestCase):
     # NOTE: the +1 is fused with a1, so a1 is not kernelized
     self.assertIs(a1.uop.base.op, Ops.REDUCE_AXIS)
     # the input to the REDUCE_AXIS is an ASSIGN though
-    self.assertIs(a1.uop.base.src[0].base.op, Ops.ASSIGN)
+    self.assertIs(a1.uop.base.src[0].base.op, Ops.AFTER)
 
 if __name__ == '__main__':
   unittest.main()

test/unit/test_simplify_valid_idx.py

@@ -5,7 +5,7 @@ from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.uop.symbolic import simplify_valid
 from tinygrad.helpers import Context
-from .test_uop_symbolic import check_uop_against_string
+from test.unit.test_uop_symbolic import check_uop_against_string
 
 def get_gated_load_uop(valid:UOp, idx:UOp):
   return UOp(Ops.LOAD, dtypes.float, (

test/unit/test_viz.py

@@ -2,7 +2,7 @@ import unittest, decimal, json, struct
 from dataclasses import dataclass
 from typing import Generator
 
-from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher, graph_rewrite, track_rewrites, TRACK_MATCH_STATS
+from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher, graph_rewrite, track_rewrites, TRACK_MATCH_STATS, profile_matches
 from tinygrad.uop.symbolic import sym
 from tinygrad.dtype import dtypes
 from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context, cpu_events, profile_marker
@@ -117,6 +117,28 @@ class TestViz(BaseTestViz):
     # NOTE: names from TracingKey do not get deduped
     self.assertEqual(lst[0]["name"], "custom_name")
 
+  def test_profile_matches(self):
+    @profile_matches
+    def nested_function(u:UOp):
+      for i in range(2): graph_rewrite(u, PatternMatcher([]), name=f"step {i+1}")
+
+    @track_rewrites()
+    def main_rewrite(u:UOp):
+      graph_rewrite(u, PatternMatcher([]), name="init")
+      nested_function(u)
+
+    main_rewrite(UOp.variable("a", 1, 10)+UOp.variable("b", 1, 10))
+    steps = get_viz_list()[0]["steps"]
+    self.assertEqual(steps[0]["name"], "init")
+    self.assertEqual(steps[1]["name"], "nested_function")
+    self.assertEqual(len(steps), 4)
+
+  def test_profile_matches_invalid_arg(self):
+    @profile_matches
+    def invalid_fxn(arg:str): return graph_rewrite(UOp(Ops.SINK), PatternMatcher([]))
+    with self.assertRaisesRegex(AssertionError, "invalid match tracing input"):
+      invalid_fxn("test")
+
   def test_colored_label(self):
     # NOTE: dataclass repr prints literal escape codes instead of unicode chars
     @dataclass(frozen=True)
@@ -126,8 +148,16 @@ class TestViz(BaseTestViz):
     a2 = uop_to_json(a)[id(a)]
     self.assertEqual(ansistrip(a2["label"]), f"CUSTOM\n{TestStruct.__qualname__}(colored_field='xyz12345')")
 
+  def test_colored_label_multiline(self):
+    arg = colored("x", "green")+"\n"+colored("y", "red")+colored("z", "yellow")+colored("ww\nw", "magenta")
+    src = [Tensor.empty(1).uop for _ in range(10)]
+    a = UOp(Ops.CUSTOM, src=tuple(src), arg=arg)
+    exec_rewrite(a, [PatternMatcher([])])
+    a2 = next(get_viz_details(0, 0))["graph"][id(a)]
+    self.assertEqual(ansistrip(a2["label"]), "CUSTOM\nx\nyzww\nw")
+
   def test_inf_loop(self):
-    a = UOp.variable('a', 0, 10)
+    a = UOp.variable('a', 0, 10, dtype=dtypes.int)
     b = a.replace(op=Ops.CONST)
     pm = PatternMatcher([
       (UPat(Ops.DEFINE_VAR, name="x"), lambda x: x.replace(op=Ops.CONST)),
@@ -142,8 +172,8 @@ class TestViz(BaseTestViz):
     self.assertEqual(graphs[2], uop_to_json(nop)[id(nop)])
 
   def test_const_node_visibility(self):
-    a = UOp.variable("a", 0, 10)
-    z = UOp.const(dtypes.index, 0)
+    a = UOp.variable("a", 0, 10, dtype=dtypes.int)
+    z = UOp.const(a.dtype, 0)
     alu = a*z
     exec_rewrite(alu, [sym])
     lst = get_viz_list()
@@ -326,14 +356,14 @@ def load_profile(lst:list[ProfileEvent]) -> dict:
     event_type, event_count = u("<BI")
     if event_type == 0:
       for _ in range(event_count):
-        name, ref, st, dur, _ = u("<IIIfI")
-        v["events"].append({"name":strings[name], "ref":option(ref), "st":st, "dur":dur})
+        name, ref, key, st, dur, _ = u("<IIIIfI")
+        v["events"].append({"name":strings[name], "ref":option(ref), "key":option(key), "st":st, "dur":dur})
     else:
       v["peak"] = u("<Q")[0]
       for _ in range(event_count):
         alloc, ts, key = u("<BII")
         if alloc: v["events"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
-        else: v["events"].append({"event":"free", "ts":ts, "key":key, "arg":{"users":[u("<I")[0] for _ in range(u("<I")[0])]}})
+        else: v["events"].append({"event":"free", "ts":ts, "key":key, "arg": {"users":[u("<IIBB") for _ in range(u("<I")[0])]}})
   return {"dur":total_dur, "peak":global_peak, "layout":layout, "markers":markers}
 
 class TestVizProfiler(unittest.TestCase):
@@ -442,7 +472,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{a.device} Memory"]
     self.assertEqual(ret["peak"], 2)
-    self.assertEqual(len(ret["events"]), 2)
+    self.assertEqual(len(ret["events"]), 4)
 
   def test_del_once(self):
     a = _alloc(1)
@@ -451,7 +481,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{b.device} Memory"]
     self.assertEqual(ret["peak"], 1)
-    self.assertEqual(len(ret["events"]), 3)
+    self.assertEqual(len(ret["events"]), 4)
 
   def test_alloc_free(self):
     a = _alloc(1)
@@ -461,7 +491,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{c.device} Memory"]
     self.assertEqual(ret["peak"], 2)
-    self.assertEqual(len(ret["events"]), 4)
+    self.assertEqual(len(ret["events"]), 6)
 
   def test_free_last(self):
     bufs = []
@@ -480,15 +510,45 @@ class TestVizMemoryLayout(BaseTestViz):
     self.assertEqual(len(profile["markers"]), 6)
 
   def test_producer_simple(self):
-    a = Tensor.empty(10, device="NULL")
-    Tensor.realize(a.add(1), a.add(2))
-    b = Tensor.empty(10, device="NULL")
-    Tensor.realize(b.add(1))
+    a = Tensor.ones(10, device="NULL")
+    Tensor.realize(a.add(1).contiguous())
+    b = Tensor.ones(10, device="NULL")
+    Tensor.realize(b.add(1).contiguous())
     profile = load_profile(cpu_events+Buffer.profile_events)
     buffers = profile["layout"]["NULL Memory"]["events"]
     programs = profile["layout"]["NULL"]["events"]
     user_cnt = [len(b["arg"]["users"]) for b in buffers if b["arg"].get("users")]
     self.assertEqual(len(user_cnt), len(programs))
 
+  def test_inflight_buf(self):
+    a = Tensor.empty(1, device="NULL")
+    n = 4
+    for i in range(n): (a+i).realize()
+    profile = load_profile(cpu_events+Buffer.profile_events)
+    buffers = profile["layout"]["NULL Memory"]["events"]
+    user_cnt = [len(b["arg"]["users"]) for b in buffers if b["arg"].get("users")]
+    self.assertEqual(max(user_cnt), n)
+    input_buf = buffers.pop()
+    assert all(u[3] == 0 for u in input_buf["arg"]["users"])
+
+  def test_annotate_read_write(self):
+    a = Tensor.ones(4, device="NULL").contiguous().realize()
+    b = a.assign(a+2)
+    c = a+1
+    Tensor.realize(b, c)
+    buf_events = load_profile(cpu_events+Buffer.profile_events)["layout"]["NULL Memory"]["events"]
+    users = next((b["arg"]["users"] for b in buf_events if len(b["arg"].get("users",[])) == 3))
+    self.assertEqual(users[0][3], 1) # write Tensor.ones
+    self.assertEqual(users[1][3], 2) # read+write Tensor.assign
+    self.assertEqual(users[2][3], 0) # readonly
+
+  def test_dedup_users(self):
+    a = Tensor.empty(1, device="NULL")
+    for _ in range(n:=4): a.add(1).realize()
+    profile = load_profile(cpu_events+Buffer.profile_events)
+    programs = profile["layout"][a.device]["events"]
+    users = profile["layout"][f"{a.device} Memory"]["events"].pop()["arg"]["users"]
+    self.assertEqual(len(programs), len(set(users)), n)
+
 if __name__ == "__main__":
   unittest.main()

tinygrad/codegen/__init__.py

@@ -14,10 +14,10 @@ from tinygrad.uop.decompositions import get_late_rewrite_patterns
 from tinygrad.codegen.late.expander import migrate_indexing, expander, pm_pre_expander, pm_group_for_reduce
 from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize, pm_reduce, \
   ReduceContext, correct_load_store, pm_render
-from tinygrad.codegen.late.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
 from tinygrad.codegen.opt.postrange import pm_postrange_opt
 from tinygrad.codegen.simplify import pm_simplify_ranges, pm_reduce_simplify, pm_flatten_range, pm_split_ranges
 from tinygrad.schedule.rangeify import pm_add_buffers, rangeify_codegen
+from tinygrad.codegen.late.control_flow import CFGContext, pm_merge_ends, pm_add_control_flow, linearize
 
 @dataclass
 class RewriteStep:
@@ -30,19 +30,13 @@ class RewriteStep:
 
 def apply_rewrites(sink:UOp, rewrites:list[RewriteStep]): return functools.reduce(lambda x,f: f(x), rewrites, sink)
 
-rewrites_for_linearizer = [
-  RewriteStep(block_create, ctx=BlockContext.from_sink, name="Linearizer: Create Blocks", bottom_up=True),
-  RewriteStep(pm_blockend_merge, name="Linearizer: Merge Blockends"),
-  RewriteStep(block_merge, name="Linearizer: Merge Blocks"),
-  RewriteStep(pm_finalize, name="Linearizer: Finalize")]
-
 def get_rewrites_for_renderer(opts:Renderer, optimize:bool=True, linearizer:bool=True) -> list[RewriteStep]:
   # cache with the values of the context vars
   return _get_rewrites_for_renderer(opts, optimize, linearizer, QUANTIZE.value, DEVECTORIZE.value, TRANSCENDENTAL.value)
 
 @functools.cache
 def _get_rewrites_for_renderer(opts:Renderer, optimize:bool, linearizer:bool, _QUANTIZE, _DEVECTORIZE, _TRANSCENDENTAL) -> list[RewriteStep]:
-  # ** lowerer (rewrite_shapetracker_with_index) **
+  # ** lowerer **
   ret: list[RewriteStep] = []
 
   if optimize:
@@ -101,11 +95,15 @@ def _get_rewrites_for_renderer(opts:Renderer, optimize:bool, linearizer:bool, _Q
   pm_final_rewrite = pm_decomp+pm_render+extra_matcher
   ret.append(RewriteStep(pm_final_rewrite, lambda _: opts.device, name="final rewrite"))
 
-  # return the list (with optional linearizer)
-  return ret + (rewrites_for_linearizer if linearizer else [])
+  # this was the linearizer
+  ret.append(RewriteStep(pm_merge_ends, name="merge ends"))
+  ret.append(RewriteStep(pm_add_control_flow, CFGContext, name="add control flow starts", bottom_up=True))
 
-def full_rewrite_to_sink(sink:UOp, opts:Renderer|None=None, optimize:bool=True, linearizer:bool=False) -> UOp:
-  return apply_rewrites(sink, get_rewrites_for_renderer(opts if opts is not None else Renderer(), optimize, linearizer))
+  # return the list
+  return ret
+
+def full_rewrite_to_sink(sink:UOp, opts:Renderer|None=None, optimize:bool=True) -> UOp:
+  return apply_rewrites(sink, get_rewrites_for_renderer(opts if opts is not None else Renderer(), optimize))
 
 def full_rewrite(sink:UOp, opts:Renderer|None=None) -> list[UOp]:
   """
@@ -119,6 +117,6 @@ def full_rewrite(sink:UOp, opts:Renderer|None=None) -> list[UOp]:
     Linear program in UOps.
   """
 
-  lst = list(full_rewrite_to_sink(sink, opts, optimize=sink.tag is None, linearizer=True).arg.lst)
+  lst = linearize(full_rewrite_to_sink(sink, opts, optimize=sink.tag is None))
   if __debug__: type_verify(lst)
   return lst

tinygrad/codegen/gpudims.py

@@ -1,4 +1,4 @@
-import math
+import math, functools, operator
 from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
 from tinygrad.helpers import all_int, dedup, get_contraction
 from tinygrad.dtype import dtypes
@@ -87,7 +87,15 @@ def add_gpudims(ctx:Renderer, s:UOp):
     except ValueError: continue
   return s.substitute(subs)
 
+def add_barrier_and_if(buf:UOp, e:UOp):
+  # TODO: this is not generic
+  local_ranges = [x for x in e.ended_ranges if x.op is Ops.RANGE and x.arg[-1] == AxisType.GROUP_REDUCE]
+  if len(local_ranges) == 0: return None
+  return buf.after(UOp(Ops.IF, dtype=dtypes.void, src=(functools.reduce(operator.and_, [x.eq(0) for x in local_ranges]), e.barrier())))
+
 pm_add_gpudims = PatternMatcher([
   # add gpudims must be last
   (UPat(Ops.SINK, name="s"), add_gpudims),
+  # add barrier and if
+  (UPat(Ops.AFTER, src=(UPat(Ops.DEFINE_LOCAL, name="buf"), UPat(Ops.END, name="e"))), add_barrier_and_if),
 ])

tinygrad/codegen/late/control_flow.py

@@ -0,0 +1,102 @@
+import heapq
+from collections import defaultdict
+from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat
+
+def linearize(u:UOp) -> list[UOp]:
+  lst = list(u.toposort())
+  in_this_block = set(lst)
+  local_children: defaultdict[UOp, list[UOp]] = defaultdict(list)
+  in_degree:dict[UOp, int] = {}
+  priorities:dict[UOp, int] = {}
+
+  # get local children and assign priorities
+  # NOTE: this requires the lst be locally toposorted
+  for u in reversed(lst):
+    in_degree[u] = 0
+    for s in u.src:
+      if s in in_this_block:
+        local_children[s].append(u)
+        in_degree[u] += 1
+    # put loads in the beginning of the block and prevent priority inversion. hack for BARRIER grouping too
+    priority = [0] + [priorities[x] for x in local_children[u]]
+    if u.op is Ops.LOAD: priority.append(-1000)
+    if u.op is Ops.BARRIER: priority.append(-1500)
+    # ranges are scheduled as late as possible so anything that can be outside is
+    #if u.op is Ops.RANGE: priority = [2000]
+    # move defines and consts to the top
+    if u.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.CONST}: priority.append(-2000)
+    priorities[u] = min(priority)
+
+  # number the uops in "ideal" order
+  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: (priorities[x],)+x.tuplize))}
+
+  # then force then to be toposorted in as close to the ideal order as possible
+  heapq.heapify(heap:=[(nkey[u],u) for u in lst if in_degree[u] == 0])
+  newlst = []
+  while heap:
+    newlst.append(u:=heapq.heappop(heap)[1])
+    for v in local_children[u]:
+      in_degree[v] -= 1
+      if in_degree[v] == 0: heapq.heappush(heap, (nkey[v],v))
+
+  assert len(newlst) == len(lst), f"len mismatch {len(newlst)} != {len(lst)}"
+  return newlst
+
+class CFGContext:
+  def __init__(self, sink:UOp):
+    # there are 3 relationships between ranges:
+    # nested, meaning endrange y is a dependency of endrange x and range x is a dependency of endrange y
+    # dependent, meaning endrange y is a dependency of endrange x and range x is not a dependency of endrange y
+    # independent, endrange y is not a dependency of endrange x
+    # everything is nested inside the sink
+    deps: dict[UOp, set[UOp]] = {}
+    nesting: dict[UOp, UOp] = {}
+    for u in sink.toposort():
+      deps[u] = set().union(*(deps[s] for s in u.src))
+      if u.op in (Ops.END, Ops.ENDIF, Ops.SINK):
+        nesting |= {x:u for x in deps[u] if x.op in (Ops.END, Ops.ENDIF) and (u.op is Ops.SINK or u.src[0] in deps[x]) and x not in nesting}
+      if u.op in (Ops.RANGE, Ops.END, Ops.IF, Ops.ENDIF): deps[u] |= {u}
+
+    self.edges: dict[UOp, UOp] = {}
+    siblings: dict[UOp, list[UOp]] = {}
+    for k,vv in nesting.items(): siblings.setdefault(vv, []).append(k)
+    for k,v in siblings.items():
+      # range/if that have dependencies on other siblings need to run after them
+      order = sorted(v, key=lambda x: len(deps[x].intersection(v)))
+      zipped = zip(order, order[1:]) if k.op is Ops.SINK else zip([k.src[0]] + order, order)
+      for x,y in zipped:
+        # TODO: is this check correct?
+        if y.src[0] not in x.backward_slice_with_self:
+          self.edges[y.src[0]] = x
+
+pm_add_control_flow = PatternMatcher([
+  (UPat((Ops.RANGE, Ops.IF), name="x"), lambda ctx,x: x.replace(src=x.src+(y,)) if (y:=ctx.edges.get(x)) is not None else None),
+])
+
+def do_merge_ends(s:UOp):
+  # NOTE: this can fail
+  stacked: dict[UOp, list[UOp]] = {}
+  dangling_ifs = []
+  for x in s.toposort():
+    if x.op in {Ops.END, Ops.ENDIF}:
+      assert x.op is not Ops.END or x.arg == 1, "ends must be single ends for linearizer"
+      stacked.setdefault(x.src[0], []).append(x)
+    if x.op is Ops.IF: dangling_ifs.append(x)
+  dangling_ifs = [x for x in dangling_ifs if x not in stacked]
+  replaces = {}
+  for k,v in stacked.items():
+    if len(v) == 1: continue
+    rep = UOp(v[0].op, src=tuple([k] + [y for x in v for y in x.src[1:]]), arg=v[0].arg)
+    for x in v: replaces[x] = rep
+  if not len(replaces) and not len(dangling_ifs): return None
+  ret = s.substitute(replaces)
+  if len(dangling_ifs):
+    assert len(dangling_ifs) == 1, "we only support 1 dangling if"
+    ret = ret.replace(src=(UOp(Ops.ENDIF, src=(dangling_ifs[0], *ret.src)),))
+  return ret
+
+pm_merge_ends = PatternMatcher([
+  # for renderering and linearizing, all ends must end one loop
+  (UPat(Ops.END, name="e"), lambda e: e.replace(src=e.src[e.arg-1:], arg=1).end(ends=e.src[:e.arg-1]) if e.arg > 1 else None),
+  (UPat(Ops.SINK, name="s"), do_merge_ends),
+])

tinygrad/codegen/late/devectorizer.py

@@ -123,7 +123,7 @@ def gep_on_store(gep:UOp, st:UOp, sto:UOp):
   return gep.src[0].store(st.gep(new_arg), *sto.src[2:])
 
 load_store_folding = PatternMatcher([
-  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines, name="buf")), UPat.var("vec"))), expand_index),
+  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines).or_after(name="buf")), UPat.var("vec"))), expand_index),
   # GEP after LOAD
   (UPat(Ops.LOAD, src=(UPat(Ops.GEP, name="gep"),), name="ld", allow_any_len=True),
    lambda gep, ld: ld.replace(dtype=ld.dtype.scalar().vec(gep.dtype.count), src=(gep.src[0],)+ld.src[1:]).gep(gep.arg)),
@@ -242,11 +242,13 @@ def no_vectorized_index(buf:UOp, cast:UOp, idx:UOp):
   return buf.broadcast(cnt).index(idx.broadcast(cnt)*cnt+UOp.const(dtypes.index.vec(cnt), tuple(range(cnt))))
 
 devectorize = PatternMatcher([
+  # CAST after AFTER
+  (UPat(Ops.CAST, name="c").f(Ops.AFTER, allow_any_len=True, name="a"), lambda c,a: c.src[0].after(*a.src[1:]).cast(c.dtype)),
   # no ALU on vectorized dtypes
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST), name="alu"), no_vectorized_alu),
   (UPat(Ops.WMMA, name="wmma"), no_vectorized_wmma),
   (UPat((Ops.DEFINE_LOCAL, Ops.DEFINE_REG), name="buf"), no_vectorized_buf),
-  (UPat((Ops.DEFINE_LOCAL, Ops.DEFINE_REG), name="buf").cast(name="cast").index(UPat.var("idx")), no_vectorized_index),
+  (UPat((Ops.DEFINE_LOCAL, Ops.DEFINE_REG)).or_after(name="buf").cast(name="cast").index(UPat.var("idx")), no_vectorized_index),
 ])
 
 pm_render = PatternMatcher([
@@ -266,9 +268,9 @@ pm_render = PatternMatcher([
     UPat.var("a")), lambda c,idx,l,a: l.replace(src=(l.src[0], a.cast(l.dtype))+l.src[2:]).cast(a.dtype)),
   (UPat.var("c").where(UPat.var("a"), UPat(Ops.LOAD, src=(UPat().index(UPat.var("idx"), UPat.var("c").logical_not()).or_casted(),),
     allow_any_len=True, name="l").or_casted()), lambda c,idx,l,a: l.replace(src=(l.src[0], a.cast(l.dtype))+l.src[2:]).cast(a.dtype)),
-  # gate any stores that aren't gated with ifs
+  # gate any stores that aren't gated with if/endif pairs
   (UPat(Ops.STORE, src=(UPat(src=(UPat(), UPat(), UPat(dtype=dtypes.bool)), name="idx").or_casted(), UPat()), name="store", allow_any_len=True),
-    lambda store,idx: UOp(Ops.STORE, dtype=store.dtype, src=store.src[:2]+(UOp(Ops.IF, src=(idx.src[2],)),)+store.src[2:]) if \
+    lambda store,idx: UOp(Ops.ENDIF, src=(uif:=UOp(Ops.IF, src=(idx.src[2],)), UOp(Ops.STORE, src=store.src[:2]+(uif,)+store.src[2:]))) if \
       len(store.src) <= 2 or store.src[2].op != Ops.IF else None),
 ])
 
@@ -293,15 +295,17 @@ def reduce_to_acc(ctx:ReduceContext, red:UOp):
   # if we have a range
   if len(reduce_range) != 0:
     topo = inp.toposort()
-    stored_ranges = flatten([x.src[2:] for x in topo if x.op is Ops.STORE])
-    input_ranges = tuple([x for x in topo if x.op is Ops.RANGE and x not in reduce_range and x not in stored_ranges])
+    ended_ranges = flatten([x.src[:x.arg] for x in topo if x.op is Ops.END])
+    input_ranges = tuple([x for x in topo if x.op is Ops.RANGE and x not in reduce_range and x not in ended_ranges])
     identity = red.const(red.dtype, identity_element(red.arg, red.dtype.scalar()))
-    acc = UOp(Ops.DEFINE_REG, red.dtype.ptr(size=1, addrspace=AddrSpace.REG), arg=(ctx.acc_num,)).index(UOp.const(dtypes.int, 0))
-    do_store = acc.store(identity, UOp(Ops.NOOP, src=input_ranges)) if len(input_ranges) else acc.store(identity)
-    lst = [acc.load(do_store, *reduce_range)] + lst  # put acc as the first element
+    acc = UOp(Ops.DEFINE_REG, red.dtype.ptr(size=1, addrspace=AddrSpace.REG), arg=(ctx.acc_num,))
+    acc_init = acc.after(*input_ranges).index(UOp.const(dtypes.int, 0)).store(identity) if len(input_ranges) else \
+               acc.index(UOp.const(dtypes.int, 0)).store(identity)
+    lst = [acc.after(acc_init, *reduce_range).index(UOp.const(dtypes.int, 0)).load()] + lst  # put acc as the first element
     ctx.acc_num += 1
   ret = functools.reduce(lambda x,y: x.alu(red.arg, y), lst)
-  return acc.load(acc.store(ret, *reduce_range)) if len(reduce_range) != 0 else ret
+  if len(reduce_range) == 0: return ret
+  return acc.after(acc.index(UOp.const(dtypes.int, 0)).store(ret).end(ends=reduce_range[::-1])).index(UOp.const(dtypes.int, 0)).load()
 
 pm_reduce = PatternMatcher([
   # REDUCE -> DEFINE_ACC+ASSIGN

tinygrad/codegen/late/expander.py

@@ -87,7 +87,7 @@ expander = PatternMatcher([
    lambda outer, inner: UOp(Ops.UNROLL, outer.dtype, (inner.src[0],), inner.arg+outer.arg)),
   # do expansion
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.GEP, Ops.WMMA, Ops.LOAD, Ops.STORE, Ops.INDEX, Ops.BUFFERIZE,
-         Ops.VECTORIZE, Ops.IF, Ops.REDUCE), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
+         Ops.VECTORIZE, Ops.IF, Ops.REDUCE, Ops.END), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
   (UPat(Ops.CONTRACT, name="con"), do_contract),
   # BARRIERs aren't actually expanded
   (UPat(Ops.BARRIER, src=(UPat(Ops.UNROLL, name="ex"),)),
@@ -145,8 +145,7 @@ def fix_group_for_reduce(x:UOp):
   reduce_loop = [x.replace(arg=(x.arg[0]+100, AxisType.REDUCE)) for x in reduce_gfr]
   buf = ret.bufferize(*upstream_locals, *reduce_gfr, arg=BufferizeOpts(reduce_gfr[0].arg[0], AddrSpace.LOCAL)).index(*upstream_locals, *reduce_loop)
 
-  # gate with an if on the store + do the final reduce
-  buf = UOp(Ops.IF, dtype=buf.dtype, src=(functools.reduce(operator.and_, [x.eq(0) for x in reduce_gfr]), buf))
+  # do the final reduce (if/barrier are added in gpudims step)
   return buf.reduce(*reduce_loop, arg=x.arg)
 
 pm_pre_expander = PatternMatcher([

tinygrad/codegen/late/linearize.py

@@ -1,243 +0,0 @@
-from __future__ import annotations
-import heapq
-from collections import defaultdict
-from dataclasses import dataclass, replace
-from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, BottomUpGate
-from tinygrad.helpers import dedup, all_same, flatten, BLOCK_REORDER
-
-# NOTE: any toposort should be valid here, unlike last time this isn't required, it's just for speed
-def block_reorder(lst:list[UOp]) -> list[UOp]:
-  in_this_block = set(lst)
-  local_children: defaultdict[UOp, list[UOp]] = defaultdict(list)
-  in_degree:dict[UOp, int] = {}
-  priorities:dict[UOp, int] = {}
-
-  # get local children and assign priorities
-  # NOTE: this requires the lst be locally toposorted
-  for u in reversed(lst):
-    in_degree[u] = 0
-    for s in u.src:
-      if s in in_this_block:
-        local_children[s].append(u)
-        in_degree[u] += 1
-    # put loads in the beginning of the block and prevent priority inversion. hack for BARRIER grouping too
-    priority = [0] + [priorities[x] for x in local_children[u]]
-    if u.op is Ops.LOAD: priority.append(-1000)
-    if u.op is Ops.BARRIER: priority.append(-1500)
-    priorities[u] = min(priority)
-
-  # number the uops in "ideal" order
-  nkey = {u:i for i,u in enumerate(sorted(lst, key=lambda x: (priorities[x],)+x.tuplize))}
-
-  # then force then to be toposorted in as close to the ideal order as possible
-  heapq.heapify(heap:=[(nkey[u],u) for u in lst if in_degree[u] == 0])
-  newlst = []
-  while heap:
-    newlst.append(u:=heapq.heappop(heap)[1])
-    for v in local_children[u]:
-      in_degree[v] -= 1
-      if in_degree[v] == 0: heapq.heappush(heap, (nkey[v],v))
-
-  assert len(newlst) == len(lst), f"len mismatch {len(newlst)} != {len(lst)}"
-  return newlst
-
-# ***** basic block *****
-
-def disp(y:UOp) -> str:
-  if y.op is Ops.IF: return f'IF{id(y)}'
-  if y.op is Ops.RANGE: return str(y.arg)
-  return "<NONE>"
-
-@dataclass(frozen=True, eq=False)
-class BasicBlock:
-  lst: tuple[UOp, ...]
-  ctx: tuple[UOp, ...] = ()
-  end: UOp|None = None
-  cnt: int = 0
-  child_ctx: tuple[UOp, ...]|None = None
-  def __lt__(self, _:BasicBlock): raise RuntimeError("no comparing basic blocks")
-  def __repr__(self):
-    return f"{(str(disp(self.end))+' ') if self.end is not None else ''}"+f'f{self.cnt} '+\
-           f"{[disp(y) for y in self.ctx]} {[disp(y) for y in self.child_ctx] if self.child_ctx is not None else '-'} "+\
-           f"{len(self.lst)}" + "\n" + '\n'.join([str(x.op) for x in self.lst])
-  def last_ctx(self): return self.child_ctx if self.child_ctx is not None else self.ctx
-
-def _sort_ctx(inp): return tuple(sorted(dedup(inp), key=lambda x: x.tuplize))
-
-# ***** block context *****
-
-@dataclass
-class BlockContext:
-  child_count: dict[UOp, int]
-  block_ctxs: dict[UOp, tuple[UOp, ...]]
-  child_ctxs: dict[UOp, tuple[UOp, ...]]
-  def last_ctx(self, u): return self.child_ctxs.get(u, self.block_ctxs[u])
-  @staticmethod
-  def from_sink(sink:UOp) -> BlockContext:
-    # get children and all block contexts
-    ctx = BlockContext({}, {}, {})
-    for u in sink.toposort(gate=lambda u:u.op is not Ops.SPECIAL):
-      this_block_ctx: list[UOp] = []
-      ctx.child_count[u] = 0
-
-      # get children and accumulate the last_ctx
-      for s in u.src:
-        if s.op is Ops.SPECIAL: continue
-        # NOTE: if a parent appears multiple times in the src, it counts multiple times as a child
-        ctx.child_count[s] += 1
-        this_block_ctx += ctx.last_ctx(s)
-
-      # save the block ctx. SINK never has anything
-      ctx.block_ctxs[u] = _sort_ctx(this_block_ctx) if u.op is not Ops.SINK else ()
-
-      # RANGE/IF add to the next ctx
-      # STORE/ASSIGN subtract from the next ctx
-      if u.op in {Ops.RANGE, Ops.IF}: ctx.child_ctxs[u] = _sort_ctx(ctx.block_ctxs[u] + (u,))
-      elif u.op is Ops.STORE: ctx.child_ctxs[u] = tuple([y for y in ctx.block_ctxs[u] if y not in u.src])
-    return ctx
-
-# ***** make blocks *****
-
-DONT_PLACE_IN_BLOCK = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.CONST}
-
-def add_blockends(base_block:UOp, new_ctx:tuple[UOp, ...], current_ctx:tuple[UOp, ...], cnt:int=1) -> UOp:
-  ends_to_add = [z for z in new_ctx if z not in current_ctx]
-  while len(ends_to_add):
-    r:UOp = ends_to_add.pop(-1)
-    new_ctx = tuple([z for z in new_ctx if z is not r])
-    end_uop = UOp(Ops.ENDIF if r.op is Ops.IF else Ops.ENDRANGE, src=(r,))
-    base_block = UOp(Ops.BLOCKEND, src=(base_block,)*cnt, arg=BasicBlock((end_uop,), tuple(new_ctx), end=r, cnt=cnt))
-  return base_block
-
-def make_block_bottom_up(ctx:BlockContext, x:UOp):
-  if x.op is Ops.BLOCKSTART:
-    current_ctx, child_ctx = x.arg
-    lst = list(x.src)
-    child_count = 1
-  else:
-    current_ctx, child_count, child_ctx = ctx.block_ctxs[x], ctx.child_count[x], ctx.child_ctxs.get(x, None)
-    lst = [x]
-
-  # count of times we've seen this block, or a seed for a new block if we can't merge it
-  unmergable: defaultdict[UOp, int] = defaultdict(int)
-  blockseeds = defaultdict(list)
-
-  # add the srcs of this to the frontier
-  # NOTE: things may be in here multiple times, that's okay
-  frontier_nodes = list(flatten(y.src[::-1] for y in lst))
-  while len(frontier_nodes):
-    u = frontier_nodes.pop(0)
-    if u.op not in DONT_PLACE_IN_BLOCK and ctx.child_count[u] == unmergable[u]+1:
-      # count is correct
-      if (newctx:=ctx.block_ctxs[u]) == current_ctx:
-        # block has same context, merge it, and put the srcs on the frontier
-        lst.append(u)
-        frontier_nodes.extend(u.src[::-1])
-      else:
-        # block has different context, add it to blockseeds
-        blockseeds[(newctx, ctx.child_ctxs.get(u, None))].append(u)
-      del unmergable[u]
-    else:
-      # count is incorrect (or it's DONT_PLACE_IN_BLOCK), add it to unmergable
-      unmergable[u] += 1
-
-  # add unmergables to sources
-  srcs = []
-  for u,cnt in unmergable.items(): srcs += [add_blockends(u, ctx.block_ctxs.get(u,()), current_ctx, cnt=cnt)]*cnt
-
-  # add blockseeds, with blockends as needed
-  for (new_ctx, new_child_ctx), v in blockseeds.items():
-    base_block = UOp(Ops.BLOCKSTART, src=tuple(v), arg=(new_ctx, new_child_ctx))
-    srcs.append(add_blockends(base_block, new_ctx, current_ctx))
-
-  lst = lst[::-1]
-  if BLOCK_REORDER: lst = block_reorder(lst)
-  bb = BasicBlock(tuple(lst), ctx=current_ctx, cnt=child_count, child_ctx=child_ctx)
-  return UOp(Ops.BLOCK, src=tuple(srcs), arg=bb)
-
-# we prevent the source of the SPECIAL from being linearized since its not part of the kernel
-def raise_bottom_up_gate(): raise BottomUpGate()
-
-block_create = PatternMatcher([
-  (UPat(GroupOp.All-DONT_PLACE_IN_BLOCK.union({Ops.BLOCK, Ops.BLOCKEND}), name="x"), make_block_bottom_up),
-  (UPat(Ops.SPECIAL), raise_bottom_up_gate)
-])
-
-# ***** blockend merging ****
-
-def merge_blockends(sink:UOp) -> UOp|None:
-  # only run on the final BLOCK with the SINK in it
-  if sink.arg.lst[-1].op is not Ops.SINK: return None
-  # combine matching BLOCKENDS, the keys of this dictionary are the RANGE UOps, values are the BLOCKENDs
-  blockends_to_arg: dict[UOp, list[UOp]] = {}
-  for be in sink.toposort():
-    if be.op is Ops.BLOCKEND: blockends_to_arg.setdefault(be.arg.end, []).append(be)
-  new_forks = {}
-  for k,v in blockends_to_arg.items():
-    # NOTE: if any BLOCKEND is the parent of any other with the same arg, this algo fails
-    if len(v) > 1:
-      bb = BasicBlock(v[0].arg.lst, _sort_ctx(flatten([y.arg.ctx for y in v])), k, cnt=sum(y.arg.cnt for y in v))
-      out = UOp(Ops.BLOCKEND, src=tuple(flatten([x.src for x in v])), arg=bb)
-      # NOTE: bb.ctx != u.arg.ctx can cause problems here
-      for u in v: new_forks[u] = out
-  if len(new_forks) == 0: return None
-  return sink.substitute(new_forks)
-
-pm_blockend_merge = PatternMatcher([(UPat(Ops.BLOCK, name="sink"), merge_blockends)])
-
-# ***** block merging ****
-
-def merge_block(x:UOp):
-  unmergable_blocks, mergable_blocks = [], []
-  mergable_dict: defaultdict[UOp, int] = defaultdict(int)
-  for y in x.src:
-    if y.op is Ops.BLOCK and x.op is Ops.BLOCK and x.arg.ctx == y.arg.ctx: mergable_dict[y] += 1
-    elif y.op is Ops.BLOCK and x.op is Ops.BLOCKEND and x.arg.end in y.arg.ctx: mergable_dict[y] += 1
-    else: unmergable_blocks.append(y)
-  for k,v in mergable_dict.items():
-    if v == k.arg.cnt: mergable_blocks.append(k)
-    else: unmergable_blocks.extend([k]*v)
-  if len(mergable_blocks) == 0: return None
-  del mergable_dict
-
-  # create the block
-  arg = replace(x.arg, lst=tuple(flatten([y.arg.lst for y in mergable_blocks]))+x.arg.lst)
-  return UOp(x.op, src=tuple(flatten([y.src for y in mergable_blocks])+unmergable_blocks), arg=arg)
-
-def remove_blockend(x:UOp):
-  # if there's any remaining blocks that need to go in this BLOCKEND, we don't remove it
-  if any(x.arg.end in y.arg.ctx for y in x.src if y.op in {Ops.BLOCK, Ops.BLOCKEND}): return None
-
-  if (parent_blocks := [y for y in x.src if y.op is Ops.BLOCK and y.arg.child_ctx is not None and x.arg.end in y.arg.child_ctx]):
-    assert all_same(parent_blocks), f"should never have two parent blocks (has {len(parent_blocks)})"
-    parent_block = parent_blocks[0]
-    assert len(parent_blocks) == parent_block.arg.cnt
-    # NOTE: DEFINE_ACC doesn't have to be handled in any special way
-    late_ops = list(x.arg.lst)
-    # NOTE: we have to add a barrier at the start if barrier is used in the range
-    if x.op is Ops.BLOCKEND and any(y.op is Ops.BARRIER for y in late_ops) and late_ops[-1].op is Ops.ENDRANGE:
-      late_ops = [UOp(Ops.BARRIER)] + late_ops
-    # peephole opt, remove any BARRIERs next to each other
-    for i in range(len(late_ops)-1):
-      if late_ops[i].op is Ops.BARRIER and late_ops[i+1].op is Ops.BARRIER: late_ops[i+1] = UOp(Ops.NOOP)
-    arg = BasicBlock(parent_block.arg.lst+tuple(late_ops), tuple([y for y in x.arg.ctx if y is not x.arg.end]), cnt=x.arg.cnt)
-    return UOp(Ops.BLOCK, src=tuple(y for y in x.src if y is not parent_block)+parent_block.src, arg=arg)
-  # else the whole context ended by the blockend is already in this block and we can safely turn it into a block
-  return UOp(Ops.BLOCK, src=x.src, arg=BasicBlock(x.arg.lst, tuple([y for y in x.arg.ctx if y is not x.arg.end]), cnt=x.arg.cnt))
-
-block_merge = PatternMatcher([
-  (UPat((Ops.BLOCK, Ops.BLOCKEND), name="x"), merge_block),
-  (UPat(Ops.BLOCKEND, name="x"), remove_blockend),
-])
-
-# ****** finalize ******
-
-def finalize(sink:UOp) -> UOp:
-  if sink.op is not Ops.BLOCK or not all(x.op in DONT_PLACE_IN_BLOCK for x in sink.src):
-    raise RuntimeError(f"linearize failure {sink.op} {[x.op for x in sink.src if x.op not in DONT_PLACE_IN_BLOCK]}")
-
-  # place the early things
-  lst = sorted(dedup(sink.src), key=lambda x: x.tuplize) + list(sink.arg.lst)
-  return UOp(Ops.BLOCKFINAL, arg=BasicBlock(tuple(lst)))
-
-pm_finalize = PatternMatcher([(UPat(Ops.BLOCK, name="sink"), finalize)])

tinygrad/codegen/opt/postrange.py

@@ -4,7 +4,7 @@ from collections import defaultdict
 from typing import cast, Final
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, AxisType, ssimplify, GroupOp
 from tinygrad.device import Buffer
-from tinygrad.dtype import AddrSpace, dtypes, ImageDType
+from tinygrad.dtype import dtypes, ImageDType
 from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up, prod, merge_dicts, get_single_element
 from tinygrad.codegen.opt import axis_colors, Opt, OptOps, KernelOptError, check, axis_letters
 from tinygrad.codegen.simplify import pm_flatten_range
@@ -64,21 +64,8 @@ class Scheduler:
     return self.ast.replace(arg=KernelInfo(name=name, applied_opts=tuple(self.applied_opts), dont_use_locals=self.dont_use_locals), tag=1)
 
   def _globalizable_rngs(self) -> list[UOp]:
-    store_rngs = self.ast.src[0].src[2:]
-
-    # filter any not in local stores
-    local_store_rngs = [x.ranges for x in self.ast.toposort() if (x.op is Ops.STORE and x.src[0].ptrdtype.addrspace == AddrSpace.LOCAL) \
-                        or (x.op is Ops.BUFFERIZE and x.arg == AddrSpace.LOCAL)]
-    for ls in local_store_rngs: store_rngs = tuple([x for x in store_rngs if x in ls])
-
-    # filter any not in reduces
-    # TODO: enable this
-    """
-    reduce_rngs = [x.ranges for x in self.ast.toposort() if x.op is Ops.REDUCE]
-    for ls in reduce_rngs: store_rngs = tuple([x for x in store_rngs if x in ls])
-    """
-
-    return [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE and x.arg[-1] == AxisType.LOOP] if store_rngs else []
+    # all ranges that end before any STOREs
+    return [x for x in self.ast.toposort(lambda x: x.op is not Ops.STORE) if x.op is Ops.RANGE and x not in self.ast.ranges]
 
   def convert_loop_to_global(self):
     if not self.opts.has_local: return None
@@ -88,7 +75,14 @@ class Scheduler:
 
     self.ast = self.ast.substitute(dict(zip(self.rngs, rng)))
 
-  def colors(self) -> list[str]: return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
+  def colors(self) -> list[str]:
+    globalizible_rngs = self._globalizable_rngs()
+    ret = []
+    for x,r in zip(self.axis_types, self.rngs):
+      if self.dont_use_locals and x == AxisType.GLOBAL: ret.append("BLUE")
+      elif r not in globalizible_rngs and x == AxisType.LOOP: ret.append("BLACK")
+      else: ret.append(axis_colors[x])
+    return ret
   def colored_shape(self) -> str: return ' '.join([colored(f'{x.src[0].render():>4s}', color) for x,color in zip(self.rngs, self.colors())])
 
   def shift_to(self, rng:UOp, amount:int, new_type:AxisType, top:bool=False, input_new_rng=None):
@@ -97,7 +91,7 @@ class Scheduler:
     new_rng = UOp.range(amount, self.maxarg+1, new_type) if input_new_rng is None else input_new_rng
     replaced_rng = rng.replace(src=(UOp.const(dtypes.int, old_sz),))
     sub_axis = (new_rng * old_sz + replaced_rng) if top else (replaced_rng * amount + new_rng)
-    self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[0]} {amount} {str(new_type).split('.')[1].lower()}")
+    self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[:-1]} {amount} {str(new_type).split('.')[1].lower()}")
     return replaced_rng, new_rng
 
   def ranges_of(self, *axis_type:AxisType) -> list[UOp]: return [r for r in self.rngs if r.arg[-1] in axis_type]
@@ -200,13 +194,14 @@ class Scheduler:
       self.ast = self.ast.substitute(replaces, f"padto {rng.arg[:-1]} {opt.arg}")
     elif opt.op is OptOps.SWAP:
       try:
-        altrng = self.rngs[opt.arg]
+        altrng:UOp = self.rngs[opt.arg]
       except IndexError:
         raise KernelOptError
       check(rng.arg[-1] == AxisType.GLOBAL and altrng.arg[-1] == AxisType.GLOBAL, "swap only for globals")
       self.ast = self.ast.substitute({rng:rng.replace(arg=(*altrng.arg[0:-1], rng.arg[-1]), tag=1),
-                                      altrng:altrng.replace(arg=(*rng.arg[0:-1], altrng.arg[-1]), tag=1)})
-      self.ast = graph_rewrite(self.ast, remove_tags)
+                                      altrng:altrng.replace(arg=(*rng.arg[0:-1], altrng.arg[-1]), tag=1)},
+                                      name=f"swap {rng.arg[:-1]} {altrng.arg[:-1]}")
+      self.ast = graph_rewrite(self.ast, remove_tags, name="swap remove tags")
     else:
       raise KernelOptError(f"unsupported opt {opt.op}")
 
@@ -340,7 +335,7 @@ def apply_opts(ctx:Renderer, ast:UOp):
   elif not NOOPT and (ast.arg is None or ast.arg.applied_opts == ()):
     from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
     # NOTE: hand_coded_optimizations doesn't support multiblock opts yet
-    if all(len(u.src) == 1 for u in ast.backward_slice if u.op is Ops.LOAD):
+    if not any(u.op is Ops.AFTER and u.src[0].op is Ops.DEFINE_LOCAL for u in ast.backward_slice):
       k = hand_coded_optimizations(k)
   return k.get_optimized_ast(name_override=ast.arg.name if ast.arg is not None and ast.arg.name != "test" else None)
 

tinygrad/codegen/opt/search.py

@@ -156,7 +156,9 @@ def beam_search(lin:Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=Tr
         if lib in seen_libs: continue
         # filter out kernels that use 1000x more compute than the smallest
         least_compute_ops = min(this_compute_ops:=sym_infer(p.estimates.ops, var_vals), least_compute_ops)
-        if least_compute_ops*1000 < this_compute_ops: continue
+        if least_compute_ops*1000 < this_compute_ops:
+          if getenv("BEAM_LOG_SURPASS_MAX"): print(f"too much compute. {this_compute_ops} when least is {least_compute_ops}")
+          continue
         seen_libs.add(lib)
         try: tms = _time_program(p, lib, var_vals, rawbufs, early_stop=beam[0][1]*3 if len(beam) else 1.0,
                                  allow_test_size=allow_test_size, clear_l2=hasattr(dev, 'invalidate_caches'))

tinygrad/codegen/simplify.py

@@ -1,5 +1,5 @@
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, graph_rewrite, _substitute, range_start, ImageDType
-from tinygrad.uop.symbolic import symbolic_flat, sym
+from tinygrad.uop.symbolic import symbolic_flat
 from tinygrad.helpers import partition
 from tinygrad.dtype import dtypes
 
@@ -13,14 +13,16 @@ def flatten_range(r:UOp):
 pm_flatten_range = PatternMatcher([
   # real ranges only
   (UPat((Ops.REDUCE, Ops.STORE), name="r"), flatten_range),
+  # END is only on RANGES. TODO: this is copied from symbolic
+  (UPat(Ops.END, name="e"), lambda e: UOp.end(*e.src[e.arg:], ends=sorted(UOp.sink(*e.src[:e.arg]).ranges, key=lambda x: x.arg))),
 ])
 
 def count_divmod(x:UOp): return len([u for u in x.toposort() if u.op in {Ops.IDIV, Ops.MOD}])
 def simplify_merge_adjacent(u:UOp) -> UOp|None:
   reduce_ranges = [x.ranges for x in u.backward_slice_with_self if x.op is Ops.REDUCE]
-  i = range_start[u.op]
-  while i < len(u.src)-1:
-    r0, r1 = u.src[i], u.src[i+1]
+  i = 0
+  while i < len(u.ended_ranges)-1:
+    r0, r1 = u.ended_ranges[i], u.ended_ranges[i+1]
     # check same type
     if r0.arg[-1] == r1.arg[-1]:
       # check if the ranges to merge are in the same reduces
@@ -39,7 +41,7 @@ def simplify_merge_adjacent(u:UOp) -> UOp|None:
   return u
 
 pm_simplify_ranges = PatternMatcher([
-  (UPat((Ops.STORE, Ops.REDUCE), name="u"), simplify_merge_adjacent),
+  (UPat((Ops.END, Ops.REDUCE), name="u"), simplify_merge_adjacent),
 ])
 
 def mark_range_mod(ctx, r:UOp, c:UOp):
@@ -57,7 +59,7 @@ def do_substitute(ctx, x: UOp):
 
 def dont_sub_ranges_for_image(ctx, x:UOp):
   if isinstance(x.src[0].dtype, ImageDType):
-    for s in x.src[1:]: ctx[s] = None
+    for s in x.src[0].ranges: ctx[s] = None
 
 pm_split_ranges = PatternMatcher([
   (UPat(Ops.RANGE, name="r")%UPat.cvar("c"), mark_range_mod),
@@ -69,59 +71,6 @@ pm_split_ranges = PatternMatcher([
 
 def no_range(u:UOp) -> bool: return not any(x.op is Ops.RANGE for x in u.backward_slice_with_self)
 
-def reduce_rangeless(red:UOp):
-  # TODO: share code with reduce_unparented
-  if red.arg not in {Ops.ADD, Ops.MAX}: return None
-  if red.src[0].dtype != red.dtype: return None
-  if not no_range(red.src[0]): return None
-  ret = red.src[0]
-  if red.arg is Ops.ADD:
-    for r in red.src[1:]:
-      ret = ret * r.src[0].cast(ret.dtype.scalar()).broadcast(ret.dtype.count)
-  return ret
-
-pm_reduce_collapse = PatternMatcher([
-  # lift x+y out of reduce on lt
-  ((UPat.var("x")+UPat.var("y")).or_casted() < UPat.var("c"), lambda x,y,c: (x < (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
-  # lift x*y out of reduce
-  ((UPat.var("x")*UPat.var("y")) < UPat.var("c"),
-   lambda x,y,c: (x < ((c+y-1) // y)) if no_range(y) and no_range(c) and y.vmin > 0 else None),
-  # lift x+y out of reduce on ne
-  ((UPat.var("x")+UPat.var("y")).or_casted() != UPat.var("c"), lambda x,y,c: (x != (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
-  # fold the range
-  ((UPat(Ops.RANGE, name="r") < UPat.var("cut")).where(0, UPat.cvar("val")).reduce(UPat.var("r"), arg=Ops.ADD),
-   lambda r,cut,val: (r.src[0]-cut).maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
-  ((UPat(Ops.RANGE, name="r") < UPat.var("cut")).where(UPat.cvar("val"), 0).reduce(UPat.var("r"), arg=Ops.ADD),
-   lambda r,cut,val: cut.maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
-  # REDUCE on ADD
-  ((UPat.var("x")+UPat.var("y")).reduce(arg=Ops.ADD, allow_any_len=True, name="r"),
-   lambda x,y,r: x.reduce(*r.src[1:], arg=Ops.ADD) + y.reduce(*r.src[1:],arg=Ops.ADD)),
-  # MUL casted bool
-  ((UPat.var("x") * UPat.var("gate", dtype=dtypes.bool).cast().or_broadcasted(name="b")),
-   lambda x,gate,b=None: gate.broadcast(x.dtype.count).where(x, 0) if b is not None else gate.where(x, 0)),
-  # reduce on gated load becomes can substitute the range and remove the reduce
-  ((UPat.var("idx")!=(UPat(Ops.RANGE, name="r").or_casted())).where(0, UPat.var("expr")).reduce(UPat.var("r"), arg=Ops.ADD),
-   lambda r,idx,expr: (v:=(idx.cast(r.dtype) >= 0) & (idx.cast(r.dtype) < r.src[0])).where(expr.substitute({r:idx.cast(r.dtype).valid(v)}),0)),
-  # AND on WHERE
-  ((UPat.any(UPat(Ops.DEFINE_VAR, name="x"), UPat(Ops.DEFINE_VAR).gep(name="x")) & UPat.var("y")) \
-   .where(UPat.cvar("c"), 0).reduce(arg=Ops.ADD, allow_any_len=True, name="r"),
-    lambda x,y,c,r: y.where(c, 0).reduce(*r.src[1:], arg=Ops.ADD)*x.cast(c.dtype)),
-  # remove REDUCEs that no longer have a RANGE in the src
-  (UPat(Ops.REDUCE, name="red"), reduce_rangeless),
-])+sym
-
-def reduce_collapse(red:UOp):
-  included, not_included = partition(red.backward_slice, lambda x: any(y in x.backward_slice_with_self for y in red.src[1:]))
-  if any(x.op in {Ops.STORE, Ops.REDUCE} for x in included): return None
-  replaces: dict[UOp, UOp] = {}
-  for u in included:
-    for s in u.src:
-      if s in not_included and s not in replaces and s.op not in {Ops.CONST, Ops.VCONST, Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_VAR}:
-        replaces[s] = UOp(Ops.DEFINE_VAR, dtype=s.dtype, arg=(f'in{len(replaces)}', s.vmin, s.vmax))
-  collapse_fxn = red.substitute(replaces)
-  sink = graph_rewrite(collapse_fxn, pm_reduce_collapse, name="reduce_collapse")
-  return sink.substitute({v:k for k,v in replaces.items()}) if no_range(sink) else None
-
 def reduce_unparented(red:UOp):
   if red.arg not in {Ops.ADD, Ops.MAX, Ops.MUL}: return None
   assert all(x.op is Ops.RANGE for x in red.src[1:]), "some reduce srcs aren't ranges"
@@ -139,6 +88,46 @@ pm_reduce_unparented = PatternMatcher([
   (UPat(Ops.REDUCE, name="red"), reduce_unparented),
 ])
 
+pm_reduce_collapse = pm_reduce_unparented + PatternMatcher([
+  # lift x+y out of reduce on lt
+  ((UPat.var("x")+UPat.var("y")).or_casted() < UPat.var("c"), lambda x,y,c: (x < (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
+  # lift x*y out of reduce
+  ((UPat.var("x")*UPat.var("y")) < UPat.var("c"),
+   lambda x,y,c: (x < ((c+y-1) // y)) if no_range(y) and no_range(c) and y.vmin > 0 else None),
+  # lift x+y out of reduce on ne
+  ((UPat.var("x")+UPat.var("y")).or_casted() != UPat.var("c"), lambda x,y,c: (x != (c.cast(y.dtype)-y)) if no_range(y) and no_range(c) else None),
+  # fold the range
+  ((UPat(Ops.RANGE, name="r") < UPat.var("cut")).where(0, UPat.cvar("val")).reduce(UPat.var("r"), arg=Ops.ADD),
+   lambda r,cut,val: (r.src[0]-cut).maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
+  (((UPat.var("r")<UPat.var("lower")).logical_not()&(UPat(Ops.RANGE, name="r")<UPat.var("upper"))).where(UPat.cvar("val"), 0).reduce(UPat.var("r"),
+    arg=Ops.ADD), lambda r,lower,upper,val: (upper.minimum(r.src[0])-lower.maximum(0)).maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
+  ((UPat(Ops.RANGE, name="r") < UPat.var("cut")).where(UPat.cvar("val"), 0).reduce(UPat.var("r"), arg=Ops.ADD),
+   lambda r,cut,val: cut.maximum(0).minimum(r.src[0]).cast(val.dtype) * val),
+  # REDUCE on ADD
+  ((UPat.var("x")+UPat.var("y")).reduce(arg=Ops.ADD, allow_any_len=True, name="r"),
+   lambda x,y,r: x.reduce(*r.src[1:], arg=Ops.ADD) + y.reduce(*r.src[1:],arg=Ops.ADD)),
+  # MUL casted bool
+  ((UPat.var("x") * UPat.var("gate", dtype=dtypes.bool).cast()), lambda x,gate: gate.where(x, 0)),
+  # reduce on gated load becomes can substitute the range and remove the reduce
+  ((UPat.var("idx")!=(UPat(Ops.RANGE, name="r").or_casted())).where(0, UPat.var("expr")).reduce(UPat.var("r"), arg=Ops.ADD),
+   lambda r,idx,expr: (v:=(idx.cast(r.dtype) >= 0) & (idx.cast(r.dtype) < r.src[0])).where(expr.substitute({r:idx.cast(r.dtype).valid(v)}),0)),
+  # AND on WHERE
+  ((UPat(Ops.DEFINE_VAR, name="x") & UPat.var("y")).where(UPat.cvar("c"), 0).reduce(arg=Ops.ADD, allow_any_len=True, name="r"),
+    lambda x,y,c,r: y.where(c, 0).reduce(*r.src[1:], arg=Ops.ADD)*x.cast(c.dtype)),
+])+symbolic_flat
+
+def reduce_collapse(red:UOp):
+  included, not_included = partition(red.backward_slice, lambda x: any(y in x.backward_slice_with_self for y in red.src[1:]))
+  if any(x.op in {Ops.STORE, Ops.REDUCE} for x in included): return None
+  replaces: dict[UOp, UOp] = {}
+  for u in included:
+    for s in u.src:
+      if s in not_included and s not in replaces and s.op not in {Ops.CONST, Ops.VCONST, Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_VAR}:
+        replaces[s] = UOp(Ops.DEFINE_VAR, dtype=s.dtype, arg=(f'in{len(replaces)}', s.vmin, s.vmax))
+  collapse_fxn = red.substitute(replaces)
+  sink = graph_rewrite(collapse_fxn, pm_reduce_collapse, name="reduce_collapse")
+  return sink.substitute({v:k for k,v in replaces.items()}) if no_range(sink) else None
+
 pm_reduce_simplify = pm_reduce_unparented + PatternMatcher([
   # remove REDUCE without loads (generic arange opt / indexing). TODO: support multi range
   (UPat(Ops.REDUCE, src=(UPat(), UPat()), name="red"), reduce_collapse),

tinygrad/device.py

@@ -5,7 +5,7 @@ from typing import Any, Generic, TypeVar, Iterator, Sequence, cast, Generator
 import importlib, inspect, functools, pathlib, os, platform, contextlib, sys, re, atexit, pickle, decimal
 from tinygrad.helpers import CI, OSX, LRU, getenv, diskcache_get, diskcache_put, DEBUG, GlobalCounters, flat_mv, PROFILE, temp, colored, CPU_LLVM
 from tinygrad.helpers import Context, DISABLE_COMPILER_CACHE, ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, cpu_events, ProfileEvent, ProfilePointEvent, dedup
-from tinygrad.helpers import unwrap_class_type
+from tinygrad.helpers import unwrap_class_type, suppress_finalizing
 from tinygrad.dtype import DType, ImageDType, PtrDType, dtypes, _to_np_dtype
 from tinygrad.renderer import Renderer
 
@@ -163,6 +163,7 @@ class Buffer:
     return self._trace_num
   @property
   def nbytes(self): return self.size*self.dtype.itemsize
+  @suppress_finalizing
   def __del__(self): (not hasattr(self, '_buf')) or self.deallocate()
   def __repr__(self):
     return f"<buf real:{self.is_allocated()} device:{self.device} size:{self.size} dtype:{self.dtype}" + \

tinygrad/engine/realize.py

@@ -166,8 +166,9 @@ class ExecItem:
     var_vals = self.fixedvars if _var_vals is None else (_var_vals|self.fixedvars)
     bufs = [cast(Buffer, x) for x in self.bufs] if jit else [cast(Buffer, x).ensure_allocated() for x in self.bufs]
     if PROFILE:
-      payload = {"metadata":self.metadata, "var_vals":var_vals, "bufs":[b.trace_num for b in bufs]}
-      cpu_events.append(ProfilePointEvent(self.prg.device, "exec", self.prg.display_name, payload))
+      payload = {"metadata":self.metadata, "var_vals":var_vals, "bufs":[b.trace_num for b in bufs], "name":self.prg.display_name}
+      payload["outputs"], payload["inputs"] = (self.prg.p.outs, self.prg.p.ins) if isinstance(self.prg, CompiledRunner) else ([0], [1])
+      cpu_events.append(ProfilePointEvent(self.prg.device, "exec", len(cpu_events), payload))
     et = self.prg(bufs, var_vals, wait=wait or DEBUG >= 2)
     if do_update_stats:
       GlobalCounters.kernel_count += 1
@@ -180,10 +181,11 @@ class ExecItem:
         header_color = 'magenta' if jit else ('green' if self.prg.first_run else None)
         ptm = colored(time_to_str(et, w=9), "yellow" if et > 0.01 else None) if et is not None else ""
         flops, membw, ldsbw = op_est/(et or 1e-20), mem_est/(et or 1e-20), lds_est/(et or 1e-20)
-        flops_str = f"{flops*1e-9:9.2f} GFLOPS" if flops < 1e14 else colored(f"{flops*1e-12:9.2f} TFLOPS", 'green')
-        mem_str = f"{membw*1e-9:6.1f}|{ldsbw*1e-9:<7.1f} GB/s" if membw < 1e13 else colored(f"{membw*1e-12:6.1f}|{ldsbw*1e-12:<7.1f} TB/s", 'green')
+        flops_str = f"{flops*1e-9:7.0f} GFLOPS" if flops < 1e14 else colored(f"{flops*1e-12:7.0f} TFLOPS", 'green')
+        mem_str = f"{membw*1e-9:4.0f}|{ldsbw*1e-9:<6.0f} GB/s" if membw < 1e13 and ldsbw < 1e15 else \
+          colored(f"{membw*1e-12:4.0f}|{ldsbw*1e-12:<6.0f} TB/s", 'green')
         print(f"{colored(f'*** {self.prg.device[:7]:7s} {GlobalCounters.kernel_count:4d}', header_color)}"+
-          f" {self.prg.display_name+' '*(44-ansilen(self.prg.display_name))} arg {len(bufs):2d} mem {GlobalCounters.mem_used/1e9:5.2f} GB"+
+          f" {self.prg.display_name+' '*(46-ansilen(self.prg.display_name))} arg {len(bufs):2d} mem {GlobalCounters.mem_used/1e9:6.2f} GB"+
           ("" if et is None else f" tm {ptm}/{GlobalCounters.time_sum_s*1e3:9.2f}ms ({flops_str} {mem_str})")+
           f" {[repr(m) if TRACEMETA >= 2 else str(m) for m in self.metadata] if self.metadata else ''}")
       self.prg.first_run = False

tinygrad/engine/schedule.py

@@ -22,18 +22,18 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
   in_degree: dict[UOp, int] = {}
   var_vals: dict[str, int] = {}
   for u in sched_sink.toposort():
-    if u.op is not Ops.ASSIGN: continue  # anything that's not an ASSIGN doesn't write a kernel, so we can skip
+    if u.op is not Ops.AFTER: continue  # anything that's not an ASSIGN doesn't write a kernel, so we can skip
     k = u.src[1]
     in_degree.setdefault(k, 0)
     for s in k.src:
-      if s.op is Ops.ASSIGN:
+      if s.op is Ops.AFTER:
         children[s.src[1]].append(k)
         in_degree[k] += 1
       elif s.op in {Ops.MSELECT, Ops.MSTACK}:
         for ss in s.src:
           if ss.op is Ops.MSELECT: ss = ss.src[0]
           if ss.op is not Ops.BUFFER:
-            assert ss.op is Ops.ASSIGN, f"ss.op is not ASSIGN, it's {ss.op}"
+            assert ss.op is Ops.AFTER, f"ss.op is not AFTER, it's {ss.op}"
             children[ss.src[1]].append(k)
             in_degree[k] += 1
       elif s.op is Ops.BUFFER:
@@ -43,7 +43,7 @@ def create_schedule_with_vars(sched_sink:UOp) -> tuple[list[ScheduleItem], dict[
         assert var.expr not in var_vals or var_vals[var.expr] == val, f"bind mismatch on {var}, {var_vals[var.expr]} != {val}"
         var_vals[var.expr] = val
       else:
-        raise RuntimeError(f"input to kernel must be ASSIGN or BUFFER, not {s.op}")
+        raise RuntimeError(f"input to kernel must be AFTER or BUFFER, not {s.op}")
 
   # linearize KERNEL UOps into ScheduleItems in BFS order
 

tinygrad/gradient.py

@@ -4,13 +4,13 @@ from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata
 from tinygrad.helpers import argsort
 
 def reduce_gradient(ctx:UOp, ret:UOp):
-  def to_inp_shape(x): return x.reshape(x.shape+(1,)*(len(ret.src[0].shape)-len(x.shape))).expand(ret.src[0].shape)
-  if ret.arg[0] == Ops.ADD: return (to_inp_shape(ctx),)
+  def broadcast_to_input(x): return x.reshape(x.shape+(1,)*(len(ret.src[0].shape)-len(x.shape))).expand(ret.src[0].shape)
+  if ret.arg[0] == Ops.ADD: return (broadcast_to_input(ctx),)
   if ret.arg[0] == Ops.MAX:
-    max_is_1s = ret.src[0].eq(to_inp_shape(ret)).cast(ctx.dtype)
-    div = to_inp_shape(max_is_1s.r(Ops.ADD, ret.arg[1]))
-    return ((max_is_1s/div) * to_inp_shape(ctx),)
-  if ret.arg[0] == Ops.MUL: return (to_inp_shape(ctx * ret) / ret.src[0],)
+    mask = ret.src[0].eq(broadcast_to_input(ret)).cast(ctx.dtype)
+    count = mask.r(Ops.ADD, ret.arg[1])
+    return ((mask/broadcast_to_input(count)) * broadcast_to_input(ctx),)
+  if ret.arg[0] == Ops.MUL: return (broadcast_to_input(ctx * ret) / ret.src[0],)
 
 # ctx is grad_output
 pm_gradient = PatternMatcher([
@@ -60,5 +60,9 @@ def compute_gradient(root:UOp, root_grad:UOp, targets:set[UOp]) -> dict[UOp, UOp
       if v is None: continue
       if k in grads: grads[k] = grads[k] + v
       else: grads[k] = v
-      if len(forward_metadata:=all_metadata.get(t0, ())): all_metadata[v] = tuple(dataclasses.replace(x, backward=True) for x in forward_metadata)
+      if len(forward_metadata:=all_metadata.get(t0, ())):
+        backward_metadata = tuple(dataclasses.replace(x, backward=True) for x in forward_metadata)
+        # we add the backward metadata to everything new in the graph
+        for bw_uop in v.toposort(lambda x: x not in (t0, *t0.src, grads[t0])):
+          all_metadata[bw_uop] = all_metadata.get(bw_uop, ())+backward_metadata
   return grads

tinygrad/helpers.py

@@ -158,7 +158,6 @@ SPLIT_REDUCEOP, NO_MEMORY_PLANNER, RING = ContextVar("SPLIT_REDUCEOP", 1), Conte
 PICKLE_BUFFERS, LRU = ContextVar("PICKLE_BUFFERS", 1), ContextVar("LRU", 1)
 CACHELEVEL, IGNORE_BEAM_CACHE, DEVECTORIZE = ContextVar("CACHELEVEL", 2), ContextVar("IGNORE_BEAM_CACHE", 0), ContextVar("DEVECTORIZE", 1)
 DISABLE_COMPILER_CACHE, BLOCK_REORDER = ContextVar("DISABLE_COMPILER_CACHE", 0), ContextVar("BLOCK_REORDER", 1)
-DONT_REALIZE_EXPAND, DONT_GROUP_REDUCES = ContextVar("DONT_REALIZE_EXPAND", 0), ContextVar("DONT_GROUP_REDUCES", 0)
 QUANTIZE, VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("QUANTIZE", 0), ContextVar("VALIDATE_WITH_CPU", 0), ContextVar("DISABLE_FAST_IDIV", 0)
 CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)
 ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0)
@@ -170,6 +169,9 @@ VIZ = PROFILE = ContextVar("VIZ", 0)
 SPEC = ContextVar("SPEC", 0)
 # TODO: disable by default due to speed
 IGNORE_OOB = ContextVar("IGNORE_OOB", 1)
+PCONTIG = ContextVar("PCONTIG", 0)  # partial contiguous in rangeify
+MULTIOUTPUT = ContextVar("MULTIOUTPUT", 0)
+DEBUG_RANGEIFY = ContextVar("DEBUG_RANGEIFY", 0)
 
 @dataclass(frozen=True)
 class Metadata:

tinygrad/nn/__init__.py

@@ -36,7 +36,7 @@ class BatchNorm:
     self.weight: Tensor|None = Tensor.ones(sz) if affine else None
     self.bias: Tensor|None = Tensor.zeros(sz) if affine else None
 
-    self.num_batches_tracked = Tensor.zeros(1, dtype='long' if is_dtype_supported(dtypes.long) else 'int', requires_grad=False)
+    self.num_batches_tracked = Tensor.zeros(dtype='long' if is_dtype_supported(dtypes.long) else 'int', requires_grad=False)
     if track_running_stats: self.running_mean, self.running_var = Tensor.zeros(sz, requires_grad=False), Tensor.ones(sz, requires_grad=False)
 
   def calc_stats(self, x:Tensor) -> tuple[Tensor, Tensor]:

tinygrad/nn/onnx.py

@@ -1242,7 +1242,8 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     G, V, H = G.detach(), V.detach(), H.detach()
     X.grad = norm_coefficient * X.detach() + G
     opt = TinyAdam([X], b1=alpha, b2=beta, eps=epsilon)
-    opt.m, opt.v, opt.lr = [V], [H], R
+    # NOTE: FUSE_OPTIM can change shapes of m and v
+    opt.m, opt.v, opt.lr = [V.reshape(opt.m[0].shape)], [H.reshape(opt.v[0].shape)], R
     # need no-op for m_hat and v_hat if T == 0
     if T == 0: opt.b1_t, opt.b2_t = opt.b1_t.zeros_like(), opt.b2_t.zeros_like()
     else:

tinygrad/nn/optim.py

@@ -50,7 +50,7 @@ class Optimizer:
     if self.fused:
       # optimizer fusion just concatenates all the buffers, runs the _step, then splits them back up
       out, extra = self._step([Tensor.cat(*[t.flatten() for t in self.params], dim=0)],
-                              [Tensor.cat(*[unwrap(t.grad).flatten() for t in self.params], dim=0)])
+                              [Tensor.cat(*[unwrap(t.grad).contiguous().flatten() for t in self.params], dim=0)])
       updated_params = [out[0][self.pos_params[i]:self.pos_params[i+1]].reshape(tt.shape) for i, tt in enumerate(self.params)]
     else:
       updated_params, extra = self._step(self.params, [unwrap(t.grad) for t in self.params])
@@ -116,9 +116,7 @@ class LARS(Optimizer):
       # classic momentum does post learning rate update
       if self.classic: g = g * r * self.lr
       if self.momentum:
-        # TODO: this contiguous is required for correctness because self.b[i] becomes a non contiguous view
-        # the scheduler should detect this and just insert contiguous
-        self.b[i].assign(self.momentum * self.b[i].contiguous() + g)  # NOTE: self.b[i] is zero on the first run, no if required
+        self.b[i].assign(self.momentum * self.b[i] + g)  # NOTE: self.b[i] is zero on the first run, no if required
         g = (g + self.momentum * self.b[i]) if self.nesterov else self.b[i]
       if self.ns_params: g = g.reshape(g.shape[0], -1).newton_schulz(self.ns_steps, self.ns_params).reshape(g.shape)
       # muon does post momentum weight decay

tinygrad/nn/state.py

@@ -209,7 +209,7 @@ def torch_load(t:Tensor) -> dict[str, Tensor]:
       assert tuple([shape_strides[i][1] for i in argsort(permute_indexes)]) == strides_for_shape(intermediate_shape), "nonpermutable strides"
       if DEBUG >= 3: print(f"WARNING: this torch load is slow. to permute {intermediate_shape} with {permute_indexes}")
       assert storage[1] != dtypes.bfloat16, "can't permute BF16"
-      # TODO: find a nice way to support all shapetracker on disktensors
+      # TODO: find a nice way to support all movement ops on disktensors
       ret = ret.to(None).reshape(intermediate_shape).permute(permute_indexes)
 
     return ret.reshape(size)

tinygrad/renderer/__init__.py

@@ -28,9 +28,12 @@ class Estimates:
     mult_stack: list[sint] = []
     dont_count: set[UOp] = set()
     if ignore_indexing:
+      def range_gate(x): return x.op is not Ops.RANGE
       for u in uops:
         if u.op in {Ops.LOAD, Ops.STORE} and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
-          dont_count = dont_count.union(u.src[0].toposort())
+          # if u.src[0] is INDEX, we have to include the buffer since it might be an AFTER
+          dont_count = dont_count.union((UOp.sink(*u.src[0].src[1:]) if u.src[0].op is Ops.INDEX else u.src[0]).toposort(range_gate))
+          # TODO: is this correct? this all needs to be cleaned up
           if len(u.src) > 2: dont_count = dont_count.union(u.src[2].toposort())
         elif u.op is Ops.IF:
           dont_count = dont_count.union(u.src[0].toposort())
@@ -45,7 +48,7 @@ class Estimates:
         mults *= cast(sint, u.src[0].ssimplify())
         # SPECIAL are already counted in mults
         mults = mults.substitute({x:x.const_like(0) for x in mults.toposort() if x.op is Ops.SPECIAL}) if isinstance(mults, UOp) else mults
-      elif u.op is Ops.ENDRANGE: mults = mult_stack.pop(-1)
+      elif u.op is Ops.END: mults = mult_stack.pop(-1)
       elif u.op is Ops.SPECIAL: mults *= cast(sint, u.src[0].ssimplify()) # NOTE: we don't push to the mult_stack here, you can't end these
       elif u.op is Ops.LOAD and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
         lds += u.dtype.itemsize * mults

tinygrad/renderer/cstyle.py

@@ -1,7 +1,7 @@
 from typing import Literal, Callable, cast
 import os, math, sys
 from collections import defaultdict, Counter
-from tinygrad.codegen.opt import tc
+from tinygrad.codegen.opt import tc, axis_letters
 from tinygrad.uop.ops import GroupOp, Ops, UOp, PatternMatcher, UPat, range_str
 from tinygrad.helpers import strip_parens, getenv, prod, dedup, AMX, CPU_COUNT
 from tinygrad.dtype import ImageDType, dtypes, DType, PtrDType, AddrSpace, truncate
@@ -11,7 +11,7 @@ from tinygrad.codegen.late.devectorizer import no_vectorized_alu
 base_rewrite = PatternMatcher([
   (UPat(Ops.DEFINE_REG, name="x"), lambda ctx,x: f"{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
   (UPat(Ops.IF, name="x"), lambda ctx,x: f"if ({ctx[x.src[0]]}) {{"),
-  (UPat((Ops.ENDIF, Ops.ENDRANGE)), lambda ctx: "}"),
+  (UPat((Ops.ENDIF, Ops.END)), lambda ctx: "}"),
   (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{x.arg[0]}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]})"),
   # r method accesses
   (UPat(Ops.RANGE, name="x"),
@@ -144,6 +144,9 @@ class CStyleLanguage(Renderer):
     name = "test"
     for u in uops:
       if u.op is Ops.NOOP: continue
+      if u.op is Ops.AFTER:
+        r[u] = r[u.src[0]]
+        continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue
@@ -160,7 +163,7 @@ class CStyleLanguage(Renderer):
       # naming
       prefix = None
       if u.op is Ops.SPECIAL: r[u] = u.arg
-      elif u.op is Ops.RANGE: r[u] = "ridx"+range_str(u)
+      elif u.op is Ops.RANGE: r[u] = f"{axis_letters[u.arg[-1]]}idx"+range_str(u)
       else:
         prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
                   Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.PRECAST: "precast",
@@ -170,7 +173,7 @@ class CStyleLanguage(Renderer):
       l = cast(str, self.string_rewrite.rewrite(u, ctx=self))
       assert l is not None, f"failed to render {u.op} {u.dtype} {[(x.op,x.dtype) for x in u.src]} {u.arg}"
 
-      if u.op in {Ops.ENDIF, Ops.ENDRANGE}: depth -= 1
+      if u.op in {Ops.ENDIF, Ops.END}: depth -= 1
       if (u.op is not Ops.CAST or u.dtype.vcount == 1) and (u.op in {Ops.CONST, Ops.GEP, Ops.INDEX, Ops.CUSTOMI} or \
         (u.op is Ops.LOAD and u.src[0].ptrdtype.addrspace == AddrSpace.REG) or \
         (u.op is Ops.CAST and isinstance(u.dtype, PtrDType)) or \

tinygrad/renderer/llvmir.py

@@ -108,7 +108,7 @@ base_rewrite = PatternMatcher([
    f"  br label %loop_entry_{range_str(x)}\nloop_entry_{range_str(x)}:\n"
    f"  br label %loop_body_{range_str(x)}\nloop_body_{range_str(x)}:\n"
    f"  {ctx[x]} = phi {ldt(x.dtype)} [ 0, %loop_entry_{range_str(x)} ], [ {ctx[x]}phi, %loop_latch_{range_str(x)} ]"),
-  (UPat(Ops.ENDRANGE, name="x"), lambda ctx,x:
+  (UPat(Ops.END, name="x"), lambda ctx,x:
    f"  br label %loop_latch_{range_str(x.src[0])}\nloop_latch_{range_str(x.src[0])}:\n"
    f"  {ctx[x.src[0]]}phi = add {ldt(x.src[0].dtype)} {ctx[x.src[0]]}, 1\n"
    f"  {ctx[x]} = icmp ult {ldt(x.src[0].dtype)} {ctx[x.src[0]]}phi, {ctx[x.src[0].src[0]]}\n"
@@ -167,6 +167,9 @@ class LLVMRenderer(Renderer):
     name = "test"
     for u in uops:
       if u.op is Ops.NOOP: continue
+      if u.op is Ops.AFTER:
+        r[u] = r[u.src[0]]
+        continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue

tinygrad/renderer/nir.py

@@ -1,4 +1,4 @@
-from typing import Callable, cast
+from typing import Callable, cast, Any
 from tinygrad.dtype import AddrSpace, DType, PtrDType, dtypes
 from tinygrad.helpers import DEBUG, OSX, unwrap
 from tinygrad.renderer import Renderer
@@ -169,10 +169,13 @@ class NIRRenderer(Renderer):
   def render(self, uops:list[UOp]):
     self.prerender(uops)
     for u in [u for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]: self.b.shader.contents.info.workgroup_size[int(u.arg[-1])] = u.src[0].arg
-    self.r, self.param_idx, ranges = {}, 0, []
+    self.r: dict[UOp, Any] = {}
+    self.param_idx, ranges = 0, []
 
     for u in uops:
       if u.op == Ops.NOOP or u.op == Ops.INDEX: pass
+      elif u.op is Ops.AFTER:
+        self.r[u] = self.r[u.src[0]]
       elif u.op == Ops.SINK:
         if u.arg is not None: self.b.shader.contents.info.name = mesa.char_pointer_cast(u.arg.function_name)
       elif u.op == Ops.DEFINE_LOCAL:
@@ -183,7 +186,7 @@ class NIRRenderer(Renderer):
         nstore(self.b, AddrSpace.REG, i, nimm(self.b, 0, u.dtype), u.dtype)
         mesa.nir_push_loop(self.b)
         self.r[u] = nload(self.b, AddrSpace.REG, i, u.dtype)
-      elif u.op == Ops.ENDRANGE:
+      elif u.op == Ops.END:
         nif(self.b, nalu(self.b, "ilt", x:=nalu(self.b, "iadd", self.r[u.src[0]], nimm(self.b, 1, u.src[0].dtype)), self.r[u.src[0].src[0]]),
             functools.partial(nstore, self.b, AddrSpace.REG, ranges.pop(), x, u.src[0].dtype), lambda: njump(self.b, mesa.nir_jump_break))
         mesa.nir_pop_loop(self.b, None)

tinygrad/renderer/ptx.py

@@ -115,7 +115,7 @@ string_rewrite = PatternMatcher([
      if x.dtype.count > 1 else f"ld.{mem_type(x)}.{ctx.mem_types[x.dtype]} {ctx.r[x]}, [{ctx.r[loc]}+0];"),
   (UPat(Ops.DEFINE_REG, src=()), lambda ctx: []),
   (UPat(Ops.RANGE, name="x"), lambda ctx, x: [f"mov.u32 {ctx.r[x]}, 0;", "LOOP_" + f"{ctx.r[x][1:]}:"]),
-  (UPat(Ops.ENDRANGE, name="x", src=(UPat.var("src0"),)), lambda ctx, x, src0: [
+  (UPat(Ops.END, name="x", src=(UPat.var("src0"),), allow_any_len=True), lambda ctx, x, src0: [
     ctx.code_for_op[Ops.ADD](ctx.r[src0], ctx.r[src0], "1", dtypes.int, ctx.types[dtypes.int]),
     ctx.code_for_op[Ops.CMPLT](ctx.r[x], ctx.r[x.src[0]], ctx.r[src0.src[0]], dtypes.int, ctx.types[dtypes.int]),
     f"@{ctx.r[x]} bra LOOP_{ctx.r[src0][1:]};"]),
@@ -179,6 +179,9 @@ class PTXRenderer(Renderer):
     name = "test"
     for u in uops:
       if u.op is Ops.NOOP: continue
+      if u.op is Ops.AFTER:
+        self.r[u] = self.r[u.src[0]]
+        continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue
@@ -216,7 +219,7 @@ class PTXRenderer(Renderer):
                        [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.src[0].dtype.scalar().itemsize)],
                        [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.dtype.scalar().itemsize)]]
         r[u] = [ssa("wmma", dtype=self.types[u.dtype.scalar()]) for _ in range(u.dtype.count)]
-      prefix, dtype = {Ops.CAST: ("cast", None), Ops.BITCAST: ("cast", None), Ops.ENDRANGE: ("pred", "pred"), Ops.RANGE: ("ridx", None),
+      prefix, dtype = {Ops.CAST: ("cast", None), Ops.BITCAST: ("cast", None), Ops.END: ("pred", "pred"), Ops.RANGE: ("ridx", None),
         Ops.DEFINE_VAR: ("dat", None), Ops.CONST: ("const", None), Ops.DEFINE_LOCAL: ("local",self.types[dtypes.ulong]),
         Ops.DEFINE_GLOBAL: ("dat", self.types[dtypes.ulong]), **{op: ("alu", None) for op in GroupOp.ALU}}.get(u.op, (None, None))
       if prefix: r[u] = ssa(prefix, u, dtype)

tinygrad/runtime/autogen/llvm.py

@@ -6,7 +6,7 @@
 # POINTER_SIZE is: 8
 # LONGDOUBLE_SIZE is: 16
 #
-import ctypes, tinygrad.runtime.support.llvm as llvm_support, tinygrad.helpers as helpers
+import ctypes, tinygrad.runtime.support.llvm as llvm_support
 
 
 class AsDictMixin:
@@ -146,7 +146,7 @@ class FunctionFactoryStub:
 # You can either re-run clan2py with -l /path/to/library.so
 # Or manually fix this by comment the ctypes.CDLL loading
 _libraries = {}
-_libraries['llvm'] = ctypes.CDLL(llvm_support.LLVM_PATH, ctypes.RTLD_GLOBAL if helpers.OSX else ctypes.DEFAULT_MODE) #  ctypes.CDLL('llvm')
+_libraries['llvm'] = ctypes.CDLL(llvm_support.LLVM_PATH) #  ctypes.CDLL('llvm')
 c_int128 = ctypes.c_ubyte*16
 c_uint128 = c_int128
 void = None

tinygrad/runtime/autogen/mesa.py

@@ -7,13 +7,14 @@
 # LONGDOUBLE_SIZE is: 16
 #
 import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers
-def brew_prefix():
-  try: return subprocess.check_output(['brew', '--prefix', 'tinymesa']).decode().strip()
-  except Exception: return ''
+def brew_path(nm):
+  try: return f"{subprocess.check_output(['brew', '--prefix', nm]).decode().strip()}/lib/lib{nm}.dylib"
+  except Exception: return 'failed'
 PATHS_TO_TRY = [
   (BASE:=os.getenv('MESA_PATH', f"/usr{'/local/' if helpers.OSX else '/'}lib"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so'),
   f'{BASE}/libtinymesa{EXT}',
-  f'{brew_prefix()}/lib/libtinymesa_cpu.dylib',
+  brew_path('tinymesa_cpu'),
+  brew_path('tinymesa'),
 ]
 def _try_dlopen_tinymesa_cpu():
   library = ctypes.util.find_library("tinymesa_cpu")
@@ -6087,7 +6088,7 @@ struct_nir_op_info._fields_ = [
 
 nir_op_info = struct_nir_op_info
 try: nir_op_infos = (struct_nir_op_info * 489).in_dll(_libraries['libtinymesa_cpu.so'], 'nir_op_infos')
-except AttributeError: pass
+except (AttributeError, ValueError): pass
 try:
     nir_op_is_selection = _libraries['FIXME_STUB'].nir_op_is_selection
     nir_op_is_selection.restype = ctypes.c_bool
@@ -8118,7 +8119,7 @@ c__EA_nir_intrinsic_index_flag = ctypes.c_uint32 # enum
 nir_intrinsic_index_flag = c__EA_nir_intrinsic_index_flag
 nir_intrinsic_index_flag__enumvalues = c__EA_nir_intrinsic_index_flag__enumvalues
 try: nir_intrinsic_index_names = (ctypes.POINTER(ctypes.c_char) * 75).in_dll(_libraries['libtinymesa_cpu.so'], 'nir_intrinsic_index_names')
-except AttributeError: pass
+except (AttributeError, ValueError): pass
 class struct_nir_intrinsic_instr(Structure):
     pass
 
@@ -8242,7 +8243,7 @@ struct_nir_intrinsic_info._fields_ = [
 
 nir_intrinsic_info = struct_nir_intrinsic_info
 try: nir_intrinsic_infos = (struct_nir_intrinsic_info * 732).in_dll(_libraries['libtinymesa_cpu.so'], 'nir_intrinsic_infos')
-except AttributeError: pass
+except (AttributeError, ValueError): pass
 try:
     nir_intrinsic_src_components = _libraries['libtinymesa_cpu.so'].nir_intrinsic_src_components
     nir_intrinsic_src_components.restype = ctypes.c_uint32
@@ -19877,4 +19878,4 @@ __all__ = \
     'union_util_format_description_0', 'util_format_colorspace',
     'util_format_layout', 'va_list']
 lvp_nir_options = gzip.decompress(base64.b64decode('H4sIAAAAAAAAA2NgZGRkYGAAkYxgCsQFsxigwgwQBoxmhCqFq2WEKwIrAEGIkQxoAEMALwCqVsCiGUwLMHA0QPn29nBJkswHANb8YpH4AAAA'))
-def __getattr__(nm): raise AttributeError() if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa (tinymesa-32dc66c, mesa-25.2.4)')
+def __getattr__(nm): raise AttributeError('LLVMpipe requires tinymesa_cpu' if 'tinymesa_cpu' not in dll._name else f'attribute {nm} not found') if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa (tinymesa-32dc66c, mesa-25.2.4)')

tinygrad/runtime/ops_amd.py

@@ -278,6 +278,7 @@ class AMDComputeQueue(HWQueue):
   def timestamp(self, signal:AMDSignal):
     with self.pred_exec(xcc_mask=0b1):
       self.release_mem(signal.timestamp_addr, 0, self.pm4.data_sel__mec_release_mem__send_gpu_clock_counter, self.pm4.int_sel__mec_release_mem__none)
+      self.acquire_mem() # ensure timestamp is written
     return self
 
   def signal(self, signal:AMDSignal, value:sint=0):
@@ -334,7 +335,7 @@ class AMDComputeAQLQueue(AMDComputeQueue):
     def flush_pm4_batch():
       nonlocal pm4_batch
       if not pm4_batch: return bytes()
-      dev.pm4_ibs.cpu_view().view(off:=dev.pm4_ib_alloc.alloc(len(pm4_batch) * 4), fmt='I')[:len(pm4_batch)] = array.array('I', pm4_batch)
+      dev.pm4_ibs.cpu_view().view(off:=dev.pm4_ib_alloc.alloc(len(pm4_batch) * 4, 16), fmt='I')[:len(pm4_batch)] = array.array('I', pm4_batch)
       pkt = [AQL_HDR | (hsa.HSA_PACKET_TYPE_VENDOR_SPECIFIC << hsa.HSA_PACKET_HEADER_TYPE) | (1 << 16),
         self.pm4.PACKET3(self.pm4.PACKET3_INDIRECT_BUFFER, 2), *data64_le(dev.pm4_ibs.va_addr+off), len(pm4_batch)|self.pm4.INDIRECT_BUFFER_VALID, 10]
       pm4_batch.clear()
@@ -701,7 +702,7 @@ class PCIIface(PCIIfaceBase):
       read_ptrs=[gart.cpu_view().view(offset=rptr, size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=wptr, size=8, fmt='Q')])
 
   def sleep(self, timeout):
-    if self.pci_dev.irq_poller is not None and (events_cnt:=len(self.pci_dev.irq_poller.poll(timeout))):
+    if hasattr(self.pci_dev, 'irq_poller') and self.pci_dev.irq_poller is not None and (events_cnt:=len(self.pci_dev.irq_poller.poll(timeout))):
       self.pci_dev.irq_fd.read(8 * events_cnt)
       self.dev_impl.ih.interrupt_handler()
 
@@ -833,7 +834,7 @@ class AMDDevice(HCQCompiled):
         read_dispatch_id_field_base_byte_offset=getattr(hsa.amd_queue_t, 'read_dispatch_id').offset,
         max_cu_id=self.max_cu_id, max_wave_id=self.max_wave_id)
       gart.cpu_view().view(fmt='B')[:ctypes.sizeof(aql_desc)] = bytes(aql_desc)
-      self.aql_desc = hsa.amd_queue_t.from_address(gart.va_addr)
+      self.aql_desc = hsa.amd_queue_t.from_address(gart.cpu_view().addr)
 
     cwsr_buffer_size = round_up((ctx_save_restore_size + debug_memory_size) * self.iface.props.get('num_xcc', 1), mmap.PAGESIZE)
     cwsr_buffer = self.iface.alloc(cwsr_buffer_size) if ctx_save_restore_size else None

tinygrad/runtime/ops_null.py

@@ -17,7 +17,7 @@ class NullRenderer(CStyleLanguage):
 class NullProgram:
   def __init__(self, device:str, name:str, lib:bytes): self.device, self.name = device, name
   def __call__(self, *bufs, global_size:tuple[int,int,int]=(1,1,1), local_size:tuple[int,int,int]=(1,1,1), vals:tuple[int, ...]=(), wait=False):
-    with cpu_profile(self.name, self.device): return 1e-4
+    with cpu_profile(self.name, self.device): return 1e-3
 
 class NullAllocator(Allocator['NullDevice']):
   def _alloc(self, size, options): pass
@@ -28,7 +28,7 @@ class NullAllocator(Allocator['NullDevice']):
   def _offset(self, buf, offset:int, size:int): pass
 
 class NullGraph(MultiGraphRunner):
-  def __call__(self, input_rawbuffers, var_vals, wait=False) -> float|None: return 1e-3
+  def __call__(self, input_rawbuffers, var_vals, wait=False) -> float|None: return 1e-1
 
 class NullDevice(Compiled):
   def __init__(self, device:str):

tinygrad/runtime/ops_nv.py

@@ -111,7 +111,7 @@ class NVCommandQueue(HWQueue[HCQSignal, 'NVDevice', 'NVProgram', 'NVArgsState'])
   def _submit_to_gpfifo(self, dev:NVDevice, gpfifo:GPFifo):
     if dev == self.binded_device: cmdq_addr = self.hw_page.va_addr
     else:
-      cmdq_addr = dev.cmdq_allocator.alloc(len(self._q) * 4)
+      cmdq_addr = dev.cmdq_allocator.alloc(len(self._q) * 4, 16)
       cmdq_wptr = (cmdq_addr - dev.cmdq_page.va_addr) // 4
       dev.cmdq[cmdq_wptr : cmdq_wptr + len(self._q)] = array.array('I', self._q)
 
@@ -156,10 +156,14 @@ class NVComputeQueue(NVCommandQueue):
       for i in range(2):
         if self.active_qmd.read(f'release{i}_enable') == 0:
           self.active_qmd.write(**{f'release{i}_enable': 1})
-          self.bind_sints_to_mem(signal.value_addr, mem=self.active_qmd_buf.cpu_view(), fmt='Q', mask=0xfffffffff,
-            offset=self.active_qmd.field_offset(f'release{i}_address_lower' if self.active_qmd.ver<4 else f'release_semaphore{i}_addr_lower'))
-          self.bind_sints_to_mem(value, mem=self.active_qmd_buf.cpu_view(), fmt='Q',
-            offset=self.active_qmd.field_offset(f'release{i}_payload_lower' if self.active_qmd.ver<4 else f'release_semaphore{i}_payload_lower'))
+
+          addr_off = self.active_qmd.field_offset(f'release{i}_address_lower' if self.active_qmd.ver<4 else f'release_semaphore{i}_addr_lower')
+          self.bind_sints_to_mem(signal.value_addr & 0xffffffff, mem=self.active_qmd_buf.cpu_view(), fmt='I', offset=addr_off)
+          self.bind_sints_to_mem(signal.value_addr >> 32, mem=self.active_qmd_buf.cpu_view(), fmt='I', mask=0xf, offset=addr_off+4)
+
+          val_off = self.active_qmd.field_offset(f'release{i}_payload_lower' if self.active_qmd.ver<4 else f'release_semaphore{i}_payload_lower')
+          self.bind_sints_to_mem(value & 0xffffffff, mem=self.active_qmd_buf.cpu_view(), fmt='I', offset=val_off)
+          self.bind_sints_to_mem(value >> 32, mem=self.active_qmd_buf.cpu_view(), fmt='I', offset=val_off+4)
           return self
 
     self.nvm(0, nv_gpu.NVC56F_SEM_ADDR_LO, *data64_le(signal.value_addr), *data64_le(value),
@@ -384,7 +388,7 @@ class NVKIface:
     if made.params.status != 0: raise RuntimeError(f"_gpu_map_to_cpu returned {get_error_str(made.params.status)}")
     return fd_dev.mmap(target, size, mmap.PROT_READ|mmap.PROT_WRITE, mmap.MAP_SHARED | (MAP_FIXED if target is not None else 0), 0)
 
-  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, map_flags=0, cpu_addr=None) -> HCQBuffer:
+  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, map_flags=0, cpu_addr=None, **kwargs) -> HCQBuffer:
     # Uncached memory is "system". Use huge pages only for gpu memory.
     page_size = (4 << (12 if OSX else 10)) if uncached or host else ((2 << 20) if size >= (8 << 20) else (4 << (12 if OSX else 10)))
     size = round_up(size, page_size)
@@ -455,13 +459,13 @@ class PCIIface(PCIIfaceBase):
   def __init__(self, dev, dev_id):
     super().__init__(dev, dev_id, vendor=0x10de, devices=[0x2204, 0x2684, 0x2b85], bars=[0, 1], vram_bar=1,
       va_start=NVMemoryManager.va_allocator.base, va_size=NVMemoryManager.va_allocator.size)
-    System.reserve_hugepages(64)
+    if not OSX: System.reserve_hugepages(64)
 
     self.pci_dev.write_config(pci.PCI_COMMAND, self.pci_dev.read_config(pci.PCI_COMMAND, 2) | pci.PCI_COMMAND_MASTER, 2)
     self.dev_impl:NVDev = NVDev(self.pci_dev.pcibus, self.pci_dev.map_bar(0, fmt='I'), self.pci_dev.map_bar(1),
       self.pci_dev.read_config(pci.PCI_VENDOR_ID, 4), self.pci_dev.read_config(pci.PCI_SUBSYSTEM_VENDOR_ID, 4),
       self.pci_dev.read_config(pci.PCI_REVISION_ID, 1), self.pci_dev.bar_info)
-    self.root, self.gpu_instance, self.p2p_base_addr = 0xc1000000, 0, self.pci_dev.bar_info[1][0]
+    self.root, self.gpu_instance = 0xc1000000, 0
     self.rm_alloc(0, nv_gpu.NV01_ROOT, nv_gpu.NV0000_ALLOC_PARAMETERS())
 
     # Setup classes for the GPU
@@ -508,7 +512,7 @@ class NVDevice(HCQCompiled[HCQSignal]):
     channel_params = nv_gpu.NV_CHANNEL_GROUP_ALLOCATION_PARAMETERS(engineType=nv_gpu.NV2080_ENGINE_TYPE_GRAPHICS)
     channel_group = self.iface.rm_alloc(self.nvdevice, nv_gpu.KEPLER_CHANNEL_GROUP_A, channel_params)
 
-    gpfifo_area = self.iface.alloc(0x200000, contiguous=True, cpu_access=True, map_flags=0x10d0000)
+    gpfifo_area = self.iface.alloc(0x200000, contiguous=True, cpu_access=True, force_devmem=True, map_flags=0x10d0000)
 
     ctxshare_params = nv_gpu.NV_CTXSHARE_ALLOCATION_PARAMETERS(hVASpace=vaspace, flags=nv_gpu.NV_CTXSHARE_ALLOCATION_FLAGS_SUBCONTEXT_ASYNC)
     ctxshare = self.iface.rm_alloc(channel_group, nv_gpu.FERMI_CONTEXT_SHARE_A, ctxshare_params)

tinygrad/runtime/ops_python.py

@@ -52,11 +52,11 @@ class PythonProgram:
       loop_ends: dict[int, int] = {}
       while i < len(self.uops):
         uop, dtype, idp, arg = self.uops[i]
-        void_ops = {Ops.ENDRANGE, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP, Ops.STORE}
+        void_ops = {Ops.END, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP, Ops.STORE}
         inp = [ul[v] for v in idp if self.uops[v][0] not in void_ops]
         dtp = [dl[v] for v in idp if self.uops[v][0] not in void_ops]
         if getenv("TRACE"): print(i, uop, dtype, arg, inp, dtp)
-        if uop is Ops.ENDRANGE:
+        if uop is Ops.END:
           loop_ends[idp[0]] = i
           i = idp[0]
           continue
@@ -72,7 +72,8 @@ class PythonProgram:
               if g: _store(m, o+j, v, dtp[1].scalar())
           i += 1
           continue
-        if uop in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
+        if uop is Ops.AFTER: ul[i] = inp[0]
+        elif uop in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
           assert isinstance(dtype, PtrDType), dtype
           storage_fmt = storage_fmt_for_dtype(dtype.base.scalar())
           if storage_fmt is None: raise RuntimeError(f"{dtype=} is not supported")

tinygrad/runtime/ops_qcom.py

@@ -9,7 +9,7 @@ from tinygrad.runtime.support.hcq import FileIOInterface, MMIOInterface
 from tinygrad.runtime.autogen import kgsl, adreno
 from tinygrad.runtime.ops_cl import CLCompiler, CLDevice
 from tinygrad.renderer.cstyle import QCOMRenderer
-from tinygrad.helpers import getenv, mv_address, to_mv, round_up, data64_le, prod, fromimport
+from tinygrad.helpers import getenv, mv_address, to_mv, round_up, data64_le, prod, fromimport, cpu_profile
 if getenv("IOCTL"): import extra.qcom_gpu_driver.opencl_ioctl  # noqa: F401  # pylint: disable=unused-import
 
 BUFTYPE_BUF, BUFTYPE_TEX, BUFTYPE_IBO = 0, 1, 2
@@ -289,20 +289,21 @@ class QCOMAllocator(HCQAllocatorBase):
       buf.texture_info = QCOMTextureInfo(pitch, real_stride, desc, [desc[0] & (~0xffff), *desc[1:len(desc)]])
     return buf
 
-  def _do_copy(self, src_addr, dest_addr, src_size, real_size, src_stride, dest_stride, dest_off=0, src_off=0):
-    while src_off < src_size:
-      ctypes.memmove(dest_addr+dest_off, src_addr+src_off, real_size)
-      src_off, dest_off = src_off+src_stride, dest_off+dest_stride
+  def _do_copy(self, src_addr, dest_addr, src_size, real_size, src_stride, dest_stride, prof_text, dest_off=0, src_off=0):
+    with cpu_profile(prof_text, self.dev.device, is_copy=True):
+      while src_off < src_size:
+        ctypes.memmove(dest_addr+dest_off, src_addr+src_off, real_size)
+        src_off, dest_off = src_off+src_stride, dest_off+dest_stride
 
   def _copyin(self, dest:HCQBuffer, src:memoryview):
     stride, pitch = (src.nbytes, src.nbytes) if (ti:=cast(QCOMTextureInfo, dest.texture_info)) is None else (ti.real_stride, ti.pitch)
-    self._do_copy(mv_address(src), dest.va_addr, src.nbytes, stride, stride, pitch)
+    self._do_copy(mv_address(src), dest.va_addr, src.nbytes, stride, stride, pitch, f"TINY -> {self.dev.device}")
 
   def _copyout(self, dest:memoryview, src:HCQBuffer):
     self.dev.synchronize()
 
     stride, pitch = (src.size, src.size) if (ti:=cast(QCOMTextureInfo, src.texture_info)) is None else (ti.real_stride, ti.pitch)
-    self._do_copy(src.va_addr, mv_address(dest), src.size, stride, pitch, stride)
+    self._do_copy(src.va_addr, mv_address(dest), src.size, stride, pitch, stride, f"{self.dev.device} -> TINY")
 
   def _as_buffer(self, src:HCQBuffer) -> memoryview:
     self.dev.synchronize()

tinygrad/runtime/support/compiler_cpu.py

@@ -58,7 +58,7 @@ class LLVMCompiler(Compiler):
         self.diag_msgs.append(msg)
     self.handle_diag = handle_diag
     llvm.LLVMContextSetDiagnosticHandler(llvm.LLVMGetGlobalContext(), handle_diag, None)
-    super().__init__(f"compile_llvm_{self.target_arch}{'_jit' if self.jit else ''}{'_opt' if opt else ''}")
+    super().__init__(f"compile_llvm_{processor}_{feats}{'_jit' if self.jit else ''}{'_opt' if opt else ''}")
 
   def __del__(self): llvm.LLVMDisposePassBuilderOptions(self.pbo)
 

tinygrad/runtime/support/compiler_cuda.py

@@ -69,7 +69,9 @@ class PTXCompiler(Compiler):
   def disassemble(self, lib:bytes): cuda_disassemble(lib, self.arch)
 
 class NVPTXCompiler(PTXCompiler):
-  def __init__(self, arch:str): super().__init__(arch, cache_key="nv_ptx")
+  def __init__(self, arch:str):
+    nvrtc_check(nvrtc.nvJitLinkVersion(ctypes.byref(ctypes.c_uint()), ctypes.byref(ctypes.c_uint())))
+    super().__init__(arch, cache_key="nv_ptx")
   def compile(self, src:str) -> bytes:
     jitlink_check(nvrtc.nvJitLinkCreate(handle := nvrtc.nvJitLinkHandle(), 1, to_char_p_p([f'-arch={self.arch}'.encode()])), handle)
     jitlink_check(nvrtc.nvJitLinkAddData(handle, nvrtc.NVJITLINK_INPUT_PTX, ptxsrc:=super().compile(src), len(ptxsrc), "<null>".encode()), handle)

tinygrad/runtime/support/hcq.py

@@ -261,20 +261,20 @@ class HCQSignal(Generic[HCQDeviceType]):
 @contextlib.contextmanager
 def hcq_profile(dev:HCQCompiled, enabled, desc, queue_type:Callable[[], HWQueue]|None=None, queue:HWQueue|None=None):
   st, en = (dev.new_signal(), dev.new_signal()) if enabled else (None, None)
+  assert queue is not None or queue_type is not None, "Either queue or queue_type must be provided"
 
   if enabled and queue is not None: queue.timestamp(st)
-  elif enabled:
-    assert queue_type is not None
+  elif enabled and queue_type is not None:
     queue_type().wait(dev.timeline_signal, dev.timeline_value - 1).timestamp(st).signal(dev.timeline_signal, dev.next_timeline()).submit(dev)
 
   try: yield (st, en)
   finally:
     if enabled and queue is not None: queue.timestamp(en)
-    elif enabled:
-      assert queue_type is not None
+    elif enabled and queue_type is not None:
       queue_type().wait(dev.timeline_signal, dev.timeline_value - 1).timestamp(en).signal(dev.timeline_signal, dev.next_timeline()).submit(dev)
 
-    if enabled and PROFILE: dev.sig_prof_records.append((cast(HCQSignal, st), cast(HCQSignal, en), desc, queue_type is dev.hw_copy_queue_t))
+    if enabled and PROFILE:
+      dev.sig_prof_records.append((cast(HCQSignal, st), cast(HCQSignal, en), desc, (queue_type or type(queue)) is dev.hw_copy_queue_t))
 
 class HCQArgsState(Generic[ProgramType]):
   def __init__(self, buf:HCQBuffer, prg:ProgramType, bufs:tuple[HCQBuffer, ...], vals:tuple[sint, ...]=()):

tinygrad/runtime/support/nv/ip.py

@@ -139,7 +139,7 @@ class NV_FLCN(NV_IP):
 
       return System.alloc_sysmem(len(patched_image), contiguous=True, data=patched_image)
 
-    self.frts_image_va, self.frts_image_sysmem = __patch(0x15, bytes(frts_cmd))
+    _, self.frts_image_sysmem = __patch(0x15, bytes(frts_cmd))
 
   def prep_booter(self):
     image = self.nvdev.extract_fw("kgspBinArchiveBooterLoadUcode", "image_prod_data")
@@ -150,7 +150,7 @@ class NV_FLCN(NV_IP):
 
     patched_image = bytearray(image)
     patched_image[patch_loc:patch_loc+sig_len] = sig[:sig_len]
-    self.booter_image_va, self.booter_image_sysmem = System.alloc_sysmem(len(patched_image), contiguous=True, data=patched_image)
+    _, self.booter_image_sysmem = System.alloc_sysmem(len(patched_image), contiguous=True, data=patched_image)
     _, _, self.booter_data_off, self.booter_data_sz, _, self.booter_code_off, self.booter_code_sz, _, _ = struct.unpack("9I", header)
 
   def init_hw(self):
@@ -327,10 +327,10 @@ class NV_GSP(NV_IP):
     # Alloc queues
     pte_cnt = ((queue_pte_cnt:=(queue_size * 2) // 0x1000)) + round_up(queue_pte_cnt * 8, 0x1000) // 0x1000
     pt_size = round_up(pte_cnt * 8, 0x1000)
-    queues_va, queues_sysmem = System.alloc_sysmem(pt_size + queue_size * 2, contiguous=False)
+    queues_view, queues_sysmem = System.alloc_sysmem(pt_size + queue_size * 2, contiguous=False)
 
     # Fill up ptes
-    for i, sysmem in enumerate(queues_sysmem): to_mv(queues_va + i * 0x8, 0x8).cast('Q')[0] = sysmem
+    for i, sysmem in enumerate(queues_sysmem): queues_view.view(i * 0x8, 0x8, fmt='Q')[0] = sysmem
 
     # Fill up arguments
     queue_args = nv.MESSAGE_QUEUE_INIT_ARGUMENTS(sharedMemPhysAddr=queues_sysmem[0], pageTableEntryCount=pte_cnt, cmdQueueOffset=pt_size,
@@ -338,7 +338,7 @@ class NV_GSP(NV_IP):
     _, self.rm_args_sysmem = self.nvdev._alloc_boot_struct(nv.GSP_ARGUMENTS_CACHED(bDmemStack=True, messageQueueInitArguments=queue_args))
 
     # Build command queue header
-    self.cmd_q_va, self.stat_q_va = queues_va + pt_size, queues_va + pt_size + queue_size
+    self.cmd_q_va, self.stat_q_va = queues_view.addr + pt_size, queues_view.addr + pt_size + queue_size
 
     cmd_q_tx = nv.msgqTxHeader(version=0, size=queue_size, entryOff=0x1000, msgSize=0x1000, msgCount=(queue_size - 0x1000) // 0x1000,
       writePtr=0, flags=1, rxHdrOff=ctypes.sizeof(nv.msgqTxHeader))
@@ -348,9 +348,9 @@ class NV_GSP(NV_IP):
 
   def init_libos_args(self):
     _, logbuf_sysmem = System.alloc_sysmem((2 << 20), contiguous=True)
-    libos_args_va, self.libos_args_sysmem = System.alloc_sysmem(0x1000, contiguous=True)
+    libos_args_view, self.libos_args_sysmem = System.alloc_sysmem(0x1000, contiguous=True)
 
-    libos_structs = (nv.LibosMemoryRegionInitArgument * 6).from_address(libos_args_va)
+    libos_structs = (nv.LibosMemoryRegionInitArgument * 6).from_address(libos_args_view.addr)
     for i, name in enumerate(["INIT", "INTR", "RM", "MNOC", "KRNL"]):
       libos_structs[i] = nv.LibosMemoryRegionInitArgument(kind=nv.LIBOS_MEMORY_REGION_CONTIGUOUS, loc=nv.LIBOS_MEMORY_REGION_LOC_SYSMEM, size=0x10000,
         id8=int.from_bytes(bytes(f"LOG{name}", 'utf-8'), 'big'), pa=logbuf_sysmem[0] + 0x10000 * i)
@@ -370,18 +370,18 @@ class NV_GSP(NV_IP):
     for i in range(3, 0, -1): npages[i-1] = ((npages[i] - 1) >> (nv.LIBOS_MEMORY_REGION_RADIX_PAGE_LOG2 - 3)) + 1
 
     offsets = [sum(npages[:i]) * 0x1000 for i in range(4)]
-    radix_va, self.gsp_radix3_sysmem = System.alloc_sysmem(offsets[-1] + len(self.gsp_image), contiguous=False)
+    radix_view, self.gsp_radix3_sysmem = System.alloc_sysmem(offsets[-1] + len(self.gsp_image), contiguous=False)
 
     # Copy image
-    to_mv(radix_va + offsets[-1], len(self.gsp_image))[:] = self.gsp_image
+    radix_view.view(offsets[-1], len(self.gsp_image))[:] = self.gsp_image
 
     # Copy level and image pages.
     for i in range(0, 3):
       cur_offset = sum(npages[:i+1])
-      to_mv(radix_va + offsets[i], npages[i+1] * 8).cast('Q')[:] = array.array('Q', self.gsp_radix3_sysmem[cur_offset:cur_offset+npages[i+1]])
+      radix_view.view(offsets[i], npages[i+1] * 8, fmt='Q')[:] = array.array('Q', self.gsp_radix3_sysmem[cur_offset:cur_offset+npages[i+1]])
 
     # Copy signature
-    self.gsp_signature_va, self.gsp_signature_sysmem = System.alloc_sysmem(len(signature), contiguous=True, data=signature)
+    _, self.gsp_signature_sysmem = System.alloc_sysmem(len(signature), contiguous=True, data=signature)
 
   def init_boot_binary_image(self):
     self.booter_image = self.nvdev.extract_fw("kgspBinArchiveGspRmBoot", "ucode_image_prod_data")
@@ -522,7 +522,7 @@ class NV_GSP(NV_IP):
     self.stat_q.wait_resp(nv.NV_VGPU_MSG_FUNCTION_SET_PAGE_DIRECTORY)
 
   def rpc_set_gsp_system_info(self):
-    def bdf_as_int(s): return (int(s[5:7],16)<<8) | (int(s[8:10],16)<<3) | int(s[-1],16)
+    def bdf_as_int(s): return 0x000 if s.startswith("usb") else (int(s[5:7],16)<<8) | (int(s[8:10],16)<<3) | int(s[-1],16)
 
     data = nv.GspSystemInfo(gpuPhysAddr=self.nvdev.bars[0][0], gpuPhysFbAddr=self.nvdev.bars[1][0], gpuPhysInstAddr=self.nvdev.bars[3][0],
       pciConfigMirrorBase=[0x88000, 0x92000][self.nvdev.fmc_boot], pciConfigMirrorSize=0x1000, nvDomainBusDeviceFunc=bdf_as_int(self.nvdev.devfmt),

tinygrad/runtime/support/nv/nvdev.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import ctypes, time, functools, re, gzip, struct
-from tinygrad.helpers import getenv, DEBUG, fetch, getbits, to_mv
+from tinygrad.helpers import getenv, DEBUG, fetch, getbits
 from tinygrad.runtime.support.hcq import MMIOInterface
 from tinygrad.runtime.support.memory import TLSFAllocator, MemoryManager
 from tinygrad.runtime.support.nv.ip import NV_FLCN, NV_FLCN_COT, NV_GSP
@@ -137,9 +137,9 @@ class NVDev(PCIDevImplBase):
     self.large_bar = self.vram.nbytes >= self.vram_size
 
   def _alloc_boot_struct(self, struct:ctypes.Structure) -> tuple[ctypes.Structure, int]:
-    va, paddrs = System.alloc_sysmem(sz:=ctypes.sizeof(type(struct)), contiguous=True)
-    to_mv(va, sz)[:] = bytes(struct)
-    return type(struct).from_address(va), paddrs[0]
+    view, paddrs = System.alloc_sysmem(sz:=ctypes.sizeof(type(struct)), contiguous=True)
+    view[:sz] = bytes(struct)
+    return type(struct).from_address(view.addr), paddrs[0]
 
   def _download(self, file:str) -> str:
     url = f"https://raw.githubusercontent.com/NVIDIA/open-gpu-kernel-modules/8ec351aeb96a93a4bb69ccc12a542bf8a8df2b6f/{file}"

tinygrad/runtime/support/system.py

@@ -1,6 +1,6 @@
 import os, mmap, array, functools, ctypes, select, contextlib, dataclasses, sys, errno, itertools
 from typing import cast, ClassVar
-from tinygrad.helpers import round_up, to_mv, getenv, OSX, temp
+from tinygrad.helpers import round_up, getenv, OSX, temp, ceildiv
 from tinygrad.runtime.autogen import libc, vfio
 from tinygrad.runtime.support.hcq import FileIOInterface, MMIOInterface, HCQBuffer
 from tinygrad.runtime.support.memory import MemoryManager, VirtMapping
@@ -49,6 +49,18 @@ class _System:
       raise RuntimeError("IOServiceOpen failed")
     return conn
 
+  def iokit_pci_memmap(self, typ:int):
+    if self.iokit.IOConnectMapMemory64(self.macos_tinygpu_conn, ctypes.c_uint32(typ), System.mach_task_self,
+      ctypes.byref(addr:=ctypes.c_uint64(0)), ctypes.byref(size:=ctypes.c_uint64(0)), 0x1): raise RuntimeError(f"IOConnectMapMemory64({typ=}) failed")
+    return MMIOInterface(addr.value, size.value)
+
+  def iokit_pci_rpc(self, sel:int, *args:int):
+    in_scalars = (ctypes.c_uint64 * len(args))(*args) if args else ctypes.POINTER(ctypes.c_uint64)()
+    if (self.iokit.IOConnectCallMethod(self.macos_tinygpu_conn, sel, in_scalars, len(args), None, ctypes.c_size_t(0),
+        out_scalars:=(ctypes.c_uint64*16)(), ctypes.byref(outcnt:=ctypes.c_uint32(16)), None, ctypes.byref(ctypes.c_size_t(0)))):
+      raise RuntimeError(f"IOConnectCallMethod({sel=}, {args=}) failed")
+    return out_scalars[:outcnt.value]
+
   def reserve_hugepages(self, cnt): os.system(f"sudo sh -c 'echo {cnt} > /proc/sys/vm/nr_hugepages'")
 
   def memory_barrier(self): lib.atomic_thread_fence(__ATOMIC_SEQ_CST:=5) if (lib:=self.libsys if OSX else self.atomic_lib) is not None else None
@@ -60,15 +72,24 @@ class _System:
     self.pagemap.seek(vaddr // mmap.PAGESIZE * 8)
     return [(x & ((1<<55) - 1)) * mmap.PAGESIZE for x in array.array('Q', self.pagemap.read(size//mmap.PAGESIZE*8, binary=True))]
 
-  def alloc_sysmem(self, size:int, vaddr:int=0, contiguous:bool=False, data:bytes|None=None) -> tuple[int, list[int]]:
-    assert not contiguous or size <= (2 << 20), "Contiguous allocation is only supported for sizes up to 2MB"
-    flags = (libc.MAP_HUGETLB if contiguous and (size:=round_up(size, mmap.PAGESIZE)) > 0x1000 else 0) | (MAP_FIXED if vaddr else 0)
-    va = FileIOInterface.anon_mmap(vaddr, size, mmap.PROT_READ|mmap.PROT_WRITE, mmap.MAP_SHARED|mmap.MAP_ANONYMOUS|MAP_POPULATE|MAP_LOCKED|flags, 0)
+  def alloc_sysmem(self, size:int, vaddr:int=0, contiguous:bool=False, data:bytes|None=None) -> tuple[MMIOInterface, list[int]]:
+    if OSX:
+      sysmem_view = System.iokit_pci_memmap(round_up(size, mmap.PAGESIZE))
+      paddrs = list(itertools.takewhile(lambda p: p[1] != 0, zip(sysmem_view.view(fmt='Q')[0::2], sysmem_view.view(fmt='Q')[1::2])))
+      assert not contiguous or len(paddrs) == 1, "not contiguous, but required"
+    else:
+      assert not contiguous or size <= (2 << 20), "Contiguous allocation is only supported for sizes up to 2MB"
+      flags = (libc.MAP_HUGETLB if contiguous and (size:=round_up(size, mmap.PAGESIZE)) > mmap.PAGESIZE else 0) | (MAP_FIXED if vaddr else 0)
+      va = FileIOInterface.anon_mmap(vaddr, size, mmap.PROT_READ|mmap.PROT_WRITE, mmap.MAP_SHARED|mmap.MAP_ANONYMOUS|MAP_POPULATE|MAP_LOCKED|flags, 0)
+      sysmem_view, paddrs = MMIOInterface(va, size), [(x, mmap.PAGESIZE) for x in self.system_paddrs(va, size)]
 
-    if data is not None: to_mv(va, len(data))[:] = data
-    return va, self.system_paddrs(va, size)
+    if data is not None: sysmem_view[:len(data)] = data
+    return sysmem_view, [p + i for p, sz in paddrs for i in range(0, sz, 0x1000)][:ceildiv(size, 0x1000)]
+
+  def pci_reset(self, gpu):
+    if OSX: System.iokit_pci_rpc(__TinyGPURPCReset:=2)
+    else: os.system(f"sudo sh -c 'echo 1 > /sys/bus/pci/devices/{gpu}/reset'")
 
-  def pci_reset(self, gpu): os.system(f"sudo sh -c 'echo 1 > /sys/bus/pci/devices/{gpu}/reset'")
   def pci_scan_bus(self, target_vendor:int, target_devices:list[int]) -> list[str]:
     result = []
     for pcibus in FileIOInterface("/sys/bus/pci/devices").listdir():
@@ -143,14 +164,12 @@ class PCIDevice:
     return MMIOInterface(loc, sz, fmt=fmt)
 
 class APLPCIDevice(PCIDevice):
-  def __init__(self, pcibus:str, bars:list[int], resize_bars:list[int]|None=None): self.pcibus, self.bars = pcibus, {b: self.map_mem(b) for b in bars}
-  def map_mem(self, typ:int) -> MMIOInterface:
-    if System.iokit.IOConnectMapMemory64(System.macos_tinygpu_conn, ctypes.c_uint32(typ), System.mach_task_self,
-      ctypes.byref(addr:=ctypes.c_uint64(0)), ctypes.byref(size:=ctypes.c_uint64(0)), 0x1): raise RuntimeError(f"IOConnectMapMemory64({typ=}) failed")
-    return MMIOInterface(addr.value, size.value)
+  def __init__(self, pcibus:str, bars:list[int], resize_bars:list[int]|None=None):
+    self.pcibus, self.bars = pcibus, {b: System.iokit_pci_memmap(b) for b in bars}
+    self.bar_info = {b:(0, self.bars[b].nbytes-1 if b in self.bars else 0, 0) for b in range(6)} # NOTE: fake bar info for nv.
   def map_bar(self, bar:int, off:int=0, addr:int=0, size:int|None=None, fmt='B') -> MMIOInterface: return self.bars[bar].view(off, size, fmt)
-  def read_config(self, offset:int, size:int): return 0
-  def write_config(self, offset:int, value:int, size:int): pass
+  def read_config(self, offset:int, size:int): return System.iokit_pci_rpc(__TinyGPURPCReadCfg:=0, offset, size)[0]
+  def write_config(self, offset:int, value:int, size:int): System.iokit_pci_rpc(__TinyGPURPCWriteCfg:=1, offset, size, value)
 
 class PCIDevImplBase:
   mm: MemoryManager
@@ -173,23 +192,23 @@ class LNXPCIIfaceBase:
     self.pci_dev, self.dev, self.vram_bar = PCIDevice(cls.gpus[dev_id], bars=bars, resize_bars=[vram_bar]), dev, vram_bar
     self.p2p_base_addr = self.pci_dev.bar_info[vram_bar][0]
 
-  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, **kwargs) -> HCQBuffer:
-    if host or (uncached and cpu_access): # host or gtt-like memory.
+  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, force_devmem=False, **kwargs) -> HCQBuffer:
+    # NOTE: logic on macos is different, since bar is small
+    should_use_sysmem = host or (((uncached or cpu_access) if OSX else (uncached and cpu_access)) and not force_devmem)
+    if should_use_sysmem:
       vaddr = self.dev_impl.mm.alloc_vaddr(size:=round_up(size, mmap.PAGESIZE), align=mmap.PAGESIZE)
-      paddrs = [(paddr, mmap.PAGESIZE) for paddr in System.alloc_sysmem(size, vaddr=vaddr, contiguous=contiguous)[1]]
-      mapping = self.dev_impl.mm.map_range(vaddr, size, paddrs, system=True, snooped=True, uncached=True)
-      return HCQBuffer(vaddr, size, meta=PCIAllocationMeta(mapping, has_cpu_mapping=True, hMemory=paddrs[0][0]),
-        view=MMIOInterface(mapping.va_addr, size, fmt='B'), owner=self.dev)
+      memview, paddrs = System.alloc_sysmem(size, vaddr=vaddr, contiguous=contiguous)
+      mapping = self.dev_impl.mm.map_range(vaddr, size, [(paddr, 0x1000) for paddr in paddrs], system=True, snooped=True, uncached=True)
+      return HCQBuffer(vaddr, size, meta=PCIAllocationMeta(mapping, has_cpu_mapping=True, hMemory=paddrs[0]), view=memview, owner=self.dev)
 
-    mapping = self.dev_impl.mm.valloc(size:=round_up(size, 4 << 10), uncached=uncached, contiguous=cpu_access)
-    if cpu_access: self.pci_dev.map_bar(bar=self.vram_bar, off=mapping.paddrs[0][0], addr=mapping.va_addr, size=mapping.size)
-    return HCQBuffer(mapping.va_addr, size, view=MMIOInterface(mapping.va_addr, size, fmt='B') if cpu_access else None,
-      meta=PCIAllocationMeta(mapping, has_cpu_mapping=cpu_access, hMemory=mapping.paddrs[0][0]), owner=self.dev)
+    mapping = self.dev_impl.mm.valloc(size:=round_up(size, 0x1000), uncached=uncached, contiguous=cpu_access)
+    barview = self.pci_dev.map_bar(bar=self.vram_bar, off=mapping.paddrs[0][0], size=mapping.size) if cpu_access else None
+    return HCQBuffer(mapping.va_addr, size, view=barview, meta=PCIAllocationMeta(mapping, cpu_access, hMemory=mapping.paddrs[0][0]), owner=self.dev)
 
   def free(self, b:HCQBuffer):
     for dev in b.mapped_devs[1:]: dev.iface.dev_impl.mm.unmap_range(b.va_addr, b.size)
     if not b.meta.mapping.system: self.dev_impl.mm.vfree(b.meta.mapping)
-    if b.owner == self.dev and b.meta.has_cpu_mapping: FileIOInterface.munmap(b.va_addr, b.size)
+    if b.owner == self.dev and b.meta.has_cpu_mapping and not OSX: FileIOInterface.munmap(b.va_addr, b.size)
 
   def map(self, b:HCQBuffer):
     if b.owner is not None and b.owner._is_cpu():
@@ -205,21 +224,6 @@ class LNXPCIIfaceBase:
 class APLPCIIfaceBase(LNXPCIIfaceBase):
   def __init__(self, dev, dev_id, vendor, devices, bars, vram_bar, va_start, va_size):
     self.pci_dev, self.dev, self.vram_bar = APLPCIDevice(pcibus=f'usb4:{dev_id}', bars=bars), dev, vram_bar
-
-  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, **kwargs) -> HCQBuffer:
-    if host or uncached or cpu_access: # cpu access memory goes here, since bar is small.
-      vaddr = self.dev_impl.mm.alloc_vaddr(size:=round_up(size, mmap.PAGESIZE), align=mmap.PAGESIZE)
-      assert size >= mmap.PAGESIZE, "Size must be at least one page"
-
-      sysmem = cast(APLPCIDevice, self.pci_dev).map_mem(size).view(fmt='Q')
-      paddrs = list(itertools.takewhile(lambda p: p[1] != 0, zip(sysmem[0::2], sysmem[1::2])))
-
-      mapping = self.dev_impl.mm.map_range(vaddr, size, paddrs, system=True, snooped=True, uncached=True)
-      return HCQBuffer(vaddr, size, meta=PCIAllocationMeta(mapping, has_cpu_mapping=True), view=sysmem.view(fmt='B'), owner=self.dev)
-
-    mapping = self.dev_impl.mm.valloc(size:=round_up(size, 4 << 10), uncached=uncached, contiguous=cpu_access)
-    return HCQBuffer(mapping.va_addr, size, view=None, meta=PCIAllocationMeta(mapping, has_cpu_mapping=False), owner=self.dev)
-
   def map(self, b:HCQBuffer): raise RuntimeError(f"map failed: {b.owner} -> {self.dev}")
 
 PCIIfaceBase:type = APLPCIIfaceBase if OSX else LNXPCIIfaceBase

tinygrad/schedule/indexing.py

@@ -1,10 +1,10 @@
-from typing import Iterator
+from typing import Iterator, cast
 import functools, operator, itertools
 from dataclasses import dataclass, field
 from tinygrad.dtype import dtypes, AddrSpace
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, graph_rewrite, sint, AxisType
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, graph_rewrite, sint, AxisType, profile_matches
 from tinygrad.uop.symbolic import symbolic, pm_simplify_valid, pm_drop_and_clauses
-from tinygrad.helpers import argsort, all_same, cpu_profile, TracingKey
+from tinygrad.helpers import argsort, all_same, cpu_profile, PCONTIG, colored, MULTIOUTPUT
 
 ALWAYS_CONTIGUOUS: set[Ops] = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
                      Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_GLOBAL,
@@ -40,25 +40,32 @@ class BufferizeOpts:
 
 @dataclass
 class IndexingContext:
-  realize_map: dict[UOp, None] = field(default_factory=dict)
+  realize_map: dict[UOp, None|list[int]] = field(default_factory=dict)
   range_map: dict[UOp, tuple[tuple[UOp, ...], tuple[UOp, ...]]] = field(default_factory=dict)
 
   # create ranges
   range_idx: Iterator[int] = field(default_factory=itertools.count)
-  def new_range(self, s:sint, axistype:AxisType=AxisType.LOOP):
+  def new_range(self, s:sint, axistype:AxisType=AxisType.LOOP) -> UOp:
+    if isinstance(s, UOp) and s.op is Ops.RANGE: return s
+    # if a range has a 1 src, it's the same as UOp.const(dtypes.index, 0)
     return UOp.range(s, next(self.range_idx), axistype) if resolve(s!=1) else UOp.const(dtypes.index, 0)
 
 def create_bufferize_and_index_based_on_ranges(ctx:IndexingContext, x:UOp):
   if x.op in {Ops.BUFFERIZE, Ops.INDEX, Ops.KERNEL}: return None
-  if x.op is Ops.ASSIGN and x.src[1].op is Ops.KERNEL: return None
+  if x.op is Ops.AFTER and x.src[1].op is Ops.KERNEL: return None
   new_srcs = []
   for s in x.src:
     new_src = s
-    if s.op in {Ops.BUFFER, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT} or (s.op is Ops.ASSIGN and s.src[1].op is Ops.KERNEL):
+    if s.op in {Ops.BUFFER, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT} or (s.op is Ops.AFTER and s.src[1].op is Ops.KERNEL):
       if x in ctx.range_map: new_src = new_src.index(*ctx.range_map[x][0])
     elif s in ctx.realize_map:
-      new_src = UOp(Ops.BUFFERIZE, s.dtype, src=(new_src,)+tuple(ctx.range_map[s][1]), arg=BufferizeOpts(device=s.device), tag=s.tag)
-      if x in ctx.range_map: new_src = new_src.index(*ctx.range_map[x][0])
+      realized_ranges = ctx.realize_map[s]
+      assert isinstance(realized_ranges, list), "realize map must contain range list"
+      closed_ranges = tuple([r for i,r in enumerate(ctx.range_map[s][1]) if i in realized_ranges])
+      # None in the device assigns it a number later
+      opts = BufferizeOpts(device=s.device) if len(ctx.range_map[s][1]) == len(realized_ranges) else BufferizeOpts(None, AddrSpace.LOCAL)
+      new_src = UOp(Ops.BUFFERIZE, s.dtype, src=(new_src,)+closed_ranges, arg=opts, tag=s.tag if opts.addrspace == AddrSpace.GLOBAL else None)
+      if x in ctx.range_map: new_src = new_src.index(*[r for i,r in enumerate(ctx.range_map[x][0]) if i in realized_ranges])
     new_srcs.append(new_src)
   # NOTE: do we need this?
   return x.replace(src=tns) if x.src != (tns:=tuple(new_srcs)) else None
@@ -132,26 +139,24 @@ def apply_movement_op(op:Ops, in_shape:tuple[sint,...], arg:tuple, rngs:tuple[UO
     case _: raise RuntimeError(f"{op} is not a MovementOp")
   return rngs
 
-@cpu_profile(TracingKey("run_rangeify"), "TINY")
+@profile_matches
 def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
+  if debug: print("**************************")
   rctx = IndexingContext()
 
   # get ops to realize
-  graph_rewrite(tsink, pm_generate_realize_map, ctx=rctx.realize_map, name="Input Graph")
+  graph_rewrite(tsink, pm_generate_realize_map, ctx=rctx.realize_map, name="get realize")
 
   # get the traversal order
-  with cpu_profile(TracingKey("reverse toposort"), "TINY"):
+  with cpu_profile("reverse toposort", "TINY"):
     tsink_reverse_toposort = tsink.reverse_toposort(consumer_map:=tsink.get_consumer_map())
 
   # explicit rangeify
-  ending_ranges: dict[UOp, bool] = {}
+  ending_ranges: dict[UOp, list[UOp]] = {}
   for x in tsink_reverse_toposort:
     if x.op in {Ops.DEVICE, Ops.UNIQUE}: continue
     if x.dtype.scalar() == dtypes.index: continue  # TODO: why do I need this?
-    ending_ranges[x] = any(ending_ranges[u] for u in consumer_map[x])
-
-    # if this element has weight and it's ending a range, we (force) realize it
-    if ending_ranges[x] and x.op in GroupOp.Elementwise.union({Ops.REDUCE_AXIS}): rctx.realize_map[x] = None
+    ending_ranges[x] = sum([ending_ranges.get(u, []) for u in consumer_map[x]], [])
 
     # *** the ranges on the output are
     #  1. new if this op is realized
@@ -161,9 +166,12 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
     consumer_rngs = [rctx.range_map[c][0] for c in consumer_map[x] if c in rctx.range_map]
     if x in rctx.realize_map:
       # if this is in the realize_map, we create new ranges (at the output)
-      out_rngs = tuple(rctx.new_range(s) if not isinstance(s, UOp) or s.op is not Ops.RANGE else s for s in x.shape)
+      out_rngs = tuple(rctx.new_range(s) for s in x.shape)
       # all ranges are ended now
-      ending_ranges[x] = False
+      ending_ranges[x] = []
+      # mark all ranges as ended
+      assert rctx.realize_map[x] is None
+      rctx.realize_map[x] = list(range(len(x.shape)))
     elif x.op in {Ops.MSTACK, Ops.MSELECT}:
       # treat MSTACK/MSELECT like SINK
       continue
@@ -175,29 +183,41 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
       out_rngs = consumer_rngs[0]
     elif len(consumer_rngs) > 1:
       # if this has two consumers, we have to merge the ranges and might create new ones
-      all_rngs = list(zip(*consumer_rngs))
+      all_rngs: list[tuple[UOp, ...]] = list(zip(*consumer_rngs))
       rngs_valids = []
       for valid_rngs in all_rngs:
         local_rngs, valids = zip(*[(r.get_idx(), r.get_valid()) for r in valid_rngs])
-        # if a range has a 1 src, it's the same as UOp.const(dtypes.index, 0)
-        same_rngs = [x if x.op is not Ops.RANGE or resolve(x.src[0] != 1) else UOp.const(dtypes.index, 0) for x in local_rngs]
-        rngs_valids.append((local_rngs, valids, all_same(same_rngs)))
+        rngs_valids.append((local_rngs, valids))
 
       # TODO: in RANGEIFY > 1 all_all_same isn't required
-      all_all_same = all(same_rngs for _,_,same_rngs in rngs_valids)
+      all_all_same = all(all_same(local_rngs) for local_rngs,_ in rngs_valids)
       _out_rngs = []
-      for i,(local_rngs,valids,same_rngs) in enumerate(rngs_valids):
+      _realize_axis = []
+      for i,(local_rngs,valids) in enumerate(rngs_valids):
         # we compare the ranges without their valids
-        if all_all_same:
+        if all_all_same or (PCONTIG and all_same(local_rngs)):
           # the new valid is the OR of all the children valids
           minimum_valid = functools.reduce(operator.or_, valids, UOp.const(dtypes.bool, False))
           _out_rngs.append(graph_rewrite(minimum_valid.where(local_rngs[0], UOp.invalid()), symbolic, name="minimum_valid"))
         else:
           _out_rngs.append(rctx.new_range(x.shape[i]))
+          _realize_axis.append(i)
       out_rngs = tuple(_out_rngs)
 
-      # we have to realize here if there's new ranges
-      if not all_all_same: rctx.realize_map[x] = None
+      # we have to (partially) realize here if there's new ranges
+      if len(_realize_axis): rctx.realize_map[x] = _realize_axis
+
+    # if this element is a reduce and there's ended ranges, we might have to end some other ranges
+    if len(ending_ranges[x]) and x.op in GroupOp.Elementwise.union({Ops.REDUCE_AXIS}):
+      _realize_axis = rctx.realize_map.get(x, []) or []
+      for i,r in enumerate(out_rngs):
+        if i in _realize_axis: continue
+        if not (PCONTIG > 1) or any(any(rr.arg > e.arg for e in ending_ranges[x]) for rr in r.ranges):
+          _realize_axis.append(i)
+      ending_ranges[x] = []
+      if len(_realize_axis):
+        rctx.realize_map[x] = _realize_axis
+        out_rngs = tuple([(rctx.new_range(x.shape[i]) if i in _realize_axis else r) for i,r in enumerate(out_rngs)])
 
     # TODO: some ops don't have shape, enable this after the `.st` property is removed
     #assert len(out_rngs) == len(x.shape), \
@@ -213,18 +233,48 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
     # apply movement ops
     if x.op in GroupOp.Movement: rngs = apply_movement_op(x.op, x.src[0].shape, x.marg, rngs)
     # if the EXPAND is used to inject a range, we don't mark it as ending_ranges. otherwise we do.
-    if x.op is Ops.EXPAND and all(isinstance(y, int) or y.op is not Ops.RANGE for y in x.shape): ending_ranges[x] = True
+    # NOTE: this doesn't actually always end a range, but this is why convs are realized, so for now we need it
+    if x.op is Ops.EXPAND and all(isinstance(y, int) or y.op is not Ops.RANGE for y in x.shape):
+      ending_ranges[x] = list(UOp.sink(*[ro for ri, ro in zip(rngs, out_rngs) if ri is not ro]).ranges.keys())
 
     # REDUCE_AXIS creates ranges for the axes it is reducing
     if x.op is Ops.REDUCE_AXIS:
       rngs = tuple(rctx.new_range(s, axistype=AxisType.REDUCE) if i in x.arg[1] else r for i,(r,s) in enumerate(zip(rngs, x.src[0].shape)))
 
     if debug:
-      print("***" if x in rctx.realize_map else "   ", len(consumer_map[x]), f"{str(x.op):20s}",
-            UOp.sink().index(*rngs).render(), " -> ", UOp.sink().index(*out_rngs).render())
+      realized_ranges = rctx.realize_map.get(x, None)
+      disp = []
+      for i, (ri, ro) in enumerate(zip([r.render() for r in rngs], [r.render() for r in out_rngs])):
+        rng = f"{ri}" if ri == ro else f"{ri} -> {ro}"
+        if realized_ranges is not None and i in realized_ranges: rng = colored(rng, "yellow")
+        disp.append("["+rng+"]")
+      print("***" if x in rctx.realize_map else "   ", len(consumer_map[x]), f"{str(x.op):20s}", ''.join(disp))
 
     # assign to the range map. rngs are the input ranges, out_rngs are the output ranges, from the x op.
     rctx.range_map[x] = (rngs, out_rngs)
 
+  if MULTIOUTPUT:
+    # second forward pass to fuse children
+    replaced_ranges = {}
+    for x in tsink.toposort():
+      if x not in rctx.realize_map: continue
+      out_rngs = rctx.range_map[x][1]
+      _realize_axis = cast(list[int], rctx.realize_map[x])
+      consumers = [rctx.range_map[u][0] for u in consumer_map[x] if u in rctx.range_map]
+      if len(consumers) < 2: continue
+      assert all(len(out_rngs) == len(rr) for rr in consumers)
+      for i,c in enumerate(zip(*consumers)):
+        out_rng = out_rngs[i]
+        # check if they are all simple ranges
+        if not all(y.op is Ops.RANGE and y.vmax == out_rng.vmax for y in c): continue
+        for r in c: replaced_ranges[r] = out_rngs[i]
+        _realize_axis.remove(i)
+      if len(_realize_axis) == 0: del rctx.realize_map[x]
+      else: rctx.realize_map[x] = _realize_axis
+
+    # do all the replaces
+    for k,(v0,v1) in rctx.range_map.items():
+      rctx.range_map[k] = (tuple(x.substitute(replaced_ranges) for x in v0), tuple(x.substitute(replaced_ranges) for x in v1))
+
   tsink = graph_rewrite(tsink, pm_apply_rangeify, ctx=rctx, bottom_up=True, name="apply rangeify")
   return tsink, rctx

tinygrad/schedule/multi.py

@@ -1,7 +1,7 @@
 from typing import cast
 import functools, itertools, operator
 from tinygrad.helpers import all_same, all_int, prod, DEBUG, RING, getenv
-from tinygrad.uop.ops import Ops, UOp, sint, PatternMatcher, UPat, GroupOp, track_rewrites, graph_rewrite_map
+from tinygrad.uop.ops import Ops, UOp, sint, PatternMatcher, UPat, GroupOp, track_rewrites, graph_rewrite_map, graph_rewrite
 from tinygrad.device import Device
 
 # *** allreduce implementation ***
@@ -219,4 +219,8 @@ multi_pm = PatternMatcher([
 ])+replace_allreduce
 
 @track_rewrites()
-def get_multi_map(big_sink:UOp) -> dict[UOp, UOp]: return graph_rewrite_map(big_sink, multi_pm, name="multi_pm")
+def get_multi_map(big_sink:UOp) -> dict[UOp, UOp]:
+  if getenv("VIZ"): graph_rewrite(big_sink, PatternMatcher([]), name="View Multi AST")
+  ret = graph_rewrite_map(big_sink, multi_pm, name="multi_pm")
+  if getenv("VIZ"): graph_rewrite(ret[big_sink], PatternMatcher([]), name="View Post Multi AST")
+  return ret

tinygrad/schedule/rangeify.py

@@ -2,11 +2,13 @@ from typing import cast
 from dataclasses import dataclass, field
 from tinygrad.dtype import dtypes, PtrDType, ImageDType, AddrSpace
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, _substitute, ssimplify, KernelInfo
-from tinygrad.uop.ops import track_rewrites, graph_rewrite, identity_element, sint, AxisType
-from tinygrad.uop.symbolic import symbolic_simple
-from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, flatten, dedup, all_int, DEBUG, SPLIT_REDUCEOP, Metadata
+from tinygrad.uop.ops import track_rewrites, graph_rewrite, identity_element, sint, AxisType, BottomUpGate
+from tinygrad.uop.symbolic import symbolic_flat
+from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, flatten, dedup, all_int, DEBUG, SPLIT_REDUCEOP, \
+  Metadata, DEBUG_RANGEIFY, MULTIOUTPUT
 from tinygrad.codegen.simplify import pm_flatten_range, pm_reduce_unparented
 from tinygrad.codegen.opt import Opt
+from tinygrad.codegen.late.control_flow import pm_merge_ends
 from tinygrad.schedule.indexing import run_rangeify, BufferizeOpts, ALWAYS_CONTIGUOUS, IndexingContext, apply_movement_op
 
 # creation can recurse a lot
@@ -136,6 +138,9 @@ def cleanup_dead_axes(b:UOp):
     # move the tag to the expand. NOTE: this expand tag might not survive
     return b.replace(src=b.src[0:1]+tuple(new_rng), tag=None).reshape(tuple(reshape)).expand(b.shape).replace(tag=b.tag)
 
+def gate_substitute(ctx, b:UOp) -> None:
+  if not any(r in b.ranges for r in ctx.keys()): raise BottomUpGate()
+pm_gate_substitute = PatternMatcher([(UPat(GroupOp.All, name="b"), gate_substitute)], compiled=False)
 # if a buffer is being stored just for permutes or something, remove it
 # we want to reexpress the indexes of idx2 in terms of the implied b1
 def remove_bufferize(src:UOp, buf:UOp, idx:UOp):
@@ -178,8 +183,7 @@ def remove_bufferize(src:UOp, buf:UOp, idx:UOp):
   # if it makes it here, the bufferize is removed
   # this is the ranges replaced
   # NOTE: if buf src is a const, we don't replace it
-  replaces = flatten([(k,v) for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST])
-  return UOp(Ops.SUBSTITUTE, dtype=src.dtype, src=(src, UOp(Ops.NOOP, src=tuple(replaces[0::2])), UOp(Ops.NOOP, src=tuple(replaces[1::2]))))
+  return src.substitute({k:v for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST}, extra_pm=pm_gate_substitute)
 
 def pre_bufferize(b:UOp, x:UOp, copy:UOp):
   nb = b.replace(src=(b.src[0].contiguous(),)+b.src[1:])
@@ -215,7 +219,9 @@ def late_buffer_view(t:UOp, b:UOp):
 
     # walk up for the INDEX
     x = t
-    while not any(u.op is Ops.INDEX for u in x.src): x = x.src[0]
+    while not any(u.op is Ops.INDEX for u in x.src):
+      assert x.op not in GroupOp.Elementwise, "can't buffer view elementwise"
+      x = x.src[0]
     x = next(u for u in x.src if u.op is Ops.INDEX)
 
     if len(shape) == 0: offset = x.src[1].arg
@@ -237,7 +243,7 @@ def limit_bufs(ctx:IndexingContext, root:UOp):
   bufs: set[UOp] = set()
   def gate_input(u:UOp):
     # TODO: add cache to fix n^2
-    if is_load:=(u.op in {Ops.BUFFERIZE, Ops.ASSIGN, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_VAR}): bufs.add(u)
+    if is_load:=(u.op in {Ops.BUFFERIZE, Ops.AFTER, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_VAR}): bufs.add(u)
     return not is_load
   root.toposort(gate=gate_input)
 
@@ -272,7 +278,8 @@ def bufferize_to_store(x:UOp):
     assert assign_target.op is Ops.INDEX, f"{assign_target.op} is not index"
     # in assign, this is the buffer size, not the bufferize size
     # TODO: assign_mops here
-    ret = assign_target.replace(dtype=sdtype).store(assign_src, *rngs, dtype=x.dtype)
+    do_store = assign_target.replace(dtype=sdtype).store(assign_src).replace(tag=x.tag).end(ends=[x for x in rngs if x.op is Ops.RANGE])
+    ret = assign_target.src[0].after(do_store)
     mops = []
     walk = assign_mops
     while walk is not assign_mops.base:
@@ -284,8 +291,8 @@ def bufferize_to_store(x:UOp):
   # NOTE: the DEFINE_LOCAL needs to be disambiguated here
   if sdtype.addrspace == AddrSpace.GLOBAL:
     buf = UOp.new_buffer(x.arg.device, size, x.dtype)
-    ret = buf.reshape(shape).index(*rngs, dtype=sdtype).store(x.src[0], *rngs, dtype=x.dtype)
-    ret = ret.forced_reshape(shape)
+    do_store = buf.reshape(shape).index(*rngs, dtype=sdtype).store(x.src[0]).replace(tag=x.tag).end(ends=[x for x in rngs if x.op is Ops.RANGE])
+    ret = buf.after(do_store).forced_reshape(shape)
     # TODO: is this right? what if it's offset
     if any(r.op is Ops.RANGE and r.src[0].op is not Ops.CONST for r in rngs):
       sym_shape = tuple([ssimplify(r.src[0]) if r.op is not Ops.CONST else 1 for r in rngs])
@@ -296,9 +303,8 @@ def bufferize_to_store(x:UOp):
   tag = x.arg.device
   if tag is None: tag = UOp.unique().arg # TODO: hack
   buf = UOp(Ops.DEFINE_LOCAL, sdtype, arg=tag)
-  # store has the other dtype here
-  # TODO: how is this unified?
-  return buf.reshape(shape).index(*rngs, dtype=sdtype).store(x.src[0], *rngs, dtype=sdtype).reshape(shape)
+  do_store = buf.reshape(shape).index(*rngs, dtype=sdtype).store(x.src[0]).end(ends=[x for x in rngs if x.op is Ops.RANGE])
+  return buf.after(do_store).reshape(shape)
 
 pm_add_buffers = pm_mops+to_bufferview+PatternMatcher([
   (UPat(Ops.BUFFERIZE, name="x"), bufferize_to_store),
@@ -330,12 +336,13 @@ def unbind_kernel(ctx:LocalAddBufferContext, b:UOp):
   ctx.vars[b] = None
   return b.src[0]
 
-def handle_assign(ctx:LocalAddBufferContext, assign:UOp):
-  buf = assign.as_buf()
+def handle_after(ctx:LocalAddBufferContext, after:UOp):
+  if isinstance(after.dtype, PtrDType) and after.ptrdtype.addrspace == AddrSpace.LOCAL: return None
+  buf = after.as_buf()
   # HACK to put the buffer in the MAP instead of MSTACK/MSELECT
   if buf.op in {Ops.MSTACK, Ops.MSELECT}: buf = buf.src[0]
   assert buf not in ctx.map
-  ctx.map[buf] = assign
+  ctx.map[buf] = after
   return buf
 
 def renumber_range(ctx:LocalAddBufferContext, r:UOp):
@@ -345,7 +352,7 @@ def renumber_range(ctx:LocalAddBufferContext, r:UOp):
   return ret
 
 def find_bufs(x:UOp):
-  idxs = [s for s in x.toposort(gate=lambda x: x.op is not Ops.ASSIGN) if s.op is Ops.INDEX]
+  idxs = [s for s in x.toposort(gate=lambda x: x.op is not Ops.AFTER) if s.op is Ops.INDEX]
   read_from: dict[UOp, Ops] = {}
   if any((buf:=idx.as_buf()).op is Ops.BUFFER and read_from.setdefault(buf, op:=idx.src[0].op) is not op for idx in idxs):
     raise RuntimeError(f"cycle detected while indexing {buf}")
@@ -354,7 +361,7 @@ to_define_global = PatternMatcher([
   (UPat(Ops.STORE, name="x"), find_bufs),
   (UPat(Ops.BUFFER, name="buf"), debuf),
   (UPat(Ops.BIND, name="b"), unbind_kernel),
-  (UPat((Ops.ASSIGN, Ops.MSTACK, Ops.MSELECT), name="assign"), handle_assign),
+  (UPat((Ops.MSTACK, Ops.MSELECT, Ops.AFTER), name="after"), handle_after),
 
   # HACK in case any CONSTs were replaced
   # this is only needed if you are using symbolic
@@ -383,16 +390,9 @@ rangeify_codegen = PatternMatcher([
 
   # add loads to non ptr indexes
   # TODO: this can be moved into codegen?
-  (UPat((Ops.DEFINE_GLOBAL, Ops.STORE), name="dg").f(Ops.INDEX, name="idx", allow_any_len=True),
-   lambda dg,idx: None if isinstance(idx.dtype, (PtrDType, ImageDType)) else idx.replace(dtype=dg.dtype, arg=None).load()),
-
-  # TODO: this can be moved into codegen
-  (UPat(Ops.STORE, name="store").f(Ops.INDEX, allow_any_len=True, name="idx").f(Ops.LOAD),
-    lambda store,idx: idx.replace(src=(store.as_buf(),)+idx.src[1:]).load(store if idx.dtype.addrspace != AddrSpace.LOCAL else store.barrier())),
-
-  # TODO: hack for group for reduce
-  (UPat(Ops.IF, src=(UPat.var("gate"), UPat(Ops.LOAD, src=(UPat.var("src"), UPat.var("barrier"))),)),
-   lambda src, barrier, gate: src.load(UOp(Ops.IF, src=(gate, barrier)))),
+  (UPat.any(UPat(Ops.DEFINE_GLOBAL, name="dg"), UPat(Ops.DEFINE_LOCAL).f(Ops.AFTER, allow_any_len=True, name="dg"))
+   .f(Ops.INDEX, name="idx", allow_any_len=True),
+    lambda dg,idx: None if isinstance(idx.dtype, (PtrDType, ImageDType)) else idx.replace(dtype=dg.dtype, arg=None).load()),
 ])
 
 def remove_metadata_tags(ctx:LocalAddBufferContext, x:UOp):
@@ -413,9 +413,8 @@ class Kernel:
     ast_rep = f"SINK{tuple(s.op for s in self.ast.src)}" if self.ast.op is Ops.SINK else repr(self.ast.op)
     return f"<Kernel {len(list(self.ast.toposort()))} {ast_rep} {self.metadata}>"
 
-def split_store(ctx:list[UOp], x:UOp):
+def split_store(ctx:list[UOp], x:UOp) -> UOp|None:
   if len(x.ranges): return None
-  if x.src[0].ptrdtype.addrspace is AddrSpace.LOCAL: return None
 
   # local kernel rewrite
   lctx = LocalAddBufferContext()
@@ -425,16 +424,22 @@ def split_store(ctx:list[UOp], x:UOp):
   metadatas = [ctx[y].metadata for y in lctx.parent_tags]
 
   # NOTE: the hack for COPY is here
-  ret = ret.sink(arg=KernelInfo(opts_to_apply=lctx.opts) if lctx.opts is not None else None) \
-    if ret.src[1].op not in {Ops.COPY, Ops.BUFFER_VIEW} else ret.src[1]
+  for u in ret.toposort():
+    # TODO: this can be wrong if there's multiple of these
+    if u.op in {Ops.COPY, Ops.BUFFER_VIEW}:
+      ret = u
+      break
+  else:
+    ret = ret.sink(arg=KernelInfo(opts_to_apply=lctx.opts) if lctx.opts is not None else None)
+
   kernel_arg = Kernel(ret,tuple(dedup(flatten([x for x in metadatas if x is not None])))[::-1])
   kernel = UOp(Ops.KERNEL, src=tuple(lctx.map.values())+tuple(lctx.vars.keys()), arg=kernel_arg)
   if ret.op is Ops.SINK and not all_same([x.device for x in kernel.src if x.op is not Ops.BIND]):
     raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in kernel.src)}")
-  return x.as_buf().assign(kernel)
+  return kernel
 
 split_kernels = PatternMatcher([
-  (UPat(Ops.STORE, name="x"), split_store),
+  (UPat((Ops.STORE, Ops.END), name="x"), split_store),
 ])
 
 def tag_uop(ctx:list[UOp], x:UOp):
@@ -465,40 +470,20 @@ replace_contiguous = PatternMatcher([
   (UPat(GroupOp.ALU, name="alu"), lambda ctx,alu: alu.replace(src=new_src) if (new_src:=tuple(ctx.get(s, s) for s in alu.src)) != alu.src else None),
 ])
 
-def do_sub_recurse(s:UOp):
-  x,keys,values = s.src[0], s.src[1].src, s.src[2].src
-  # SUBSTITUTE applied to SUBSTITUTE runs the child SUB on the parents. though this is probably wrong in the generic case
-  if x.op is Ops.SUBSTITUTE:
-    sub_k = UOp(Ops.SUBSTITUTE, src=(x.src[1],)+s.src[1:])
-    sub_v = UOp(Ops.SUBSTITUTE, src=(x.src[2],)+s.src[1:])
-    return UOp(Ops.SUBSTITUTE, dtype=x.dtype, src=(x.src[0], sub_k, sub_v))
-  # here we actually do the SUBSTITUTE
-  if x in keys: return values[keys.index(x)]
-  # we filter any keys where the ranges don't overlap. this keeps the algorithm O(output graph size)
-  x_ranges = x.ranges
-  new_kv = {k:v for k,v in zip(keys,values) if any(r in x_ranges for r in k.ranges)}
-  # if there's no SUBSTITUTEs left, we can just return x
-  if len(new_kv) == 0: return x
-  # then we add SUBSTITUTE to all parents
-  uop_keys, uop_values = UOp(Ops.NOOP, src=tuple(new_kv.keys())), UOp(Ops.NOOP, src=tuple(new_kv.values()))
-  return x.replace(src=tuple([UOp(Ops.SUBSTITUTE, dtype=y.dtype, src=(y,uop_keys,uop_values)) for y in x.src]))
-pm_substitute_recurse = PatternMatcher([(UPat(Ops.SUBSTITUTE, src=(UPat(), UPat(Ops.NOOP), UPat(Ops.NOOP)), name="s"), do_sub_recurse)])
-
 @track_rewrites(lambda _,ret: f"Schedule {pluralize('Kernel', len([u for u in UOp.sink(*ret.values()).toposort() if u.op is Ops.KERNEL]))}", True)
 def get_rangeify_map(sink:UOp) -> dict[UOp, UOp]:
+  if getenv("VIZ"): graph_rewrite(sink, PatternMatcher([]), name="View Input Graph")
   uop_list: list[UOp] = []
   tsink = graph_rewrite(sink, add_tags, ctx=uop_list, bottom_up=True, name="number the uops")
 
   tsink = graph_rewrite(tsink, earliest_rewrites+replace_contiguous, ctx={}, name="earliest rewrites")
 
   # convert movement ops to ranges
-  tsink, rctx = run_rangeify(tsink, getenv("DEBUG_RANGEIFY", 0))
+  tsink, rctx = run_rangeify(tsink, DEBUG_RANGEIFY)
 
   # NOTE: sym (vs symbolic_simple) breaks things here because ranges with len 1 aren't handled right
-  tsink = graph_rewrite(tsink, symbolic_simple+pm_reduce_unparented, name="symbolic")  # this supports const folding
+  tsink = graph_rewrite(tsink, symbolic_flat+pm_reduce_unparented, name="symbolic")  # this supports const folding
   tsink = graph_rewrite(tsink, pm_cleanups, bottom_up=True, name="remove costly buffers")
-  # TODO: can you substitute and remove costly buffers at the same time?
-  tsink = graph_rewrite(tsink, pm_substitute_recurse, bottom_up=True, name="run substitutes")
   tsink = graph_rewrite(tsink, pm_limit_bufs, ctx=rctx, name="limit buffers")
 
   # rebuild the sink with all the BUFFERIZEs with tags, this is what's ending up in the tensor graph
@@ -511,19 +496,20 @@ def get_rangeify_map(sink:UOp) -> dict[UOp, UOp]:
 
   # bufferize -> store
   tsink = graph_rewrite(tsink, pm_add_buffers, bottom_up=True, name="bufferize to store")
+  if MULTIOUTPUT: tsink = graph_rewrite(tsink, pm_merge_ends, name="merge end ranges")
   tsink = graph_rewrite(tsink, split_kernels, ctx=uop_list, name="split kernels")
 
   # if a kernel depends on a buffer, and that buffer is later assigned to, make the assign depend on the kernel's assign
   kernel_assign: dict[UOp, UOp] = {}
   assign_rep: dict[UOp, UOp] = {}
   for u in tsink.toposort():
-    if u.op is not Ops.ASSIGN: continue
+    if u.op is not Ops.AFTER: continue
     kernel_assign[u.buf_uop] = u
     for s in u.src[1].src:
       # TODO: this is probably broken for MSELECT/MSTACK
       if s.op is not Ops.BUFFER or s is u.buf_uop or (a:=kernel_assign.get(s)) is None: continue
-      if any(x.op is Ops.ASSIGN and x.buf_uop is s for x in u.toposort()):
-        raise RuntimeError(f"cycle detected in graph, kernel for {u.buf_uop} must either depend on ASSIGN or BUFFER")
+      if any(x.op is Ops.AFTER and x.buf_uop is s for x in u.toposort()):
+        raise RuntimeError(f"cycle detected in graph, kernel for {u.buf_uop} must either depend on AFTER or BUFFER")
       assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,))
   if assign_rep: tsink = graph_rewrite(tsink, _substitute, ctx=assign_rep, bottom_up=True, name="fix_assign")
 

tinygrad/tensor.py

@@ -249,9 +249,9 @@ class Tensor(MathTrait):
     self.kernelize(*lst)
     sink = UOp.sink(*[x.uop for x in (self,)+lst])
 
-    # remove all ASSIGNs, after scheduling, the tensors are just buffers
-    remove_assign_map = {u:u.buf_uop for u in sink.toposort() if u.op is Ops.ASSIGN}
-    _apply_map_to_tensors(remove_assign_map, name="Remove Assigns")
+    # remove all AFTERs, after scheduling, the tensors are just buffers
+    remove_assign_map = {u:u.buf_uop for u in sink.toposort() if u.op is Ops.AFTER}
+    _apply_map_to_tensors(remove_assign_map, name="Remove After")
 
     # create the schedule
     schedule, var_vals = create_schedule_with_vars(sink)
@@ -294,8 +294,7 @@ class Tensor(MathTrait):
     assert self.shape == x.shape, f"assign shape mismatch {self.shape} != {x.shape}"
     assert self.device == x.device, f"assign device mismatch {self.device} != {x.device}"
     assert self.dtype == x.dtype, f"assign dtype mismatch {self.dtype} != {x.dtype}"
-    self.uop = self.uop.assign(x.uop)
-    return self
+    return self.replace(self._apply_uop(UOp.assign, x))
 
   def detach(self) -> Tensor:
     """

tinygrad/uop/__init__.py

@@ -12,19 +12,18 @@ class Ops(FastEnum):
   NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto(); PRECAST = auto(); REWRITE_ERROR = auto()  # noqa: E702
   SENTINEL = auto()
 
+  # AFTER passes src[0] through and promises in the toposort that any consumers of the AFTER run after src[1:]
+  AFTER = auto()
+
   # buffer ops
   COPY = auto(); BUFFER = auto(); BUFFER_VIEW = auto(); MSELECT = auto(); MSTACK = auto() # noqa: E702
 
   # create buffer
   BUFFERIZE = auto()
-  SUBSTITUTE = auto()
 
   # ops that adjust the behavior of the scheduler
   CONTIGUOUS = auto(); CONTIGUOUS_BACKWARD = auto(); DETACH = auto(); FUSE = auto() # noqa: E702
 
-  # blocks in linearizer (only used there)
-  BLOCK = auto(); BLOCKSTART = auto(); BLOCKEND = auto(); BLOCKFINAL = auto() # noqa: E702
-
   # movement ops! these only exist in the tensor graph
   RESHAPE = auto(); PERMUTE = auto(); EXPAND = auto(); PAD = auto(); SHRINK = auto(); FLIP = auto() # noqa: E702
   MULTI = auto()  # MULTI is really a movement op
@@ -67,7 +66,7 @@ class Ops(FastEnum):
   WHERE = auto(); MULACC = auto() # noqa: E702
 
   # control flow ops
-  BARRIER = auto(); RANGE = auto(); IF = auto(); ENDRANGE = auto(); ENDIF = auto() # noqa: E702
+  BARRIER = auto(); RANGE = auto(); IF = auto(); END = auto(); ENDIF = auto() # noqa: E702
 
   # consts. VCONST is a vectorized const
   VCONST = auto(); CONST = auto() # noqa: E702
@@ -91,7 +90,6 @@ class GroupOp:
   Movement = {Ops.RESHAPE, Ops.EXPAND, Ops.PERMUTE, Ops.PAD, Ops.SHRINK, Ops.FLIP}
 
   Buffer = {Ops.LOAD, Ops.STORE, Ops.CONST, Ops.DEFINE_VAR}
-  Block = {Ops.BLOCK, Ops.BLOCKEND, Ops.BLOCKSTART}
 
   # BinaryOps that can be flipped
   Commutative = {Ops.ADD, Ops.MUL, Ops.MAX, Ops.CMPNE, Ops.CMPEQ, Ops.XOR, Ops.AND, Ops.OR}

tinygrad/uop/ops.py

@@ -169,7 +169,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
 
   @property
   def ptrdtype(self) -> PtrDType:
-    if not isinstance(self.dtype, PtrDType): raise RuntimeError("ptrdtype called on UOp without PtrDType")
+    if not isinstance(self.dtype, PtrDType): raise RuntimeError(f"ptrdtype called on UOp with type {self.dtype}")
     return self.dtype
 
   # *** uop shape stuff ***
@@ -179,7 +179,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     match self.op:
       # late ops don't have shape
       case Ops.UNIQUE | Ops.DEVICE | Ops.RANGE | Ops.INDEX | Ops.LOAD | Ops.IF | Ops.BARRIER | Ops.CUSTOM | Ops.CUSTOMI | \
-           Ops.VECTORIZE | Ops.VCONST | Ops.SUBSTITUTE | Ops.GEP | Ops.SPECIAL | Ops.UNROLL | Ops.PRECAST:
+           Ops.VECTORIZE | Ops.VCONST | Ops.GEP | Ops.SPECIAL | Ops.UNROLL | Ops.PRECAST:
         return None
 
       # some ops init the shape
@@ -190,7 +190,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       case Ops.DEFINE_GLOBAL | Ops.DEFINE_LOCAL | Ops.DEFINE_REG: return (self.ptrdtype.size,)
 
       # passthrough ops
-      case Ops.REDUCE | Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.FUSE: return self.src[0]._shape
+      case Ops.REDUCE | Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.FUSE | Ops.AFTER | Ops.END:
+        return self.src[0]._shape
 
       # ops with custom handling
       case Ops.KERNEL: return self.arg.ast._shape
@@ -275,6 +276,10 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       for s in self.src[:range_start[self.op]]: ret.update(s.ranges)
       for s in UOp.sink(*self.src[range_start[self.op]:]).ranges:
         if s in ret: del ret[s]
+    elif self.op is Ops.END:
+      for s in self.src[self.arg:]: ret.update(s.ranges)
+      for s in UOp.sink(*self.src[:self.arg]).ranges:
+        if s in ret: del ret[s]
     else:
       for s in self.src: ret.update(s.ranges)
     return ret
@@ -284,6 +289,13 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.RANGE: return {self:None}
     return self._ranges
 
+  @functools.cached_property
+  def ended_ranges(self):
+    match self.op:
+      case Ops.REDUCE: return self.src[1:]
+      case Ops.END: return self.src[:self.arg]
+      case _: raise RuntimeError(f"{self.op} doesn't end ranges")
+
   # *** uop evaluation ***
 
   def simplify(self, tracked=False, full_symbolic=True):
@@ -324,7 +336,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def detach(self): return UOp(Ops.DETACH, self.dtype, (self,))
   def index(self, *srcs:UOp|None, **kwargs):
     return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
-  def __getitem__(self, idx): return self.index(idx)
+  def __getitem__(self, *idx): return self.index(*idx)
   def const_like(self, b:ConstLike):
     # constants can optionally have a DEVICE source
     return UOp.const(self.dtype, b, device=self._device, shape=self._shape)
@@ -349,6 +361,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
   def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
   def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, kwargs.pop("dtype", dtypes.void), (self,)+src, **kwargs)
+  def end(self, *src:UOp, ends:Sequence[UOp]):
+    if len(ends) == 0:
+      if len(src): return UOp(Ops.NOOP, src=(self,*src))
+      return self
+    return UOp(Ops.END, src=(*ends, self, *src), arg=len(ends))
+  def after(self, *src:UOp): return UOp(Ops.AFTER, self.dtype, (self,)+src)
   def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
   def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
   def alu(self, op, *src:UOp, **kwargs):
@@ -370,15 +388,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     return UOp(Ops.RANGE, dtype=dtypes.index, src=(sint_to_uop(end),), arg=arg)
   def r(self, op:Ops, axis:tuple[int, ...]):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
-    if len(axis) == 0: return self
-    # move any non reduce axis before the first reduce axis
-    move_early, rest = partition(range(axis[0], len(self.shape)), lambda i: i not in axis and resolve(self.shape[i] != 1))
-    permaxis = tuple(range(axis[0])) + tuple(move_early) + tuple(rest)
-    ret = self.permute(permaxis)
-    new_axis = tuple([x for x in range(axis[0]+len(move_early), len(self.shape)) if resolve(ret.shape[x] != 1)])
-    assert len(axis) == len(new_axis)
-    ret = UOp(Ops.REDUCE_AXIS, self.dtype, (ret,), (op, new_axis))
-    return ret.reshape(tuple([x if i not in axis else 1 for i,x in enumerate(self.shape)]))
+    return UOp(Ops.REDUCE_AXIS, self.dtype, (self,), (op, axis)) if len(axis) else self
   @staticmethod
   def invalid(count=1): return UOp(Ops.CONST, dtypes.index.vec(count), src=(), arg=Invalid)
   def valid(self, cond): return self if cond.op is Ops.WHERE and cond.arg else cond.where(self, UOp.invalid(self.dtype.count))
@@ -533,6 +543,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def _device(self) -> str|tuple[str, ...]|None:
     if self.op is Ops.DEVICE: return self.arg
     if self.op is Ops.BUFFERIZE: return self.arg.device
+    if self.op is Ops.AFTER: return self.src[0]._device
     if self.op is Ops.MSELECT:
       assert isinstance(self.src[0].device, tuple), "mselect must be on tuple device"
       return self.src[0].device[self.arg]
@@ -546,8 +557,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.BUFFER: return self
     if self.op is Ops.MSELECT: return self.src[0].buf_uop.mselect(self.arg)
     if self.op is Ops.MSTACK: return UOp(Ops.MSTACK, self.dtype, src=tuple(x.buf_uop for x in self.src))
-    assert self.op is Ops.ASSIGN, f"must be ASSIGN {self.op}"
-    return self.src[0].base
+    assert self.op is Ops.AFTER, f"must be AFTER {self.op}"
+    return self.src[0].buf_uop.base
 
   def as_buf(self) -> UOp:
     if self.op is Ops.MSELECT: return self.src[0].as_buf().mselect(self.arg)
@@ -660,6 +671,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       new_count.subtract(div_fac.split_uop(Ops.MUL))
       if const%div_const==0 and all(v>=0 for v in new_count.values()): return math.prod([*new_count.elements(), self.const_like(const//div_const)])
     return None # generic None if we aren't sure
+  def sum(self:UOp, *uops:UOp) -> UOp: return functools.reduce(operator.or_ if self.dtype is dtypes.bool else operator.add, uops, self)
+  def prod(self:UOp, *uops:UOp) -> UOp: return functools.reduce(operator.and_ if self.dtype is dtypes.bool else operator.mul, uops, self)
   @property
   def vmin(self) -> ConstType: return self._min_max[0]
   @property
@@ -816,6 +829,8 @@ class UPat(MathTrait):
   @staticmethod
   def any(*src): return UPatAny(src=src)
   def or_casted(self, name:str|None=None): return UPat.any(self if name is None else self.named(name), UPat(Ops.CAST, name=name, src=(self,)))
+  def or_after(self, name:str|None=None):
+    return UPat.any(self if name is None else self.named(name), UPat(Ops.AFTER, name=name, src=(self,), allow_any_len=True))
 
   @staticmethod
   @functools.cache
@@ -842,7 +857,6 @@ class UPat(MathTrait):
   def reduce(self, *src:UPat, **kwargs): return UPat(Ops.REDUCE, self.dtype, src=(self,)+src, **kwargs)
   def fuse(self): return self.alu(Ops.FUSE)
   def broadcast(self, **kwargs): return UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs)
-  def or_broadcasted(self, **kwargs): return UPat.any(self, self.broadcast(**kwargs))
   def contiguous(self, *args, **kwargs): return UPat(Ops.CONTIGUOUS, dtype=self.dtype, src=(self,)+args, **kwargs)
 
   def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))
@@ -936,7 +950,7 @@ class TrackedGraphRewrite:
   loc:tuple[str, int]                           # location that called graph_rewrite
   sink:int                                      # the sink input to graph_rewrite
   matches:list[tuple[int, int, tuple, float]]   # before/after UOp, UPat location and time
-  name:str|None                                 # optional name of the rewrite
+  name:str                                      # name of the rewrite
   depth:int                                     # depth if it's a subrewrite
   bottom_up:bool
 
@@ -981,19 +995,21 @@ def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=Fal
   return _decorator
 
 active_rewrites:list[TrackedGraphRewrite] = []
-def track_matches(func):
-  def _track_func(*args, **kwargs):
+def profile_matches(fxn:Callable):
+  def wrap(*args, **kwargs):
+    name = str(kwargs.get("name", None) or fxn.__name__)
+    assert args and isinstance(args[0], UOp), f"invalid match tracing inputs for {name} with {args}"
     if tracking:=(TRACK_MATCH_STATS >= 2):
       loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)
       depth = len(active_rewrites)
-      if not tracked_ctxs: add_trace_group(TracingKey(f"default {func.__name__}"))
-      tracked_ctxs[-1].append(ctx:=TrackedGraphRewrite(loc, args[0].trace_num, [], kwargs.get("name", None), depth, kwargs.get("bottom_up", False)))
+      if not tracked_ctxs: add_trace_group(TracingKey(f"default {fxn.__name__}"))
+      tracked_ctxs[-1].append(ctx:=TrackedGraphRewrite(loc, args[0].trace_num, [], name, depth, kwargs.get("bottom_up", False)))
       active_rewrites.append(ctx)
-    with cpu_profile(kwargs.get("name", "<unnamed>"), "TINY", display=tracking):
-      ret = func(*args, **kwargs)
+    with cpu_profile(name, "TINY", display=tracking):
+      ret = fxn(*args, **kwargs)
     if tracking: active_rewrites.pop()
     return ret
-  return _track_func
+  return wrap
 
 class TrackedPatternMatcher(PatternMatcher):
   def rewrite(self, uop:UOp, ctx=None) -> UOp|None:
@@ -1095,7 +1111,7 @@ class RewriteContext:
               new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
           except BottomUpGate:
             # if the bpm matching raised a gate, we are done with this node and dont continue down the srcs
-            self.replace[n] = new_n
+            self.replace[n] = unwrap(test_n)
             continue
         stack.append((n, 1, new_n))
         for x in reversed(new_n.src):
@@ -1133,12 +1149,12 @@ class RewriteContext:
           self.replace[n] = replaced_new_n
     return self.replace[root]
 
-@track_matches
+@profile_matches
 def graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None) -> UOp:
   rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx)
   return rewrite_ctx.unified_rewrite(sink)
 
-@track_matches
+@profile_matches
 def graph_rewrite_map(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None,
                       input_map:dict[UOp, UOp]|None=None, ) -> dict[UOp, UOp]:
   rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx)
@@ -1175,6 +1191,7 @@ pm_lower_index_dtype = PatternMatcher([
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast(), UPat.var("valid"))), lambda buf,idx,valid: buf.index(idx, valid)),
   (UPat((Ops.STORE, Ops.LOAD), src=(UPat(), UPat(), UPat().cast(dtypes.index)), allow_any_len=True, name="s"),
     lambda s: s.replace(src=s.src[:2]+tuple(u.src[0] for u in s.src[2:]))),
+  # TODO: this is only triggering if they are all casts, correct?
   (UPat((Ops.SINK, Ops.NOOP), src=UPat().cast(dtypes.index), name="n"), lambda n: n.replace(src=tuple(s.src[0] for s in n.src))),
 ])
 def _index_to_concrete_int(u:UOp): return graph_rewrite(u.sink(), pm_lower_index_dtype).src[0]

tinygrad/uop/spec.py

@@ -66,8 +66,8 @@ buffer_spec = PatternMatcher([
 ])
 
 assign_spec = PatternMatcher([
-  # KERNEL can attach to an ASSIGN to describe the compute required to realize a BUFFER
-  (UPat(Ops.KERNEL, src=UPat((Ops.BUFFER, Ops.BUFFER_VIEW, Ops.ASSIGN, Ops.MSELECT, Ops.MSTACK, Ops.BIND))), lambda: True),
+  # KERNEL can attach to an AFTER to describe the compute required to realize a BUFFER
+  (UPat(Ops.KERNEL, src=UPat((Ops.BUFFER, Ops.BUFFER_VIEW, Ops.AFTER, Ops.MSELECT, Ops.MSTACK, Ops.BIND))), lambda: True),
 
   # ASSIGN has a target and a value. It can also optionally depend on other assigns
   (UPat(Ops.ASSIGN, name="x"), lambda x: len(x.src) >= 2 and all(s.op is Ops.ASSIGN for s in x.src[2:])),
@@ -111,6 +111,9 @@ tensor_uop_spec = buffer_spec+assign_spec+PatternMatcher([
 
   # REDUCE with an outerworld range
   (UPat(Ops.REDUCE, src=(UPat(),), allow_any_len=True, name="x"), lambda x: all(y.dtype == dtypes.index for y in x.src[1:])),
+
+  # AFTER if things were kernelized
+  (UPat(Ops.AFTER, src=(UPat((Ops.BUFFER, Ops.AFTER)),), allow_any_len=True), lambda: True)
 ])
 
 # ***** uop type spec *****
@@ -156,12 +159,16 @@ spec = PatternMatcher([
   (UPat(Ops.DEFINE_REG, src=()), lambda: True),
   (UPat(Ops.DEFINE_VAR, name="x"), lambda x: isinstance(x.arg[1], int) and isinstance(x.arg[2], int)),
 
-  (UPat(Ops.RANGE, src=(UPat.var("x"),), name="rng"), lambda rng,x: rng.dtype == x.dtype and isinstance(rng.arg, tuple) and len(rng.arg) >= 2 and \
-     all(isinstance(ra, int) for ra in rng.arg[0:-1]) and isinstance(rng.arg[-1], AxisType)),
+  (UPat(Ops.RANGE, src=(UPat.var("x"),), allow_any_len=True, name="rng"), lambda rng,x:
+    rng.dtype == x.dtype and isinstance(rng.arg, tuple) and len(rng.arg) >= 2 and \
+      all(isinstance(ra, int) for ra in rng.arg[0:-1]) and isinstance(rng.arg[-1], AxisType)),
   (UPat(Ops.SPECIAL, src=(UPat.var("x"),), name="s"), lambda s,x: s.dtype == x.dtype == dtypes.int32 and isinstance(s.arg, str)),
 
   (UPat(Ops.CONST, src=(), name="x"), lambda x: type(x.arg) is type(dtypes.as_const(x.arg, x.dtype))),
 
+  # allow AFTER on buffers
+  (UPat(Ops.AFTER, src=(UPat(GroupOp.Defines),), allow_any_len=True), lambda: True),
+
   # **** new style load/store ****
 
   # make sure all index dtypes have been lowered
@@ -171,18 +178,14 @@ spec = PatternMatcher([
 
   # INDEX is used in new style load/store
   # INDEX takes a <buf, alu, gate?>
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat())), lambda: True),
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
-
-  # LOAD on STORE
-  (UPat(Ops.LOAD, src=(UPat(Ops.STORE),), allow_any_len=True), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines).or_after(), UPat())), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines).or_after(), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
 
   # LOAD takes a <bufidx, alt?, barrier?>
   (UPat(Ops.LOAD, src=(index_pat, UPat(Ops.IF, name="cond")), allow_any_len=True), lambda idx,cond: validate_index(idx,cond.src[0])),
   (UPat(Ops.LOAD, src=(index_pat,), allow_any_len=True), validate_index),
 
-  # STORE takes a <bufidx, val, gate?>
-  (UPat(Ops.STORE, src=(index_pat, UPat(name="val"), UPat(Ops.IF, name="gate")), allow_any_len=True), validate_store),
+  # STORE takes a <bufidx, val, ranges...>
   (UPat(Ops.STORE, src=(index_pat, UPat(name="val")), allow_any_len=True), validate_store),
 
   # most ALUs have all matching dtypes, except CMPLT, CMPNE, and WHERE
@@ -193,7 +196,7 @@ spec = PatternMatcher([
   (UPat((Ops.IDIV, Ops.MOD), name="x"), lambda x: None if dtypes.is_int(x.dtype) else False),
   (UPat(GroupOp.ALU, name="x"), lambda x: all(x.dtype.base == y.dtype.base for y in x.src)),
 
-  (UPat(Ops.ENDRANGE, dtype=dtypes.void, src=(UPat(Ops.RANGE),)), lambda: True),
+  (UPat(Ops.END, dtype=dtypes.void), lambda: True),
 
   # WMMA has a <a, b, acc>
   (UPat(Ops.WMMA, src=(UPat(), UPat(), UPat()), name="x"), lambda x: isinstance(x.arg, tuple) and len(x.arg) == 8),
@@ -201,9 +204,8 @@ spec = PatternMatcher([
   (UPat(Ops.UNROLL, name="x"), lambda x: x.src[0].dtype.count == prod(y[1] for y in x.arg)),
 
   # if has a <gate, barrier?>
-  (UPat(Ops.IF, dtype=dtypes.void, src=(UPat(),)), lambda: True),
-  (UPat(Ops.IF, dtype=dtypes.void, src=(UPat(), UPat(Ops.BARRIER))), lambda: True),
-  (UPat(Ops.ENDIF, dtype=dtypes.void, src=(UPat(Ops.IF),)), lambda: True),
+  (UPat(Ops.IF, dtype=dtypes.void, src=(UPat(),), allow_any_len=True), lambda: True),
+  (UPat(Ops.ENDIF, dtype=dtypes.void, src=(UPat(Ops.IF),), allow_any_len=True), lambda: True),
 
   (UPat(Ops.REDUCE_AXIS, name="x"), lambda x: isinstance(x.arg, tuple) and len(x.arg) >= 2 and x.arg[0] in {Ops.ADD, Ops.MUL, Ops.MAX}),
   (UPat(Ops.GEP, src=(UPat.var("src"),), name="gep"), lambda gep,src: gep.dtype == src.dtype.scalar()),
@@ -234,9 +236,6 @@ full_spec = PatternMatcher([
   # SENTINEL should never be in the graph
   (UPat(Ops.SENTINEL), lambda: False),
 
-  # allow any SUBSTITUTE
-  (UPat(Ops.SUBSTITUTE), lambda: True),
-
   # Invalid must have type Index
   (UPat(Ops.CONST, arg=Invalid, name="x"), lambda x: x.dtype.scalar() == dtypes.index),
   # where on index in rhs position is fine
@@ -269,7 +268,7 @@ full_spec = PatternMatcher([
   (UPat(Ops.INDEX, src=(UPat((Ops.VECTORIZE, Ops.CAST)), UPat())), lambda: True),
 
   # linearizer: outputs + intermediate KERNELs
-  (UPat((Ops.BLOCKSTART, Ops.BLOCK, Ops.BLOCKFINAL, Ops.BLOCKEND, Ops.KERNEL), dtype=dtypes.void), lambda: True),
+  (UPat(Ops.KERNEL, dtype=dtypes.void), lambda: True),
 
   # allow index dtype on a restricted set of UOps
   (UPat((Ops.ADD, Ops.MUL, Ops.MOD, Ops.IDIV, Ops.MAX, Ops.WHERE,
@@ -283,6 +282,8 @@ full_spec = PatternMatcher([
   (UPat(Ops.DEFINE_VAR), lambda: True),
   # reshape on STORE
   (UPat(Ops.RESHAPE, src=(UPat(Ops.STORE),)), lambda: True),
+  # allow any AFTER
+  (UPat(Ops.AFTER, src=(UPat(),), allow_any_len=True), lambda: True),
 ])+tensor_uop_spec+spec
 
 # ***** uop helpers *****

tinygrad/uop/symbolic.py

@@ -28,6 +28,7 @@ invalid_gate = UPat.var("cond").where(UPat.var("x"), invalid_pat)
 propagate_invalid = PatternMatcher([
   # this needs to be before symbolic so that 0*something_that_might_be_invalid doesnt become 0
   # propagate invalid, push it past children
+  (invalid_gate.cast(name="cast"), lambda i,x,cond,cast: x.cast(cast.dtype) if cast.dtype is not dtypes.index else None),
   *((invalid_gate.alu(op, UPat.var("y")).named("alu"), lambda cond,x,y,alu,i: cond.where(x.alu(alu.op,y), i))
     for op in GroupOp.Binary-GroupOp.Comparison),
   *((invalid_gate.alu(op, UPat.var("y")).named("alu"), lambda cond,x,y,alu,i: x.alu(alu.op,y)) for op in GroupOp.Comparison),
@@ -130,7 +131,7 @@ symbolic_simple = propagate_invalid + PatternMatcher([
 def lt_folding(x:UOp, c:int) -> UOp|None:
   p, np = partition(x.split_uop(Ops.ADD), lambda u: u.const_factor() == 1)
   if np and (d:=math.gcd(*[u.const_factor() for u in np], c)) > 1 and 0 <= sum(u.vmin for u in p) and sum(u.vmax for u in p) < d:
-    return cast(UOp, functools.reduce(operator.add, np).divides(d))<(c//d)
+    return cast(UOp, UOp.sum(*np).divides(d))<(c//d)
   return None
 
 def canonicalize_simplex(X:UOp) -> UOp|None:
@@ -144,7 +145,7 @@ def canonicalize_simplex(X:UOp) -> UOp|None:
       u = u.src[0]
     if not (u.op in GroupOp.Irreducible and u.vmin >= 0): return None
     ret.append(u)
-  return functools.reduce(operator.add, ret) if changed else None
+  return UOp.sum(*ret) if changed else None
 
 def cancel_divmod(d: UOp, x: UOp, y: UOp) -> UOp|None:
   # simple cancel div/mod case when the range of the numerator lies within a single denominator interval
@@ -167,7 +168,7 @@ def remove_nested_mod(m: UOp, x: UOp, y: UOp) -> UOp|None:
         something_changed = True
         u = u.src[0]
     new_xs.append(u)
-  new_x: UOp = functools.reduce(operator.add, new_xs)
+  new_x: UOp = UOp.sum(*new_xs)
   if something_changed and new_x.vmin>=0: return new_x % y
   return None
 
@@ -300,6 +301,7 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
   ((UPat.var("y") + UPat.var("x")) + UPat.var("x"), lambda y,x: y+x*2),
   ((UPat.var("x") / UPat.var("x2")) / UPat.var("x3"), lambda x,x2,x3: x/(x2*x3) if x2 is not x3 else None), # (x/x2)/x3 -> x/(x2*x3)
   (-1 * (UPat.var("x") + UPat.cvar("c")), lambda x,c: (-x)+(-c)),  # -(x+c) -> -x + -c
+  (UPat.cvar("y") * (UPat.var("x", dtype=dtypes.index) + UPat.cvar("c")), lambda x,y,c: (y*x)+(y*c)),  # -(x+c) -> -x + -c
   # ** where folding **
   (UPat.var("cond", dtype=dtypes.bool).logical_not().where(UPat.var("t"), UPat.var("f")), lambda cond, t, f: cond.where(f,t)
     if f.arg is not Invalid else None),
@@ -375,6 +377,13 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
   (UPat(GroupOp.Binary, src=(UPat.var("x", dtypes.long), UPat.var("y", dtypes.long)), name="u"), lambda u,x,y:
     x.cast(dtypes.int).alu(u.op, y.cast(dtypes.int)).cast(u.dtype) if not any(v.overflows(dtypes.int) for v in (u,x,y)) else None),
   ((UPat.var("x", dtypes.index) + UPat.cvar("c")).cast(dtypes.sints, name="cast"), lambda x,c,cast:x.cast(cast.dtype)+c.cast(cast.dtype)),
+  # only RANGE/IF/STORE/KERNEL have side effects
+  (UPat(Ops.AFTER, name="x"), lambda x: x.replace(src=(x.src[0],)+
+    tuple(flatten([(y,) if y.op in {Ops.RANGE, Ops.IF, Ops.STORE, Ops.KERNEL, Ops.BARRIER, Ops.END} else y.src for y in x.src[1:]])))),
+  # after with 1 src is just src[0]
+  (UPat(Ops.AFTER, src=(UPat.var("s"),)), lambda s: s),
+  # END is only on RANGES
+  (UPat(Ops.END, name="e"), lambda e: UOp.end(*e.src[e.arg:], ends=sorted(UOp.sink(*e.src[:e.arg]).ranges, key=lambda x: x.arg))),
 ])+gep_pushing
 
 symbolic_flat = symbolic+PatternMatcher([
@@ -452,9 +461,9 @@ def simplify_valid(valid:UOp) -> UOp|None:
   something_changed = False
   valids = list(valid.split_uop(Ops.AND))
   for stmt in sorted(valids, key=lambda v: _valid_priority(v, valids)):
-    ret.append(uop_given_valid(functools.reduce(operator.and_, ret), stmt) if ret else stmt)
+    ret.append(uop_given_valid(UOp.prod(*ret), stmt) if ret else stmt)
     if ret[-1] is not stmt: something_changed = True
-  return functools.reduce(operator.and_, ret) if something_changed else None
+  return UOp.prod(*ret) if something_changed else None
 
 # ******** phase 3 is the complete symbolic, and deals with very complex things like loop rewriting and threefry transform ********
 
@@ -471,7 +480,7 @@ def reduce_mul_chain(r:UOp):
 
 def drop_and_clauses(cond:UOp, x:UOp, i:UOp) -> UOp|None:
   if not (dropped_clauses:=[c for c in cond.split_uop(Ops.AND) if not any(r in x.ranges for r in c.ranges)]): return None
-  return functools.reduce(operator.and_, [c for c in cond.split_uop(Ops.AND) if c not in dropped_clauses], UOp.const(dtypes.bool, True)).where(x, i)
+  return UOp.const(dtypes.bool, True).prod(*[c for c in cond.split_uop(Ops.AND) if c not in dropped_clauses]).where(x, i)
 pm_drop_and_clauses = PatternMatcher([(UPat.var("cond").where(UPat.var("x", dtype=dtypes.index), invalid_pat), drop_and_clauses)])
 
 def where_on_load(l, c1, buf, x):
@@ -483,7 +492,7 @@ def where_on_load(l, c1, buf, x):
     and not c.op_in_backward_slice_with_self(Ops.LOAD)]
   if not (removed:=moved_clauses+duplicate_clauses): return None
   # aditionally we can drop the clause on the where if it already exists in the load
-  remaining_clause = functools.reduce(operator.and_, [c for c in c1.split_uop(Ops.AND) if c not in removed], UOp.const(dtypes.bool, True))
+  remaining_clause = UOp.const(dtypes.bool, True).prod(*[c for c in c1.split_uop(Ops.AND) if c not in removed])
   return remaining_clause.where(UOp.load(buf.index(x.get_idx().valid(functools.reduce(operator.and_, moved_clauses, c2)), *l.src[1:])), 0)
 pm_move_where_on_load = PatternMatcher([
   (UPat.var("c1").where(UPat(Ops.LOAD, src=(UPat.var("buf").index(UPat.var("x")),), name="l"), 0), where_on_load),

tinygrad/viz/index.html

@@ -2,6 +2,7 @@
 <html>
 <head>
   <title>tinygrad viz</title>
+  <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   <link rel="icon" href="data:;base64,iVBORw0KGgo=">
   <script src="assets/d3js.org/d3.v7.min.js" charset="utf-8"></script>
@@ -38,24 +39,37 @@
   ::-webkit-scrollbar-thumb { background: #686977; }
   a {
     color: #4a90e2;
+    text-decoration: underline;
+    cursor: pointer;
   }
   ul {
     padding: 0;
-    opacity: 0.6;
     white-space: nowrap;
     cursor: pointer;
   }
-  ul.active {
+  ul > p {
+    opacity: 0.6;
+  }
+  ul.active > p {
     opacity: 1;
   }
   ul > ul {
     display: none;
+    margin-left: 6px;
+  }
+  ul.has-children > p::before {
+    content:"▸ ";
+  }
+  ul.has-children.expanded > p::before {
+    content:"▾ ";
   }
   ul.expanded > ul {
     display: block;
   }
-  ul.disabled {
+  ul.disabled > p {
     opacity: 0.4;
+  }
+  ul.disabled {
     pointer-events: none;
   }
   label {
@@ -137,7 +151,7 @@
   .metadata > * + *, .rewrite-container > * + *, .ctx-list > * + * {
     margin-top: 12px;
   }
-  .ctx-list > ul > * + * {
+  ul > * + * {
     margin-top: 4px;
   }
   .graph {

tinygrad/viz/js/index.js

@@ -51,7 +51,7 @@ function addTags(root) {
   root.selectAll("text").data(d => [d]).join("text").text(d => d).attr("dy", "0.35em");
 }
 
-let [workerUrl, worker] = [null, null];
+let workerUrl = null, worker = null;
 async function initWorker() {
   const resp = await Promise.all(["/assets/dagrejs.github.io/project/dagre/latest/dagre.min.js","/js/worker.js"].map(u => fetch(u)));
   workerUrl = URL.createObjectURL(new Blob([(await Promise.all(resp.map((r) => r.text()))).join("\n")], { type: "application/javascript" }));
@@ -78,7 +78,7 @@ function renderDag(graph, additions, recenter) {
         if (parents == null && children == null) return;
         const src = [...parents, ...children, d.id];
         nodes.classed("highlight", n => src.includes(n.id)).classed("child", n => children.includes(n.id));
-        const matchEdge = (v, w) => (v===d.id && children.includes(w)) ? "highlight child "  : (parents.includes(v) && w===d.id) ? "highlight " : "";
+        const matchEdge = (v, w) => (v===d.id && children.includes(w)) ? "highlight child " : (parents.includes(v) && w===d.id) ? "highlight " : "";
         d3.select("#edges").selectAll("path.edgePath").attr("class", e => matchEdge(e.v, e.w)+"edgePath");
         d3.select("#edge-labels").selectAll("g.port").attr("class",  (_, i, n) => matchEdge(...n[i].id.split("-"))+"port");
         e.stopPropagation();
@@ -92,10 +92,9 @@ function renderDag(graph, additions, recenter) {
     }).selectAll("text").data(d => {
       const ret = [[]];
       for (const { st, color } of parseColors(d.label, defaultColor="initial")) {
-        for (const [i, l] of st.split("\n").entries()) {
-          if (i > 0) ret.push([]);
-          ret.at(-1).push({ st:l, color });
-        }
+        const lines = st.split("\n");
+        ret.at(-1).push({ st:lines[0], color });
+        for (let i=1; i<lines.length; i++) ret.push([{ st:lines[i], color }]);
       }
       return [ret];
     }).join("text").selectAll("tspan").data(d => d).join("tspan").attr("x", "0").attr("dy", 14).selectAll("tspan").data(d => d).join("tspan")
@@ -174,10 +173,16 @@ function tabulate(rows) {
   return root;
 }
 
-var data, focusedDevice, focusedShape, canvasZoom, zoomLevel = d3.zoomIdentity;
+var data, focusedDevice, focusedShape, canvasZoom, zoomLevel = d3.zoomIdentity, shapeMetadata = new Map();
+function focusShape(shape) {
+  saveToHistory({ shape:focusedShape });
+  focusedShape = shape?.key; d3.select("#timeline").call(canvasZoom.transform, zoomLevel);
+  return document.querySelector(".metadata").replaceChildren(shapeMetadata.get(focusedShape) ?? "");
+}
+
 async function renderProfiler() {
   displayGraph("profiler");
-  d3.select(".metadata").node().replaceChildren(focusedShape?.html ?? "");
+  d3.select(".metadata").node().replaceChildren(shapeMetadata.get(focusedShape) ?? "");
   // layout once!
   if (data != null) return updateProgress({ start:false });
   const profiler = d3.select(".profiler").html("");
@@ -202,6 +207,8 @@ async function renderProfiler() {
   const canvasTop = rect(canvas).top;
   // color by key (name/device)
   const colorMap = new Map();
+  // map shapes by event key
+  const shapeMap = new Map();
   data = {tracks:new Map(), axes:{}};
   const heightScale = d3.scaleLinear().domain([0, tracePeak]).range([4,maxheight=100]);
   for (let i=0; i<layoutsLen; i++) {
@@ -216,10 +223,10 @@ async function renderProfiler() {
     if (eventType === EventTypes.TIMELINE) {
       const levelHeight = baseHeight-padding;
       const levels = [];
-      data.tracks.set(k, { shapes, visible, offsetY });
+      data.tracks.set(k, { shapes, visible, offsetY, pcolor:"#9ea2ad" });
       let colorKey, ref;
       for (let j=0; j<eventsLen; j++) {
-        const e = {name:strings[u32()], ref:optional(u32()), st:u32(), dur:f32(), info:strings[u32()] || null};
+        const e = {name:strings[u32()], ref:optional(u32()), key:optional(u32()), st:u32(), dur:f32(), info:strings[u32()] || null};
         // find a free level to put the event
         let depth = levels.findIndex(levelEt => e.st >= levelEt);
         const et = e.st+Math.trunc(e.dur);
@@ -239,7 +246,20 @@ async function renderProfiler() {
           const stepIdx = ctxs[ref.ctx+1].steps.findIndex((s, i) => i >= start && s.name == e.name);
           if (stepIdx !== -1) { ref.step = stepIdx; shapeRef = ref; }
         }
-        const arg = { tooltipText:colored(e.name).outerHTML+"\n"+formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...shapeRef };
+        const html = document.createElement("div");
+        html.appendChild(tabulate([["Name", colored(e.name)], ["Duration", formatTime(e.dur)], ["Start Time", formatTime(e.st)]]).node());
+        const argsDiv = document.createElement("div"); argsDiv.id = "args"; html.appendChild(document.createElement("br")); html.appendChild(argsDiv);
+        if (e.info != null) html.appendChild(document.createElement("p")).innerText = "\n"+e.info;
+        if (shapeRef != null) {
+          const a = html.appendChild(document.createElement("a"));
+          a.innerText = "\nView codegen rewrite";
+          a.onclick = () => setCtxWithHistory(shapeRef.ctx, shapeRef.step);
+        }
+        // tiny device events go straight to the rewrite rule
+        const key = k.startsWith("TINY") ? null : `${k}-${j}`;
+        if (key != null) shapeMetadata.set(key, html);
+        const arg = { tooltipText:colored(e.name).outerHTML+"\n"+formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), key, ...shapeRef };
+        if (e.key != null) shapeMap.set(e.key, arg);
         // offset y by depth
         shapes.push({x:e.st, y:levelHeight*depth, width:e.dur, height:levelHeight, arg, label, fillColor });
       }
@@ -259,7 +279,7 @@ async function renderProfiler() {
           x += 1; y += nbytes; valueMap.set(ts, y);
         } else {
           const free = buf_shapes.get(key);
-          free.users = Array.from({ length: u32() }, () => strings[u32()]);
+          free.users = Array.from({ length: u32() }, () => ({shape:shapeMap.get(u32()), repr:strings[u32()], num:u8(), mode:u8()}));
           timestamps.push(ts); valueMap.set(ts, y);
           x += 1; y -= free.nbytes;
           free.x.push(x);
@@ -272,10 +292,6 @@ async function renderProfiler() {
           }
         }
       }
-      for (const [_, v] of temp) {
-        v.x.push(x);
-        v.y.push(v.y.at(-1));
-      }
       timestamps.push(dur);
       const height = heightScale(peak);
       const yscale = d3.scaleLinear().domain([0, peak]).range([height, 0]);
@@ -286,15 +302,30 @@ async function renderProfiler() {
         const rows = [["DType", dtype], ["Len", formatUnit(sz)], ["Size", formatUnit(nbytes, "B")], ["Lifetime", formatTime(dur)]];
         if (users != null) rows.push(["Users", users.length]);
         const info = html.appendChild(tabulate(rows).node());
+        const arg = {tooltipText:info.outerHTML, key:`${k}-${num}`};
         for (let u=0; u<users?.length; u++) {
-          const p = html.appendChild(document.createElement("p")); p.style.marginTop = "4px"; p.style.cursor = "pointer";
-          const name = users[u]; p.appendChild(colored(`[${u}] ${name}`));
-          p.onclick = () => {
-            const cid = ctxs.findIndex(c => c.name === name);
-            if (cid != null) setCtxWithHistory(cid-1);
+          const p = html.appendChild(document.createElement("p")); p.style.marginTop = "4px";
+          const { repr, num, mode, shape } = users[u];
+          const bufInfo = `${mode == 2 ? 'read+write' : mode == 1 ? 'write' : 'read'}@data${num}`
+          p.appendChild(colored(`[${u}] ${repr} ${bufInfo}`));
+          const metadata = shape?.tooltipText?.split("\n").at(-1);
+          if (metadata != null) p.appendChild(document.createElement("span")).innerText = "\n"+metadata;
+          if (shape != null) {
+            p.style.cursor = "pointer";
+            p.onclick = () => focusShape(shape);
+            const args = shapeMetadata.get(shape.key).querySelector("#args");
+            const bufArg = d3.create("p").text(`${bufInfo} ${rows[2][1]}`).style("cursor", "pointer").style("margin-top", "4px").on("click", () => {
+              const device = document.getElementById(k);
+              if (!isExpanded(device)) device.click();
+              focusShape(arg);
+            }).node();
+            bufArg.dataset.num = num;
+            let before = null;
+            for (const c of args.children) { if (+c.dataset.num > num) { before = c; break; } }
+            args.insertBefore(bufArg, before);
           }
         }
-        const arg = {tooltipText:info.outerHTML, html, key:`${k}-${num}`};
+        shapeMetadata.set(arg.key, html)
         shapes.push({ x, y0:y.map(yscale), y1:y.map(y0 => yscale(y0+nbytes)), arg, fillColor:cycleColors(colorScheme.BUFFER, shapes.length) });
       }
       // generic polygon merger
@@ -317,7 +348,7 @@ async function renderProfiler() {
         sum.x.push(allX[i], allX[i+1]);
         const y = maxY.get(allX[i]); sum.y1.push(y, y); sum.y0.push(base0, base0);
       }
-      data.tracks.set(k, { shapes:[sum], visible, offsetY, height, peak, scaleFactor:maxheight*4/height, views:[[sum], shapes], valueMap });
+      data.tracks.set(k, { shapes:[sum], visible, offsetY, pcolor:"#c9a8ff", height, peak, scaleFactor:maxheight*4/height, views:[[sum], shapes], valueMap });
       div.style("height", height+padding+"px").style("cursor", "pointer").on("click", (e) => {
         const newFocus = e.currentTarget.id === focusedDevice ? null : e.currentTarget.id;
         let offset = 0;
@@ -327,6 +358,7 @@ async function renderProfiler() {
           else if (tid === focusedDevice) { track.shapes = track.views[0]; offset += rescaleTrack(track, tid, 1/track.scaleFactor); }
         }
         data.axes.y = newFocus != null ? { domain:[0, (t=data.tracks.get(newFocus)).peak], range:[t.offsetY+t.height, t.offsetY], fmt:"B" } : null;
+        toggleCls(document.getElementById(focusedDevice), document.getElementById(newFocus), "expanded");
         focusedDevice = newFocus;
         return resize();
       });
@@ -346,14 +378,13 @@ async function renderProfiler() {
     xscale.domain(visibleX);
     // draw shapes
     const paths = [];
-    for (const [_, { offsetY, shapes, visible, valueMap }] of data.tracks) {
+    for (const [_, { offsetY, shapes, visible, valueMap, pcolor }] of data.tracks) {
       visible.length = 0;
       for (const e of shapes) {
-        // generic polygon
-        if (e.width == null) {
+        const p = new Path2D();
+        if (e.width == null) { // generic polygon
           if (e.x[0]>et || e.x.at(-1)<st) continue;
           const x = e.x.map(xscale);
-          const p = new Path2D();
           p.moveTo(x[0], offsetY+e.y0[0]);
           for (let i=1; i<x.length; i++) {
             p.lineTo(x[i], offsetY+e.y0[i]);
@@ -364,32 +395,29 @@ async function renderProfiler() {
           for (let i=x.length-1; i>=0; i--) p.lineTo(x[i], offsetY+e.y1[i]);
           p.closePath();
           ctx.fillStyle = e.fillColor; ctx.fill(p);
-          if (focusedShape?.key && e.arg?.key === focusedShape.key) { paths.push(p); }
-          continue;
-        }
-        // contiguous rect
-        if (e.x>et || e.x+e.width<st) continue;
-        const x = xscale(e.x);
-        const y = offsetY+e.y;
-        const width = xscale(e.x+e.width)-x;
-        ctx.fillStyle = e.fillColor; ctx.fillRect(x, y, width, e.height);
-        visible.push({ y0:y, y1:y+e.height, x0:x, x1:x+width, arg:e.arg });
-        // add label
-        if (e.label == null) continue;
-        ctx.textAlign = "left";
-        ctx.textBaseline = "middle";
-        let labelX = x+2, labelWidth = 0;
-        const labelY = y+e.height/2;
-        for (const [i,l] of e.label.entries()) {
-          if (labelWidth+l.width+(i===e.label.length-1 ? 0 : ellipsisWidth)+2 > width) {
-            if (labelWidth !== 0) ctx.fillText("...", labelX, labelY);
-            break;
+        } else { // contiguous rect
+          if (e.x>et || e.x+e.width<st) continue;
+          const x = xscale(e.x);
+          const y = offsetY+e.y;
+          const width = xscale(e.x+e.width)-x;
+          p.rect(x, y, width, e.height);
+          visible.push({ y0:y, y1:y+e.height, x0:x, x1:x+width, arg:e.arg });
+          ctx.fillStyle = e.fillColor; ctx.fill(p);
+          // add label
+          let lw = 0;
+          const lx = x+2, ly = y+e.height/2;
+          for (let li=0; li<e.label?.length; li++) {
+            if (lw+e.label[li].width+(li===e.label.length-1 ? 0 : ellipsisWidth)+2 > width) {
+              if (lw>0) ctx.fillText("...", lx+lw, ly);
+              break;
+            }
+            ctx.textBaseline = "middle";
+            ctx.fillStyle = e.label[li].color;
+            ctx.fillText(e.label[li].st, lx+lw, ly);
+            lw += e.label[li].width;
           }
-          ctx.fillStyle = l.color;
-          ctx.fillText(l.st, labelX, labelY);
-          labelWidth += l.width;
-          labelX += l.width;
         }
+        if (focusedShape != null && e.arg?.key === focusedShape) { paths.push([p, pcolor]); }
       }
     }
     // draw axes
@@ -419,7 +447,7 @@ async function renderProfiler() {
       drawLine(ctx, [x, x], [0, canvas.clientHeight], { color:m.color });
       ctx.fillText(m.name, x+2, 1);
     }
-    for (const p of paths) { ctx.lineWidth = 1.4; ctx.strokeStyle = "#c9a8ff"; ctx.stroke(p); }
+    for (const [p, color] of paths) { ctx.lineWidth = 1.4; ctx.strokeStyle = color; ctx.stroke(p); }
   }
 
   function resize() {
@@ -459,9 +487,8 @@ async function renderProfiler() {
   canvas.addEventListener("click", e => {
     e.preventDefault();
     const foundRect = findRectAtPosition(e.clientX, e.clientY);
-    if (foundRect?.step != null) return setCtxWithHistory(foundRect.ctx, foundRect.step);
-    if (foundRect?.key != focusedShape?.key) { focusedShape = foundRect; render(zoomLevel); }
-    return document.querySelector(".metadata").replaceChildren(foundRect?.html ?? "");
+    if (foundRect?.step != null && foundRect?.key == null) { return setCtxWithHistory(foundRect.ctx, foundRect.step); }
+    if (foundRect?.key != focusedShape) { focusShape(foundRect); }
   });
 
   canvas.addEventListener("mousemove", e => {
@@ -520,15 +547,10 @@ function codeBlock(st, language, { loc, wrap }={}) {
   return ret;
 }
 
-function appendTd(tr, value, unit=null) {
-  const fmt = (typeof value === "number" && !Number.isInteger(value)) ? value.toFixed(2) : value;
-  tr.appendChild(document.createElement("td")).innerText = unit == "us" ? formatTime(value) : fmt+(unit ?? "");
-}
-
-function setActive(e) {
-  if (e == null) return;
-  e.classList.add("active");
-  requestAnimationFrame(() => e.scrollIntoView({ behavior: "auto", block: "nearest" }));
+function toggleCls(prev, next, cls, value) {
+  prev?.classList.remove(cls);
+  next?.classList.toggle(cls, value ?? true);
+  requestAnimationFrame(() => next?.scrollIntoView({ behavior: "auto", block: "nearest" }));
 }
 
 // ** hljs extra definitions for UOps and float4
@@ -558,31 +580,41 @@ const evtSources = [];
 // context: collection of steps
 const state = {currentCtx:-1, currentStep:0, currentRewrite:0, expandSteps:false};
 function setState(ns) {
-  const { currentCtx:prevCtx, currentStep:prevStep } = state;
+  const { ctx:prevCtx, step:prevStep } = select(state.currentCtx, state.currentStep);
   Object.assign(state, ns);
   // update element styles if needed
-  document.getElementById(`ctx-${state.currentCtx}`)?.classList.toggle("expanded", state.expandSteps);
-  if (state.currentCtx !== prevCtx) {
-    document.getElementById(`ctx-${prevCtx}`)?.classList.remove("active", "expanded");
-    setActive(document.getElementById(`ctx-${state.currentCtx}`));
-  }
-  if (state.currentCtx !== prevCtx || state.currentStep !== prevStep) {
-    document.getElementById(`step-${prevCtx}-${prevStep}`)?.classList.remove("active");
-    setActive(document.getElementById(`step-${state.currentCtx}-${state.currentStep}`));
+  const { ctx, step } = select(state.currentCtx, state.currentStep);
+  toggleCls(prevCtx, ctx, "expanded", state.expandSteps);
+  if (ctx?.id !== prevCtx?.id) toggleCls(prevCtx, ctx, "active");
+  if (ctx?.id !== prevCtx?.id || step?.id !== prevStep?.id) {
+    toggleCls(prevStep, step, "active");
+    // walk the tree back until all parents expanded so that the child is visible
+    let e = step;
+    while (e?.parentElement?.id.startsWith("step")) {
+      e.parentElement.classList.add("expanded");
+      e = e.parentElement;
+    }
   }
   // re-render
   main();
 }
 
+const getSubrewrites = (ul) => ul.querySelectorAll(":scope > ul");
+
+function saveToHistory(ns) {
+  // NOTE: browser does a structured clone, passing a mutable object is safe.
+  history.replaceState(ns, "");
+  history.pushState(ns, "");
+}
+
 // set a new context and keep the old one in browser history
 function setCtxWithHistory(newCtx, step=0) {
-  // NOTE: browser does a structured clone, passing a mutable object is safe.
-  history.replaceState(state, "");
-  history.pushState(state, "");
+  saveToHistory(state);
   setState({ expandSteps:true, currentCtx:newCtx+1, currentStep:step, currentRewrite:0 });
 }
 
 window.addEventListener("popstate", (e) => {
+  if (e.state?.shape != null) return focusShape({ key:e.state?.shape });
   if (e.state != null) setState(e.state);
 });
 
@@ -600,15 +632,25 @@ async function main() {
       p.onclick = () => {
         setState(i === state.currentCtx ? { expandSteps:!state.expandSteps } : { expandSteps:true, currentCtx:i, currentStep:0, currentRewrite:0 });
       }
+      const stack = []; let list = ul;
       for (const [j,u] of steps.entries()) {
-        const inner = ul.appendChild(document.createElement("ul"));
-        inner.id = `step-${i}-${j}`;
-        inner.innerText = `${u.name ?? u.loc[0].replaceAll("\\", "/").split("/").pop()+':'+u.loc[1]}`+(u.match_count ? ` - ${u.match_count}` : '');
-        inner.style.marginLeft = `${8*u.depth}px`;
-        inner.onclick = (e) => {
+        while (stack.length && stack.at(-1).depth >= u.depth) stack.pop();
+        const list = stack.length > 0 ? stack.at(-1).li : ul;
+        u.li = list.appendChild(document.createElement("ul"));
+        u.li.id = `step-${i}-${j}`;
+        const p = u.li.appendChild(document.createElement("p"));
+        p.innerText = `${u.name}`+(u.match_count ? ` - ${u.match_count}` : '');
+        p.onclick = (e) => {
           e.stopPropagation();
-          setState({ currentStep:j, currentCtx:i, currentRewrite:0 });
+          const subrewrites = getSubrewrites(e.currentTarget.parentElement);
+          if (subrewrites.length) { e.currentTarget.parentElement.classList.toggle("expanded"); }
+          setState({ currentStep:j, currentCtx:i });
         }
+        stack.push(u);
+      }
+      for (const l of ul.querySelectorAll("ul > ul > p")) {
+        const subrewrites = getSubrewrites(l.parentElement);
+        if (subrewrites.length > 0) { l.innerText += ` (${subrewrites.length})`; l.parentElement.classList.add("has-children"); }
       }
     }
     return setState({ currentCtx:-1 });
@@ -649,7 +691,7 @@ async function main() {
         tr.className = "main-row code-row";
         for (const [i,value] of r.entries()) {
           // string format scalar values
-          if (!Array.isArray(value)) appendTd(tr, value);
+          if (!Array.isArray(value)) tr.appendChild(document.createElement("td")).innerText = value;
           // display arrays in a bar graph
           else {
             const segmentsTd = tr.appendChild(document.createElement("td"));
@@ -701,8 +743,9 @@ async function main() {
     rewriteList.className = "rewrite-list";
     for (let s=0; s<=step.match_count; s++) {
       const ul = rewriteList.appendChild(document.createElement("ul"));
-      ul.innerText = s;
       ul.id = `rewrite-${s}`;
+      const p = ul.appendChild(document.createElement("p"));
+      p.innerText = s;
       ul.onclick = () => setState({ currentRewrite:s });
       ul.className = s > ret.length-1 ? "disabled" : s === currentRewrite ? "active" : "";
       if (s > 0 && s === currentRewrite) {
@@ -757,22 +800,32 @@ appendResizer(document.querySelector(".metadata-parent"), { minWidth: 20, maxWid
 
 // **** keyboard shortcuts
 
+const select = (ctx, step) => ({ ctx:document.getElementById(`ctx-${ctx}`), step:document.getElementById(`step-${ctx}-${step}`) });
+const deselect = (element) => {
+  const parts = element?.id.split("-").map(Number);
+  return element?.id.startsWith("ctx") ? { ctx:parts[1], step:null } : element?.id.startsWith("step") ? {ctx:parts[1], step:parts[2]} : {};
+}
+const isExpanded = (el) => el?.classList.contains("expanded");
+
 document.addEventListener("keydown", (event) => {
   const { currentCtx, currentStep, currentRewrite, expandSteps } = state;
   // up and down change the step or context from the list
   const changeStep = expandSteps && ctxs[currentCtx].steps?.length;
+  const { step, ctx } = select(currentCtx, currentStep);
   if (event.key == "ArrowUp") {
     event.preventDefault();
     if (changeStep) {
-      return setState({ currentRewrite:0, currentStep:Math.max(0, currentStep-1) });
+      let prev = deselect(step.previousElementSibling);
+      if (prev.step == null && isExpanded(step.parentElement)) prev = deselect(step.parentElement);
+      return prev.step != null && !isExpanded(step) && setState({ currentRewrite:0, currentStep:prev.step });
     }
     return setState({ currentStep:0, currentRewrite:0, currentCtx:Math.max(0, currentCtx-1), expandSteps:false });
   }
   if (event.key == "ArrowDown") {
     event.preventDefault();
     if (changeStep) {
-      const totalUOps = ctxs[currentCtx].steps.length-1;
-      return setState({ currentRewrite:0, currentStep:Math.min(totalUOps, currentStep+1) });
+      const next = deselect(isExpanded(step) ? step.children[1] : step.nextElementSibling);
+      return next.step != null && setState({ currentRewrite:0, currentStep:next.step });
     }
     return setState({ currentStep:0, currentRewrite:0, currentCtx:Math.min(ctxs.length-1, currentCtx+1), expandSteps:false });
   }
@@ -782,6 +835,7 @@ document.addEventListener("keydown", (event) => {
     if (currentCtx === -1) {
       return setState({ currentCtx:0, expandSteps:true });
     }
+    if (expandSteps && getSubrewrites(step).length) return step.children[0].click();
     return setState({ expandSteps:!expandSteps });
   }
   // left and right go through rewrites in a single UOp

tinygrad/viz/serve.py

@@ -17,10 +17,10 @@ uops_colors = {Ops.LOAD: "#ffc0c0", Ops.STORE: "#87CEEB", Ops.CONST: "#e0e0e0",
                Ops.DEFINE_GLOBAL: "#ffe0b0", Ops.DEFINE_LOCAL: "#ffe0d0", Ops.DEFINE_REG: "#f0ffe0", Ops.REDUCE_AXIS: "#FF6B6B",
                Ops.RANGE: "#c8a0e0", Ops.ASSIGN: "#909090", Ops.BARRIER: "#ff8080", Ops.IF: "#c8b0c0", Ops.SPECIAL: "#c0c0ff",
                Ops.INDEX: "#e8ffa0", Ops.WMMA: "#efefc0", Ops.MULTI: "#f6ccff", Ops.KERNEL: "#3e7f55",
-               **{x:"#D8F9E4" for x in GroupOp.Movement}, **{x:"#ffffc0" for x in GroupOp.ALU}, Ops.THREEFRY:"#ffff80", Ops.BUFFER_VIEW: "#E5EAFF",
-               Ops.BLOCK: "#C4A484", Ops.BLOCKEND: "#C4A4A4", Ops.BUFFER: "#B0BDFF", Ops.COPY: "#a040a0", Ops.FUSE: "#FFa500",
+               **{x:"#D8F9E4" for x in GroupOp.Movement}, **{x:"#ffffc0" for x in GroupOp.ALU}, Ops.THREEFRY:"#ffff80",
+               Ops.BUFFER_VIEW: "#E5EAFF", Ops.BUFFER: "#B0BDFF", Ops.COPY: "#a040a0", Ops.FUSE: "#FFa500",
                Ops.ALLREDUCE: "#ff40a0", Ops.MSELECT: "#d040a0", Ops.MSTACK: "#d040a0", Ops.CONTIGUOUS: "#FFC14D",
-               Ops.BUFFERIZE: "#FF991C", Ops.REWRITE_ERROR: "#ff2e2e", Ops.SUBSTITUTE: "#ffff00"}
+               Ops.BUFFERIZE: "#FF991C", Ops.REWRITE_ERROR: "#ff2e2e", Ops.AFTER: "#8A7866", Ops.END: "#524C46"}
 
 # VIZ API
 
@@ -33,8 +33,8 @@ def get_rewrites(t:RewriteTrace) -> list[dict]:
     steps = [{"name":s.name, "loc":s.loc, "match_count":len(s.matches), "code_line":printable(s.loc),
               "query":f"/ctxs?ctx={i}&idx={j}", "depth":s.depth} for j,s in enumerate(v)]
     if isinstance(k.ret, ProgramSpec):
-      steps.append({"name":"View Program", "query":f"/render?ctx={i}&fmt=src"})
-      steps.append({"name":"View Disassembly", "query":f"/render?ctx={i}&fmt=asm"})
+      steps.append({"name":"View Program", "query":f"/render?ctx={i}&fmt=src", "depth":0})
+      steps.append({"name":"View Disassembly", "query":f"/render?ctx={i}&fmt=asm", "depth":0})
     for key in k.keys: ref_map[key] = i
     ret.append({"name":k.display_name, "steps":steps})
   return ret
@@ -56,7 +56,7 @@ def pystr(u:UOp, i:int) -> str:
     except Exception: pass
   return str(u)
 
-def uop_to_json(x:UOp) -> dict[int, dict]:
+def uop_to_json(x:UOp, ignore_indexing=False) -> dict[int, dict]:
   assert isinstance(x, UOp)
   graph: dict[int, dict] = {}
   excluded: set[UOp] = set()
@@ -64,13 +64,14 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
     # always exclude DEVICE/CONST/UNIQUE
     if u.op in {Ops.DEVICE, Ops.CONST, Ops.UNIQUE} and u is not x: excluded.add(u)
     if u.op is Ops.VCONST and u.dtype.scalar() == dtypes.index and u is not x: excluded.add(u)
+    if u.dtype.scalar() is dtypes.index and ignore_indexing: excluded.update(u.backward_slice_with_self)
   for u in toposort:
     if u in excluded: continue
     argst = codecs.decode(str(u.arg), "unicode_escape")
     if u.op in GroupOp.Movement: argst = (mask_to_str if u.op in {Ops.SHRINK, Ops.PAD} else shape_to_str)(u.marg)
     label = f"{str(u.op).split('.')[1]}{(chr(10)+word_wrap(argst.replace(':', ''))) if u.arg is not None else ''}"
     if u.dtype != dtypes.void: label += f"\n{u.dtype}"
-    for idx,x in enumerate(u.src):
+    for idx,x in enumerate(u.src[:1] if u.op in {Ops.BUFFERIZE, Ops.INDEX} else (u.src if u.op is not Ops.END else [])):
       if x in excluded:
         arg = f"{x.arg:g}" if x.op is Ops.CONST and dtypes.is_float(x.dtype) else f"{x.arg}"
         label += f"\n{x.op.name}{idx} {arg}" + (f" {x.src[0].op}" if len(x.src) else "")
@@ -81,6 +82,8 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
         label += f"\n{shape_to_str(u.shape)}"
       if u.op in {Ops.INDEX, Ops.BUFFERIZE}:
         label += f"\n{u.render()}"
+      if u.op is Ops.END:
+        label += "\n"+' '.join([f"{colored(u.src[i].arg[0], axis_colors[u.src[i].arg[-1]])}({u.src[i].vmax+1})" for i in range(u.arg)])
     except Exception:
       label += "\n<ISSUE GETTING LABEL>"
     if (ref:=ref_map.get(u.arg.ast) if u.op is Ops.KERNEL else None) is not None: label += f"\ncodegen@{ctxs[ref]['name']}"
@@ -97,14 +100,19 @@ def _reconstruct(a:int):
   return UOp(op, dtype, tuple(_reconstruct(s) for s in src), arg, *rest)
 
 def get_full_rewrite(ctx:TrackedGraphRewrite, i:int=0) -> Generator[GraphRewriteDetails, None, None]:
-  yield {"graph":uop_to_json(next_sink:=_reconstruct(ctx.sink)), "uop":pystr(next_sink,i), "changed_nodes":None, "diff":None, "upat":None}
+  next_sink = _reconstruct(ctx.sink)
+  # in the schedule graph we don't show indexing ops (unless it's in a kernel AST or rewriting dtypes.index sink)
+  ignore_indexing = trace.keys[i].display_name.startswith("Schedule") and not (ctx.name in {"kernel split"} or \
+      any(s.dtype is dtypes.index for s in next_sink.src+(next_sink,)))
+  yield {"graph":uop_to_json(next_sink, ignore_indexing), "uop":pystr(next_sink,i), "changed_nodes":None, "diff":None, "upat":None}
   replaces: dict[UOp, UOp] = {}
   for u0_num,u1_num,upat_loc,dur in tqdm(ctx.matches):
     replaces[u0:=_reconstruct(u0_num)] = u1 = _reconstruct(u1_num)
     try: new_sink = next_sink.substitute(replaces)
     except RuntimeError as e: new_sink = UOp(Ops.NOOP, arg=str(e))
     match_repr = f"# {dur*1e6:.2f} us\n"+printable(upat_loc)
-    yield {"graph":(sink_json:=uop_to_json(new_sink)), "uop":pystr(new_sink,i), "changed_nodes":[id(x) for x in u1.toposort() if id(x) in sink_json],
+    yield {"graph":(sink_json:=uop_to_json(new_sink, ignore_indexing)), "uop":pystr(new_sink,i),
+           "changed_nodes":[id(x) for x in u1.toposort() if id(x) in sink_json],
            "diff":list(difflib.unified_diff(pystr(u0,i).splitlines(),pystr(u1,i).splitlines())), "upat":(upat_loc, match_repr)}
     if not ctx.bottom_up: next_sink = new_sink
 
@@ -136,28 +144,38 @@ def flatten_events(profile:list[ProfileEvent]) -> Generator[tuple[Decimal, Decim
 # normalize event timestamps and attach kernel metadata
 def timeline_layout(dev_events:list[tuple[int, int, float, DevEvent]], start_ts:int, scache:dict[str, int]) -> bytes|None:
   events:list[bytes] = []
-  exec_points:dict[str, dict] = {}
+  exec_points:dict[str, ProfilePointEvent] = {}
   for st,et,dur,e in dev_events:
-    if isinstance(e, ProfilePointEvent) and e.name == "exec": exec_points[e.key] = e.arg
+    if isinstance(e, ProfilePointEvent) and e.name == "exec": exec_points[e.arg["name"]] = e
     if dur == 0: continue
-    name, info = e.name, None
+    name, info, key = e.name, None, None
     if (ref:=ref_map.get(name)) is not None:
       name = ctxs[ref]["name"]
       if isinstance(p:=trace.keys[ref].ret, ProgramSpec) and (ei:=exec_points.get(p.name)) is not None:
-        info = f"{sym_infer(p.estimates.ops, ei['var_vals'])/(t:=dur*1e3):.2f} GFLOPS {sym_infer(p.estimates.mem, ei['var_vals'])/t:4.1f}"+ \
-               f"|{sym_infer(p.estimates.lds,ei['var_vals'])/t:.1f} GB/s\n{ei['metadata']}"
+        info = f"{sym_infer(p.estimates.ops, ei.arg['var_vals'])/(t:=dur*1e3):.2f} GFLOPS {sym_infer(p.estimates.mem, ei.arg['var_vals'])/t:4.1f}"+ \
+               f"|{sym_infer(p.estimates.lds,ei.arg['var_vals'])/t:.1f} GB/s\n{ei.arg['metadata']}"
+        key = ei.key
     elif isinstance(e.name, TracingKey):
       name = e.name.display_name
       ref = next((v for k in e.name.keys if (v:=ref_map.get(k)) is not None), None)
-    events.append(struct.pack("<IIIfI", enum_str(name, scache), option(ref), st-start_ts, dur, enum_str(info or "", scache)))
+    events.append(struct.pack("<IIIIfI", enum_str(name, scache), option(ref), option(key), st-start_ts, dur, enum_str(info or "", scache)))
   return struct.pack("<BI", 0, len(events))+b"".join(events) if events else None
 
+def encode_mem_free(key:int, ts:int, execs:list[ProfilePointEvent], scache:dict) -> bytes:
+  ei_encoding:list[tuple[int, int, int, int]] = [] # <[u32, u32, u8, u8] [run id, display name, buffer number and mode (2 = r/w, 1 = w, 0 = r)]
+  for e in execs:
+    num = next(i for i,k in enumerate(e.arg["bufs"]) if k == key)
+    mode = 2 if (num in e.arg["inputs"] and num in e.arg["outputs"]) else 1 if (num in e.arg["outputs"]) else 0
+    ei_encoding.append((e.key, enum_str(e.arg["name"], scache), num, mode))
+  return struct.pack("<BIII", 0, ts, key, len(ei_encoding))+b"".join(struct.pack("<IIBB", *t) for t in ei_encoding)
+
 def mem_layout(dev_events:list[tuple[int, int, float, DevEvent]], start_ts:int, end_ts:int, peaks:list[int], dtype_size:dict[str, int],
                scache:dict[str, int]) -> bytes|None:
   peak, mem = 0, 0
   temp:dict[int, int] = {}
   events:list[bytes] = []
   buf_ei:dict[int, list[ProfilePointEvent]] = {}
+
   for st,_,_,e in dev_events:
     if not isinstance(e, ProfilePointEvent): continue
     if e.name == "alloc":
@@ -170,9 +188,9 @@ def mem_layout(dev_events:list[tuple[int, int, float, DevEvent]], start_ts:int,
     if e.name == "exec" and e.arg["bufs"]:
       for b in e.arg["bufs"]: buf_ei.setdefault(b, []).append(e)
     if e.name == "free":
-      kernel_names = [enum_str(ei.key, scache) for ei in buf_ei.pop(e.key, [])]
-      events.append(struct.pack(f"<BIII{len(kernel_names)}I", 0, int(e.ts) - start_ts, e.key, len(kernel_names), *kernel_names))
+      events.append(encode_mem_free(e.key, int(e.ts) - start_ts, buf_ei.pop(e.key, []), scache))
       mem -= temp.pop(e.key)
+  for t in temp: events.append(encode_mem_free(t, end_ts-start_ts, buf_ei.pop(t, []), scache))
   peaks.append(peak)
   return struct.pack("<BIQ", 1, len(events), peak)+b"".join(events) if events else None