ignore expand

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

@@ -121,7 +121,7 @@ runs:
         echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
         echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
         echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' | sudo tee -a /etc/apt/apt.conf.d/99keep-debs
-
+    
     - name: Add OpenCL Repo
       if: inputs.opencl == 'true' && runner.os == 'Linux'
       shell: bash
@@ -174,7 +174,7 @@ runs:
         if [[ "${{ inputs.llvm }}" == "true" ]]; then
           pkgs+=" libllvm20 clang-20 lld-20"
         fi
-
+        
         echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
         echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
@@ -183,21 +183,21 @@ runs:
       uses: actions/cache@v4
       with:
         path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.APT_CACHE_VERSION }}
+        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}
 
     - name: Run apt Update + Install
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
       shell: bash
       run: |
         sudo apt -qq update || true
-
+        
         # ******** do install ********
         if [[ -n "${{ steps.apt-pkgs.outputs.pkgs }}" ]]; then
           sudo apt-get -y --allow-unauthenticated --no-install-recommends install ${{ steps.apt-pkgs.outputs.pkgs }}
         fi
-
+        
         sudo chown -R $USER:$USER /var/cache/apt/archives/
-
+    
     # **** AMD ****
     - name: Setup AMD (Linux)
       if: inputs.amd == 'true' && runner.os == 'Linux'
@@ -234,7 +234,7 @@ runs:
         cache-name: cache-gpuocelot-build
       with:
         path: ${{ github.workspace }}/gpuocelot/ocelot
-        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.BUILD_CACHE_VERSION }}
+        key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-0
     - name: Clone/compile gpuocelot
       if: inputs.ocelot == 'true' && steps.cache-build.outputs.cache-hit != 'true'
       shell: bash

.github/workflows/benchmark.yml

@@ -63,7 +63,7 @@ jobs:
     - name: Run model inference benchmark
       run: METAL=1 python3.11 test/external/external_model_benchmark.py
     - name: Test speed vs torch
-      run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
+      run: BIG=2 MPS=1 python3.11 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test tensor cores
       run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test AMX tensor cores
@@ -187,7 +187,7 @@ jobs:
     - name: Run model inference benchmark
       run: NV=1 CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
     - name: Test speed vs torch
-      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
+      run: NV=1 CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
       run: NV=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test benchmark allreduce
@@ -389,7 +389,7 @@ jobs:
     #- name: Test speed vs torch
     #  run: |
     #    python3 -c "import torch; print(torch.__version__)"
-    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py | tee torch_speed.txt
+    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
     - name: Test speed vs theoretical
       run: AMD=1 IGNORE_BEAM_CACHE=1 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
     - name: Test tensor cores

.github/workflows/test.yml

@@ -1,10 +1,8 @@
 name: Unit Tests
 env:
   # increment this when downloads substantially change to avoid the internet
-  DOWNLOAD_CACHE_VERSION: '12'
-  PYTHON_CACHE_VERSION: '3'
-  APT_CACHE_VERSION: '1'
-  BUILD_CACHE_VERSION: '1'
+  DOWNLOAD_CACHE_VERSION: '10'
+  PYTHON_CACHE_VERSION: '2'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
@@ -32,9 +30,9 @@ jobs:
     - name: External Benchmark Schedule
       run: PYTHONPATH="." python3 test/external/external_benchmark_schedule.py
     - name: Speed Test
-      run: LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: LLVM=1 python3 test/test_speed_v_torch.py
     - name: Speed Test (BEAM=2)
-      run: BEAM=2 LLVM=1 python3 test/speed/external_test_speed_v_torch.py
+      run: BEAM=2 LLVM=1 python3 test/test_speed_v_torch.py
 
   docs:
     name: Docs
@@ -48,11 +46,6 @@ jobs:
       with:
         deps: docs
         pydeps: "capstone"
-    - name: Build wheel and show size
-      run: |
-        pip install build
-        python -m build --wheel --outdir dist
-        ls -lh dist/*.whl
     - name: Use as an external package
       run: |
         mkdir $HOME/test_external_dir
@@ -460,7 +453,7 @@ jobs:
   testopenpilot:
     name: 'openpilot Compile Tests'
     runs-on: ubuntu-22.04
-    timeout-minutes: 15
+    timeout-minutes: 10
     env:
       IGNORE_OOB: 0
     steps:
@@ -591,29 +584,6 @@ jobs:
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
-  testdevectorize:
-    name: Linux (devectorize)
-    runs-on: ubuntu-24.04
-    timeout-minutes: 15
-    env:
-      IGNORE_OOB: 0
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: devectorize-minimal
-        deps: testing_minimal
-        pydeps: "pillow"
-        llvm: "true"
-    - name: Test LLVM=1 DEVECTORIZE=0
-      run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
-    - name: Test LLVM=1 DEVECTORIZE=0 for model
-      run: PYTHONPATH="." LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
-    - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
-
   testdsp:
     name: Linux (DSP)
     runs-on: ubuntu-24.04
@@ -649,6 +619,12 @@ jobs:
       run: CC=clang-20 PYTHONPATH="." DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
     - name: Test quantize onnx
       run: PYTHONPATH="." DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
+    - name: Test LLVM=1 DEVECTORIZE=0
+      run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+    - name: Test LLVM=1 DEVECTORIZE=0 for model
+      run: PYTHONPATH="." LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
+    - name: Test CPU=1 DEVECTORIZE=0
+      run: CPU=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
 
   testwebgpu:
     name: Linux (WebGPU)
@@ -708,9 +684,9 @@ jobs:
           DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run LLVM test
         if: matrix.backend=='amdllvm'
-        run: python test/device/test_amd_llvm.py
+        run: python test/test_amd_llvm.py
       - name: Run pytest (amd)
-        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/device/test_hcq.py --durations=20
+        run: python -m pytest -n=auto test/test_ops.py test/test_dtype.py test/test_dtype_alu.py test/test_linearizer.py test/test_randomness.py test/test_jit.py test/test_graph.py test/test_multitensor.py test/test_hcq.py --durations=20
       - name: Run pytest (amd)
         run: python -m pytest test/external/external_test_am.py --durations=20
       - name: Run TRANSCENDENTAL math
@@ -835,14 +811,14 @@ jobs:
         AMD: 1
         FORWARD_ONLY: 1
       run: |
-        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
+        python3 -m pytest -n=auto test/test_hcq.py test/test_tiny.py --durations=20
     - name: Run pytest (amd with llvm backend)
       env:
         MOCKGPU: 1
         AMD: 1
         FORWARD_ONLY: 1
       run: |
-        python -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py test/device/test_amd_llvm.py --durations=20
+        python -m pytest -n=auto test/test_hcq.py test/test_tiny.py test/test_amd_llvm.py --durations=20
     - name: Run pytest (ptx)
       env:
         MOCKGPU: 1
@@ -850,7 +826,7 @@ jobs:
         NV: 1
         FORWARD_ONLY: 1
       run: |
-        python3 -m pytest -n=auto test/device/test_hcq.py test/test_tiny.py --durations=20
+        python3 -m pytest -n=auto test/test_hcq.py test/test_tiny.py --durations=20
     - name: Run process replay tests
       uses: ./.github/actions/process-replay
 
@@ -961,18 +937,18 @@ jobs:
         env:
           HOST: 127.0.0.1:6667*6,127.0.0.1:6668*6
         run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py --durations 20
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_remote.py test/test_tensor_variable.py
       - name: Run REMOTE=1 Test (GPU)
         env:
           HOST: 127.0.0.1:7667*6
         run: |
-          python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py --durations 20
+          python3 -m pytest test/test_tiny.py test/test_image_dtype.py test/test_jit.py
           IMAGE=2 python3 -m pytest test/test_tiny.py test/test_image_dtype.py
       - name: Run REMOTE=1 Test (CPU)
         env:
           HOST: 127.0.0.1:8667*6
         run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py --durations 20
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_multitensor.py
       - name: Show remote server logs
         if: always()
         run: |

autogen_stubs.sh

@@ -198,7 +198,11 @@ generate_amd() {
   clang2py -k cdefstum \
     extra/hip_gpu_driver/sdma_registers.h \
     extra/hip_gpu_driver/nvd.h \
+    extra/hip_gpu_driver/kfd_pm4_headers_ai.h \
+    extra/hip_gpu_driver/soc21_enum.h \
+    extra/hip_gpu_driver/sdma_v6_0_0_pkt_open.h \
     extra/hip_gpu_driver/gc_11_0_0_offset.h \
+    extra/hip_gpu_driver/gc_10_3_0_offset.h \
     extra/hip_gpu_driver/sienna_cichlid_ip_offset.h \
     --clang-args="-I/opt/rocm/include -x c++" \
     -o $BASE/amd_gpu.py
@@ -372,6 +376,26 @@ generate_am() {
     -o $BASE/am/pm4_nv.py
   fixup $BASE/am/pm4_nv.py
 
+  clang2py -k cdefstum \
+    $AMKERN_INC/vega10_enum.h \
+    -o $BASE/am/vega10.py
+  fixup $BASE/am/vega10.py
+
+  clang2py -k cdefstum \
+    $AMKERN_INC/navi10_enum.h \
+    -o $BASE/am/navi10.py
+  fixup $BASE/am/navi10.py
+
+  clang2py -k cdefstum \
+    $AMKERN_INC/soc21_enum.h \
+    -o $BASE/am/soc21.py
+  fixup $BASE/am/soc21.py
+
+  clang2py -k cdefstum \
+    $AMKERN_INC/soc24_enum.h \
+    -o $BASE/am/soc24.py
+  fixup $BASE/am/soc24.py
+
   clang2py -k cdefstum \
     extra/hip_gpu_driver/sdma_registers.h \
     $AMKERN_AMD/amdgpu/vega10_sdma_pkt_open.h \

pytest.ini

@@ -1,2 +0,0 @@
-[pytest]
-norecursedirs = extra

setup.py

@@ -18,7 +18,7 @@ testing_minimal = [
 ]
 
 setup(name='tinygrad',
-      version='0.11.0',
+      version='0.10.3',
       description='You like pytorch? You like micrograd? You love tinygrad! <3',
       author='George Hotz',
       license='MIT',

test/external/external_benchmark_kernel_launch.py

@@ -2,7 +2,7 @@ import time
 from tinygrad import Tensor, TinyJit, Device, Context
 from tinygrad.helpers import Profiling, Timing, GlobalCounters
 
-# python3 test/speed/external_test_speed_v_torch.py TestSpeed.test_add_a
+# python3 test/test_speed_v_torch.py TestSpeed.test_add_a
 
 @TinyJit
 def plus(a:Tensor, b:Tensor): return a+b

test/external/process_replay/process_replay.py

@@ -99,6 +99,7 @@ def diff(offset:int, fxns:dict[str, Callable[..., tuple|None]]) -> None:
     except Exception as e:
       changed += 1
       warnings.warn(f"{name=} {loc=} {e=}", ProcessReplayWarning)
+  conn.commit()
   cur.close()
 
 # *** generic runner to map rows of a table to a function in parallel
@@ -110,11 +111,12 @@ def _pmap(fxns:dict[str, Callable]) -> None:
   except sqlite3.OperationalError:
     raise RuntimeError(f"{TABLE_NAME} isn't accessible in master, did DB_VERSION change?")
   finally:
+    conn.commit()
     cur.close()
 
   with multiprocessing.get_context("spawn").Pool(multiprocessing.cpu_count()) as pool:
-    bar = tqdm(total=row_count)
-    for _ in pool.imap_unordered(functools.partial(diff, fxns=fxns), range(0, row_count, PAGE_SIZE)): bar.update(PAGE_SIZE)
+    inputs = list(range(0, row_count, PAGE_SIZE))
+    list(tqdm(pool.imap_unordered(functools.partial(diff, fxns=fxns), inputs), total=len(inputs)))
     pool.close()
     pool.join()
     pool.terminate()

test/mockgpu/amd/amdgpu.py

@@ -1,7 +1,7 @@
 import ctypes, time
 from test.mockgpu.gpu import VirtGPU
 from tinygrad.helpers import getbits, to_mv, init_c_struct_t
-import tinygrad.runtime.autogen.amd_gpu as amd_gpu, tinygrad.runtime.autogen.am.pm4_nv as pm4
+import tinygrad.runtime.autogen.amd_gpu as amd_gpu, tinygrad.runtime.autogen.am.pm4_nv as pm4, tinygrad.runtime.autogen.am.soc21 as soc21
 
 SDMA_MAX_COPY_SIZE = 0x400000
 
@@ -14,9 +14,6 @@ regSQ_THREAD_TRACE_BUF0_SIZE = 0x39e9 + amd_gpu.GC_BASE__INST0_SEG1
 regSQ_THREAD_TRACE_WPTR = 0x39ef + amd_gpu.GC_BASE__INST0_SEG1
 regSQ_THREAD_TRACE_STATUS = 0x39f4 + amd_gpu.GC_BASE__INST0_SEG1
 
-class SQTT_EVENTS:
-  THREAD_TRACE_FINISH = 0x00000037
-
 CACHE_FLUSH_AND_INV_TS_EVENT = 0x14
 
 WAIT_REG_MEM_FUNCTION_ALWAYS = 0
@@ -211,7 +208,7 @@ class PM4Executor(AMDQueue):
     assert n == 0
     event_dw = self._next_dword()
     match (event_dw & 0xFF): # event type
-      case SQTT_EVENTS.THREAD_TRACE_FINISH:
+      case soc21.THREAD_TRACE_FINISH:
         old_idx = self.gpu.regs.grbm_index
         for se in range(self.gpu.regs.n_se):
           self.gpu.regs.grbm_index = 0b011 << 29 | se << 16 # select se, broadcast sa and instance

test/models/test_onnx.py

@@ -32,7 +32,6 @@ OPENPILOT_MODEL = "https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/mod
 np.random.seed(1337)
 
 class TestOnnxModel(unittest.TestCase):
-  @unittest.skip("this isn't a test, it can't fail")
   def test_benchmark_openpilot_model(self):
     onnx_model = fetch(OPENPILOT_MODEL)
     run_onnx = OnnxRunner(onnx_model)

test/models/test_whisper.py

@@ -16,7 +16,7 @@ TRANSCRIPTION_2 = "a slightly longer audio file so that we can test batch transc
 TEST_FILE_3_URL = 'https://homepage.ntu.edu.tw/~karchung/miniconversations/mc45.mp3'
 TRANSCRIPTION_3 = "Just lie back and relax. Is the level of pressure about right? Yes, it's fine, and I'd like conditioner please. Sure. I'm going to start the second lathering now. Would you like some Q-tips? How'd you like it cut? I'd like my bangs and the back trimmed, and I'd like the rest thinned out a bit and layered. Where would you like the part? On the left, right about here. Here, have a look. What do you think? It's fine. Here's a thousand anti-dollars. It's 30-ant extra for the rants. Here's your change and receipt. Thank you, and please come again. So how do you like it? It could have been worse, but you'll notice that I didn't ask her for her card. Hmm, yeah. Maybe you can try that place over there next time."   # noqa: E501
 
-@unittest.skipIf(Device.DEFAULT in ["CPU", "LLVM"], "slow")
+@unittest.skipIf(CI and Device.DEFAULT in ["CPU"], "slow")
 @unittest.skipUnless(is_dtype_supported(dtypes.float16), "need float16 support")
 class TestWhisper(unittest.TestCase):
   @classmethod

test/test_compile_failures.py

@@ -1,10 +1,7 @@
-import unittest, io
-from contextlib import redirect_stdout
+import unittest
 from tinygrad import Tensor, dtypes, Device
-from tinygrad.helpers import OSX
 from tinygrad.engine.realize import lower_schedule
 from tinygrad.device import is_dtype_supported
-from tinygrad.engine.realize import get_program
 
 class TestCompileFailures(unittest.TestCase):
   def compile(self, out:Tensor):
@@ -17,17 +14,5 @@ class TestCompileFailures(unittest.TestCase):
   def test_add_max_uchar(self):
     self.compile((Tensor.empty(1024, dtype='uint8') + Tensor.empty(1024, dtype='uint8')).max())
 
-class TestDisassembly(unittest.TestCase):
-  # TODO: fails on llvm. llvm.LLVMGetHostCPUName() returns "generic"
-  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and OSX, "m series cpus support fp16 arithmetic")
-  def test_float16_alu(self):
-    c = Tensor([1], dtype=dtypes.float16) + Tensor([1], dtype=dtypes.float16)
-    s = c.schedule()[-1]
-    p = get_program(s.ast, Device[Device.DEFAULT].renderer)
-    lib = Device[Device.DEFAULT].compiler.compile(p.src)
-    out = io.StringIO()
-    with redirect_stdout(out): Device[Device.DEFAULT].compiler.disassemble(lib)
-    assert "fcvt" not in out.getvalue()
-
 if __name__ == '__main__':
   unittest.main()

test/test_copy_speed.py

@@ -3,7 +3,7 @@ from tinygrad import Tensor, Device, TinyJit
 from tinygrad.helpers import Timing, CI, OSX
 import multiprocessing.shared_memory as shared_memory
 
-N = 256
+N = 256 if CI else 4096
 class TestCopySpeed(unittest.TestCase):
   @classmethod
   def setUpClass(cls): Device[Device.DEFAULT].synchronize()

test/test_disassembly.py

@@ -0,0 +1,21 @@
+import unittest, io
+from tinygrad import Tensor, dtypes
+from contextlib import redirect_stdout
+from tinygrad.device import Device
+from tinygrad.helpers import OSX
+from tinygrad.engine.realize import get_program
+
+class TestDisassembly(unittest.TestCase):
+  # TODO: fails on llvm. llvm.LLVMGetHostCPUName() returns "generic"
+  @unittest.skipUnless(Device.DEFAULT in ("CPU",) and OSX, "m series cpus support fp16 arithmetic")
+  def test_float16_alu(self):
+    c = Tensor([1], dtype=dtypes.float16) + Tensor([1], dtype=dtypes.float16)
+    s = c.schedule()[-1]
+    p = get_program(s.ast, Device[Device.DEFAULT].renderer)
+    lib = Device[Device.DEFAULT].compiler.compile(p.src)
+    out = io.StringIO()
+    with redirect_stdout(out): Device[Device.DEFAULT].compiler.disassemble(lib)
+    assert "fcvt" not in out.getvalue()
+
+if __name__ == "__main__":
+  unittest.main()

test/test_multitensor.py

@@ -373,11 +373,11 @@ class TestMultiTensor(unittest.TestCase):
     np.testing.assert_allclose(y.numpy(), y_shard.numpy(), atol=1e-6, rtol=1e-6)
 
   # NOTE: this is failing on LLVM CI, no idea why. Works locally.
-  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU", "AMD"), "slow, and flaky on LLVM/CPU")
+  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
   def test_data_parallel_resnet(self):
     from extra.models.resnet import ResNet18
 
-    fake_image = Tensor.rand((2, 3, 224//16, 224//16))
+    fake_image = Tensor.rand((2, 3, 224//8, 224//8))
     fake_image_sharded = fake_image.shard(devices_2, axis=0)
     m = ResNet18()
     m.load_from_pretrained()
@@ -409,10 +409,10 @@ class TestMultiTensor(unittest.TestCase):
     # sometimes there is zeros in these grads... why?
     np.testing.assert_allclose(grad, shard_grad, atol=1e-5, rtol=1e-5)
 
-  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU", "AMD"), "slow, and flaky on LLVM/CPU")
+  @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
   def test_data_parallel_resnet_train_step(self):
     from extra.models.resnet import ResNet18
-    fake_image = Tensor.rand((2, 3, 224//16, 224//16))
+    fake_image = Tensor.rand((2, 3, 224//8, 224//8))
     labels = Tensor.randint(2, low=0, high=1000)
     m = ResNet18()
     self._test_model_train_step(m, fake_image, labels)

test/test_net_speed.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+import time
+import unittest
+import torch
+from tinygrad import Tensor, Device
+from tinygrad.helpers import Profiling, CI
+
+@unittest.skipIf(CI and Device.DEFAULT in {"CUDA", "NV"}, "slow")
+class TestConvSpeed(unittest.TestCase):
+
+  def test_mnist(self):
+    # https://keras.io/examples/vision/mnist_convnet/
+    conv = 3
+    inter_chan, out_chan = 32, 64
+
+    # ****** torch baseline *******
+
+    torch.backends.mkldnn.enabled = False
+
+    conv = 3
+    inter_chan, out_chan = 32, 64
+    c1 = torch.randn(inter_chan,1,conv,conv, requires_grad=True)
+    c2 = torch.randn(out_chan,inter_chan,conv,conv, requires_grad=True)
+    l1 = torch.randn(out_chan*5*5, 10, requires_grad=True)
+
+    c2d = torch.nn.functional.conv2d
+    mp = torch.nn.MaxPool2d((2,2))
+    lsm = torch.nn.LogSoftmax(dim=1)
+
+    cnt = 5
+    fpt, bpt = 0.0, 0.0
+    for i in range(cnt):
+      et0 = time.time()
+      x = torch.randn(128, 1, 28, 28, requires_grad=True)
+      x = mp(c2d(x,c1).relu())
+      x = mp(c2d(x,c2).relu())
+      x = x.reshape(x.shape[0], -1)
+      out = lsm(x.matmul(l1))
+      out = out.mean()
+      et1 = time.time()
+      out.backward()
+      et2 = time.time()
+      fpt += (et1-et0)
+      bpt += (et2-et1)
+
+    fpt_baseline = (fpt*1000/cnt)
+    bpt_baseline = (bpt*1000/cnt)
+    print("torch forward pass:  %.3f ms" % fpt_baseline)
+    print("torch backward pass: %.3f ms" % bpt_baseline)
+
+    # ****** tinygrad compare *******
+
+    c1 = Tensor(c1.detach().numpy(), requires_grad=True)
+    c2 = Tensor(c2.detach().numpy(), requires_grad=True)
+    l1 = Tensor(l1.detach().numpy(), requires_grad=True)
+
+    cnt = 5
+    fpt, bpt = 0.0, 0.0
+    for i in range(1+cnt):
+      et0 = time.time()
+      x = Tensor.randn(128, 1, 28, 28)
+      x = x.conv2d(c1).relu().avg_pool2d()
+      x = x.conv2d(c2).relu().max_pool2d()
+      x = x.reshape(shape=(x.shape[0], -1))
+      out = x.dot(l1).log_softmax()
+      out = out.mean()
+      out.backward()  # NOTE: we have to now compute this here, but it doesn't realize
+      out.realize()
+      et1 = time.time()
+      [x.grad.realize() for x in [c1, c2, l1]]
+      et2 = time.time()
+      if i == 0:
+        pr = Profiling(sort='time', frac=0.2)
+        pr.__enter__()
+      else:
+        fpt += (et1-et0)
+        bpt += (et2-et1)
+
+    pr.__exit__()
+    fpt = (fpt*1000/cnt)
+    bpt = (bpt*1000/cnt)
+    print("forward pass:  %.3f ms, %.2fx off baseline %.3f ms" % (fpt, fpt/fpt_baseline, fpt_baseline))
+    print("backward pass: %.3f ms, %.2fx off baseline %.3f ms" % (bpt, bpt/bpt_baseline, bpt_baseline))
+
+
+if __name__ == '__main__':
+  unittest.main()

test/test_rangeify.py

@@ -0,0 +1,113 @@
+import unittest
+from tinygrad import Tensor
+
+class TestRangeify(unittest.TestCase):
+  def test_add(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    (A+B).realize()
+
+  def test_double_gemm(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    (A@B@C).realize()
+
+  def test_double_gemm_exp(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    (((A@B).exp()@C).exp()).realize()
+
+  def test_double_gemm_relu(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    (((A@B).relu()@C).relu()).realize()
+
+  def test_double_gemm_relu_half_contig(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    (((A@B).relu().contiguous(arg=(1,))@C).relu()).realize()
+
+  def test_double_gemm_half_contig(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    ((A@B).contiguous(arg=(1,))@C).realize()
+
+  def test_double_gemm_contig(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    ((A@B).contiguous()@C).realize()
+
+  def test_many_gemm(self):
+    N = 1024
+    A = Tensor.empty(N, N)
+    B = Tensor.empty(N, N)
+    C = Tensor.empty(N, N)
+    D = Tensor.empty(N, N)
+    E = Tensor.empty(N, N)
+    F = Tensor.empty(N, N)
+    (A@B@C@D@E@F).realize()
+
+  def test_conv2d(self):
+    x = Tensor.empty(1, 4, 32, 32)
+    w1 = Tensor.empty(8, 4, 3, 3)
+    x.conv2d(w1).realize()
+
+  def test_conv2d_t(self):
+    x = Tensor.empty(1, 4, 32, 32)
+    w1 = Tensor.empty(8, 4, 3, 3)
+    (x*2).conv2d(w1).realize()
+
+  def test_double_conv2d(self):
+    x = Tensor.empty(1, 4, 32, 32)
+    w1 = Tensor.empty(8, 4, 3, 3)
+    w2 = Tensor.empty(12, 8, 3, 3)
+    x.conv2d(w1).conv2d(w2).realize()
+
+  def test_double_conv2d_half_contig(self):
+    x = Tensor.empty(1, 4, 32, 32)
+    w1 = Tensor.empty(8, 4, 3, 3)
+    w2 = Tensor.empty(12, 8, 3, 3)
+    # NOTE: this contiguous doesn't help
+    x.conv2d(w1).contiguous(arg=(1,)).conv2d(w2).permute(0,2,3,1).contiguous().realize()
+
+  def test_double_conv2d_contig(self):
+    x = Tensor.empty(1, 4, 32, 32)
+    w1 = Tensor.empty(8, 4, 3, 3)
+    w2 = Tensor.empty(12, 8, 3, 3)
+    x.conv2d(w1).contiguous().conv2d(w2).realize()
+
+  def test_ffn(self):
+    from tinygrad.apps.llm import TransformerBlock
+    from tinygrad import nn
+    blk = TransformerBlock(1024, 4096, 1, 1, 1e-5)
+    for p in nn.state.get_parameters(blk): p.replace(Tensor.empty(p.shape))
+
+    x = Tensor.empty(128, 1024)
+    out = blk._feed_forward(x)
+    out.realize()
+
+  def test_flash_attention(self):
+    BS = 4
+    HEADS = 2
+    MATDIM = 16
+    EMB = 8
+    q = Tensor.empty(BS, HEADS, MATDIM, EMB)
+    k = Tensor.empty(BS, HEADS, MATDIM, EMB)
+    v = Tensor.empty(BS, HEADS, MATDIM, EMB)
+    q.scaled_dot_product_attention(k, v).realize()
+
+if __name__ == '__main__':
+  unittest.main()

test/test_schedule.py

@@ -1044,7 +1044,7 @@ 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, 5))
     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()))

test/test_softmax_fusion.py

@@ -41,7 +41,7 @@ class TestFuse(unittest.TestCase):
 
   def test_fuse_norm(self):
     a = Tensor.rand(50,50).realize()
-    self._test_fuse(lambda a: a / a.mean(axis=1), a, atol=1e-6)
+    self._test_fuse(lambda a: a / a.mean(axis=1), a)
 
   def test_fuse_argmax(self):
     a = Tensor.rand(50,50).realize()
@@ -117,14 +117,6 @@ class TestFuse(unittest.TestCase):
     c = (a.sum(axis=1) + b.sum(axis=1)).fuse()
     self.assertListEqual(c.tolist(), [30]*16)
 
-  @unittest.skipUnless(Device.DEFAULT == "METAL", "METAL TC")
-  def test_fuse_and_tc_opt(self):
-    A = Tensor.randn(8, 8).realize()
-    B = Tensor.randn(8, 8).realize()
-    C = Tensor.ones(1, 8, 8).pad(((1,1), None, None),).sum(0)
-    out = (C + (A @ B)).fuse()
-    out.realize()
-
 class TestSoftmaxFusion(unittest.TestCase):
   @classmethod
   def setUpClass(cls):

test/test_tiny.py

@@ -100,10 +100,10 @@ class TestTiny(unittest.TestCase):
       lambda x: x.flatten(1), nn.Linear(576, 10)]
 
     # replace random weights with ones
-    Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
+    for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))
 
     # run model inference
-    probs = Tensor.rand(1, 1, 28, 28).sequential(layers).tolist()
+    probs = Tensor.empty(1, 1, 28, 28).sequential(layers).tolist()
     self.assertEqual(len(probs[0]), 10)
 
   # TODO: this is failing because of how swizzling rewrites the ShapeTracker of the final STORE
@@ -116,8 +116,8 @@ class TestTiny(unittest.TestCase):
 
     # replace random weights with ones
     # TODO: there's a bug here where it's tying two of the biases together. we need UNIQUE const
-    #Tensor.realize(*[p.replace(Tensor.ones_like(p).contiguous()) for p in nn.state.get_parameters(layers)])
     for p in nn.state.get_parameters(layers): p.replace(Tensor.empty(p.shape))
+    #for p in nn.state.get_parameters(layers): p.replace(Tensor.ones_like(p).contiguous().realize())
 
     # realize gradients
     for x in nn.state.get_parameters(layers): x.requires_grad_()

test/test_uops_stats.py

@@ -20,7 +20,6 @@ def get_stats(x:Tensor):
   ei = lower_schedule_item(si)
   return ei.prg.estimates.ops, ei.prg.estimates.mem
 
-@unittest.skipIf(Device.DEFAULT == "WEBGPU", "webgpu does extra load/store for packed types")
 class TestMemoryCount(unittest.TestCase):
   def test_add(self):
     a = Tensor.empty(1024, 1024, dtype=dtypes.uint8)

test/unit/test_dtype.py

@@ -53,13 +53,5 @@ class TestCastConvenienceMethod(unittest.TestCase):
       self.assertEqual(t.float().dtype, dtypes.float)
       self.assertEqual(t.double().dtype, dtypes.double)
 
-class TestDtypeTolist(unittest.TestCase):
-  def test_bfloat16(self):
-    self.assertEqual(Tensor([-60000, 1.5, 3.1, 60000], device="PYTHON", dtype=dtypes.bfloat16).tolist(), [-59904.0, 1.5, 3.09375, 59904.0])
-    # 448
-    self.assertEqual(Tensor([-30000, 1.5, 3.1, 30000], device="PYTHON", dtype=dtypes.fp8e4m3).tolist(), [-448.0, 1.5, 3.0, 448.0])
-    # 57344
-    self.assertEqual(Tensor([-30000, 1.5, 3.1, 30000], device="PYTHON", dtype=dtypes.fp8e5m2).tolist(), [-28672.0, 1.5, 3.0, 28672.0])
-
 if __name__ == "__main__":
   unittest.main()

test/unit/test_hashing.py

@@ -2,6 +2,7 @@ from typing_extensions import Callable
 import hashlib, random, unittest
 from tinygrad import Tensor, Device, getenv, dtypes
 from tinygrad.device import is_dtype_supported
+from tinygrad.helpers import CI
 
 @unittest.skipUnless(is_dtype_supported(dtypes.uint8) and is_dtype_supported(dtypes.uint64), "Device must support uint8 and uint64")
 @unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashes in NV CI")
@@ -11,7 +12,7 @@ class TestHashing(unittest.TestCase):
     chunk_hashes = [hashlib.shake_128(chunk).digest(16) for chunk in chunks]
     return hashlib.shake_128(b''.join(chunk_hashes)).digest(16)
 
-  @unittest.skip("very slow")
+  @unittest.skipIf(CI, "very slow")
   def test_abc(self):
     expected = self._python_hash_1mb(b"abc" + b"\x00" * (2**20 - 3))
     out = Tensor(b"abc").hash()
@@ -64,7 +65,7 @@ class TestKeccak(unittest.TestCase):
     data = b"\x00" * 4
     self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))
 
-    data = b"\x00" * 1000
+    data = b"\x00" * (1000 if CI else 4096)
     self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))
 
 if __name__ == "__main__":

test/unit/test_uop_symbolic.py

@@ -622,8 +622,6 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(cond, 0, 1, "(a<2)")
     self.helper_test_variable(cond.where(u1, u0), 0, 1, "(a<2)")
     self.helper_test_variable(cond.where(u1, u0).where(u1, u0), 0, 1, "(a<2)")
-    self.helper_test_variable(cond.where(u0, u1), 0, 1, "((a<2)!=True)")
-    self.helper_test_variable(cond.where(u0, u1).where(u0, u1), 0, 1, "(a<2)")
 
   def test_where_combine(self):
     cond = Variable("x", 0, 3) < 2

test/unit/test_viz.py

@@ -250,7 +250,7 @@ class TestVizProfiler(unittest.TestCase):
 
     j = json.loads(get_profile(prof))
 
-    dev_events = j['layout']['NV']['shapes']
+    dev_events = j['layout']['NV']['timeline']['shapes']
     self.assertEqual(len(dev_events), 1)
     event = dev_events[0]
     self.assertEqual(event['name'], 'E_2')
@@ -263,7 +263,7 @@ class TestVizProfiler(unittest.TestCase):
 
     j = json.loads(get_profile(prof))
 
-    event = j['layout']['NV']['shapes'][0]
+    event = j['layout']['NV']['timeline']['shapes'][0]
     self.assertEqual(event['name'], 'COPYxx')
     self.assertEqual(event['st'], 900) # diff clock
     self.assertEqual(event['dur'], 10)
@@ -278,23 +278,23 @@ class TestVizProfiler(unittest.TestCase):
 
     j = json.loads(get_profile(prof))
 
-    tracks = list(j['layout'])
-    self.assertEqual(tracks[0], 'NV Graph')
-    self.assertEqual(tracks[2], 'NV')
-    self.assertEqual(tracks[4], 'NV:1')
+    devices = list(j['layout'])
+    self.assertEqual(devices[0], 'NV Graph')
+    self.assertEqual(devices[1], 'NV')
+    self.assertEqual(devices[2], 'NV:1')
 
-    nv_events = j['layout']['NV']['shapes']
+    nv_events = j['layout']['NV']['timeline']['shapes']
     self.assertEqual(nv_events[0]['name'], 'E_25_4n2')
     self.assertEqual(nv_events[0]['st'], 0)
     self.assertEqual(nv_events[0]['dur'], 2)
     #self.assertEqual(j['devEvents'][6]['pid'], j['devEvents'][0]['pid'])
 
-    nv1_events = j['layout']['NV:1']['shapes']
+    nv1_events = j['layout']['NV:1']['timeline']['shapes']
     self.assertEqual(nv1_events[0]['name'], 'NV -> NV:1')
     self.assertEqual(nv1_events[0]['st'], 954)
     #self.assertEqual(j['devEvents'][7]['pid'], j['devEvents'][3]['pid'])
 
-    graph_events = j['layout']['NV Graph']['shapes']
+    graph_events = j['layout']['NV Graph']['timeline']['shapes']
     self.assertEqual(graph_events[0]['st'], nv_events[0]['st'])
     self.assertEqual(graph_events[0]['st']+graph_events[0]['dur'], nv1_events[0]['st']+nv1_events[0]['dur'])
 
@@ -308,7 +308,7 @@ class TestVizMemoryLayout(BaseTestViz):
     a = _alloc(1)
     _b = _alloc(1)
     profile_ret = json.loads(get_profile(Buffer.profile_events))
-    ret = profile_ret["layout"][f"{a.device} Memory"]
+    ret = profile_ret["layout"][a.device]["mem"]
     self.assertEqual(ret["peak"], 2)
     self.assertEqual(ret["shapes"][0]["x"], [0, 2])
     self.assertEqual(ret["shapes"][1]["x"], [1, 2])
@@ -318,7 +318,7 @@ class TestVizMemoryLayout(BaseTestViz):
     del a
     b = _alloc(1)
     profile_ret = json.loads(get_profile(Buffer.profile_events))
-    ret = profile_ret["layout"][f"{b.device} Memory"]
+    ret = profile_ret["layout"][b.device]["mem"]
     self.assertEqual(ret["peak"], 1)
     self.assertEqual(ret["shapes"][0]["x"], [0, 2])
     self.assertEqual(ret["shapes"][1]["x"], [2, 3])
@@ -331,7 +331,7 @@ class TestVizMemoryLayout(BaseTestViz):
     del a
     c = _alloc(1)
     profile_ret = json.loads(get_profile(Buffer.profile_events))
-    ret = profile_ret["layout"][f"{c.device} Memory"]
+    ret = profile_ret["layout"][c.device]["mem"]
     self.assertEqual(ret["peak"], 2)
     self.assertEqual(ret["shapes"][0]["x"], [0, 3])
     self.assertEqual(ret["shapes"][1]["x"], [1, 3, 3, 4])

test_driven_development.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+python3 test/external/process_replay/reset.py
+CAPTURE_PROCESS_REPLAY=1 pytest -n auto test/test_tiny.py test/test_uop_graph.py test/test_ops.py test/test_linearizer.py
+while true; do
+  if python3 test/test_tiny.py; then
+    PYTHONPATH="." python3 test/external/process_replay/process_replay.py
+  fi
+done

tinygrad/codegen/__init__.py

@@ -87,7 +87,7 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
 
   # decompositions
   pm_decomp = symbolic_simple+get_late_rewrite_patterns(supported_ops, _TRANSCENDENTAL>=2)
-  ret.append(RewriteStep(pm_decomp, lambda _: opts.device, name="decompositions"))
+  ret.append(RewriteStep(pm_decomp, name="decompositions"))
 
   # final rules for the renderer (without sym)
   pm_final_rewrite = pm_decomp+pm_render+extra_matcher

tinygrad/codegen/devectorizer.py

@@ -364,7 +364,8 @@ def reduce_collapse(red:UOp):
         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")
-  if any(x.op is Ops.RANGE for x in sink.toposort()): return None
+  # TODO: why is REDUCE needed here and just RANGE isn't enough?
+  if any(x.op in {Ops.REDUCE, Ops.RANGE} for x in sink.toposort()): return None
   return sink.substitute({v:k for k,v in replaces.items()})
 
 def reduce_unparented(red:UOp):

tinygrad/codegen/opt/kernel.py

@@ -378,9 +378,9 @@ class Kernel:
       tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
       for tc in tensor_cores:
         tensor_core_opts = [self._create_tc_opts(reduceop, tc, axis, opt_level) for reduceop in self.reduceops]
-        if tensor_core_opts[0] is None: continue
         # can only fuse reduces with the same tc options
         assert all_same(tensor_core_opts)
+        if tensor_core_opts[0] is None: continue
         self.tensor_core_opts = tc_opts = tensor_core_opts[0]
 
         # attempt to pad the tensor axes that require it

tinygrad/frontend/onnx.py

@@ -5,7 +5,7 @@ from io import BufferedReader
 from tinygrad.nn.state import TensorIO
 from tinygrad.tensor import Tensor, _broadcast_shape, ReductionStr
 from tinygrad.helpers import getenv, DEBUG, all_same, prod, flatten, make_tuple, argsort, is_numpy_ndarray, get_single_element, polyN
-from tinygrad.dtype import DType, ConstType, dtypes, _from_np_dtype, truncate
+from tinygrad.dtype import DType, ConstType, dtypes, _from_np_dtype
 from tinygrad.device import is_dtype_supported, Device
 
 # ***** protobuf definitions ******
@@ -105,7 +105,9 @@ class PBBufferedReader(BufferedReader):
   def read_bytes(self) -> Tensor: return self.read_delimited(use_tensor=True)
   def read_float(self) -> float: return struct.unpack("<f", self.read(4))[0]
   def read_packed_floats(self) -> Tensor: return self.read_delimited(use_tensor=True)
-  def read_int64(self) -> int: return truncate[dtypes.int64](self.decode_varint())
+  def read_int64(self) -> int:
+    val = self.decode_varint()
+    return val - 2**64 if val & (1 << 63) else val
   def read_packed_int64s(self) -> list[int]:
     total_bytes_len = self.decode_varint()
     old_pos = self.tell()
@@ -1072,7 +1074,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     if X.ndim == 4: X = X.permute(0, 2, 1, 3)
     elif X.ndim == 3:
       assert num_heads is not None, "num_heads must be provided for 3D input"
-      X = X.unflatten(-1, (num_heads, X.shape[-1] // num_heads))
+      X = X.reshape(*X.shape[:-1], num_heads, X.shape[-1] // num_heads)
 
     head_size = cast(int, X.shape[-1])
     rot_dim = rotary_embedding_dim or head_size
@@ -1083,10 +1085,16 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     cos = cos[..., :rot_dim//2].unsqueeze(2)
     sin = sin[..., :rot_dim//2].unsqueeze(2)
 
-    x1, x2 = (x_rotate[..., ::2], x_rotate[..., 1::2]) if interleaved else x_rotate.chunk(2, dim=-1)
-    real = x1 * cos - x2 * sin
-    imag = x1 * sin + x2 * cos
-    x_rotated = real.stack(imag, dim=-1).flatten(start_dim=-2) if interleaved else real.cat(imag, dim=-1)
+    if interleaved:
+      x1, x2 = x_rotate[..., ::2], x_rotate[..., 1::2]
+      real = x1 * cos - x2 * sin
+      imag = x1 * sin + x2 * cos
+      x_rotated = Tensor.stack(real, imag, dim=-1).flatten(start_dim=-2)
+    else:
+      x1, x2 = x_rotate.chunk(2, dim=-1)
+      real = x1 * cos - x2 * sin
+      imag = x1 * sin + x2 * cos
+      x_rotated = real.cat(imag, dim=-1)
 
     output = x_rotated.cat(x_pass, dim=-1)
     return output.flatten(start_dim=2) if len(original_input_shape) == 3 else output.permute(0, 2, 1, 3)

tinygrad/helpers.py

@@ -137,7 +137,7 @@ PICKLE_BUFFERS, PROFILE, LRU = ContextVar("PICKLE_BUFFERS", 1), ContextVar("PROF
 CACHELEVEL, IGNORE_BEAM_CACHE, DEVECTORIZE = ContextVar("CACHELEVEL", 2), ContextVar("IGNORE_BEAM_CACHE", 0), ContextVar("DEVECTORIZE", 1)
 DISABLE_COMPILER_CACHE = ContextVar("DISABLE_COMPILER_CACHE", 0)
 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)
+QUANTIZE, VALIDATE_WITH_CPU = ContextVar("QUANTIZE", 0), ContextVar("VALIDATE_WITH_CPU", 0)
 CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)
 ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, AMD_LLVM = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0), ContextVar("AMD_LLVM", 1)
 
@@ -293,7 +293,7 @@ def fetch(url:str, name:pathlib.Path|str|None=None, subdir:str|None=None, gunzip
   else: fp = _ensure_downloads_dir() / (subdir or "") / ((name or hashlib.md5(url.encode('utf-8')).hexdigest()) + (".gunzip" if gunzip else ""))
   if not fp.is_file() or not allow_caching:
     (_dir := fp.parent).mkdir(parents=True, exist_ok=True)
-    with urllib.request.urlopen(urllib.request.Request(url, headers={"User-Agent": "tinygrad 0.11.0"}), timeout=10) as r:
+    with urllib.request.urlopen(urllib.request.Request(url, headers={"User-Agent": "tinygrad 0.10.3"}), timeout=10) as r:
       assert r.status == 200, r.status
       length = int(r.headers.get('content-length', 0)) if not gunzip else None
       readfile = gzip.GzipFile(fileobj=r) if gunzip else r

tinygrad/renderer/__init__.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 from typing import Callable, cast, TYPE_CHECKING
-import functools
-from dataclasses import dataclass, field
+import functools, itertools
+from dataclasses import dataclass, field, replace
 from tinygrad.helpers import to_function_name, dedup, prod
 from tinygrad.uop.ops import Ops, UOp, sym_infer, sint, Variable, ssimplify, GroupOp, PatternMatcher
 from tinygrad.dtype import AddrSpace, PtrDType
@@ -23,7 +23,6 @@ class Estimates:
   def from_uops(uops:list[UOp], ignore_indexing=False) -> Estimates:
     flops: sint = 0
     lds: sint = 0
-    mem: dict[tuple[UOp, Ops], sint] = {}
     mults: sint = 1
     mult_stack: list[sint] = []
     dont_count: set[UOp] = set()
@@ -35,11 +34,6 @@ class Estimates:
         elif u.op is Ops.IF:
           dont_count = dont_count.union(u.src[0].toposort())
     for u in uops:
-      if u.op in {Ops.LOAD, Ops.STORE}:
-        buf = u
-        while len(buf.src): buf = buf.src[0]
-        if buf.op is Ops.DEFINE_GLOBAL: # assume all DEFINE_GLOBAL memory is accessed
-          mem[(buf, u.op)] = cast(PtrDType, buf.dtype).size * buf.dtype.itemsize
       if u.op is Ops.RANGE:
         mult_stack.append(mults)
         mults *= cast(sint, u.src[0].ssimplify())
@@ -53,7 +47,7 @@ class Estimates:
         lds += u.src[1].dtype.itemsize * mults
       elif u.op in GroupOp.ALU and u not in dont_count: flops += (mults * (2 if u.op is Ops.MULACC else 1)) * u.dtype.count
       elif u.op is Ops.WMMA and u not in dont_count: flops += 2 * prod(u.arg[1]) // u.arg[5] * mults
-    return Estimates(flops, lds, sum(mem.values()))
+    return Estimates(flops, lds, lds) # TODO: properly track memory, lds is always a high estimate
 
 @dataclass
 class ProgramSpec:
@@ -90,9 +84,17 @@ class ProgramSpec:
       self.ins = sorted(dedup(self.ins))
       self._ran_post_init = True
 
+  @functools.cached_property
+  def mem_estimate(self) -> sint:
+    # group non-local bufs by the op type (LOAD or STORE) and the buffer arg. take the max access of that buffer in bytes
+    # TODO: these max and min don't work on symbolic, and results are very wrong.
+    return sum(max(x.src[0].dtype.nbytes() for x in group)
+      for _, group in itertools.groupby([x for x in self.ast.toposort() if x.op in {Ops.LOAD, Ops.STORE} and x.src[0].base.op is Ops.DEFINE_GLOBAL],
+                        key=lambda x: (x.op, x.src[0].base.arg)))
+
   @functools.cached_property
   def estimates(self) -> Estimates:
-    return Estimates() if self.uops is None else Estimates.from_uops(self.uops, ignore_indexing=True)
+    return replace(Estimates() if self.uops is None else Estimates.from_uops(self.uops, ignore_indexing=True), mem=self.mem_estimate)
 
   @functools.cached_property
   def function_name(self) -> str: return to_function_name(self.name)

tinygrad/renderer/cstyle.py

@@ -63,6 +63,8 @@ extra_pm = PatternMatcher([
   # insert a PRECAST before BITCAST to force it to be rendered. not needed on all backends?
   (UPat(Ops.BITCAST, name="x"), lambda x: UOp(Ops.BITCAST, x.dtype, (UOp(Ops.PRECAST, x.src[0].dtype, x.src),))
    if x.src[0].op not in {Ops.PRECAST, Ops.LOAD, Ops.CUSTOM} else None),
+  # rewrite MAX to CMPLT + WHERE (max function is annoying on many cstyle backends)
+  (UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])),
   # devectorize any bools
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.INDEX), dtype=dtypes.bool, name="alu"), no_vectorized_alu),
   # CAST (from bool) can't be vectorized
@@ -102,7 +104,9 @@ class CStyleLanguage(Renderer):
     Ops.ADD: lambda a,b,dtype: f"({a}+{b})", Ops.SUB: lambda a,b,dtype: f"({a}-{b})", Ops.MUL: lambda a,b,dtype: f"({a}*{b})",
     Ops.MOD: lambda a,b,dtype: f"({a}%{b})", Ops.IDIV: lambda a,b,dtype: f"({a}/{b})", Ops.CMPNE: lambda a,b,dtype: f"({a}!={b})",
     Ops.SHR: lambda a,b,dtype: f"({a}>>{b})", Ops.SHL: lambda a,b,dtype: f"({a}<<{b})", Ops.CMPLT: lambda a,b,dtype: f"({a}<{b})",
-    Ops.WHERE: lambda a,b,c,dtype: f"({a}?{b}:{c})", Ops.CMPEQ: lambda a,b,dtype: f"({a}=={b})"}
+    Ops.WHERE: lambda a,b,c,dtype: f"({a}?{b}:{c})", Ops.CMPEQ: lambda a,b,dtype: f"({a}=={b})",
+    # NOTE: these don't work, but they are nice for rendering
+    Ops.THREEFRY: lambda a,b,dtype: f"threefry({a},{b})", Ops.MAX: lambda a,b,dtype: f"max({a},{b})"}
 
   string_rewrite = base_rewrite
   extra_matcher = extra_pm

tinygrad/renderer/llvmir.py

@@ -206,13 +206,12 @@ class AMDLLVMRenderer(LLVMRenderer):
   string_rewrite = PatternMatcher([
     (UPat(Ops.SPECIAL, name="x"), lambda ctx, x: f"  {ctx[x]} = " + f"{ code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; "),
     (UPat(Ops.BARRIER), lambda ctx: barrier),
+    (UPat(Ops.CAST, name="x", dtype=dtypes.half.vec(16), src=UPat.var("y", dtypes.half.vec(8))), lambda ctx, x, y: f"  {ctx[x]} = shufflevector "\
+      f"<8 x half> {ctx[y]}, <8 x half> zeroinitializer, <16 x i32> <{', '.join([f'i32 {i}, i32 {j}' for i, j in zip(range(0, 8), range(8, 16))])}>"),
+    (UPat(Ops.CAST, name="x", dtype=dtypes.half.vec(8), src=UPat.var("y", dtypes.half.vec(16))), lambda ctx, x, y:
+      f"  {ctx[x]}= shufflevector <16 x half> {ctx[y]}, <16 x half> undef, <8 x i32> <{', '.join([f'i32 {x}' for x in range(0, 16, 2)])}>"),
   ]) + base_rewrite
-  extra_matcher = LLVMRenderer.extra_matcher + PatternMatcher([
-    (UPat(Ops.CAST, name="x", dtype=dtypes.half.vec(16), src=UPat.var("y", dtypes.half.vec(8))),
-      lambda x, y: UOp(Ops.VECTORIZE, dtypes.half.vec(16), tuple(y.gep(i // 2) if i % 2 == 0 else UOp.const(dtypes.half, 0.0) for i in range(16)))),
-    (UPat(Ops.CAST, name="x", dtype=dtypes.half.vec(8), src=UPat.var("y", dtypes.half.vec(16))),
-      lambda x, y: UOp(Ops.VECTORIZE, dtypes.half.vec(8), tuple(y.gep(i * 2) for i in range(8)))),
-  ])
+  extra_matcher = LLVMRenderer.extra_matcher
   def _render_footer(self, uops: list[UOp]) -> str:
     # TODO: this is copied from cstyle
     requiredMaxThreadsPerBlock = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")

tinygrad/runtime/ops_amd.py

@@ -15,7 +15,7 @@ from tinygrad.runtime.autogen.am import am
 from tinygrad.runtime.support.compiler_amd import HIPCompiler, AMDLLVMCompiler
 from tinygrad.runtime.support.elf import elf_loader
 from tinygrad.runtime.support.am.amdev import AMDev, AMMemoryManager
-from tinygrad.runtime.support.amd import AMDReg, AMDIP, import_module, import_soc, setup_pci_bars
+from tinygrad.runtime.support.amd import AMDReg, AMDIP, import_module, setup_pci_bars
 from tinygrad.runtime.support.system import System, PCIIfaceBase, PCIAllocationMeta, MAP_FIXED, MAP_NORESERVE
 from tinygrad.runtime.support.usb import ASM24Controller, USBMMIOInterface
 if getenv("IOCTL"): import extra.hip_gpu_driver.hip_ioctl  # noqa: F401 # pylint: disable=unused-import
@@ -751,7 +751,7 @@ class AMDDevice(HCQCompiled):
     debug_memory_size = round_up((self.max_cu_id + 1 if self.target >= (10,1,0) else 1) * (self.max_wave_id + 1) * 32, 64)
     if self.target[0] == 10: ctl_stack_size = min(ctl_stack_size, 0x7000)
 
-    self.soc = import_soc(self.target)
+    self.soc = importlib.import_module(f"tinygrad.runtime.autogen.am.{({9: 'vega10', 10: 'navi10', 11: 'soc21', 12: 'soc24'}[self.target[0]])}")
     self.pm4 = importlib.import_module(f"tinygrad.runtime.autogen.am.pm4_{'nv' if self.target[0] >= 10 else 'soc15'}")
     self.sdma = import_module('sdma', min(self.iface.ip_versions[am.SDMA0_HWIP], (6, 0, 0)))
     self.gc = AMDIP('gc', self.iface.ip_versions[am.GC_HWIP], self.iface.ip_offsets[am.GC_HWIP])

tinygrad/runtime/ops_null.py

@@ -1,13 +1,12 @@
-from tinygrad.device import Compiled, Compiler, Allocator
-from tinygrad.engine.jit import MultiGraphRunner
-from tinygrad.renderer.cstyle import CStyleLanguage
+from tinygrad.device import Compiled, Compiler, Renderer, Allocator
 from tinygrad.uop.ops import Ops
+from tinygrad.engine.jit import MultiGraphRunner
 
-class NullRenderer(CStyleLanguage):
+class NullRenderer(Renderer):
   device = "NULL"
+  code_for_op = {k:lambda:None for k in [Ops.EXP2, Ops.LOG2, Ops.SIN, Ops.SQRT]}
   has_local = False
-  float4 = "float4"
-  code_for_op = {**CStyleLanguage.code_for_op, Ops.THREEFRY: lambda a,b,dtype: f"threefry({a},{b})", Ops.MAX: lambda a,b,dtype: f"max({a},{b})"}
+  def render(self, uops:list) -> str: return ""
 
 class NullProgram:
   def __init__(self, name:str, lib:bytes): pass

tinygrad/runtime/support/am/amdev.py

@@ -29,7 +29,7 @@ class AMFirmware:
     # Load SOS firmware
     self.sos_fw = {}
 
-    blob, sos_hdr = self.load_fw(f"psp_{fmt_ver(am.MP0_HWIP)}_sos.bin", versioned_header='struct_psp_firmware_header')
+    blob, sos_hdr = self.load_fw(f"psp_{fmt_ver(am.MP0_HWIP)}_sos.bin", am.struct_psp_firmware_header_v2_0)
     fw_bin = sos_hdr.psp_fw_bin
 
     for fw_i in range(sos_hdr.psp_fw_bin_count):
@@ -45,11 +45,11 @@ class AMFirmware:
     self.smu_psp_desc = self.desc(blob, hdr.header.ucode_array_offset_bytes, hdr.header.ucode_size_bytes, am.GFX_FW_TYPE_SMU)
 
     # SDMA firmware
-    blob, hdr = self.load_fw(f"sdma_{fmt_ver(am.SDMA0_HWIP)}.bin", versioned_header='struct_sdma_firmware_header')
+    blob, hdr, hdr_v3 = self.load_fw(f"sdma_{fmt_ver(am.SDMA0_HWIP)}.bin", am.struct_sdma_firmware_header_v2_0, am.struct_sdma_firmware_header_v3_0)
     if hdr.header.header_version_major < 3:
       self.descs += [self.desc(blob, hdr.ctl_ucode_offset, hdr.ctl_ucode_size_bytes, am.GFX_FW_TYPE_SDMA_UCODE_TH1)]
       self.descs += [self.desc(blob, hdr.header.ucode_array_offset_bytes, hdr.ctx_ucode_size_bytes, am.GFX_FW_TYPE_SDMA_UCODE_TH0)]
-    else: self.descs += [self.desc(blob, hdr.header.ucode_array_offset_bytes, hdr.ucode_size_bytes, am.GFX_FW_TYPE_SDMA_UCODE_TH0)]
+    else: self.descs += [self.desc(blob, hdr_v3.header.ucode_array_offset_bytes, hdr_v3.ucode_size_bytes, am.GFX_FW_TYPE_SDMA_UCODE_TH0)]
 
     # PFP, ME, MEC firmware
     for (fw_name, fw_cnt) in ([('PFP', 1), ('ME', 1)] if self.adev.ip_ver[am.GC_HWIP] >= (12,0,0) else []) + [('MEC', 1)]:
@@ -83,13 +83,10 @@ class AMFirmware:
 
     self.descs += [self.desc(blob, hdr0.header.ucode_array_offset_bytes, hdr0.header.ucode_size_bytes, am.GFX_FW_TYPE_RLC_G)]
 
-  def load_fw(self, fname:str, *headers, versioned_header:str|None=None):
+  def load_fw(self, fname:str, *headers):
     fpath = fetch(f"https://gitlab.com/kernel-firmware/linux-firmware/-/raw/45f59212aebd226c7630aff4b58598967c0c8c91/amdgpu/{fname}", subdir="fw")
     blob = memoryview(bytearray(fpath.read_bytes()))
     if AM_DEBUG >= 1: print(f"am {self.adev.devfmt}: loading firmware {fname}: {hashlib.sha256(blob).hexdigest()}")
-    if versioned_header:
-      chdr = am.struct_common_firmware_header.from_address(mv_address(blob))
-      headers += (getattr(am, versioned_header + f"_v{chdr.header_version_major}_{chdr.header_version_minor}"),)
     return tuple([blob] + [hdr.from_address(mv_address(blob)) for hdr in headers])
 
   def desc(self, blob:memoryview, offset:int, size:int, *types:int) -> tuple[list[int], memoryview]: return (list(types), blob[offset:offset+size])
@@ -226,7 +223,7 @@ class AMDev(PCIDevImplBase):
 
     self.bhdr = am.struct_binary_header.from_buffer(bytearray(self.vram.view(tmr_offset, tmr_size)[:]))
     ihdr = am.struct_ip_discovery_header.from_address(ctypes.addressof(self.bhdr) + self.bhdr.table_list[am.IP_DISCOVERY].offset)
-    assert self.bhdr.binary_signature == am.BINARY_SIGNATURE and ihdr.signature == am.DISCOVERY_TABLE_SIGNATURE, "discovery signatures mismatch"
+    assert ihdr.signature == am.DISCOVERY_TABLE_SIGNATURE and not ihdr.base_addr_64_bit, f"0x{ihdr.signature:X} != 0x{am.DISCOVERY_TABLE_SIGNATURE:X}"
 
     # Mapping of HW IP to Discovery HW IP
     hw_id_map = {am.__dict__[x]: int(y) for x,y in am.hw_id_map}
@@ -259,3 +256,4 @@ class AMDev(PCIDevImplBase):
     for prefix, hwip in mods:
       self.__dict__.update(import_asic_regs(prefix, self.ip_ver[hwip], cls=functools.partial(AMRegister, adev=self, bases=self.regs_offset[hwip])))
     self.__dict__.update(import_asic_regs('mp', (11, 0), cls=functools.partial(AMRegister, adev=self, bases=self.regs_offset[am.MP1_HWIP])))
+

tinygrad/runtime/support/am/ip.py

@@ -1,8 +1,7 @@
-import ctypes, time, contextlib, functools
+import ctypes, time, contextlib, importlib, functools
 from typing import Literal
-from tinygrad.helpers import to_mv, data64, lo32, hi32, DEBUG, wait_cond
 from tinygrad.runtime.autogen.am import am
-from tinygrad.runtime.support.amd import import_soc
+from tinygrad.helpers import to_mv, data64, lo32, hi32, DEBUG, wait_cond
 
 class AM_IP:
   def __init__(self, adev): self.adev = adev
@@ -12,7 +11,9 @@ class AM_IP:
   def set_clockgating_state(self): pass # Set clockgating state for this IP
 
 class AM_SOC(AM_IP):
-  def init_sw(self): self.module = import_soc(self.adev.ip_ver[am.GC_HWIP])
+  def init_sw(self):
+    self.soc_ver = 24 if self.adev.ip_ver[am.GC_HWIP] >= (12,0,0) else 21
+    self.module = importlib.import_module(f"tinygrad.runtime.autogen.am.soc{self.soc_ver}")
 
   def init_hw(self):
     self.adev.regRCC_DEV0_EPF2_STRAP2.update(strap_no_soft_reset_dev0_f2=0x0)

tinygrad/runtime/support/amd.py

@@ -43,22 +43,12 @@ def fixup_ip_version(ip:str, version:tuple[int, ...]) -> list[tuple[int, ...]]:
 
   return [version, version[:2], version[:2]+(0,), version[:1]+(0, 0)]
 
-def header_download(file, name=None, subdir="defines") -> str:
-  url = "https://gitlab.com/linux-kernel/linux-next/-/raw/cf6d949a409e09539477d32dbe7c954e4852e744/drivers/gpu/drm/amd"
-  return fetch(f"{url}/{file}", name=name, subdir=subdir).read_text()
-
-def import_header(path:str):
-  t = re.sub(r'//.*|/\*.*?\*/','', header_download(path, subdir="defines"), flags=re.S)
-  return {k:int(v,0) for k,v in re.findall(r'\b([A-Za-z_]\w*)\s*=\s*(0x[0-9A-Fa-f]+|\d+)', t)}
-
 def import_module(name:str, version:tuple[int, ...], version_prefix:str=""):
   for ver in fixup_ip_version(name, version):
     try: return importlib.import_module(f"tinygrad.runtime.autogen.am.{name}_{version_prefix}{'_'.join(map(str, ver))}")
     except ImportError: pass
   raise ImportError(f"Failed to load autogen module for {name.upper()} {'.'.join(map(str, version))}")
 
-def import_soc(ip): return type("SOC", (object,), import_header(f"include/{({9: 'vega10', 10: 'navi10', 11: 'soc21', 12: 'soc24'}[ip[0]])}_enum.h"))
-
 def import_asic_regs(prefix:str, version:tuple[int, ...], cls=AMDReg) -> dict[str, AMDReg]:
   def _split_name(name): return name[:(pos:=next((i for i,c in enumerate(name) if c.isupper()), len(name)))], name[pos:]
   def _extract_regs(txt):
@@ -66,7 +56,8 @@ def import_asic_regs(prefix:str, version:tuple[int, ...], cls=AMDReg) -> dict[st
   def _download_file(ver, suff) -> str:
     dir_prefix = {"osssys": "oss"}.get(prefix, prefix)
     fetch_name, file_name = f"{prefix}_{'_'.join(map(str, ver))}_{suff}.h", f"{prefix}_{'_'.join(map(str, version))}_{suff}.h"
-    return header_download(f"include/asic_reg/{dir_prefix}/{fetch_name}", name=file_name, subdir="asic_regs")
+    url = "https://gitlab.com/linux-kernel/linux-next/-/raw/cf6d949a409e09539477d32dbe7c954e4852e744/drivers/gpu/drm/amd/include/asic_reg"
+    return fetch(f"{url}/{dir_prefix}/{fetch_name}", name=file_name, subdir="asic_regs").read_text()
 
   for ver in fixup_ip_version(prefix, version):
     try: offs, sh_masks = _extract_regs(_download_file(ver, "offset")), _extract_regs(_download_file(ver, "sh_mask"))

tinygrad/schedule/kernelize.py

@@ -91,8 +91,8 @@ kernelize_sym = symbolic_simple+PatternMatcher([
   (UPat(Ops.CAST, name="cast", src=(UPat(Ops.VIEW, name="vm"),)), lambda cast,vm: vm.base.cast(cast.dtype).view(vm.st)
     if cast.dtype.itemsize <= vm.dtype.itemsize and resolve(prod(vm.shape) > vm.st.real_size()) else None),
   # put UnaryOps before EXPANDs, if it can fuse with the input
-  #(UPat(GroupOp.Unary, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="inp"),), name="v"),), name="alu"),
-  # lambda inp,v,alu: inp.alu(alu.op).view(v.st) if resolve(prod(alu.shape) > v.st.real_size()) else None),
+  (UPat(GroupOp.Unary, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="inp"),), name="v"),), name="alu"),
+   lambda inp,v,alu: inp.alu(alu.op).view(v.st) if resolve(prod(alu.shape) > v.st.real_size()) else None),
 ])
 
 # support for using a contiguous permuted view instead of the parent view if one exists

tinygrad/schedule/multi.py

@@ -13,10 +13,6 @@ def handle_allreduce_multirank(buf:UOp, red:UOp) -> UOp|None:
   for i,dev in enumerate(buf.device):
     groups.setdefault(Device[dev].group_id, []).append(buf.mselect(i))
 
-  # Put reduce leader of each group first
-  reduce_leaders = set(getenv("REDUCE_LEADERS", "").split(","))
-  groups = {gid: sorted(bufs, key=lambda x: (x.device not in reduce_leaders, x.device)) for gid,bufs in groups.items()}
-
   # Skip if only one group or if every group has only one buffer
   if len(groups) <= 1 or not any(len(g) > 1 for g in groups.values()): return None
 

tinygrad/schedule/rangeify.py

@@ -0,0 +1,663 @@
+from typing import Any
+from dataclasses import dataclass, field
+from tinygrad.dtype import dtypes, AddrSpace, PtrDType
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, RewriteNotReady, _substitute
+from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, colored, flatten, dedup
+from tinygrad.uop.symbolic import symbolic_simple, sym
+
+from tinygrad.schedule.kernelize import Kernel
+from tinygrad.uop.ops import track_rewrites, graph_rewrite_map, graph_rewrite, KernelInfo, identity_element
+
+imported_rewrites = PatternMatcher([
+  # UOp with size 0 is zero
+  (UPat(GroupOp.All-{Ops.SINK}, name="root"), lambda root: root.const_like(0) if root.base.st is not None and root.size == 0 else None),
+  # DETACH and CONTIGUOUS_BACKWARD are NOOPs here
+  (UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]),
+  # reduce of size 0 is the identity element
+  (UPat(Ops.REDUCE_AXIS, name="reduce", src=(UPat.var("x"),)),
+   lambda reduce,x: reduce.const_like(identity_element(reduce.arg[0], reduce.dtype)) if x.size == 0 and reduce.size != 0 else None),
+])
+
+earliest_rewrites = imported_rewrites+PatternMatcher([
+  # RESHAPE on RESHAPE is the second reshape
+  (UPat(Ops.RESHAPE, src=(UPat(Ops.RESHAPE),), name="x"), lambda x: x.replace(src=(x.src[0].src[0],))),
+  # non shape changing RESHAPE is NOOP
+  (UPat(Ops.RESHAPE, name="x"), lambda x: x.src[0] if x.src[0].shape == x.arg else None),
+  # RESHAPE after COPY
+  (UPat(Ops.COPY, src=(UPat(Ops.RESHAPE, name="r"),UPat(name="d")), name="c"), lambda c,r,d: c.replace(src=(r.src[0],d)).reshape(r.arg)),
+  # TODO: this should be BUFFER_VIEW
+  (UPat(Ops.COPY, src=(UPat(Ops.SHRINK, name="r"),UPat(name="d")), name="c"), lambda c,r,d: c.replace(src=(r.src[0],d)).shrink(r.arg)),
+  # const hacks
+  (UPat(Ops.CONST, name="x"), lambda x:
+   x.replace(src=(x.src[0].src[0],)).reshape((1,)*len(x.shape)).expand(x.shape) if \
+    len(x.src) and x.src[0].op is Ops.VIEW and not any(s == 0 for s in x.shape) else None),
+  # assign only to buffer
+  (UPat(Ops.ASSIGN, src=(UPat(GroupOp.All-{Ops.BUFFER}), UPat(name="x"))), lambda x: x if x.src[0].base.op is not Ops.BUFFER else None),
+])
+
+# 1. add contiguous where we have to
+
+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,
+                     Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.LOAD}
+
+def realize(ctx:dict[UOp, None], tr:UOp) -> None: ctx[tr] = None
+
+def realize_parents(ctx:dict[UOp, None], rb:UOp) -> None:
+  for s in rb.src:
+    if s.op not in ALWAYS_CONTIGUOUS: ctx[s] = None
+
+do_realize = PatternMatcher([
+  # always realize SINK parents
+  (UPat(Ops.SINK, name="s"), lambda ctx,s: ctx.update((x.base, None) for x in s.src if x.base.op not in ALWAYS_CONTIGUOUS)),
+  # always realize ASSIGN/CONTIGUOUS/COPY/BUFFER_VIEW
+  (UPat({Ops.ASSIGN, Ops.COPY, Ops.BUFFER_VIEW}, name="tr"), realize),
+  # realize parents of COPY, MSELECT, MSTACK
+  (UPat((Ops.COPY, Ops.MSELECT, Ops.MSTACK), name="rb"), realize_parents),
+])
+
+add_contiguous = PatternMatcher([(UPat(GroupOp.All-{Ops.CONTIGUOUS}, name="x"),
+                                  lambda ctx,x: x.replace(tag=1).contiguous() if x in ctx and x.tag is None else None)])
+remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
+early_cleanups = PatternMatcher([(UPat().contiguous(name="c").contiguous(), lambda c: c),])
+
+# 2. mark all children
+
+@dataclass
+class ChildrenContext: children: dict[UOp, list[UOp]]|None = None
+def extract_children(ctx:ChildrenContext, x:UOp):
+  if ctx.children is not None: return
+  children_map = x.get_children_map()
+  ctx.children = {}
+  for k,v in children_map.items():
+    non_sink_children = [u for u in v if u.op is not Ops.SINK]
+    if len(non_sink_children) <= 1: continue
+    if any(x.op is Ops.REDUCE_AXIS for x in k.toposort()):
+      ctx.children[k] = non_sink_children
+
+def mark_children(ctx:ChildrenContext, x:UOp):
+  new_srcs = [(UOp(Ops.CHILD, s.dtype, src=(UOp(Ops.CHILDREN, s.dtype, (s,), arg=len(ctx.children[s])),),
+                   arg=(ctx.children[s].index(x), len(ctx.children[s]))) if s in ctx.children else s) for s in x.src]
+  return x.replace(src=tuple(new_srcs))
+
+pm_children = PatternMatcher([
+  (UPat(Ops.SINK, name="x"), extract_children),
+  (UPat(GroupOp.All-{Ops.CHILD, Ops.CHILDREN}, name="x"), mark_children),
+
+  # hack for one kernel threefry
+  #(UPat(Ops.CHILD, src=(UPat(Ops.THREEFRY, name="x"),)), lambda x: x),
+])
+
+# 3. rangeify
+
+@dataclass
+class RangeifyContext:
+  idx: int = 0
+  regs: int = 0
+  seen_children: dict[UOp, dict[int, UOp]] = field(default_factory=dict)
+  seen_child: dict[UOp, Any] = field(default_factory=dict)
+  progress: int = 0
+  children: dict[UOp, list[UOp]]|None = None
+
+def map_reshape(idx:UOp, r:UOp):
+  acc = 1
+  to_sum = []
+  for s,src in list(zip(idx.shape, idx.src[1:]))[::-1]:
+    to_sum.append(acc*src)
+    acc *= s
+  mish = sum(to_sum)
+  ret = []
+  for s in r.src[0].shape[::-1]:
+    if resolve(s!=1):
+      # this MOD should limit any ranges outside s
+      ret.append(mish % s)
+      mish //= s
+    else:
+      ret.append(UOp.const(dtypes.int, 0))
+  ret = UOp.sink(*ret).simplify().src[::-1] if len(ret) else ()
+  return r.src[0].index(*ret, dtype=idx.dtype, arg=idx.arg)
+
+def map_pad(idx:UOp, r:UOp):
+  ret = list(idx.src[1:])
+  bigwhere = UOp.const(dtypes.bool, True)
+  for i,(sh,(s,e)) in enumerate(zip(r.shape, r.arg)):
+    if s == 0 and e == 0: continue
+    where = UOp.const(dtypes.bool, True)
+    if e > 0: where = where & (ret[i] < (sh-e))
+    if s > 0: where = where & (ret[i] >= s)
+    bigwhere = bigwhere & where
+    # this is safe but dumb
+    ret[i] = (ret[i] - s).maximum(0).minimum(r.src[0].shape[i]-1)
+  # PAD is with 0
+  return bigwhere.simplify().where(r.src[0].index(*ret, dtype=idx.dtype, arg=idx.arg), UOp.const(r.dtype, 0))
+
+def map_expand(r:UOp, idx:UOp):
+  new_rngs = []
+  ending_ranges = []
+  non_ending_ranges = []
+  for a,x,y in zip(idx.src[1:], r.src[0].shape, r.shape):
+    axis_to_range = [u for u in a.toposort() if u.op is Ops.RANGE]
+    if resolve(x!=y, False):
+      ending_ranges.extend(axis_to_range)
+      new_rngs.append(a.const_like(0))
+    else:
+      non_ending_ranges.extend(axis_to_range)
+      new_rngs.append(a)
+  ending_ranges = [x.arg for x in ending_ranges if x not in non_ending_ranges]
+  if idx.arg is not None: ending_ranges.append(idx.arg)
+  return r.src[0].index(*new_rngs, arg=min([x for x in ending_ranges]) if ending_ranges else None)
+
+pm_mops = PatternMatcher([
+  # this is like the definitions of these
+  (UPat(Ops.INDEX, src=(UPat(Ops.SHRINK, name="r"),), allow_any_len=True, name="idx"),
+   lambda r,idx: r.src[0].index(*[a+ss if resolve(ss != 0) else a for a,(ss,_) in zip(idx.src[1:], r.arg)], dtype=idx.dtype, arg=idx.arg)),
+  (UPat(Ops.INDEX, src=(UPat(Ops.PERMUTE, name="r"),), allow_any_len=True, name="idx"),
+   lambda r,idx: r.src[0].index(*[idx.src[1+p] for p in argsort(idx.src[0].arg)], dtype=idx.dtype, arg=idx.arg)),
+  (UPat(Ops.INDEX, src=(UPat(Ops.FLIP, name="r"),), allow_any_len=True, name="idx"),
+   lambda r,idx: r.src[0].index(*[((s-1)-a) if f else a for a,s,f in zip(idx.src[1:], r.shape, r.arg)], dtype=idx.dtype, arg=idx.arg)),
+  # expand needs to end ranges
+  (UPat(Ops.INDEX, src=(UPat(Ops.EXPAND, name="r"),), allow_any_len=True, name="idx"), map_expand),
+  # reshape does a lot of symbolic stuff
+  (UPat(Ops.INDEX, src=(UPat(Ops.RESHAPE, name="r"),), allow_any_len=True, name="idx"), map_reshape),
+  # pad adds min and max
+  (UPat(Ops.INDEX, src=(UPat(Ops.PAD, name="r"),), allow_any_len=True, name="idx"), map_pad),
+])
+
+def map_contiguous(ctx:RangeifyContext, x:UOp, idx:UOp|None=None):
+  # NOTE: partial contig is disabled for now
+  #arg = x.arg
+  arg = None
+  if arg is None and idx is not None: return None
+  if arg is not None and idx is None: return None
+
+  ranges = []
+  new_ranges = []
+  passthrough_idx = []
+  for i,s in enumerate(x.shape):
+    if arg is not None and i not in arg:
+      assert idx is not None, "partial contig requires index"
+      ranges.append(idx.src[1+i])
+      continue
+    if idx is not None: passthrough_idx.append(idx.src[1+i])
+    if resolve(s!=1):
+      ranges.append(UOp.range(dtypes.int, s, ctx.idx))
+      new_ranges.append(ranges[-1])
+      ctx.idx += 1
+    else:
+      ranges.append(UOp.const(dtypes.int, 0))
+  ret = x.src[0].index(*ranges).bufferize(*new_ranges, arg=x.device)
+  ret = ret.index(*passthrough_idx) if len(passthrough_idx) else ret.reshape(x.shape)
+  return ret
+
+def map_reduce(ctx:RangeifyContext, idx:UOp, red:UOp):
+  rngs = list(idx.src[1:])
+  new_ranges = []
+  for i,s in enumerate(red.src[0].shape):
+    if i in red.arg[1]:
+      rngs[i] = UOp.range(dtypes.int, s, ctx.idx)
+      ctx.idx += 1
+      new_ranges.append(rngs[i])
+  return UOp(Ops.REDUCE, red.dtype, src=(red.src[0].index(*rngs),)+tuple(new_ranges), arg=red.arg[0])
+
+def index_child(ctx:RangeifyContext, c:UOp, x:UOp, idx:UOp):
+  if c not in ctx.seen_children: ctx.seen_children[c] = {}
+  # wait here until we have seen all the children
+  if len(ctx.seen_children[c]) != x.arg[1]:
+    ctx.progress += 1
+    if ctx.progress > 10000: raise RuntimeError("children not making progress")
+    # NOTE: we mark this here
+    ctx.seen_children[c][x.arg[0]] = idx
+    raise RewriteNotReady
+  ctx.progress = 0
+
+  if c not in ctx.seen_child:
+    all_rngs = zip(*[ch.src[1:] for ch in ctx.seen_children[c].values()])
+    out_rngs = []
+    end_ranges = []
+    idx_ranges = []
+    for i,r in enumerate(all_rngs):
+      if all_same(r):
+        out_rngs.append(r[0])
+      else:
+        out_rngs.append(UOp.range(dtypes.int, c.shape[i], ctx.idx))
+        ctx.idx += 1
+        end_ranges.append(out_rngs[-1])
+        idx_ranges.append(i)
+    ctx.seen_child[c] = (idx_ranges, end_ranges)
+  else:
+    out_rngs = list(idx.src[1:])
+    idx_ranges, end_ranges = ctx.seen_child[c]
+    for i,nr in zip(idx_ranges, end_ranges): out_rngs[i] = nr
+  if len(idx_ranges) == 0: return c.index(*out_rngs)
+  # NOTE: partial contigs can still come from here
+  return c.index(*out_rngs).bufferize(*end_ranges, arg=x.device).index(*[idx.src[1+i] for i in idx_ranges])
+
+def children_gate(ctx:RangeifyContext, idx:UOp, c:UOp):
+  if len(ctx.seen_children[c]) != c.arg: raise RuntimeError("all children should have been seen by now")
+  return idx.replace(src=(idx.src[0].src[0],)+idx.src[1:])
+
+def might_end_axis(idx:UOp):
+  if idx.arg is None: return None
+  to_end_axis = []
+  for i,a in enumerate(idx.src[1:]):
+    if any(x.arg > idx.arg for x in a.toposort() if x.op is Ops.RANGE):
+      to_end_axis.append(i)
+  if to_end_axis: return idx.replace(src=(idx.src[0].contiguous(arg=tuple(to_end_axis)),)+idx.src[1:], arg=None)
+  return idx.replace(arg=None)
+
+pm_rangeify = pm_mops+PatternMatcher([
+  # sink contigs to kick it off
+  (UPat(Ops.CONTIGUOUS, src=(UPat(),), name="x"), map_contiguous),
+
+  # if there are new ended children, tag the SINK
+  (UPat(Ops.INDEX, src=(UPat(Ops.CHILD, src=(UPat(name="c"), ), name="x"),), allow_any_len=True, name="idx"), index_child),
+  (UPat(Ops.INDEX, src=(UPat(Ops.CHILDREN, name="c"),), allow_any_len=True, name="idx"), children_gate),
+
+  # if we come across this, remove it. it was a CHILD unused in an INDEX
+  (UPat(Ops.CHILD, src=(UPat(Ops.CHILDREN, src=(UPat.var("x"),)),)), lambda x: x),
+
+  # if there's an INDEX it can support partial contig
+  (UPat(Ops.INDEX, src=(UPat(Ops.CONTIGUOUS, src=(UPat(),), name="x"),), allow_any_len=True, name="idx"), map_contiguous),
+
+  # CONST can't have axes. remove srcs when we idx
+  (UPat(Ops.INDEX, src=(UPat(Ops.CONST, name="c"),)), lambda c: c.replace(src=())),
+
+  # handle arg on any op with weight. old endrange stuff
+  (UPat(Ops.INDEX, src=(UPat(GroupOp.Elementwise.union({Ops.REDUCE_AXIS})),), allow_any_len=True, name="idx"), might_end_axis),
+
+  # move MAP through elementwise ALU / reduce. these are the items with cost
+  (UPat(Ops.INDEX, src=(UPat(GroupOp.Elementwise.union({Ops.STORE, Ops.ASSIGN, Ops.COPY, Ops.DEVICE})),), allow_any_len=True, name="x"),
+   lambda x: x.src[0].replace(src=tuple([s.index(*x.src[1:]) for s in x.src[0].src]))),
+  (UPat(Ops.INDEX, src=(UPat(Ops.REDUCE_AXIS, name="red"),), allow_any_len=True, name="idx"), map_reduce),
+])
+
+# 4. remove bufferize
+
+def bufferize_to_store(ctx, x:UOp):
+  rngs = x.src[1:]
+  shape = tuple([r.vmax+1 for r in rngs])
+  assert prod(shape) > 0, f"no zero sized buffers {shape}"
+  store_rngs = [x for x in UOp.sink(*rngs).toposort() if x.op is Ops.RANGE]
+  if x.src[0].op is Ops.ASSIGN:
+    return x.src[0].src[0].replace(dtype=x.dtype.ptr(size=prod(shape))).store(x.src[0].src[1], *store_rngs)
+  #buf = UOp.new_buffer(x.arg, prod(shape), x.dtype)
+  buf = UOp(Ops.DEFINE_LOCAL, x.dtype.ptr(size=prod(shape)), arg=ctx[0])
+  ctx[0] += 1
+  return buf.reshape(shape).index(*rngs, dtype=x.dtype.ptr(size=prod(shape))).store(x.src[0], *store_rngs)
+
+def add_load_on_buffer(idx:UOp, b:UOp):
+  if isinstance(idx.dtype, PtrDType): return None
+  return idx.replace(dtype=idx.dtype.ptr(b.size), arg=None).load()
+
+def add_load_on_store(x:UOp, st:UOp):
+  if isinstance(x.dtype, PtrDType): return None
+  rngs = x.src[1:]
+  shape = tuple([r.vmax+1 for r in rngs])
+  b = st.src[0].src[0]
+  #assert b.op is Ops.BUFFER
+  return b.shrink(((0,prod(shape)),)).reshape(shape).index(*rngs, dtype=x.dtype.ptr(size=b.size)).load(st)
+
+def shp(shp, rng):
+  acc = 1
+  ss = []
+  for s,r in list(zip(shp,rng))[::-1]:
+    ss.append(r*acc)
+    acc *= s
+  return sum(ss)
+
+pm_add_buffers = pm_mops+PatternMatcher([
+  (UPat(Ops.BUFFERIZE, name="x"), bufferize_to_store),
+  (UPat(Ops.INDEX, src=(UPat(Ops.BUFFER, name="b"), UPat()), name="idx"), add_load_on_buffer),
+  (UPat(Ops.INDEX, src=(UPat(Ops.STORE, name="st"),), allow_any_len=True, name="x"), add_load_on_store),
+
+  # HACK
+  #(UPat(Ops.CONST, name="c"), lambda c: c.replace(src=()) if len(c.src) else None),
+
+  (UPat(Ops.INDEX, name="idx").contiguous(),
+   lambda idx: UOp.new_buffer(idx.device, prod(idx.arg), idx.dtype).index(shp(idx.arg, idx.src[1:]),
+                                                                          dtype=idx.dtype.ptr(prod(idx.arg))).store(*idx.src))
+])
+
+# 5 (alt). create pointers
+
+def debuf(ctx, b:UOp):
+  ret = UOp(Ops.DEFINE_GLOBAL, b.dtype.ptr(b.arg), arg=ctx[0])
+  ctx[0] += 1
+  return ret
+
+pm_debuf = PatternMatcher([
+  (UPat(Ops.BUFFER, name="b"), debuf),
+  # HACK: consts shouldn't have srcs by here
+  (UPat(Ops.CONST, name="x"), lambda x: x.replace(src=()) if len(x.src) else None),
+  # no movement ops
+  (UPat(GroupOp.Movement, name="x"), lambda x: x.src[0]),
+  # HACK: no copy
+  (UPat(Ops.COPY, name="x"), lambda x: x.src[0]),
+])
+
+# 5. split into kernels
+
+@dataclass
+class LocalAddBufferContext:
+  dg:int = 0
+  map:dict = field(default_factory=dict)
+  vars:dict = field(default_factory=dict)
+
+def debuf(ctx:LocalAddBufferContext, b:UOp): return UOp(Ops.DEFINE_GLOBAL, b.dtype.ptr(b.arg), arg=ctx.map[b][1])
+def unbind_kernel(ctx:LocalAddBufferContext, b:UOp):
+  ctx.vars[b] = None
+  return b.src[0]
+
+def split_load(ctx:LocalAddBufferContext, s:UOp):
+  b = s.src[0].src[0]
+  if b.op is not Ops.BUFFER: return None
+
+  if len(s.src) == 2 and s.src[1].op is Ops.ASSIGN:
+    assert len(s.src) == 2
+    lb = s.src[1]
+    assert b not in ctx.map or ctx.map[b][0] == lb
+  else:
+    lb = b
+  if b not in ctx.map:
+    ctx.map[b] = (lb, ctx.dg)
+    ctx.dg += 1
+  return s.replace(src=s.src[0:1]) if b is not lb else None
+
+def handle_store(ctx:LocalAddBufferContext, s:UOp):
+  b = s.src[0].src[0]
+  if b.op is not Ops.BUFFER: return None
+  if b not in ctx.map:
+    ctx.map[b] = (b, ctx.dg)
+    ctx.dg += 1
+  if s.src[1].op is Ops.COPY: return s.src[1]
+  return None
+
+to_define_global = PatternMatcher([
+  (UPat(Ops.BUFFER, name="b"), debuf),
+  (UPat(Ops.BIND, name="b"), unbind_kernel),
+  (UPat(Ops.LOAD, name="s"), split_load),
+  (UPat(Ops.STORE, name="s"), handle_store),
+])
+
+def split_store(x:UOp):
+  if len(x.ranges): return None
+  store_rngs = x.src[2:]
+
+  ctx = LocalAddBufferContext()
+  ret = graph_rewrite(x, to_define_global, ctx=ctx, name="kernel split", bottom_up=True)
+  rng = sorted([u for u in ret.toposort() if u.op is Ops.RANGE], key=lambda x: x.arg)
+  name = "k"+colored('_', 'BLACK').join(['']+[colored(str(s.vmax+1), "WHITE") if s in store_rngs else colored(str(s.vmax+1), "red") for s in rng])
+
+  ret = ret.sink(arg=KernelInfo(name=name)) if ret.op is Ops.STORE else ret
+  kernel = UOp(Ops.KERNEL, src=tuple([x[0] for x in ctx.map.values()])+tuple(ctx.vars.keys()), arg=Kernel(ret, ()))
+  return kernel.src[0].assign(kernel)
+
+split_kernels = PatternMatcher([
+  (UPat(Ops.STORE, name="x"), split_store),
+])
+
+pm_children_fixup = PatternMatcher([
+  # clone all movement ops
+  (UPat(Ops.CHILD, src=(UPat(Ops.CHILDREN, src=(UPat(GroupOp.Movement, name="m"),)),), name="c"),
+    lambda c,m: UOp(m.op, m.dtype, (c.replace(src=(c.src[0].replace(src=(m.src[0],)),)),), m.arg)),
+])
+
+
+@dataclass
+class RContext:
+  range_num = 0
+
+def new_range(ctx, s):
+  ret = UOp.range(dtypes.int, s, ctx.range_num)
+  ctx.range_num += 1
+  return ret
+
+def td_reshape(ctx, idx:UOp, r:UOp):
+  acc = 1
+  to_sum = []
+  for s,i in list(zip(idx.arg, idx.src[1:]))[::-1]:
+    to_sum.append(i*acc)
+    acc *= s
+  mish = sum(to_sum)
+  ret = []
+  for s in r.arg[::-1]:
+    if resolve(s!=1):
+      # this MOD should limit any ranges outside s
+      ret.append(mish % s)
+      mish //= s
+    else:
+      ret.append(UOp.const(dtypes.int, 0))
+  ret = UOp.sink(*ret).simplify().src[::-1] if len(ret) else ()
+  ii = idx.src[0]
+  out_rng = ret
+
+  """
+  out_rng = []
+  for i,rr in enumerate(ret):
+    if rr.op not in {Ops.RANGE, Ops.CONST}:
+      out_rng.append(new_range(ctx, r.arg[i]))
+    else:
+      out_rng.append(rr)
+
+  mm = [idx.src[0]]
+  for x,y in zip(ret, out_rng):
+    if x is not y:
+      mm.append(x)
+      mm.append(y)
+  if len(mm) > 1:
+    ii = UOp(Ops.MERGE, idx.dtype, tuple(mm))
+  """
+  return ii.index(*out_rng, dtype=idx.dtype, arg=r.arg)
+
+def td_elementwise(ctx, e:UOp):
+  # if the range is closed by a reduce to the left, we can't reuse it
+  # TODO: handle composite ranges better
+  reduces_left = flatten([x.src[1:] for x in e.toposort() if x.op is Ops.REDUCE])
+  shps = [u.arg for u in e.src]
+  assert all_same(shps)
+  rngs = [u.src[1:] for u in e.src]
+  out_rng = []
+  need_merge = False
+  for i,r in enumerate(zip(*rngs)):
+    r = [x for x in r if x is not UOp.const(dtypes.int, 0)]
+    if len(r) == 0:
+      out_rng.append(UOp.const(dtypes.int, 0))
+    elif all_same(r) and r[0] not in reduces_left:
+      out_rng.append(r[0])
+    else:
+      out_rng.append(new_range(ctx, shps[0][i]))
+      need_merge = True
+  if need_merge:
+    new_src = []
+    for u in e.src:
+      assert u.op is Ops.INDEX
+      out = [u.src[0]]
+      rngs_in_src = [x for x in out[0].toposort() if x.op is Ops.RANGE]
+      for i,idx in list(enumerate(u.src[1:]))[::-1]:
+        rngs_in_idx = [x for x in idx.toposort() if x.op is Ops.RANGE]
+        if all(x not in rngs_in_src for x in rngs_in_idx):
+          # for expands
+          continue
+        if idx is not out_rng[i]:
+          out.append(idx)
+          out.append(out_rng[i])
+          #out = UOp(Ops.MERGE, out.dtype, src=(out, idx, out_rng[i]))
+      if len(out) > 1:
+        new_src.append(UOp(Ops.MERGE, u.dtype, tuple(out)))
+      else:
+        new_src.append(out[0])
+      #mm = []
+      #for i,idx in enumerate(u.src[1:]):
+      #  if idx is not out_rng[i] and idx is not UOp.const(dtypes.int, 0):
+      #    mm.append(UOp(Ops.MERGE, src=(idx, out_rng[i])))
+      #new_src.append(UOp(Ops.MBLOCK, u.dtype, (u.src[0],)+tuple(mm)))
+  else:
+    new_src = list([x.src[0] for x in e.src])
+  return e.replace(src=tuple(new_src)).index(*out_rng, arg=shps[0])
+
+def td_shrink(idx:UOp, r:UOp):
+  ret = []
+  shp = []
+  for u,(s,e),shape in zip(idx.src[1:], r.arg, idx.arg):
+    assert s == 0
+    #if u.vmax >= e: u = (u<e).where(u, UOp(Ops.INVALID, u.dtype))
+    ret.append(u)
+    shp.append(min(shape, e))
+  return idx.src[0].index(*ret, dtype=idx.dtype, arg=tuple(shp))
+
+def td_reduce(ctx, idx:UOp, r:UOp):
+  rngs = idx.src[1:]
+  new_shp = tuple([s if i not in r.arg[1] else 1 for i,s in enumerate(idx.arg)])
+  return UOp(Ops.REDUCE, r.dtype, (idx.src[0],)+tuple([x for i,x in enumerate(rngs) if i in r.arg[1]]),
+                     r.arg[0]).index(*[x if i not in r.arg[1] else UOp.const(dtypes.int, 0) for i,x in enumerate(rngs)], arg=new_shp)
+
+pm_td_rangeify = PatternMatcher([
+  #(UPat(Ops.INDEX, src=(UPat(Ops.MERGE, src=(UPat(Ops.LOAD, name="b"),), allow_any_len=True),), allow_any_len=True, name="idx"),
+  # lambda idx,b: b.src[0].src[0].index(*idx.src[1:], dtype=b.src[0].dtype).load().index(*idx.src[1:], arg=idx.arg)),
+  (UPat(Ops.BUFFER, name="b"), lambda ctx, b:
+    b.replace(tag=1).index(nr:=new_range(ctx, b.size), dtype=b.dtype.ptr(size=b.size)).load().index(nr, arg=(b.size,)) if b.tag is None else None),
+    #b.replace(tag=1).index(new_range(ctx, b.size), arg=(b.size,)) if b.tag is None else None),
+  (UPat(Ops.CONST, src=(UPat(Ops.DEVICE),), name="c"), lambda c: c.replace(src=()).index(arg=())),
+  (UPat(Ops.RESHAPE, src=(UPat(Ops.INDEX, name="idx"),), name="r"), td_reshape),
+  (UPat(Ops.SHRINK, src=(UPat(Ops.INDEX, name="idx"),), name="r"), td_shrink),
+  (UPat(Ops.PERMUTE, src=(UPat(Ops.INDEX, name="idx"),), name="r"),
+    lambda r,idx: idx.src[0].index(*[idx.src[1+p] for p in r.arg], dtype=idx.dtype, arg=tuple(idx.arg[p] for p in r.arg))),
+  # 0s are already in place for EXPAND
+  #(UPat(Ops.EXPAND, src=(UPat(Ops.INDEX, name="idx"),), name="r"), lambda r,idx: idx.replace(arg=r.arg)),
+  (UPat(Ops.EXPAND, src=(UPat(Ops.INDEX, name="idx"),), name="r"),
+    lambda ctx,r,idx: idx.src[0].index(*[ii if s1==s2 else new_range(ctx, s1) for s1,s2,ii in zip(r.arg, idx.arg, idx.src[1:])], arg=r.arg)),
+  (UPat(GroupOp.Elementwise, src=UPat(Ops.INDEX), name="e"), td_elementwise),
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.INDEX, name="idx"),), name="r"), td_reduce),
+])
+
+def remove_merge(m):
+  tr0, tr1 = [], []
+  for r0,r1 in zip(m.src[1::2], m.src[2::2]):
+    if r0 is r1: continue
+    tr0.append(r0)
+    tr1.append(r1)
+  if m.src[0].op is Ops.LOAD and False:
+    # hack for LOAD
+    reps = {k:v for k,v in zip(tr0, tr1)}
+    return m.src[0].substitute(reps)
+  return UOp(Ops.BUFFERIZE, m.dtype, (m.src[0],)+tuple(tr0), arg=m.device).index(*tr1)
+
+no_merge = PatternMatcher([
+  (UPat(Ops.MERGE, name="m"), remove_merge),
+])
+
+@track_rewrites(name=lambda sink,ret: f"Schedule {pluralize('Kernel',len([u for u in ret[sink].toposort() if u.op is Ops.KERNEL]))}", replay=True)
+def get_kernelize_map(sink:UOp) -> dict[UOp, UOp]:
+  tensor_map = graph_rewrite_map(sink, earliest_rewrites, name="earliest")
+  realize_map = {}
+  graph_rewrite(tensor_map[sink], do_realize, ctx=realize_map, name="Input Graph")
+  tensor_map = graph_rewrite_map(tensor_map[sink], add_contiguous, ctx=realize_map, bottom_up=True, input_map=tensor_map, name="add contiguous")
+  tensor_map = graph_rewrite_map(tensor_map[sink], early_cleanups+remove_tags, input_map=tensor_map, name="cleanup")
+  rsink = tensor_map[sink]
+
+  ctx = RContext()
+  rsink = graph_rewrite(rsink, pm_td_rangeify, ctx=ctx, name="td rangeify")
+  rsink = graph_rewrite(rsink, sym, name="symbolic")
+
+  # find MOD on RANGE to split
+  while 1:
+    #break
+    reps = {}
+    for u in rsink.toposort():
+      if u.op is Ops.MOD and u.src[0].op is Ops.RANGE and u.src[1].op is Ops.CONST:
+        r = u.src[0].vmax+1
+        c = u.src[1].arg
+        if r%c == 0:
+          reps[u.src[0]] = new_range(ctx, r//c)*c + new_range(ctx, c)
+    print(len(reps))
+    if len(reps) == 0: break
+    rsink = rsink.substitute(reps)
+    rsink = graph_rewrite(rsink, sym, name="symbolic")
+
+  for i in range(0):
+    print("loop")
+    real_rngs = rsink.ranges.copy()
+    for u in rsink.toposort():
+      if u.op is Ops.REDUCE:
+        for s in u.src[1:]: real_rngs[s] = None
+    real_rngs = {x:[] for x in real_rngs}
+    print("unmovable", [x.arg for x in real_rngs])
+
+    for u in rsink.toposort():
+      if u.op is not Ops.MERGE: continue
+      assert all(x.op is Ops.RANGE for x in u.src)
+      r0, r1 = [x for x in u.src]
+      if r0 is r1: continue
+      if r0 in real_rngs: real_rngs[r0].append(r1)
+      if r1 in real_rngs: real_rngs[r1].append(r0)
+
+    rew = {}
+    for k,v in real_rngs.items():
+      print(k.arg, [x.arg for x in v])
+      for u in v:
+        rew[u] = k
+    rsink = rsink.substitute(rew)
+
+
+  """
+  rngs = [x for x in rsink.toposort() if x.op is Ops.RANGE]
+  mmap = {16:1000, 2:8, 3:9}
+  rep = {}
+  for x in rngs:
+    if x.arg in mmap:
+      rep[x] = x.replace(arg=mmap[x.arg])
+  rsink = rsink.substitute(rep)
+  """
+
+  rsink = graph_rewrite(rsink, no_merge, name="remove merge")
+
+  """
+  tensor_map = graph_rewrite_map(tensor_map[sink], pm_children, ctx=ChildrenContext(), bottom_up=True, input_map=tensor_map, name="children")
+  tensor_map = graph_rewrite_map(tensor_map[sink], pm_children_fixup, bottom_up=True, input_map=tensor_map, name="fixup children")
+  tensor_map = graph_rewrite_map(tensor_map[sink], pm_rangeify, ctx=RangeifyContext(), bottom_up=True, input_map=tensor_map, name="rangeify")
+  """
+  #tensor_map = graph_rewrite_map(tensor_map[sink], symbolic_simple, input_map=tensor_map, name="symbolic")
+  #tensor_map = graph_rewrite_map(tensor_map[sink], pm_add_buffers, bottom_up=True, input_map=tensor_map, name="add buffers")
+  #if getenv("VIZ"): graph_rewrite(tensor_map[sink], PatternMatcher([]), name="View Rangeify Graph")
+  if getenv("VIZ"): graph_rewrite(rsink, PatternMatcher([]), name="View Rangeify Graph")
+
+  rsink = graph_rewrite(rsink, pm_add_buffers, ctx=[0], bottom_up=True, name="add buffers")
+
+  # render
+  if getenv("SRC") or True:
+    #rsink = tensor_map[sink]
+    from tinygrad.codegen.devectorizer import pm_reduce, ReduceContext
+    rsink = graph_rewrite(rsink, pm_reduce, ctx=ReduceContext(), name="remove reduce")
+    rsink = graph_rewrite(rsink, pm_debuf, ctx=[0], name="debuf", bottom_up=True)
+    rsink = graph_rewrite(rsink, sym, name="symbolic 2")
+
+    # renumber ranges
+    #rngs = dedup([x for x in flatten([x.src[2:] for x in list(rsink.toposort())[::-1] if x.op is Ops.STORE]) if x.op is Ops.RANGE])
+    #rsink = rsink.substitute({x:x.replace(arg=i) for i,x in enumerate(rngs)})
+
+    from tinygrad.codegen import rewrites_for_linearizer, apply_rewrites
+    rsink = apply_rewrites(rsink, rewrites_for_linearizer)
+    from tinygrad.renderer.cstyle import CStyleLanguage
+    src = CStyleLanguage().render(rsink.arg.lst)
+    print(src)
+    return {sink:sink}
+
+  tensor_map = graph_rewrite_map(tensor_map[sink], split_kernels, input_map=tensor_map, 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 tensor_map[sink].toposort():
+    if u.op is not Ops.ASSIGN: 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")
+      assign_rep[a] = kernel_assign[s] = a.replace(src=a.src+(u,))
+  if assign_rep:
+    tensor_map = graph_rewrite_map(tensor_map[sink], _substitute, ctx=assign_rep, bottom_up=True, input_map=tensor_map, name="fix_assign")
+
+  if getenv("VIZ"): graph_rewrite(tensor_map[sink], PatternMatcher([]), name="View Kernel Graph")
+  return tensor_map

tinygrad/tensor.py

@@ -14,7 +14,7 @@ from tinygrad.device import Device, Buffer
 from tinygrad.engine.realize import run_schedule
 from tinygrad.engine.memory import memory_planner
 from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars
-from tinygrad.schedule.kernelize import get_kernelize_map
+from tinygrad.schedule.rangeify import get_kernelize_map
 
 # *** all in scope Tensors are here. this gets relevant UOps ***
 
@@ -252,7 +252,8 @@ class Tensor(MathTrait):
     # create the schedule
     schedule, var_vals = create_schedule_with_vars(sink)
     schedule = memory_planner(schedule)
-    if DEBUG >= 1 and len(schedule) > 1: print(f"scheduled {len(schedule)} kernels in {(time.perf_counter()-st)*1000:.2f} ms")
+    if (DEBUG >= 1 and len(schedule) >= 10) or (DEBUG >= 2 and len(schedule) > 1):
+      print(f"scheduled {len(schedule)} kernels in {(time.perf_counter()-st)*1000:.2f} ms")
     return schedule, var_vals
 
   def schedule(self, *lst:Tensor) -> list[ScheduleItem]:
@@ -345,7 +346,6 @@ class Tensor(MathTrait):
     print(t.tolist())
     ```
     """
-    if self.dtype in (dtypes.bfloat16, *dtypes.fp8s): return self.cast(dtypes.float32).tolist()
     return self.data().tolist()
 
   def numpy(self) -> 'np.ndarray':  # type: ignore [name-defined] # noqa: F821
@@ -1128,12 +1128,11 @@ class Tensor(MathTrait):
     if (isinstance(indices, list) and all_int(indices)) or not isinstance(indices, (tuple, list)): indices = [indices]
     x, indices = self, list(indices)
 
-    # fill ellipsis or rest of indices with slice(None)
+    # filter ellipsis and fill with slice(None) or fill rest of indices with slice(None)
     if len(ellipsis_idx := [dim for dim, i in enumerate(indices) if i is Ellipsis]) > 1: raise IndexError("indices can only have a single ellipsis")
-    # NOTE: None adds a dim later
+    fill_idx = ellipsis_idx[0] if ellipsis_idx else len(indices)
     num_indices = len(indices) - len(ellipsis_idx) - sum(1 for i in indices if i is None)
     if num_indices > self.ndim: raise IndexError(f"too many {num_indices=} for {self.ndim=}")
-    fill_idx = ellipsis_idx[0] if ellipsis_idx else len(indices)
     indices[fill_idx:fill_idx+1] = [slice(None)] * (self.ndim - num_indices)
 
     indices_parsed, dim = [], 0
@@ -1149,7 +1148,6 @@ class Tensor(MathTrait):
           index = Tensor([i+size if i<0 else i for i in fully_flatten(index)], self.device, requires_grad=False).reshape(ti.shape)
         case int() | UOp(): # sint
           if index >= size or index < -size: raise IndexError(f"{index=} is out of bounds with {size=}")
-          # TODO: is this right for (negative) symbolic?
           boundary = [index, index+1] if index >= 0 else [index+size, index+size+1]
         case slice():
           if index.step == 0: raise ValueError(f"{index=} cannot have 0 as step")
@@ -1163,9 +1161,9 @@ class Tensor(MathTrait):
             elif stride < 0: boundary = [boundary[1] + 1, boundary[0] + 1]
             # update size for slice
             size = ceildiv((boundary[1] - boundary[0]), abs(stride))
-          elif resolve(step == 1, False) and all(isinstance(s,sint) for s in (start, stop)) and resolve((stop-start) > 0, False):
+          elif (step == 1) and isinstance(step, int) and all(isinstance(s,(int,UOp)) for s in (start, stop)) and resolve((stop-start) > 0, False):
             # simple symbolic slice
-            size = cast(sint, cast(UOp, (stop - start)).ssimplify())
+            size = cast(UOp|int, cast(UOp, (stop - start)).ssimplify())
           else: raise TypeError(f"slice {index=} is not supported")
         case None: pass # do nothing
         case _: raise IndexError(f"{type(index).__name__} indexing is not supported")
@@ -1177,9 +1175,9 @@ class Tensor(MathTrait):
       # flip negative strides
       shrinks, strides = zip(*((i['boundary'], i['stride']) for i in mops))
       x = x.shrink(shrinks).flip(tuple(i for i,st in enumerate(strides) if st < 0))
-      strides = tuple(map(abs, strides))
-      # apply stride
-      if any(st != 1 for st in strides):
+      # handle stride != 1 or -1
+      if any(abs(st) != 1 for st in strides):
+        strides = tuple(abs(s) for s in strides)
         # pad shape to multiple of stride
         if not all_int(x.shape): raise RuntimeError("symbolic shape not supported")
         x = x.pad(tuple((0, round_up(s, st) - s) for s, st in zip(x.shape, strides)))
@@ -2683,7 +2681,7 @@ class Tensor(MathTrait):
     print(t.triu(diagonal=-1).numpy())
     ```
     """
-    return Tensor._tri(self.shape[-2], self.shape[-1], diagonal=diagonal, device=self.device, dtype=dtypes.bool).where(self, self.zeros_like())
+    return Tensor._tri(self.shape[-2], self.shape[-1], diagonal=diagonal, device=self.device, dtype=dtypes.bool).where(self, 0).cast(self.dtype)
 
   def tril(self, diagonal:int=0) -> Tensor:
     """
@@ -2706,7 +2704,7 @@ class Tensor(MathTrait):
     print(t.tril(diagonal=-1).numpy())
     ```
     """
-    return Tensor._tri(self.shape[-2], self.shape[-1], diagonal=diagonal+1, device=self.device, dtype=dtypes.bool).where(self.zeros_like(), self)
+    return Tensor._tri(self.shape[-2], self.shape[-1], diagonal=diagonal+1, device=self.device, dtype=dtypes.bool).where(0, self).cast(self.dtype)
 
   def interpolate(self, size:tuple[int, ...], mode:str="linear", align_corners:bool=False) -> Tensor:
     """

tinygrad/uop/__init__.py

@@ -9,19 +9,18 @@ class FastEnum(IntEnum):
 # the order of these Ops controls the order of the toposort
 class Ops(FastEnum):
   # uops that aren't rendered
-  NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto(); PRECAST = auto(); REWRITE_ERROR = auto()  # noqa: E702
+  NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto(); PRECAST = auto()  # noqa: E702
 
   # track children
   CHILD = auto(); CHILDREN = auto() # noqa: E702
+  MERGE = auto(); MBLOCK = auto(); INVALID = auto()
 
   # buffer ops
   COPY = auto(); BUFFER = auto(); BUFFER_VIEW = auto(); MSELECT = auto(); MSTACK = auto() # noqa: E702
 
-  # create buffer
-  BUFFERIZE = auto()
-
   # ops that adjust the behavior of the scheduler
   CONTIGUOUS = auto(); CONTIGUOUS_BACKWARD = auto(); DETACH = auto(); FUSE = auto() # noqa: E702
+  BUFFERIZE = auto()
 
   # blocks in linearizer (only used there)
   BLOCK = auto(); BLOCKSTART = auto(); BLOCKEND = auto(); BLOCKFINAL = auto() # noqa: E702

tinygrad/uop/decompositions.py

@@ -2,7 +2,7 @@ from typing import Callable
 import math, functools
 from tinygrad.dtype import dtypes, DType, promo_lattice
 from tinygrad.device import is_dtype_supported
-from tinygrad.helpers import polyN, DISABLE_FAST_IDIV
+from tinygrad.helpers import polyN, getenv
 from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher
 
 TRANSCENDENTAL_SUPPORTED_DTYPES = (dtypes.float16, dtypes.float32, dtypes.float64)
@@ -317,10 +317,8 @@ powers_of_two = {2**i:i for i in range(64)}
 def get_late_rewrite_patterns(ops:tuple[Ops, ...], force_transcendental=False):
   pat: list[tuple[UPat, Callable]] = [(UPat(op, dtype=TRANSCENDENTAL_SUPPORTED_DTYPES, src=(UPat.var("d"),)), f) for op,f in \
            ((Ops.EXP2, xexp2), (Ops.LOG2, xlog2), (Ops.SIN, xsin)) if op not in ops or force_transcendental]
-  # no real hardware supports THREEFRY, but NullRenderer does
-  if Ops.THREEFRY not in ops: pat.append((UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32))
-  # MAX can be rewritten as CMPLT + WHERE (max function is annoying on many cstyle backends)
-  if Ops.MAX not in ops and Ops.CMPLT in ops: pat.append((UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])))
+  # no real hardware supports THREEFRY
+  pat.append((UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32))
   # rewrite SQRT to xpow 0.5
   if Ops.SQRT not in ops: pat.append((UPat(Ops.SQRT, src=UPat.var("d")), lambda d: xpow(d, d.const_like(0.5))))
   # rewrite MOD to AND (which should always be supported, but not for generic in tests): x % (2**y) -> x & (2**y-1)
@@ -332,7 +330,7 @@ def get_late_rewrite_patterns(ops:tuple[Ops, ...], force_transcendental=False):
     pat += [(UPat.var("x", dtypes.uints)//UPat.cvar("c"), lambda x,c: x >> v if (v:=powers_of_two.get(c.arg, 0)) else None)]
     pat += [(UPat.var("x", dtypes.ints)//UPat.cvar("c"), lambda x,c: (x+(l.const_like(l.vmin) if (l:=(x<0)).vmin==l.vmax else l).where(
       c-1, 0)) >> v if (v:=powers_of_two.get(c.arg, 0)) else None)]  # (x+(x<0).where(c-1, 0)) >> v
-    if not DISABLE_FAST_IDIV:
+    if not getenv("DISABLE_FAST_IDIV"):
       pat += [(UPat.var("x", dtypes.ints)//UPat.cvar("d"), lambda ctx, x, d: fast_idiv(ctx, x, d.arg))]
       pat += [(UPat.var("x", dtypes.ints)%UPat.var("d"), lambda x, d: x-d*(x//d))]
   if Ops.NEG in ops:

tinygrad/uop/ops.py

@@ -136,13 +136,17 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
 
   @functools.cached_property
   def st(self) -> ShapeTracker|None:
-    if self.op in GroupOp.Block or self.op is Ops.INDEX: return None
+    if self.op is Ops.INDEX and self.src[0].op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.BUFFER, Ops.BUFFERIZE}: return None
+    if self.op is Ops.MBLOCK: return None
+    if self.op in GroupOp.Block: return None
     from tinygrad.shape.shapetracker import ShapeTracker
     # VIEW and MovementOps define a new ShapeTracker from the arg
     if self.op is Ops.VIEW: return self.arg
-    # allow reshape from nothing
+    if self.op is Ops.BUFFERIZE: return ShapeTracker.from_shape((prod([r.vmax+1 for r in self.src[1:]]),))
     if self.op is Ops.RESHAPE and self.src[0].st is None: return ShapeTracker.from_shape(self.arg)
-    if self.op in GroupOp.Movement: return unwrap(self.src[0].st).mop(self.op, self.arg)
+    if self.op in GroupOp.Movement:
+      if self.src[0].st is None: return None
+      return unwrap(self.src[0].st).mop(self.op, self.arg)
     # CONST with a DEVICE has a shape of ()
     if self.op is Ops.CONST and len(self.src) and self.src[0].op is Ops.DEVICE: return ShapeTracker.from_shape(())
     # BufferOps and ASSIGN flow ShapeTracker from a direct edge
@@ -160,7 +164,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.CAST and self.src[0].op is Ops.DEFINE_GLOBAL: return None
 
     # otherwise we get the shape from sources
-    if not (src_sts := [x.st for x in self.src if x.st is not None]): return None
+    if not (src_sts := [x.st for x in self.src if x.st is not None and x.op is not Ops.INDEX]): return None
     assert all_same([x.shape for x in src_sts]), f"UOp sources must have the same shape {self} {[x.shape for x in src_sts]}"
     match self.op:
       case Ops.MULTI: shape = tuple(self.src[0].shape[a]*len(self.device) if a == self.axis else s for a,s in enumerate(self.src[0].shape))
@@ -188,6 +192,13 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   @functools.cached_property
   def ranges(self) -> dict[UOp, None]:
     if self.op is Ops.RANGE: return {self:None}
+    if self.op is Ops.MERGE:
+      ret = self.src[0].ranges.copy()
+      for s in self.src[1::2]:
+        if s in ret: del ret[s]
+      for s in self.src[2::2]:
+        ret.update(s.ranges)
+      return ret
     if self.op in {Ops.BUFFERIZE, Ops.REDUCE}:
       ret = self.src[0].ranges.copy()
       for s in self.src[1:]:
@@ -372,7 +383,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.st == ret.st: return self  # ignore NOOPs, also check ret.st
     return ret
 
-  def forced_reshape(self, arg:tuple[sint, ...]): return UOp(Ops.RESHAPE, self.dtype, src=(self,), arg=arg)
   def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg)
   def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg)
   def expand(self, arg:tuple[sint, ...]): return self._mop(Ops.EXPAND, arg)
@@ -667,9 +677,6 @@ class UPat(MathTrait):
   @staticmethod
   def const(dtype:DType|tuple[DType, ...]|None, b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)
 
-  # lil helper
-  def f(self, op, **kwargs): return UPat(op, src=(self,), **kwargs)
-
   # copied from UOp
   def sink(self, *srcs:UPat|None, **kwargs): return UPat(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
   def index(self, idx:UPat, valid:UPat|None=None): return UPat(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))
@@ -863,7 +870,7 @@ class TrackedPatternMatcher(PatternMatcher):
       try: ret = match(uop, ctx)
       except Exception as e:
         if TRACK_MATCH_STATS >= 2 and active_rewrites and not isinstance(e, RewriteNotReady):
-          active_rewrites[-1].matches.append((track_uop(uop), track_uop(UOp(Ops.REWRITE_ERROR, src=uop.src, arg=str(sys.exc_info()[1]))), p.location))
+          active_rewrites[-1].matches.append((track_uop(uop), track_uop(UOp(Ops.NOOP, arg=str(sys.exc_info()[1]))), p.location))
         raise
       if ret is not None and ret is not uop:
         match_stats[p][0] += 1

tinygrad/uop/symbolic.py

@@ -41,7 +41,6 @@ symbolic_simple = PatternMatcher([
   (UPat(GroupOp.Idempotent, src=(UPat.var("x"), UPat.var("x"))), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).logical_not().logical_not(), lambda x: x),
   (UPat.var("x", dtype=dtypes.bool).where(UPat.const(dtypes.bool, True), UPat.const(dtypes.bool, False)), lambda x: x),
-  (UPat.var("x", dtype=dtypes.bool).where(UPat.const(dtypes.bool, False), UPat.const(dtypes.bool, True)), lambda x: x.logical_not()),
   (UPat.var("x", dtype=dtypes.ints+(dtypes.bool,)).trunc(), lambda x: x),
   # ** zero folding **
   (UPat.var("x") < UPat.var("x"), lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x < x -> False
@@ -243,18 +242,18 @@ def gep_through_wmma(gep:UOp, wmma:UOp):
 
 gep_pushing = PatternMatcher([
   # GEP/VECTORIZE, GEP/GEP, GEP/CONST, GEP/VCONST
-  (UPat(Ops.GEP, name='g2').f(Ops.GEP, name='g1'),
+  (UPat(Ops.GEP, src=(UPat(Ops.GEP, name='g2'),), name='g1'),
    lambda g1, g2: g2.src[0].gep(tuple(g2.arg[g1.arg[i]] for i in range(len(g1.arg))))),
-  (UPat(Ops.VECTORIZE, name='vec').f(Ops.GEP, name='gep'),
+  (UPat(Ops.GEP, src=(UPat(Ops.VECTORIZE, name="vec"),), name="gep"),
    lambda gep, vec: UOp(Ops.VECTORIZE, gep.dtype, tuple(vec.src[i] for i in gep.arg)) if len(gep.arg) > 1 else vec.src[gep.arg[0]]),
-  (UPat.cvar("c", vec=False).f(Ops.GEP, name="gep"), lambda gep, c: gep.const_like(c.arg)),
-  (UPat(Ops.VCONST, name="c").f(Ops.GEP, name="gep"), lambda gep, c: gep.const_like(tuple(c.arg[x] for x in gep.arg))),
+  (UPat(Ops.GEP, src=(UPat.cvar("c", vec=False),), name="gep"), lambda gep, c: gep.const_like(c.arg)),
+  (UPat(Ops.GEP, src=(UPat(Ops.VCONST, name="c"),), name="gep"), lambda gep, c: gep.const_like(tuple(c.arg[x] for x in gep.arg))),
   # GEP on void is skipped
   (UPat(Ops.GEP, src=(UPat(dtype=dtypes.void, name="x"),)), lambda x: x),
   # GEP in order is removed
   (UPat(Ops.GEP, name="g"), lambda g: g.src[0] if not isinstance(g.dtype, PtrDType) and g.arg == tuple(range(g.src[0].dtype.count)) else None),
   # push all GEPs through ALUs (fix arange stuff)
-  (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST), name='alu').f(Ops.GEP, name='gep'),
+  (UPat(Ops.GEP, src=(UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST), name='alu'),), name='gep'),
    lambda gep,alu: UOp(alu.op, alu.dtype.scalar().vec(gep.dtype.count), tuple(x.gep(gep.arg) for x in alu.src), alu.arg) \
      if not isinstance(gep.dtype, PtrDType) else None),
   # CAT can't be rendered. it's a VECTORIZE on vectors, we expand to a single VECTORIZEs with GEPs (TODO: move this later)
@@ -263,7 +262,7 @@ gep_pushing = PatternMatcher([
   # VECTORIZE on same GEP
   (UPat(Ops.VECTORIZE, name="v", src=UPat(Ops.GEP, src=(UPat.var("x"),))), lambda v,x: x.gep(tuple(get_single_element(i.arg) for i in v.src))),
   # push some GEPs through WMMAs
-  (UPat(Ops.WMMA, name="wmma").f(Ops.GEP, name="gep"), gep_through_wmma),
+  (UPat(Ops.GEP, src=(UPat(Ops.WMMA, name="wmma"),), name="gep"), gep_through_wmma),
 ])
 
 commutative = PatternMatcher([

tinygrad/viz/index.html

@@ -228,7 +228,7 @@
   }
   #device-list > div {
     min-height: 32px;
-    max-width: 132px;
+    max-width: 100px;
     overflow-x: auto;
     overflow-y: hidden;
     white-space: nowrap;

tinygrad/viz/js/index.js

@@ -122,18 +122,17 @@ const colorScheme = {TINY:["#1b5745", "#354f52", "#354f52", "#1d2e62", "#63b0cd"
   CATEGORICAL:["#ff8080", "#F4A261", "#C8F9D4", "#8D99AE", "#F4A261", "#ffffa2", "#ffffc0", "#87CEEB"],}
 const cycleColors = (lst, i) => lst[i%lst.length];
 
-const rescaleTrack = (source, tid, k) => {
-  for (const e of source.shapes) {
-    for (let i=0; i<e.y0.length; i++) {
-      e.y0[i] = e.y0[i]*k;
-      e.y1[i] = e.y1[i]*k;
-    }
+const createPolygons = (source, area) => {
+  const shapes = [];
+  const yscale = d3.scaleLinear().domain([0, source.peak]).range([area, 0]);
+  for (const [i,e] of source.shapes.entries()) {
+    const x = e.x.map((i,_) => (source.timestamps[i] ?? data.et)-data.st);
+    const y0 = e.y.map(yscale);
+    const y1 = e.y.map(y => yscale(y+e.arg.nbytes));
+    const arg = { tooltipText:`${e.arg.dtype} len:${formatUnit(e.arg.sz)}\n${formatUnit(e.arg.nbytes, "B")}` };
+    shapes.push({ x, y0, y1, arg, fillColor:cycleColors(colorScheme.BUFFER, i) });
   }
-  const change = (source.height*k)-source.height;
-  const div = document.getElementById(tid);
-  div.style.height = rect(div).height+change+"px";
-  source.height = source.height*k;
-  return change;
+  return shapes;
 }
 
 const drawLine = (ctx, x, y) => {
@@ -151,68 +150,77 @@ async function renderProfiler() {
   // layout once!
   if (data != null) return;
   const profiler = d3.select(".profiler").html("");
-  const { layout, st, et } = await (await fetch("/get_profile")).json();
-  // place devices on the y axis and set vertical positions
-  const [tickSize, padding] = [10, 8];
-  const deviceList = profiler.append("div").attr("id", "device-list").style("padding-top", tickSize+padding+"px");
+  const deviceList = profiler.append("div").attr("id", "device-list").node();
   const canvas = profiler.append("canvas").attr("id", "timeline").node();
   // NOTE: scrolling via mouse can only zoom the graph
   canvas.addEventListener("wheel", e => (e.stopPropagation(), e.preventDefault()), { passive:false });
+  const profileRet = await (await fetch("/get_profile")).json()
+  const { layout, st, et } = profileRet;
+  // place devices on the y axis and set vertical positions
+  const [tickSize, padding] = [10, 8];
+  deviceList.style.paddingTop = `${tickSize+padding}px`;
   const ctx = canvas.getContext("2d");
   const canvasTop = rect(canvas).top;
   // color by key (name/category/device)
   const colorMap = new Map();
   data = {tracks:new Map(), axes:{}, st, et};
-  const heightScale = d3.scaleLinear().domain([0, Object.entries(layout).reduce((peak, [_,d]) => Math.max(peak, d.peak||0), 0)]).range([4,maxheight=100]);
-  for (const [k, v] of Object.entries(layout)) {
-    if (v.shapes.length === 0) continue;
-    const div = deviceList.append("div").attr("id", k).text(k).style("padding", padding+"px");
-    const { y:baseY, height:baseHeight } = rect(div.node());
-    const offsetY = baseY-canvasTop+padding/2;
-    if (v.shapes[0].dur != null) {
-      const levelHeight = baseHeight-padding;
-      const shapes = [];
-      data.tracks.set(k, { shapes, offsetY });
-      let colorKey, ref;
-      for (const e of v.shapes) {
-        if (e.depth === 0) colorKey = e.cat ?? e.name;
-        if (!colorMap.has(colorKey)) colorMap.set(colorKey, cycleColors(colorScheme[k] ?? colorScheme.DEFAULT, colorMap.size));
-        const fillColor = d3.color(colorMap.get(colorKey)).brighter(e.depth).toString();
-        const label = parseColors(e.name).map(({ color, st }) => ({ color, st, width:ctx.measureText(st).width }));
-        if (e.ref != null) ref = {ctx:e.ref, step:0};
-        else if (ref != null) {
-          const start = ref.step>0 ? ref.step+1 : 0;
-          const stepIdx = ctxs[ref.ctx+1].steps.findIndex((s, i) => i >= start && s.name == e.name);
-          ref = stepIdx === -1 ? null : {ctx:ref.ctx, step:stepIdx};
-        }
-        const arg = { tooltipText:formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
-        // offset y by depth
-        shapes.push({x:e.st-st, y:levelHeight*e.depth, width:e.dur, height:levelHeight, arg, label, fillColor });
+  const areaScale = d3.scaleLinear().domain([0, Object.entries(layout).reduce((peak, [_,d]) => Math.max(peak, d.mem.peak), 0)]).range([4,maxArea=100]);
+  for (const [k, { timeline, mem }] of Object.entries(layout)) {
+    if (timeline.shapes.length === 0 && mem.shapes.length == 0) continue;
+    const div = deviceList.appendChild(document.createElement("div"));
+    div.innerText = k;
+    div.style.padding = `${padding}px`;
+    div.onclick = () => { // TODO: make this feature more visible
+      const prevScroll = profiler.node().scrollTop;
+      let newOffset = null;
+      for (const [track, v] of data.tracks) {
+        if (track === `${k} memory`)  {
+          // expand the y axis or reset to default size
+          const pick = [areaScale(mem.peak), maxArea*4];
+          const expand = k !== focusedDevice;
+          const [newArea, prevArea] = expand ? pick.reverse() : pick;
+          focusedDevice = expand ? k : null;
+          data.axes.y = expand ? { domain:[0, mem.peak], range:[v.offsetY+newArea, v.offsetY], fmt:"B" } : null;
+          // either way update all offsets
+          v.shapes = createPolygons(mem, newArea);
+          newOffset = newArea-prevArea;
+          v.div.style.height = rect(v.div).height+newOffset+"px";
+        } else if (newOffset != null) v.offsetY += newOffset;
       }
-      div.style("height", levelHeight*v.maxDepth+padding+"px").style("pointerEvents", "none");
-    } else {
-      const height = heightScale(v.peak);
-      const yscale = d3.scaleLinear().domain([0, v.peak]).range([height, 0]);
-      const shapes = [];
-      for (const [i,e] of v.shapes.entries()) {
-        const x = e.x.map(tsIdx => v.timestamps[tsIdx]-st);
-        const arg = {tooltipText:`${e.arg.dtype} len:${formatUnit(e.arg.sz)}\n${formatUnit(e.arg.nbytes, "B")}`};
-        shapes.push({ x, y0:e.y.map(yscale), y1:e.y.map(y => yscale(y+e.arg.nbytes)), arg, fillColor:cycleColors(colorScheme.BUFFER, i) });
-      }
-      data.tracks.set(k, { shapes, offsetY, height, peak:v.peak, scaleFactor:maxheight*4/height });
-      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;
-        for (const [tid, track] of data.tracks) {
-          track.offsetY += offset;
-          if (tid === newFocus) offset += rescaleTrack(track, tid, track.scaleFactor);
-          else if (tid === focusedDevice) 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;
-        focusedDevice = newFocus;
-        return resize();
-      });
+      d3.select(canvas).call(canvasZoom.transform, zoomLevel);
+      if (prevScroll) profiler.node().scrollTop = prevScroll;
     }
+    const { y:baseY, height:baseHeight } = rect(div);
+    const levelHeight = baseHeight-padding;
+    const offsetY = baseY-canvasTop+padding/2;
+    const shapes = [];
+    data.tracks.set(k, { shapes, offsetY });
+    let colorKey, ref;
+    for (const e of timeline.shapes) {
+      if (e.depth === 0) colorKey = e.cat ?? e.name;
+      if (!colorMap.has(colorKey)) colorMap.set(colorKey, cycleColors(colorScheme[k] ?? colorScheme.DEFAULT, colorMap.size));
+      const fillColor = d3.color(colorMap.get(colorKey)).brighter(e.depth).toString();
+      const label = parseColors(e.name).map(({ color, st }) => ({ color, st, width:ctx.measureText(st).width }));
+      if (e.ref != null) ref = {ctx:e.ref, step:0};
+      else if (ref != null) {
+        const start = ref.step>0 ? ref.step+1 : 0;
+        const stepIdx = ctxs[ref.ctx+1].steps.findIndex((s, i) => i >= start && s.name == e.name);
+        ref = stepIdx === -1 ? null : {ctx:ref.ctx, step:stepIdx};
+      }
+      const arg = { tooltipText:formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
+      // offset y by depth
+      shapes.push({x:e.st-st, y:levelHeight*e.depth, width:e.dur, height:levelHeight, arg, label, fillColor });
+    }
+    // position shapes on the canvas and scale to fit fixed area
+    let area = mem.shapes.length === 0 ? 0 : areaScale(mem.peak);
+    if (area === 0) div.style.pointerEvents = "none";
+    else {
+      const startY = offsetY+(levelHeight*timeline.maxDepth)+padding/2;
+      data.tracks.set(`${k} memory`, { shapes:createPolygons(mem, area), offsetY:startY, div });
+      div.style.cursor = "pointer";
+    }
+    // lastly, adjust device rect by number of levels
+    div.style.height = `${Math.max(levelHeight*timeline.maxDepth, baseHeight)+area+padding}px`;
   }
   updateProgress({ "show":false });
   // draw events on a timeline

tinygrad/viz/serve.py

@@ -19,7 +19,7 @@ uops_colors = {Ops.LOAD: "#ffc0c0", Ops.STORE: "#87CEEB", Ops.CONST: "#e0e0e0",
                **{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",
                Ops.ALLREDUCE: "#ff40a0", Ops.MSELECT: "#d040a0", Ops.MSTACK: "#d040a0", Ops.CONTIGUOUS: "#FFC14D",
-               Ops.CHILDREN: "#80ffc0", Ops.CHILD: "#80fff0", Ops.BUFFERIZE: "#FF991C", Ops.REWRITE_ERROR: "#ff2e2e"}
+               Ops.CHILDREN: "#80ffc0", Ops.CHILD: "#80fff0", Ops.BUFFERIZE: "#FF991C"}
 
 # VIZ API
 
@@ -73,8 +73,8 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
     if u.dtype != dtypes.void: label += f"\n{u.dtype}"
     for idx,x in enumerate(u.src):
       if x in excluded:
-        arg = f"{x.arg:g}" if x.op is Ops.CONST and dtypes.is_float(u.dtype) else f"{x.arg}"
-        label += f"\n{x.op.name}{idx} {arg}" + (f" {x.src[0].op}" if len(x.src) else "")
+        if x.op is Ops.CONST and dtypes.is_float(u.dtype): label += f"\nCONST{idx} {x.arg:g}" + (f" {x.src[0].op}" if len(x.src) else "")
+        else: label += f"\n{x.op.name}{idx} {x.arg}"
     try:
       if u.op not in {Ops.VIEW, Ops.BUFFER, Ops.KERNEL, Ops.ASSIGN, Ops.COPY, Ops.SINK, *GroupOp.Buffer} and u.st is not None:
         label += f"\n{shape_to_str(u.shape)}"
@@ -144,7 +144,7 @@ def timeline_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
     shapes.append({"name":name, "ref":ref, "st":st, "dur":dur, "depth":depth, "cat":cat, "info":info})
   return {"shapes":shapes, "maxDepth":len(levels)}
 
-def mem_layout(events:list[tuple[int, int, float, DevEvent]], max_ts:int) -> dict:
+def mem_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
   step, peak, mem = 0, 0, 0
   shps:dict[int, dict] = {}
   temp:dict[int, dict] = {}
@@ -170,10 +170,9 @@ def mem_layout(events:list[tuple[int, int, float, DevEvent]], max_ts:int) -> dic
   for v in temp.values():
     v["x"].append(step)
     v["y"].append(v["y"][-1])
-  timestamps.append(max_ts)
   return {"shapes":list(shps.values()), "peak":peak, "timestamps":timestamps}
 
-def get_profile(profile:list[ProfileEvent]) -> bytes|None:
+def get_profile(profile:list[ProfileEvent]):
   # start by getting the time diffs
   for ev in profile:
     if isinstance(ev,ProfileDeviceEvent): device_ts_diffs[ev.device] = (ev.comp_tdiff, ev.copy_tdiff if ev.copy_tdiff is not None else ev.comp_tdiff)
@@ -185,14 +184,10 @@ def get_profile(profile:list[ProfileEvent]) -> bytes|None:
     dev_events.setdefault(e.device,[]).append((st:=int(ts), et:=int(en), float(en-ts), e))
     if min_ts is None or st < min_ts: min_ts = st
     if max_ts is None or et > max_ts: max_ts = et
-  if min_ts is None: return None
   # return layout of per device events
-  layout:dict[str, dict] = {}
-  for k,v in dev_events.items():
-    v.sort(key=lambda e:e[0])
-    layout[k] = timeline_layout(v)
-    layout[f"{k} Memory"] = mem_layout(v, unwrap(max_ts))
-  return json.dumps({"layout":layout, "st":min_ts, "et":max_ts}).encode("utf-8")
+  for events in dev_events.values(): events.sort(key=lambda v:v[0])
+  dev_layout = {k:{"timeline":timeline_layout(v), "mem":mem_layout(v)} for k,v in dev_events.items()}
+  return json.dumps({"layout":dev_layout, "st":min_ts, "et":max_ts}).encode("utf-8")
 
 def get_runtime_stats(key) -> list[dict]:
   ret:list[dict] = []