fix test amx

* refactor and split test_linearizer

* forget that file

* imports

* remove from docs

* test gen float4

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

@@ -225,13 +225,22 @@ runs:
     - name: Install gpuocelot dependencies (MacOS)
       if: inputs.ocelot == 'true' && runner.os == 'macOS'
       shell: bash
-      run: brew install --quiet cmake ninja llvm@15 zlib glew flex bison boost zstd ncurses
+      run: |
+        pkgs=(cmake ninja llvm@15 zlib glew flex bison [email protected] zstd ncurses)
+        for f in "${pkgs[@]}"; do
+          brew ls --versions "$f" >/dev/null 2>&1 || brew install --quiet "$f"
+        done
+
+        # Fix boost 1.85 for gpuocelot
+        ln -s /opt/homebrew/opt/[email protected] /opt/homebrew/opt/boost || true
+        ln -s /opt/homebrew/opt/boost/lib/libboost_atomic-mt.dylib /opt/homebrew/opt/boost/lib/libboost_atomic.dylib || true
+        ln -s /opt/homebrew/opt/boost/lib/libboost_thread-mt.dylib /opt/homebrew/opt/boost/lib/libboost_thread.dylib || true
     - name: Cache gpuocelot
       if: inputs.ocelot == 'true'
       id: cache-build
       uses: actions/cache@v4
       env:
-        cache-name: cache-gpuocelot-build
+        cache-name: cache-gpuocelot-build-1
       with:
         path: ${{ github.workspace }}/gpuocelot/ocelot
         key: ${{ runner.os }}-gpuocelot-b16039dc940dc6bc4ea0a98380495769ff35ed99-rebuild-${{ env.BUILD_CACHE_VERSION }}
@@ -244,7 +253,8 @@ runs:
         git checkout b16039dc940dc6bc4ea0a98380495769ff35ed99
         mkdir build
         cd build
-        cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF -DCMAKE_POLICY_VERSION_MINIMUM=3.5
+        cmake .. -Wno-dev -G Ninja -DOCELOT_BUILD_TOOLS=OFF -DCMAKE_BUILD_ALWAYS=0 -DBUILD_TESTS_CUDA=OFF \
+          -DBoost_INCLUDE_DIR=$(brew --prefix boost)/include -DBoost_LIBRARY_DIR=$(brew --prefix boost)/lib -DCMAKE_POLICY_VERSION_MINIMUM=3.5
         ninja
     - name: Install gpuocelot
       if: inputs.ocelot == 'true'

.github/workflows/benchmark.yml

@@ -68,8 +68,10 @@ jobs:
       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
       run: |
-        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
-        DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
+        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 CPU=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
+        DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 LLVM=1 AMX=1 python3.11 test/opt/test_gen_float4.py TestFloat4.test_float4_multidim_amx TestFloat4.test_float4_multidim_unaligned_load_amx
     - name: Run Tensor Core GEMM (float)
       run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt
     - name: Run Tensor Core GEMM (half)
@@ -688,6 +690,10 @@ jobs:
       run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
     - name: Test DISK copy time
       run: AMD=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
+    - name: Test CPU copy time
+      run: |
+        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
+        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
     - name: Run full CIFAR training w 1 GPU
       run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
     # TODO: enable
@@ -745,6 +751,10 @@ jobs:
       run: NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test DISK copy time
       run: NV=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
+    - name: Test CPU copy time
+      run: |
+        NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
+        NV=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
     - name: Test LLAMA-3
       run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
     - name: Run full CIFAR training w 1 GPU

.github/workflows/test.yml

@@ -7,6 +7,7 @@ env:
   BUILD_CACHE_VERSION: '1'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+  PYTHONPATH: ${{ github.workspace }}
 
 on:
   push:
@@ -79,7 +80,7 @@ jobs:
         python docs/abstractions2.py
         python docs/abstractions3.py
     - name: Test Quickstart
-      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py &&  PYTHONPATH=. python quickstart.py
+      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' docs/quickstart.md > quickstart.py &&  python quickstart.py
     - name: Test DEBUG
       run: DEBUG=100 python3 -c "from tinygrad import Tensor; N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N); c = (a.reshape(N, 1, N) * b.T.reshape(1, N, N)).sum(axis=2); print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
     - name: Compile EfficientNet to C and test it
@@ -182,19 +183,19 @@ jobs:
         pip3 install --upgrade --force-reinstall ruff==0.11.0
         python3 -m ruff check extra/torch_backend/backend.py
     - name: Test one op
-      run: PYTHONPATH=. FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
+      run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
     - name: Test ResNet-18
-      run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/example.py
+      run: DEBUG=2 python3 extra/torch_backend/example.py
     - name: My (custom) tests
-      run: PYTHONPATH=. python3 extra/torch_backend/test.py
+      run: python3 extra/torch_backend/test.py
     - name: Test one op in torch tests
-      run: PYTHONPATH=. DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
+      run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
     - name: Test Ops with TINY_BACKEND
-      run: PYTHONPATH=. LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
+      run: LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
     - name: Test in-place operations on views
-      run: PYTHONPATH=. TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
+      run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
     - name: Test multi-gpu
-      run: PYTHONPATH=. LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+      run: LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
 
   torchbackendmore:
     name: Torch Backend Tests More
@@ -216,9 +217,9 @@ jobs:
         sudo apt update || true
         sudo apt install -y --no-install-recommends ninja-build
     - name: Test beautiful_mnist in torch with TINY_BACKEND
-      run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 PYTHONPATH=. LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+      run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
     - name: Test some torch tests (expect failure)
-      run: PYTHONPATH=. python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
+      run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
 
   tc:
     name: Tensor Core tests
@@ -240,55 +241,55 @@ jobs:
         IMAGE=2 PYTHON=1 python3 test/test_ops.py TestOps.test_simple_conv2d
     - name: Test emulated METAL tensor cores
       run: |
-        DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
-        PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_big_gemm
+        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test emulated AMX tensor cores
-      run: PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
+      run: DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
     - name: Test emulated AMD tensor cores
       run: |
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
     - name: Test emulated AMD MFMA tensor cores
       run: |
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_MFMA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=AMD_MFMA FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test emulated AMD RDNA4 tensor cores
       run: |
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD_RDNA4=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=AMD_RDNA4 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test emulated CUDA tensor cores
       run: |
-        DEBUG=2 EMULATE_CUDA=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
-        DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
-        DEBUG=2 EMULATE_CUDA_SM75=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
-        PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH="." DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
+        DEBUG=2 EMULATE=CUDA FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
+        DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm
+        DEBUG=2 EMULATE=CUDA_SM75 FORWARD_ONLY=1 PYTHON=1 python3 test/test_ops.py TestOps.test_gemm_fp16
+        PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores
+        PYTHONPATH="." DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
     - name: Test emulated INTEL OpenCL tensor cores
-      run: DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
+      run: DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 HALF=1 N=64 python3 ./extra/gemm/simple_matmul.py
     - name: Full test tensor cores
       run: |
-        PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
-        PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=METAL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=AMD FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=CUDA ALLOW_TF32=1 FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 EMULATE=INTEL FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
+        DEBUG=2 AMX=1 EMULATE=AMX FORWARD_ONLY=1 PYTHON=1 python3 ./test/test_linearizer.py TestLinearizer.test_tensor_cores
     - name: Test device flop counts
       run: |
-        PYTHONPATH=. DEBUG=2 EMULATE_METAL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        PYTHONPATH=. DEBUG=2 EMULATE_AMD=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        PYTHONPATH=. DEBUG=2 EMULATE_CUDA=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        PYTHONPATH=. DEBUG=2 EMULATE_INTEL=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
-        PYTHONPATH=. DEBUG=2 AMX=1 EMULATE_AMX=1 PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
+        DEBUG=2 EMULATE=METAL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=AMD PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=CUDA PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 EMULATE=INTEL PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 AMX=1 EMULATE=AMX PYTHON=1 python3 ./test/test_uops_stats.py TestUOpsStats.test_simple_matmul
 
   bepython:
     name: Python Backend
@@ -305,15 +306,15 @@ jobs:
         key: be-minimal
         deps: testing_minimal
     - name: Test dtype with Python emulator
-      run: DEBUG=1 PYTHONPATH=. PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
+      run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
     - name: Test ops with Python emulator
       run: DEBUG=2 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py -k "not (test_split or test_simple_cumsum or test_cumsum or test_einsum or test_dot or test_dot_1d or test_big_gemm or test_broadcastdot or test_multidot or test_var_axis or test_std_axis or test_broadcast_full or test_broadcast_partial or test_simple_conv3d or test_dilated_conv_transpose2d or test_simple_conv_transpose3d or test_large_input_conv2d or test_max_pool2d or test_max_pool2d_simple or test_max_pool2d_bigger_stride or test_avg_pool2d or test_cat or test_scaled_product_attention or test_scaled_product_attention_causal or test_slice_fancy_indexing_dim_inject_none or test_slice_fancy_indexing_list_indices or test_slice_fancy_indexing_no_dim_collapse or test_slice_fancy_indexing_tuple_indices or test_slice_fancy_indexing_list_with_tensors or test_slice_fancy_indexing_dim_collapse_int or test_interpolate_bilinear or test_interpolate_bilinear_corners_aligned or test_scaled_dot_product_attention or test_cummax or test_simple_cummax or test_logcumsumexp or test_sort or test_cumprod)" --durations=20
     - name: Test uops with Python emulator
       run: PYTHON=1 python3 -m pytest test/test_uops.py --durations=20
     - name: Test symbolic with Python emulator
-      run: PYTHONPATH=. PYTHON=1 python3 test/test_symbolic_ops.py
+      run: PYTHON=1 python3 test/test_symbolic_ops.py
     - name: test_renderer_failures with Python emulator
-      run: PYTHONPATH=. PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
+      run: PYTHON=1 python3 -m pytest -rA test/test_renderer_failures.py::TestRendererFailures
 
   linter:
     name: Linters
@@ -361,7 +362,7 @@ jobs:
         pydeps: "pillow"
         deps: testing_unit
     - name: Test README
-      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py &&  PYTHONPATH=. python README.py
+      run: awk '/```python/{flag=1;next}/```/{flag=0}flag' README.md > README.py &&  python README.py
     - name: Run unit tests
       run: PYTHONPATH="." python -m pytest -n=auto test/unit/ --durations=20
     - name: Run targetted tests on NULL backend
@@ -379,11 +380,11 @@ jobs:
       run: |
         test/external/process_replay/reset.py
         CAPTURE_PROCESS_REPLAY=1 python test/test_tiny.py TestTiny.test_plus
-        PYTHONPATH=. python extra/optimization/extract_dataset.py
+        python extra/optimization/extract_dataset.py
         gzip -c /tmp/sops > extra/datasets/sops.gz
-        DEBUG=1 MIN_ASTS=1 PYTHONPATH=. python extra/optimization/get_action_space.py
-    - name: Repo line count < 17500 lines
-      run: MAX_LINE_COUNT=17500 python sz.py
+        DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
+    - name: Repo line count < 18000 lines
+      run: MAX_LINE_COUNT=18000 python sz.py
 
   fuzzing:
     name: Fuzzing
@@ -513,11 +514,11 @@ jobs:
       - name: Test ONNX (LLVM)
         run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
       - name: Test ONNX Runner (CPU)
-        run: CPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
+        run: CPU=1 python3 test/external/external_test_onnx_runner.py
       - name: Test Additional ONNX Ops (CPU)
-        run: CPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_ops.py
+        run: CPU=1 python3 test/external/external_test_onnx_ops.py
       - name: Test Quantize ONNX
-        run: CPU=1 PYTHONPATH=. python3 test/test_quantize_onnx.py
+        run: CPU=1 python3 test/test_quantize_onnx.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -542,15 +543,15 @@ jobs:
       - name: Test ONNX (GPU)
         run: GPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
       - name: Test Optimization Helpers
-        run: PYTHONPATH="." DEBUG=1 python3 extra/optimization/test_helpers.py
+        run: DEBUG=1 python3 extra/optimization/test_helpers.py
       #- name: Test Action Space
-      #  run: PYTHONPATH="." DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
+      #  run: DEBUG=1 GPU=1 python3 extra/optimization/get_action_space.py
       - name: Test Beam Search
-        run: PYTHONPATH="." GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
+        run: GPU=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
       - name: Test MLPerf stuff
         run: GPU=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
       - name: Test llama 3 training
-        run: MAX_BUFFER_SIZE=0 PYTHONPATH="." DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
+        run: MAX_BUFFER_SIZE=0 DEV=NULL SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -639,7 +640,7 @@ jobs:
     - 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
+      run: 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"
 
@@ -675,9 +676,9 @@ jobs:
     - name: Run test_tiny on DSP
       run: DEBUG=2 DSP=1 python test/test_tiny.py
     - name: Test transcendentals
-      run: CC=clang-20 PYTHONPATH="." DEBUG=2 DSP=1 python test/test_transcendental.py TestTranscendentalVectorized
+      run: CC=clang-20 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
+      run: DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
 
   testwebgpu:
     name: Linux (WebGPU)
@@ -720,7 +721,6 @@ jobs:
       MOCKGPU: 1
       FORWARD_ONLY: 1
       AMD_LLVM: ${{ matrix.backend == 'amdllvm' && '1' || matrix.backend != 'amdllvm' && '0' }}
-      PYTHONPATH: ${{ github.workspace }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -760,7 +760,9 @@ jobs:
     name: Linux (${{ matrix.backend }})
     runs-on: ubuntu-22.04
     timeout-minutes: 20
-
+    env:
+      MOCKGPU: 1
+      FORWARD_ONLY: 1
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -772,11 +774,11 @@ jobs:
           cuda: 'true'
           ocelot: 'true'
       - name: Set env
-        run: printf "${{ matrix.backend == 'PTX' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nCUDA=1\nPTX=1\nMOCKGPU=1' || matrix.backend == 'nv' && 'NV=1\nMOCKGPU=1\nFORWARD_ONLY=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'PTX' && 'CUDA=1\nPTX=1' || matrix.backend == 'nv' && 'NV=1' }}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
-          PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
-          DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
+          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CUDA','NV'], Device.DEFAULT"
+          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run pytest (cuda)
         # skip multitensor because it's slow
         run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --ignore test/test_gc.py --ignore test/test_multitensor.py --durations=20
@@ -809,8 +811,8 @@ jobs:
         run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' }}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
-          PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
-          DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
+          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','GPU'], Device.DEFAULT"
+          DEBUG=5 FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run pytest (not cuda)
         run: python -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
       - name: Run TRANSCENDENTAL math
@@ -851,11 +853,11 @@ jobs:
     - name: Test tensor core ops (real)
       run: METAL=1 DEBUG=3 python test/test_ops.py TestOps.test_big_gemm
     - name: Test LLaMA compile speed
-      run: PYTHONPATH="." METAL=1 python test/external/external_test_speed_llama.py
+      run: METAL=1 python test/external/external_test_speed_llama.py
     - name: Test Beam Search
-      run: PYTHONPATH="." METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
+      run: METAL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
     #- name: Fuzz Test linearizer
-    #  run: PYTHONPATH="." METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
+    #  run: METAL=1 DEPTH=4 FUZZ_N=50 FUZZ_MAX_SIZE=1000000 python test/external/fuzz_linearizer.py
     - name: Run TRANSCENDENTAL math
       run: TRANSCENDENTAL=2 python -m pytest -n=auto test/test_ops.py::TestOps::test_sin test/test_ops.py::TestOps::test_cos test/test_ops.py::TestOps::test_tan test/test_ops.py::TestOps::test_exp test/test_ops.py::TestOps::test_log --durations=20
     - name: Run pytest (amd)
@@ -918,7 +920,7 @@ jobs:
     #    cp $GITHUB_WORKSPACE/test/web/test_viz.js .
     #    node test_viz.js
     - name: Test ONNX Runner (WEBGPU)
-      run: WEBGPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
+      run: WEBGPU=1 python3 test/external/external_test_onnx_runner.py
 
   osxremote:
    name: MacOS (remote metal)
@@ -950,7 +952,6 @@ jobs:
     timeout-minutes: 20
     env:
       REMOTE: 1
-      PYTHONPATH: ${{ github.workspace }}
     steps:
       - name: Checkout Code
         uses: actions/checkout@v4
@@ -1071,7 +1072,7 @@ jobs:
         run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'webgpu' && 'WEBGPU=1'}}" >> $GITHUB_ENV
       - name: Run unit tests
         if: matrix.backend=='llvm'
-        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py
+        run: python -m pytest -n=auto test/unit/ --ignore=test/unit/test_disk_tensor.py --ignore=test/unit/test_elf.py --ignore=test/unit/test_tar.py --durations=20
       - name: Run pytest (${{ matrix.backend }})
         shell: bash
         run: |

.pylintrc

@@ -54,11 +54,12 @@ confidence=
 # --enable=similarities". If you want to run only the classes checker, but have
 # no Warning level messages displayed, use"--disable=all --enable=classes
 # --disable=W"
-disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method
+disable=C,R,W0613,W0511,W0212,W0201,W0106,W0603,W0621,W0703,W1201,W1203,E1136,W1514,E1101,W0221,W0105,E0401,abstract-method,W0707
 # E1101 for function binding
 # W0221 for Function class
 # W0105 for comment strings
 # E0401 for missing imports
+# W0707 for not reraising
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

docs/developer/layout.md

@@ -22,12 +22,6 @@ Group UOps into kernels.
 
 Transforms the ast into an optimized ast. This is where BEAM search and heuristics live.
 
-::: tinygrad.codegen.opt.get_optimized_ast
-    options:
-        members: false
-        show_labels: false
-        show_source: false
-
 ---
 
 ## tinygrad/codegen

examples/beautiful_cifar.py

@@ -2,7 +2,6 @@ import time
 start_tm = time.perf_counter()
 import math
 from typing import Tuple, cast
-import numpy as np
 from tinygrad import Tensor, nn, GlobalCounters, TinyJit, dtypes, Device
 from tinygrad.helpers import partition, trange, getenv, Context
 from extra.lr_scheduler import OneCycleLR
@@ -150,13 +149,12 @@ if __name__ == "__main__":
       acc.append((out.argmax(-1) == Y).sum() / eval_batchsize)
     return Tensor.stack(*loss).mean() / (batchsize*loss_batchsize_scaler), Tensor.stack(*acc).mean()
 
-  np.random.seed(1337)
+  Tensor.manual_seed(1337)
+  num_train_samples = X_train.shape[0]
+
   for epoch in range(math.ceil(hyp['misc']['train_epochs'])):
-    # TODO: move to tinygrad
     gst = time.perf_counter()
-    idxs = np.arange(X_train.shape[0])
-    np.random.shuffle(idxs)
-    tidxs = Tensor(idxs, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)  # NOTE: long doesn't fold
+    tidxs = Tensor.randperm(num_train_samples, dtype='int')[:num_steps_per_epoch*batchsize].reshape(num_steps_per_epoch, batchsize)
     train_loss:float = 0
     for epoch_step in (t:=trange(num_steps_per_epoch)):
       st = time.perf_counter()

examples/mlperf/dataloader.py

@@ -758,6 +758,27 @@ def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0
       batch.append(tokens)
     yield Tensor.stack(batch, dim=0)
 
+def batch_load_llama3_small(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
+  if val:
+    dataset = BlendedGPTDataset([
+      base_dir / "c4-validation-91205-samples.en_text_document",
+    ], [
+      1.0
+    ], samples, seqlen, seed, False)
+  else:
+    dataset = BlendedGPTDataset([
+      base_dir / "c4-train.en_6_text_document",
+    ], [
+      1.0
+    ], samples, seqlen, seed, True)
+
+  for b in range(math.ceil(samples / bs)):
+    batch = []
+    for i in range(bs):
+      tokens = dataset.get(b * bs + i)
+      batch.append(tokens)
+    yield Tensor.stack(batch, dim=0)
+
 if __name__ == "__main__":
   def load_unet3d(val):
     assert not val, "validation set is not supported due to different sizes on inputs"

examples/mlperf/model_eval.py

@@ -243,31 +243,49 @@ def eval_mrcnn():
 
 def eval_llama3():
   from extra.models.llama import Transformer
-  from examples.llama3 import MODEL_PARAMS
+  from examples.llama3 import MODEL_PARAMS, load, convert_from_huggingface
   from tinygrad.helpers import tqdm
 
-  bs = 4
-  sequence_length = 512
+  BASEDIR = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
+  BS = getenv("BS", 4)
+  SMALL = getenv("SMALL", 0)
+  SEQLEN = getenv("SEQLEN", 8192)
+  MODEL_PATH = Path(getenv("MODEL_PATH", "/raid/weights/llama31_8b/"))
 
-  model = Transformer(**(MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}), max_context=sequence_length, jit=False, disable_kv_cache=True)
+  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
+  params = params | {"vocab_size": 32000} if not SMALL else params
+  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
+  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
+
+  # load weights
+  weights = load(str(MODEL_PATH / "model.safetensors.index.json"))
+  if "model.embed_tokens.weight" in weights:
+    print("converting from huggingface format")
+    weights = convert_from_huggingface(weights, params["n_layers"], params["n_heads"], params["n_kv_heads"])
+
+  load_state_dict(model, weights, strict=False, consume=True)
 
   @TinyJit
   def eval_step(model, tokens):
     logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
     loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
-    return loss.flatten()
+    return loss.flatten().float()
 
-  from examples.mlperf.dataloader import batch_load_llama3
-  iter = batch_load_llama3(bs, 5760, sequence_length, Path(getenv("BASEDIR", "/raid/datasets/c4/")), True)
+  if SMALL:
+    from examples.mlperf.dataloader import batch_load_llama3_small
+    iter = batch_load_llama3_small(BS, 5760, SEQLEN, BASEDIR, val=True)
+  else:
+    from examples.mlperf.dataloader import batch_load_llama3
+    iter = batch_load_llama3(BS, 5760, SEQLEN, BASEDIR, val=True)
 
   losses = []
-  for tokens in tqdm(iter, total=5760//bs):
+  for tokens in tqdm(iter, total=5760//BS):
     GlobalCounters.reset()
     losses += eval_step(model, tokens).tolist()
     tqdm.write(f"loss: {np.mean(losses)}")
 
-  log_perplexity = Tensor(losses).mean()
-  print(f"Log Perplexity: {log_perplexity.item()}")
+  log_perplexity = np.mean(losses)
+  print(f"Log Perplexity: {log_perplexity}")
 
 if __name__ == "__main__":
   # inference only

examples/mlperf/model_train.py

@@ -4,7 +4,7 @@ import multiprocessing
 
 from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
 from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW, Profiling
-from tinygrad.nn.state import get_parameters, get_state_dict, safe_load, safe_save
+from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
 from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
 
 from extra.lr_scheduler import LRSchedulerGroup
@@ -1290,12 +1290,14 @@ def train_llama3():
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
 
   config = {}
+  BASEDIR            = config["BASEDIR"]                = Path(getenv("BASEDIR", "/raid/datasets/c4/"))
   BS                 = config["BS"]                     = getenv("BS", 16)
   grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
   GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
   SEED               = config["SEED"]                   = getenv("SEED", 5760)
   SEQLEN             = config["SEQLEN"]                 = getenv("SEQLEN", 8192)
   TRAIN_ON_VAL       = config["TRAIN_ON_VAL"]           = getenv("TRAIN_ON_VAL", 0)
+  SMALL              = config["SMALL"]                  = getenv("SMALL", 0)
   SAMPLES            = config["SAMPLES"]                = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000 * 1152)
   EVAL_FREQ          = config["EVAL_FREQ"]              = getenv("EVAL_FREQ", 46080)
   EVAL_BS            = config["EVAL_BS"]                = getenv("EVAL_BS", 16)
@@ -1311,13 +1313,14 @@ def train_llama3():
 
   opt_gradient_clip_norm = 1.0
   opt_learning_rate_warmup_steps = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
-  opt_learning_rate_decay_steps = getenv("DECAY_STEPS", math.ceil(1_200_000 * 1152 / GBS) - opt_learning_rate_warmup_steps)
+  opt_learning_rate_decay_steps = getenv("MAX_STEPS", math.ceil(1_200_000 * 1152 / GBS)) - opt_learning_rate_warmup_steps
   opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152)  # NOTE: cannot change for benchmark
-  opt_end_learning_rate = 8e-7
+  opt_end_learning_rate = getenv("END_LR", 8e-7)
 
   # TODO: confirm weights are in bf16
   # vocab_size from the mixtral tokenizer
-  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}
+  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]
+  params = params | {"vocab_size": 32000} if not SMALL else params
   if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
   model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
 
@@ -1353,6 +1356,15 @@ def train_llama3():
                 b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
   scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
 
+  if resume_ckpt := getenv("RESUME_CKPT"):
+    fn = f"./ckpts/llama3_{resume_ckpt}.safe"
+    print(f"loading initial checkpoint from {fn}")
+    load_state_dict(model, safe_load(fn), realize=False)
+
+    fn = f"./ckpts/llama3_{resume_ckpt}_optim.safe"
+    print(f"loading optim checkpoint from {fn}")
+    load_state_dict(scheduler, safe_load(fn), realize=False)
+
   @TinyJit
   @Tensor.train()
   def train_step(model, tokens:Tensor, grad_acc:int):
@@ -1403,43 +1415,55 @@ def train_llama3():
   # ** data iters **
   def fake_data(bs, samples):
     for _ in range(samples // bs):
-      yield Tensor.randint(bs, SEQLEN + 1, low=0, high=32000, dtype=dtypes.int32, device=Device.DEFAULT)
+      yield Tensor.randint(bs, SEQLEN + 1, low=0, high=params["vocab_size"], dtype=dtypes.int32, device=Device.DEFAULT)
 
   def get_train_iter():
     if getenv("FAKEDATA", 0):
       return fake_data(GBS, SAMPLES)
     else:
-      from examples.mlperf.dataloader import batch_load_llama3
-      return batch_load_llama3(GBS, SAMPLES, SEQLEN, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=SEED, val=bool(TRAIN_ON_VAL))
+      if SMALL:
+        from examples.mlperf.dataloader import batch_load_llama3_small
+        return batch_load_llama3_small(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
+      else:
+        from examples.mlperf.dataloader import batch_load_llama3
+        return batch_load_llama3(GBS, SAMPLES, SEQLEN, BASEDIR, seed=SEED, val=bool(TRAIN_ON_VAL))
 
   def get_eval_iter():
     if getenv("FAKEDATA", 0):
       return fake_data(EVAL_BS, 5760)
     else:
-      from examples.mlperf.dataloader import batch_load_llama3
-      return batch_load_llama3(EVAL_BS, 5760, SEQLEN, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=SEED, val=True)
+      if SMALL:
+        from examples.mlperf.dataloader import batch_load_llama3_small
+        return batch_load_llama3_small(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
+      else:
+        from examples.mlperf.dataloader import batch_load_llama3
+        return batch_load_llama3(EVAL_BS, 5760, SEQLEN, BASEDIR, val=True)
 
   iter = get_train_iter()
-  i, sequences_seen = 0, 0
+  i, sequences_seen = resume_ckpt, 0
   for tokens in tqdm(iter, total=SAMPLES//GBS):
     t = time.perf_counter()
     GlobalCounters.reset()
     loss, lr = train_step(model, tokens, grad_acc)
     loss = loss.float().item()
-    # above as tqdm.write f-string
+
+    i += 1
+    sequences_seen += tokens.shape[0]
+
     tqdm.write(f"{loss:.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
     if (fname:=getenv("LOSS_FILE", "")):
       with open(fname, "a") as f:
         f.write(f"{i} {loss:.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
 
-    if getenv("CKPT") and (i % 200 == 0 or i == 10):
+    if (ckpt_freq := getenv("CKPT")) and (i % ckpt_freq == 0 and (i != 1 or ckpt_freq == 1)):
       tqdm.write("saving checkpoint")
       if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
       fn = f"{ckpt_dir}/llama3_{i}.safe"
       safe_save(get_state_dict(model), fn)
 
-    i += 1
-    sequences_seen += tokens.shape[0]
+      tqdm.write("saving optim checkpoint")
+      fn = f"{ckpt_dir}/llama3_{i}_optim.safe"
+      safe_save(get_state_dict(scheduler), fn)
 
     if sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1):
       tqdm.write(f"evaluating after {sequences_seen} sequences")

extra/amdpci/proclogs.py

@@ -37,7 +37,7 @@ def main():
   dev = PCIIface(None, 0)
   for x, y in dev.dev_impl.__dict__.items():
     if isinstance(y, AMRegister):
-      for inst, addr in y.addr.keys(): reg_names[addr] = f"{x}, xcc={inst}"
+      for inst, addr in y.addr.items(): reg_names[addr] = f"{x}, xcc={inst}"
 
   with open(sys.argv[1], 'r') as f:
     log_content = log_content_them = f.read()

extra/backends/rdna.py

@@ -29,10 +29,10 @@ def uops_to_rdna(function_name:str, uops:UOpGraph) -> str:
   r: Dict[UOp, str] = {}
   for u in uops:
     if u.uop == UOps.SPECIAL:
-      if u.arg[1].startswith("lidx"):
-        r[u] = f'v{u.arg[0]}'
-      elif u.arg[1].startswith("gidx"):
-        r[u] = f's{2+u.arg[0]}'
+      if u.arg.startswith("lidx"):
+        r[u] = f'v{u.src[0].arg}'
+      elif u.arg.startswith("gidx"):
+        r[u] = f's{2+u.src[0].arg}'
       else:
         raise NotImplementedError
     elif u.uop == UOps.CONST:

extra/gemm/amd_uop_matmul.py

@@ -65,7 +65,7 @@ def top_spec_kernel3():
   c = a@b
   sink = c.schedule()[-1].ast
   L = 16
-  sink = sink.reshape((N//L, L, N//L, L)) #.lift({0:UOp.range(dtypes.int, N//BM, 0), 2:UOp.range(dtypes.int, N//BN, 1)})
+  sink = sink.reshape((N//L, L, N//L, L)) #.lift({0:UOp.range(N//BM, 0), 2:UOp.range(N//BN, 1)})
   sink = graph_rewrite(sink, view_left+pm)
   axis_types = (AxisType.GLOBAL, AxisType.LOCAL, AxisType.GLOBAL, AxisType.LOCAL, AxisType.REDUCE)
   return sink.replace(arg=KernelInfo(name="top_"+to_colored(sink.full_shape, axis_types), axis_types=axis_types))
@@ -186,7 +186,7 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
 
   c_regs = UOp(Ops.DEFINE_REG, dtypes.float.ptr(TM * nbIterWaveM * TN * nbIterWaveN), arg=2)
 
-  i = UOp.range(dtypes.int, c_regs.dtype.size, 16)
+  i = UOp.range(c_regs.dtype.size, 16)
   init_store = c_regs[i].store(UOp.const(dtypes.float, 0.0), i)
 
   if kernel4:
@@ -197,53 +197,53 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
     kId = 0
 
     # load from globals into locals
-    i = UOp.range(dtypes.int, nbReadsB, 0)
+    i = UOp.range(nbReadsB, 0)
     index_x = BN * blockIdx_x + rBIdx
     index_y = rBIdy + i * strideReadB + kId
     Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
 
-    i = UOp.range(dtypes.int, nbReadsA, 1)
+    i = UOp.range(nbReadsA, 1)
     index_x = rAIdx + kId
     index_y = BM * blockIdx_y + rAIdy + i * strideReadA
     As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
 
     # iterate over the middle chunk
-    kId_range = UOp.range(dtypes.int, N//BK-1, 2)
+    kId_range = UOp.range(N//BK-1, 2)
     kId = kId_range*BK
 
     barrier = UOp.barrier(As_store, Bs_store)
 
     # load from globals into registers (next round)
-    i = UOp.range(dtypes.int, nbReadsB, 3)
+    i = UOp.range(nbReadsB, 3)
     index_x = BN * blockIdx_x + rBIdx
     index_y = rBIdy + i * strideReadB + kId + BK
     regB_store = regB[i].store(b[N * index_y + index_x].load(), i)
 
-    i = UOp.range(dtypes.int, nbReadsA, 4)
+    i = UOp.range(nbReadsA, 4)
     index_x = rAIdx + kId + BK
     index_y = BM * blockIdx_y + rAIdy + i * strideReadA
     regA_store = regA[i].store(a[N * index_y + index_x].load(), i)
 
     def inner_loop(first_range, inp_dep=()):
       # inner unroll
-      k = UOp.range(dtypes.int, BK, first_range+0)
+      k = UOp.range(BK, first_range+0)
 
       # load from locals into registers
-      iterWave = UOp.range(dtypes.int, nbIterWaveN, first_range+1)
-      i = UOp.range(dtypes.int, TN, first_range+2)
+      iterWave = UOp.range(nbIterWaveN, first_range+1)
+      i = UOp.range(TN, first_range+2)
       index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
       B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(*inp_dep), iterWave, i)
 
-      iterWave = UOp.range(dtypes.int, nbIterWaveM, first_range+3)
-      i = UOp.range(dtypes.int, TM, first_range+4)
+      iterWave = UOp.range(nbIterWaveM, first_range+3)
+      i = UOp.range(TM, first_range+4)
       index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
       A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(*inp_dep), iterWave, i)
 
       # do the GEMM math
-      iterWaveM = UOp.range(dtypes.int, nbIterWaveM, first_range+5)
-      yt = UOp.range(dtypes.int, TM, first_range+6)
-      iterWaveN = UOp.range(dtypes.int, nbIterWaveN, first_range+7)
-      xt = UOp.range(dtypes.int, TN, first_range+8)
+      iterWaveM = UOp.range(nbIterWaveM, first_range+5)
+      yt = UOp.range(TM, first_range+6)
+      iterWaveN = UOp.range(nbIterWaveN, first_range+7)
+      xt = UOp.range(TN, first_range+8)
       x = iterWaveN * TN + xt
       y = iterWaveM * TM + yt
       c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
@@ -256,12 +256,12 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
     sink = inner_loop(5, (barrier, regB_store, regA_store)).barrier()
 
     # load from registers into locals
-    i = UOp.range(dtypes.int, nbReadsB, 14)
+    i = UOp.range(nbReadsB, 14)
     index_x = BN * blockIdx_x + rBIdx
     index_y = rBIdy + i * strideReadB + kId + BK
     Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(regB[i].load(sink), i, kId_range)
 
-    i = UOp.range(dtypes.int, nbReadsA, 15)
+    i = UOp.range(nbReadsA, 15)
     index_x = rAIdx + kId + BK
     index_y = BM * blockIdx_y + rAIdy + i * strideReadA
     As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(regA[i].load(sink), i, kId_range)
@@ -269,40 +269,40 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
     # final iteration without the copy
     sink = inner_loop(16, (UOp.barrier(Bs_store, As_store),))
   else:
-    kId_range = UOp.range(dtypes.int, N//BK, 0)
+    kId_range = UOp.range(N//BK, 0)
     kId = kId_range*BK
 
     # load from globals into locals
-    i = UOp.range(dtypes.int, nbReadsB, 1)
+    i = UOp.range(nbReadsB, 1)
     index_x = BN * blockIdx_x + rBIdx
     index_y = rBIdy + i * strideReadB + kId
     Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
 
-    i = UOp.range(dtypes.int, nbReadsA, 2)
+    i = UOp.range(nbReadsA, 2)
     index_x = rAIdx + kId
     index_y = BM * blockIdx_y + rAIdy + i * strideReadA
     As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
 
     barrier = UOp.barrier(As_store, Bs_store)
 
-    k = UOp.range(dtypes.int, BK, 3)
+    k = UOp.range(BK, 3)
 
     # load from locals into registers
-    iterWave = UOp.range(dtypes.int, nbIterWaveN, 4)
-    i = UOp.range(dtypes.int, TN, 5)
+    iterWave = UOp.range(nbIterWaveN, 4)
+    i = UOp.range(TN, 5)
     index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
     B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(barrier), iterWave, i)
 
-    iterWave = UOp.range(dtypes.int, nbIterWaveM, 6)
-    i = UOp.range(dtypes.int, TM, 7)
+    iterWave = UOp.range(nbIterWaveM, 6)
+    i = UOp.range(TM, 7)
     index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
     A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(barrier), iterWave, i)
 
     # do the GEMM math
-    iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 8)
-    yt = UOp.range(dtypes.int, TM, 9)
-    iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 10)
-    xt = UOp.range(dtypes.int, TN, 12)
+    iterWaveM = UOp.range(nbIterWaveM, 8)
+    yt = UOp.range(TM, 9)
+    iterWaveN = UOp.range(nbIterWaveN, 10)
+    xt = UOp.range(TN, 12)
     x = iterWaveN * TN + xt
     y = iterWaveM * TM + yt
     c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
@@ -310,10 +310,10 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
                             iterWaveM, iterWaveN, yt, xt, k, kId_range)
 
   # store c_regs into c
-  iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 1000)
-  yt = UOp.range(dtypes.int, TM, 1001)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 1002)
-  xt = UOp.range(dtypes.int, TN, 1003)
+  iterWaveM = UOp.range(nbIterWaveM, 1000)
+  yt = UOp.range(TM, 1001)
+  iterWaveN = UOp.range(nbIterWaveN, 1002)
+  xt = UOp.range(TN, 1003)
   xOut = blockIdx_x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave
   yOut = blockIdx_y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave
   indexC = N * (yOut + yt) + xOut + xt

extra/gemm/max_matmul.py

@@ -56,7 +56,7 @@ def randoms():
 def ast_to_cuda_prog(compiler, ast, opts):
   k = Kernel(ast)
   k.apply_opts(opts)
-  p = get_program(k.get_optimized_ast(), k.opts)
+  p = get_program(k.ast, k.opts, k.applied_opts)
   return CUDAProgram(device, p.function_name, compiler.compile(p.src))
 
 if __name__ == "__main__":

extra/gemm/tinygrad_nv_matmul.py

@@ -29,7 +29,7 @@ if __name__ == "__main__":
       Opt(op=OptOps.LOCAL, axis=0, amt=2),
     ]
   k.apply_opts(opts)
-  prg = get_program(k.get_optimized_ast(), k.opts)
+  prg = get_program(k.ast, k.opts, k.applied_opts)
   new_src = prg.src
   # can mod source here
   prg = replace(prg, src=new_src)

extra/optimization/test_beam_search.py

@@ -16,9 +16,9 @@ class TestBeamSearch(unittest.TestCase):
     BEAM.value = self.old_beam
 
   def test_variable_ast_beam(self):
-    with Context(IGNORE_OOB=1):
-      a = rand(3, 3).reshape((Variable("a", 1, 10).bind(3), 3))
-      a = (a+1).realize()
+    vi = Variable("a", 1, 10).bind(3)
+    a = rand(10, 3)[:vi]
+    a = (a+1).realize()
 
   def test_big_prime_number(self):
     a = rand(367, 367)
@@ -42,18 +42,16 @@ class TestBeamSearch(unittest.TestCase):
 
   def test_variable_big_prime_number(self):
     v = Variable("v", 1, 400).bind(367)
-    a = rand(367, 367)
-    b = rand(367, 367)
-    with Context(IGNORE_OOB=1):
-      c = (a.reshape(367, v) @ b.reshape(v, 367)).realize()
-      np.testing.assert_allclose(c.numpy(), a.numpy() @ b.numpy(), atol=1e-4, rtol=1e-4)
+    a = rand(367, 400)
+    b = rand(400, 367)
+    c = (a[:, :v] @ b[:v, :]).realize()
+    np.testing.assert_allclose(c.numpy(), a[:, :367].numpy() @ b[:367, :].numpy(), atol=1e-4, rtol=1e-4)
 
   def test_variable_shrink_prime_number(self):
     v = Variable("v", 1, 400).bind(367)
     a = rand(400, 367)
-    with Context(IGNORE_OOB=1):
-      b = (a.shrink(((0,v), None))+1).reshape(367,367).realize()
-      np.testing.assert_allclose(b.numpy(), a.numpy()[:367]+1, atol=1e-4, rtol=1e-4)
+    b = (a.shrink(((0,v), None))+1).reshape(367,367).realize()
+    np.testing.assert_allclose(b.numpy(), a.numpy()[:367]+1, atol=1e-4, rtol=1e-4)
 
   def test_no_mutate_rawbuffers(self):
     a = rand(3, 3).realize()

extra/remu/src/thread.rs

@@ -673,6 +673,7 @@ impl<'a> Thread<'a> {
                                         39 => f32::log2(s0),
                                         42 => 1.0 / s0,
                                         43 => 1.0 / s0,
+                                        46 => 1.0 / f32::sqrt(s0),
                                         51 => f32::sqrt(s0),
                                         _ => todo_instr!(instruction)?,
                                     }
@@ -1246,7 +1247,7 @@ impl<'a> Thread<'a> {
                             }
 
                             let ret = match op {
-                                257 | 259 | 299 | 260 | 261 | 264 | 272 | 392 | 426 | 531 | 537 | 540 | 551 | 567 | 796 => {
+                                257 | 259 | 299 | 260 | 261 | 264 | 272 | 392 | 426 | 430 | 531 | 537 | 540 | 551 | 567 | 796 => {
                                     let s0 = f32::from_bits(s0).negate(0, neg).absolute(0, abs);
                                     let s1 = f32::from_bits(s1).negate(1, neg).absolute(1, abs);
                                     let s2 = f32::from_bits(s2).negate(2, neg).absolute(2, abs);
@@ -1258,6 +1259,7 @@ impl<'a> Thread<'a> {
                                         272 => f32::max(s0, s1),
                                         299 => f32::mul_add(s0, s1, f32::from_bits(self.vec_reg[vdst])),
                                         426 => s0.recip(),
+                                        430 => 1.0 / f32::sqrt(s0),
                                         531 => f32::mul_add(s0, s1, s2),
                                         537 => f32::min(f32::min(s0, s1), s2),
                                         540 => f32::max(f32::max(s0, s1), s2),
@@ -2625,6 +2627,14 @@ mod test_vop1 {
         assert_eq!(thread.vec_reg[3], 1071644672);
     }
 
+    #[test]
+    fn test_v_rsq_f32() {
+        let mut thread = _helper_test_thread();
+        thread.vec_reg[0] = f32::to_bits(4.0);
+        r(&vec![0x7E005D00, END_PRG], &mut thread);
+        assert_eq!(f32::from_bits(thread.vec_reg[0]), 0.5);
+    }
+
     #[test]
     fn test_v_frexp_exp_i32_f64() {
         [(3573412790272.0, 42), (69.0, 7), (2.0, 2), (f64::NEG_INFINITY, 0)]

extra/replay_pkl.py

@@ -58,7 +58,7 @@ if __name__ == "__main__":
             GlobalCounters.kernel_count -= 1
 
         if not getenv("NOOPT"): k.apply_opts(hand_coded_optimizations(k))
-        p2 = get_program(k.get_optimized_ast(), k.opts)
+        p2 = get_program(k.ast, k.opts, k.applied_opts)
         new_ei = replace(ei, prg=CompiledRunner(p2))
         new_ei.run()
         new_jit.append(new_ei)

extra/test_hcopt.py

@@ -0,0 +1,22 @@
+from extra.optimization.helpers import load_worlds, ast_str_to_lin
+from tinygrad.codegen.lowerer import pm_lowerer, get_index
+from tinygrad.uop.ops import graph_rewrite
+from tinygrad.codegen.opt.postrange import Scheduler
+from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+
+if __name__ == "__main__":
+  ast_strs = load_worlds()
+  for i, ast_str in enumerate(ast_strs):
+    lin = ast_str_to_lin(ast_str)
+    opt1 = hand_coded_optimizations(lin)
+
+    lowered = graph_rewrite(lin.ast, pm_lowerer, ctx=get_index(lin.ast), bottom_up=True)
+    sch = Scheduler(lowered, lin.opts)
+    opt2 = hand_coded_optimizations(sch)
+
+    if opt1 != opt2:
+      print("*******")
+      print("Kernel:    ", opt1)
+      print("Scheduler: ", opt2)
+    else:
+      print("******* MATCH")

test/external/external_benchmark_sdxl_softmax.py

@@ -24,5 +24,5 @@ if __name__ == "__main__":
   #k.apply_opt(Opt(OptOps.GROUP, 1, 32))
   #k.apply_opt(Opt(OptOps.GROUP, 0, 32))
   from tinygrad.engine.realize import CompiledRunner, ExecItem
-  run = CompiledRunner(prg:=get_program(k.get_optimized_ast(), k.opts))
+  run = CompiledRunner(prg:=get_program(k.ast, k.opts, k.applied_opts))
   ExecItem(run, si.bufs).run()

test/external/external_debug_metal_sd_conv.py

@@ -35,7 +35,7 @@ k = Kernel(ast)
 k.apply_opts(opts)
 bufs = bufs_from_lin(k)
 
-prg = CompiledRunner(get_program(k.get_optimized_ast(), k.opts))
+prg = CompiledRunner(get_program(k.ast, k.opts, k.applied_opts))
 
 for i in range(10):
   speed = prg(bufs, var_vals={}, wait=True)

test/external/external_metal_compile_slow.py

@@ -0,0 +1,56 @@
+# ruff: noqa: E501
+from tinygrad import dtypes
+from tinygrad.helpers import Timing, getenv
+from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.engine.realize import get_program, CompiledRunner
+from tinygrad.uop.ops import UOp, Ops, AxisType
+
+if __name__ == "__main__":
+  if getenv("TC", 0) == 0:
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1179648), arg=0, src=())
+    c1 = UOp.range(UOp.const(dtypes.int, 512), 0, AxisType.GLOBAL)
+    c2 = UOp.range(UOp.const(dtypes.int, 64), 1, AxisType.GLOBAL)
+    c3 = UOp.range(UOp.const(dtypes.int, 6), 2, AxisType.GLOBAL)
+    c4 = UOp.range(UOp.const(dtypes.int, 6), 3, AxisType.GLOBAL)
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(2097152), arg=1, src=())
+    c6 = UOp.range(UOp.const(dtypes.int, 64), 1004, AxisType.REDUCE)
+    c7 = UOp.range(UOp.const(dtypes.int, 3), 1005, AxisType.REDUCE)
+    c8 = UOp.range(UOp.const(dtypes.int, 3), 1006, AxisType.REDUCE)
+    c9 = c5.index(((((((c1*UOp.const(dtypes.int, 4096))+(c3*UOp.const(dtypes.int, 8)))+c4)+(c6*UOp.const(dtypes.int, 64)))+(c7*UOp.const(dtypes.int, 8)))+c8), UOp.const(dtypes.bool, True)).load()
+    c10 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(36864), arg=2, src=())
+    c11 = c10.index(((((c2*UOp.const(dtypes.int, 576))+(c6*UOp.const(dtypes.int, 9)))+(c7*UOp.const(dtypes.int, 3)))+c8), UOp.const(dtypes.bool, True)).load()
+    c12 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64), arg=3, src=())
+    c13 = c12.index(c2, UOp.const(dtypes.bool, True)).load()
+    c14 = ((c9*c11).reduce(c6, c7, c8, arg=Ops.ADD)+c13)
+    c15 = c0.index(((((c1*UOp.const(dtypes.int, 2304))+(c2*UOp.const(dtypes.int, 36)))+(c3*UOp.const(dtypes.int, 6)))+c4), UOp.const(dtypes.bool, True)).store(c14, c1, c2, c3, c4)
+    ast = c15.sink()
+
+    # this does have tons of locals
+    opts = [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=3, arg=0),
+            Opt(op=OptOps.LOCAL, axis=0, arg=16), Opt(op=OptOps.UPCAST, axis=3, arg=2),
+            Opt(op=OptOps.GROUPTOP, axis=0, arg=16)]
+  else:
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(10616832), arg=0, src=())
+    c1 = UOp.range(UOp.const(dtypes.int, 512), 0, AxisType.GLOBAL)
+    c2 = UOp.range(UOp.const(dtypes.int, 64), 1, AxisType.GLOBAL)
+    c3 = UOp.range(UOp.const(dtypes.int, 36), 2, AxisType.GLOBAL)
+    c4 = UOp.range(UOp.const(dtypes.int, 9), 3, AxisType.GLOBAL)
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(36864), arg=1, src=())
+    c6 = UOp.range(UOp.const(dtypes.int, 64), 1004, AxisType.REDUCE)
+    c7 = c5.index((((c2*UOp.const(dtypes.int, 9))+c4)+(c6*UOp.const(dtypes.int, 576))), UOp.const(dtypes.bool, True)).load()
+    c8 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1179648), arg=2, src=())
+    c9 = c8.index((((c1*UOp.const(dtypes.int, 2304))+c3)+(c6*UOp.const(dtypes.int, 36))), UOp.const(dtypes.bool, True)).load()
+    c10 = (c7*c9).reduce(c6, arg=Ops.ADD)
+    c11 = c0.index(((((c1*UOp.const(dtypes.int, 20736))+(c2*UOp.const(dtypes.int, 324)))+(c3*UOp.const(dtypes.int, 9)))+c4), UOp.const(dtypes.bool, True)).store(c10, c1, c2, c3, c4)
+    ast = c11.sink()
+
+    opts = [Opt(op=OptOps.TC, axis=0, arg=(0, 0, 1)), Opt(op=OptOps.UPCAST, axis=2, arg=4),
+            Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=0)]
+
+  prg = get_program(ast, opts=opts)
+  print(prg.src)
+  for i in range(10):
+    with Timing(f"try {i}: "):
+      # NOTE: this doesn't even run the kernel
+      try: CompiledRunner(prg)
+      except RuntimeError: pass

test/external/speed_beam_v_hcopt.py

@@ -1,41 +0,0 @@
-from tinygrad import Device
-from tinygrad.helpers import getenv, DEBUG, BEAM
-from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-from extra.optimization.helpers import load_worlds, ast_str_to_lin, time_linearizer
-
-if __name__ == "__main__":
-  filter_reduce = bool(getenv("FILTER_REDUCE"))
-  ast_strs = load_worlds(filter_reduce=filter_reduce, filter_novariable=True)
-  dev = Device[Device.DEFAULT]
-
-  test_n = getenv("TEST_N", 10)
-  single = getenv("NUM", -1)
-  if single != -1: ast_strs = ast_strs[single:single+1]
-
-  beam_won, tested = 0, 0
-
-  for num, ast in enumerate(ast_strs[:test_n]):
-    def new_lin(): return ast_str_to_lin(ast, opts=dev.renderer)
-
-    k = new_lin()
-
-    if not (used_tensor_cores:=k.apply_tensor_cores(getenv("TC", 1))): k.apply_opts(hand_coded_optimizations(k))
-
-    assert BEAM > 0
-
-    lins = [(("tc" if used_tensor_cores else "hc"), k)]
-    if used_tensor_cores:
-      lins.append(("hc", new_lin()))
-      lins[-1][1].apply_opts(hand_coded_optimizations(lins[-1][1]))
-    kb = new_lin()
-    test_rawbuffers = bufs_from_lin(kb)    # allocate scratch buffers for optimization
-    lins.append((f"beam{BEAM.value}", beam_search(kb, test_rawbuffers, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))))
-    timed = sorted([(nm, tk, time_linearizer(tk, test_rawbuffers, allow_test_size=False, clear_l2=True)) for nm, tk in lins], key=lambda x: x[2])
-    if DEBUG >= 1: print("  <  ".join(f"{nm:6s} : {lin.colored_shape(30, dense=True)} : {tm*1e6:8.2f} us" for nm, lin, tm in timed))
-
-    tested += 1
-    if timed[0][0].startswith("beam"):
-      beam_won += 1
-
-  print(f"{beam_won=} / {tested=} = {beam_won/tested:.3f}")

test/mockgpu/amd/amdgpu.py

@@ -1,5 +1,6 @@
 import ctypes, time
 from test.mockgpu.gpu import VirtGPU
+from test.mockgpu.helpers import _try_dlopen_remu
 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
 
@@ -24,19 +25,6 @@ WAIT_REG_MEM_FUNCTION_EQ  = 3 # ==
 WAIT_REG_MEM_FUNCTION_NEQ = 4 # !=
 WAIT_REG_MEM_FUNCTION_GEQ = 5 # >=
 
-REMU_PATHS = ["extra/remu/target/release/libremu.so", "libremu.so", "/usr/local/lib/libremu.so",
-              "extra/remu/target/release/libremu.dylib", "libremu.dylib", "/usr/local/lib/libremu.dylib", "/opt/homebrew/lib/libremu.dylib"]
-def _try_dlopen_remu():
-  for path in REMU_PATHS:
-    try:
-      remu = ctypes.CDLL(path)
-      remu.run_asm.restype = ctypes.c_int32
-      remu.run_asm.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32,
-        ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_void_p]
-    except OSError: pass
-    else: return remu
-  print("Could not find libremu.so")
-  return None
 remu = _try_dlopen_remu()
 
 def create_sdma_packets():

test/mockgpu/cuda/cuda.py

@@ -2,16 +2,14 @@ from __future__ import annotations
 from typing import Any
 import ctypes, time
 from tinygrad.runtime.autogen import cuda as orig_cuda
+from test.mockgpu.helpers import _try_dlopen_gpuocelot
 from tinygrad.helpers import mv_address
 
 for attr in dir(orig_cuda):
   if not attr.startswith('__'):
     globals()[attr] = getattr(orig_cuda, attr)
 
-try:
-  gpuocelot_lib = ctypes.CDLL(ctypes.util.find_library("gpuocelot"))
-  gpuocelot_lib.ptx_run.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_void_p), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]  # noqa: E501
-except Exception: pass
+gpuocelot_lib = _try_dlopen_gpuocelot()
 
 # Global state
 class CUDAState:
@@ -130,7 +128,10 @@ def cuModuleUnload(hmod) -> int:
 def cuLaunchKernel(f, gx: int, gy: int, gz: int, lx: int, ly: int, lz: int, sharedMemBytes: int,
                    hStream: Any, kernelParams: Any, extra: Any) -> int:
   cargs = [ctypes.cast(getattr(extra, field[0]), ctypes.c_void_p) for field in extra._fields_]
-  gpuocelot_lib.ptx_run(ctypes.cast(f.value, ctypes.c_char_p), len(cargs), (ctypes.c_void_p*len(cargs))(*cargs), lx, ly, lz, gx, gy, gz, 0)
+  try: gpuocelot_lib.ptx_run(ctypes.cast(f.value, ctypes.c_char_p), len(cargs), (ctypes.c_void_p*len(cargs))(*cargs), lx, ly, lz, gx, gy, gz, 0)
+  except Exception as e:
+    print("Error in cuLaunchKernel:", e)
+    return orig_cuda.CUDA_ERROR_LAUNCH_FAILED
   return orig_cuda.CUDA_SUCCESS
 
 def cuDeviceComputeCapability(major, minor, dev: int) -> int:

test/mockgpu/helpers.py

@@ -0,0 +1,29 @@
+import ctypes, ctypes.util
+
+def _try_dlopen_gpuocelot():
+  GPUOCELOT_PATHS = [ctypes.util.find_library("gpuocelot")] if ctypes.util.find_library("gpuocelot") is not None else []
+  GPUOCELOT_PATHS += ["libgpuocelot.so", "/usr/local/lib/libgpuocelot.so",
+                      "libgpuocelot.dylib", "/usr/local/lib/libgpuocelot.dylib", "/opt/homebrew/lib/libgpuocelot.dylib"]
+  for path in GPUOCELOT_PATHS:
+    try:
+      gpuocelot_lib = ctypes.CDLL(path)
+      gpuocelot_lib.ptx_run.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_void_p), ctypes.c_int, ctypes.c_int,
+        ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]
+    except OSError: pass
+    else: return gpuocelot_lib
+  print("Could not find libgpuocelot.so")
+  return None
+
+def _try_dlopen_remu():
+  REMU_PATHS = ["extra/remu/target/release/libremu.so", "libremu.so", "/usr/local/lib/libremu.so",
+               "extra/remu/target/release/libremu.dylib", "libremu.dylib", "/usr/local/lib/libremu.dylib", "/opt/homebrew/lib/libremu.dylib"]
+  for path in REMU_PATHS:
+    try:
+      remu = ctypes.CDLL(path)
+      remu.run_asm.restype = ctypes.c_int32
+      remu.run_asm.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32,
+        ctypes.c_uint32, ctypes.c_uint32, ctypes.c_uint32, ctypes.c_void_p]
+    except OSError: pass
+    else: return remu
+  print("Could not find libremu.so")
+  return None

test/mockgpu/nv/nvgpu.py

@@ -2,6 +2,7 @@ import ctypes, ctypes.util, time
 import tinygrad.runtime.autogen.nv_gpu as nv_gpu
 from enum import Enum, auto
 from test.mockgpu.gpu import VirtGPU
+from test.mockgpu.helpers import _try_dlopen_gpuocelot
 from tinygrad.helpers import to_mv, init_c_struct_t
 
 def make_qmd_struct_type():
@@ -16,10 +17,7 @@ def make_qmd_struct_type():
 qmd_struct_t = make_qmd_struct_type()
 assert ctypes.sizeof(qmd_struct_t) == 0x40 * 4
 
-try:
-  gpuocelot_lib = ctypes.CDLL(ctypes.util.find_library("gpuocelot"))
-  gpuocelot_lib.ptx_run.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_void_p), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]  # noqa: E501
-except Exception: pass
+gpuocelot_lib = _try_dlopen_gpuocelot()
 
 class SchedResult(Enum): CONT = auto(); YIELD = auto() # noqa: E702
 
@@ -99,7 +97,10 @@ class GPFIFO:
     cargs = [ctypes.cast(args[i], ctypes.c_void_p) for i in range(args_cnt)] + [ctypes.cast(vals[i], ctypes.c_void_p) for i in range(vals_cnt)]
     gx, gy, gz = qmd.cta_raster_width, qmd.cta_raster_height, qmd.cta_raster_depth
     lx, ly, lz = qmd.cta_thread_dimension0, qmd.cta_thread_dimension1, qmd.cta_thread_dimension2
-    gpuocelot_lib.ptx_run(ctypes.cast(prg_addr, ctypes.c_char_p), args_cnt+vals_cnt, (ctypes.c_void_p*len(cargs))(*cargs), lx, ly, lz, gx, gy, gz, 0)
+    try:
+      gpuocelot_lib.ptx_run(ctypes.cast(prg_addr, ctypes.c_char_p), args_cnt+vals_cnt,
+        (ctypes.c_void_p*len(cargs))(*cargs), lx, ly, lz, gx, gy, gz, 0)
+    except Exception as e: print("failed to execute:", e)
     if qmd.release0_enable:
       rel0 = to_mv(qmd.release0_address_lower + (qmd.release0_address_upper << 32), 0x10).cast('Q')
       rel0[0] = qmd.release0_payload_lower + (qmd.release0_payload_upper << 32)

test/opt/test_gen_float4.py

@@ -0,0 +1,229 @@
+import unittest
+from tinygrad import Device, Tensor, dtypes
+from tinygrad.uop.ops import UOp, Ops
+from tinygrad.codegen.opt import Opt, OptOps
+from tinygrad.shape.shapetracker import ShapeTracker, View
+from tinygrad.engine.realize import get_program
+from tinygrad.helpers import AMX
+
+@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4")
+class TestFloat4(unittest.TestCase):
+  @staticmethod
+  def count_float4(uops: list[UOp], n=4):
+    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]),
+            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float.vec(n)]))
+  @staticmethod
+  def count_half4(uops: list[UOp]):
+    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]),
+            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half.vec(4)]))
+
+  def test_float4_basic(self):
+    a = Tensor.empty(2, 8).realize()
+    b = Tensor.empty(2, 8).realize()
+    c = a + b
+
+    s = c.schedule()[0]
+    realized_ast = s.ast
+    opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
+    program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
+
+    assert TestFloat4.count_float4(program.uops) == (2, 1)
+
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
+  def test_float4_multidim(self):
+    a = Tensor.empty(2, 8).realize()
+    b = Tensor.empty(2, 8).realize()
+    c = a + b
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
+    assert TestFloat4.count_float4(uops) == (4, 2)
+
+  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
+  def test_float4_multidim_amx(self):
+    def kernel_for_shape(size, shift):
+      a = Tensor.empty(2, size).realize()
+      b = Tensor.empty(2, size).realize()
+      c = a + b
+
+      s = c.schedule()[0]
+      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]).uops
+
+    sizes = [12, 8, 16]
+    shifts = [3, 2, 4]
+    expected_upcast_size = [4, 8, 16]
+    expected_output = [(6,3), (2,1), (2,1)]
+
+    for i in range(len(sizes)):
+      assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
+
+  def test_float4_unaligned_load(self):
+    a = Tensor.empty(9).realize().shrink(((1, 9),))
+    b = Tensor.empty(9).realize().shrink(((1, 9),))
+    c = a + b
+
+    s = c.schedule()[0]
+    realized_ast = s.ast
+    opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
+    program = get_program(realized_ast, Device[Device.DEFAULT].renderer, opts=opts_to_apply)
+
+    assert TestFloat4.count_float4(program.uops) == (0, 1)
+
+  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
+  def test_float4_multidim_unaligned_load(self):
+    a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
+    b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
+    c = a + b
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]).uops
+
+    assert TestFloat4.count_float4(uops) == (0, 2)
+
+  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
+  def test_float4_multidim_unaligned_load_amx(self):
+    def kernel_for_shape(size, shift):
+      a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
+      b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
+      c = a + b
+
+      s = c.schedule()[0]
+      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]).uops
+
+    sizes = [13, 9, 17]
+    shifts = [3, 2, 4]
+    expected_upcast_size = [4, 8, 16]
+    expected_output = [(0,3), (0,1), (0,1)]
+
+    for i in range(len(sizes)):
+      assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
+
+  def test_float4_sometimes_unaligned(self):
+    a = Tensor.empty(1, 1, 8).realize()
+    b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
+    c = a.conv2d(b)
+    # only the first and last conv dot products are aligned in a, and b is never aligned, so no
+    # float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UNROLL, axis=0, arg=4)]).uops
+
+    assert TestFloat4.count_float4(uops) == (0, 0)
+
+  def test_float4_multidim_sometimes_unaligned(self):
+    a = Tensor.empty(1, 1, 7).realize()
+    b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
+    c = a.conv2d(b)
+    # the first conv dot product is aligned in a. If we upcast the output and reduce
+    # dimension, then we could do float4 for only that one set of loads, but we currently
+    # don't.
+    # UPDATE: now we do this fusion
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
+
+    assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
+
+  def test_float4_expand(self):
+    a = Tensor.empty(9).realize().shrink(((1, 9),))
+    b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
+    c = a + b
+
+    # we will upcast the top axis of sz 4. they should not be coalesced into float4,
+    # since the top axis is not contiguous.
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
+
+    assert TestFloat4.count_float4(uops) == (0, 1)
+
+  def test_float4_heterogeneous(self):
+    a = Tensor.empty(8).realize()
+    b = Tensor.empty(9).realize().shrink(((1, 9),))
+    c = a + b
+
+    # should float4 b but not a
+
+    s = c.schedule()[0]
+    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
+
+    assert TestFloat4.count_float4(uops) == (1, 1)
+
+  def test_half4_load_unrolled(self):
+    # from llama 7B shard 4 gpus
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)),
+        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
+          UOp(Ops.CAST, dtypes.float, arg=None, src=(
+            UOp(Ops.MUL, dtypes.half, arg=None, src=(
+              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)),
+              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),))
+
+    # TODO: fix this, expected might change but should be positive
+    for expected, opts in [
+      ((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+      ((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+      ((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
+    ]:
+      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
+
+      count = TestFloat4.count_half4(program.uops)
+      assert count == expected, f"{count=}, {expected=}"
+
+  @unittest.skip("this doesn't happen anymore")
+  def test_float4_acc(self):
+    # from float32 stable diffusion red tinybox
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
+          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)),
+        UOp(Ops.ADD, dtypes.float, arg=None, src=(
+          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
+            UOp(Ops.MUL, dtypes.float, arg=None, src=(
+              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)),
+              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)),
+          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
+            UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
+              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),))
+
+    for expected, opts in [
+      (1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]),
+      (4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]),
+    ]:
+      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
+      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)])
+      assert count == expected, f"{count=}, {expected=}"
+
+  @unittest.skip("this doesn't happen anymore")
+  def test_float2_acc(self):
+    # from resnet
+    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
+      UOp(Ops.STORE, dtypes.void, arg=None, src=(
+        UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
+          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)),
+        UOp(Ops.CAST, dtypes.half, arg=None, src=(
+          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=(
+            UOp(Ops.CAST, dtypes.float, arg=None, src=(
+              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
+                UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501
+                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),))
+    for expected, opts in [
+      (16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]),  # noqa: E501
+      (4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]),
+    ]:
+      program = get_program(ast, Device[Device.DEFAULT].renderer, opts=opts)
+      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)])
+      assert count == expected, f"{count=}, {expected=}"
+
+if __name__ == '__main__':
+  unittest.main()

test/opt/test_kernel_opts.py

@@ -0,0 +1,326 @@
+import unittest
+from tinygrad import Device, Tensor, dtypes
+from tinygrad.helpers import CI
+from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
+
+# TODO: write a clean version of this
+from test.test_linearizer import helper_linearizer_opt
+
+class TestKernelOpts(unittest.TestCase):
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  def test_local_and_grouped_reduce(self):
+    N = 128
+    Tensor.manual_seed(1882)
+    a = Tensor.rand(4, 4, N, N)
+    b = Tensor.rand(4, 4, N)
+    r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.LOCAL, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 8)],
+      [Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals
+      [Opt(OptOps.GROUPTOP, 0, 2)],
+      [Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)],
+      [Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)],
+      # Checking how it works with locals + grouped reduce
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)],
+      # Checking how it works with locals + grouped reduce + upcasts
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)],
+      # many local + many group
+      [Opt(OptOps.GROUP, 0, 2)] * 4,
+      [Opt(OptOps.LOCAL, 0, 2)] * 4,
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)] * 4,
+    ])
+
+  def test_upcasts(self):
+    N = 16
+    Tensor.manual_seed(1772)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    r = (a+b).sqrt() * ((a+1).exp())
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.UPCAST, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 4)],
+      [Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts
+    ])
+
+  def test_full_upcast(self):
+    Tensor.manual_seed(1772)
+    a = Tensor.rand(4)
+    b = Tensor.rand(4)
+    r = (a+b).sqrt() * ((a+1).exp())
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts
+    ])
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  def test_matmul(self):
+    N = 128
+    Tensor.manual_seed(1552)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    r = a@b
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.UPCAST, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts
+      [Opt(OptOps.LOCAL, 0, 2)],
+      [Opt(OptOps.LOCAL, 1, 32)],
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)],
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)],
+      [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals
+      [Opt(OptOps.GROUPTOP, 0, 2)],
+      [Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce
+      # Checking all together
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4),
+       Opt(OptOps.UPCAST, 1, 2)],
+      # Full global upcast + local
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)],
+    ])
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  def test_double_reduce(self):
+    N = 128
+    Tensor.manual_seed(1552)
+    a = Tensor.rand(8, N, 8, N)
+    r = a.sum(axis=(1,3))
+    helper_linearizer_opt(r, [
+      # openCL / GPU=1 is 256 max threads
+      [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
+      [Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
+      [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
+      [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)],
+      [Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces.
+      [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)],
+      [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts.
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)],
+      # Checking how it works with 2 grouped_reduces + upcasts + locals.
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)],
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
+       Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals.
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
+       Opt(OptOps.UPCAST, 0, 2)], # No globals
+    ])
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
+                      "test requires tensor cores with accumulation in half") # testing with half suffices.
+  def test_tensor_core_opts(self):
+    N = 128
+    Tensor.manual_seed(1552)
+    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
+    r = a.matmul(b, dtype=dtypes.half)
+    atol, rtol = 0.25, 0.01
+    helper_linearizer_opt(r, [
+      [],
+      [Opt(OptOps.UPCAST, 0, 4)],
+      [Opt(OptOps.UPCAST, 1, 4)],
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts
+      [Opt(OptOps.UNROLL, 0, 2)], # check unroll
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)],
+      [Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations
+      [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)],
+      [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)],
+    ], apply_tc=True, atol=atol, rtol=rtol)
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
+                      "test requires tensor cores with accumulation in half") # testing with half suffices.
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  def test_tensor_core_opts_locals(self):
+    N = 128
+    Tensor.manual_seed(1552)
+    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
+    r = a.matmul(b, dtype=dtypes.half)
+    atol, rtol = 0.25, 0.01
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals
+      [Opt(OptOps.LOCAL, 0, 4)], # check local
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
+    ], apply_tc=True, atol=atol, rtol=rtol)
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared memory")
+  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
+                      "test requires tensor cores with accumulation in half") # testing with half suffices.
+  # NOTE: the METAL test is broken, likely due to a compiler bug. passes on CI with -O0 and with default opt level locally on M3
+  @unittest.skipIf(Device.DEFAULT == "METAL", "broken for METAL")
+  @unittest.skip("feature was removed")
+  def test_tensor_core_opts_group(self):
+    N = 128
+    Tensor.manual_seed(1552)
+    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
+    r = a.matmul(b, dtype=dtypes.half)
+    atol, rtol = 0.25, 0.01
+    helper_linearizer_opt(r, [
+      [Opt(OptOps.GROUP, 0, 2)],
+      [Opt(OptOps.GROUPTOP, 0, 4)],
+      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.GROUP, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
+      [Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
+      [Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)],
+      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 2)],
+    ], apply_tc=True, atol=atol, rtol=rtol)
+
+  def test_padto_matmul(self):
+    if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]):
+      self.skipTest("super slow on CUDA and AMD because of the big grid dims")
+    N = 17 * 17
+    Tensor.manual_seed(289)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    helper_linearizer_opt(a@b, [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 1, 32)],
+      [Opt(OptOps.PADTO, 2, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.PADTO, 2, 32)],
+      # can optimize further post PADTO
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),],
+    ])
+
+  def test_padto_upcasted_not_ok(self):
+    N = 4
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    helper_linearizer_opt(a@b, [
+      [Opt(OptOps.UPCAST, 0, 0)],
+      [Opt(OptOps.UPCAST, 1, 0)],
+      [Opt(OptOps.UNROLL, 0, 0)],
+      [Opt(OptOps.PADTO, 0, 8)],
+      [Opt(OptOps.PADTO, 1, 8)],
+      [Opt(OptOps.PADTO, 2, 8)],
+    ])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 0, 0), Opt(OptOps.PADTO, 1, 8)]])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 1, 0), Opt(OptOps.PADTO, 1, 8)]])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]])
+
+  def test_padto_sum_ok(self):
+    N = 18 * 18
+    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
+    a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100
+    b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17)))
+
+    helper_linearizer_opt(a.sum(0), [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+    helper_linearizer_opt(a.sum(1), [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+
+    # can pad sum reduce axis if there's no unsafe ops prior to sum
+    for axis in (0, 1):
+      helper_linearizer_opt(a.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
+      helper_linearizer_opt(a.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
+      helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
+      helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
+      helper_linearizer_opt(b.sum(dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
+      # TODO: why?
+      if Device.DEFAULT != "WEBGPU":
+        helper_linearizer_opt(b.sum(0, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
+        helper_linearizer_opt(b.sum(1, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
+
+    # having unsafe ops after sum is fine
+    helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],])
+    helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])
+
+  def test_padto_sum_not_ok(self):
+    N = 18 * 18
+    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
+    a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp()
+    # exp is not safe to pad
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a.exp().sum(), [[Opt(OptOps.PADTO, 0, 32)],])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a.exp().sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
+
+    b = a < 1
+    # lt is not safe to pad
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, 0, 32)],])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
+
+  def test_padto_max(self):
+    N = 18 * 18
+    # NOTE: this setup prevents 17 * 17 contiguous merged into one axis
+    a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100
+
+    helper_linearizer_opt(a.max(0), [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+    helper_linearizer_opt(a.max(1), [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+
+    # cannot pad max kernel on reduce
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],])
+    with self.assertRaises(KernelOptError):
+      helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],])
+
+  def test_padto_where(self):
+    Tensor.manual_seed(0)
+    N = 17 * 17
+    a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0)
+    helper_linearizer_opt(a.max(0), [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+
+  def test_padto_where_multioutput(self):
+    Tensor.manual_seed(0)
+    N = 17 * 17
+    r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1
+    a0 = r.where(1, 0)
+    a1 = r.where(2, 0)
+    helper_linearizer_opt([a0.max(0), a1.max(0)], [
+      [Opt(OptOps.PADTO, 0, 32)],
+      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
+    ])
+
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
+  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
+  def test_color_shapes_with_local(self):
+    N = 32
+    Tensor.manual_seed(1552)
+    a = Tensor.rand(N, N)
+    b = Tensor.rand(N, N)
+    r = a@b
+    opts_shapes = [
+      ([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]),
+      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]),
+      # check to ensure local_dims are stable for full UNROLL of the first reduce
+      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
+      ([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
+      # check behavior for full UNROLL on an existing GROUP
+      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]),
+      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
+      ([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
+      ([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]),
+    ]
+    helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
+
+if __name__ == '__main__':
+  unittest.main()

test/speed/external_test_copy_speed.py

@@ -1,9 +1,9 @@
 import unittest, numpy as np
 from tinygrad import Tensor, Device, TinyJit
-from tinygrad.helpers import Timing, CI, OSX
+from tinygrad.helpers import Timing, CI, OSX, getenv
 import multiprocessing.shared_memory as shared_memory
 
-N = 256
+N = getenv("NSZ", 256)
 class TestCopySpeed(unittest.TestCase):
   @classmethod
   def setUpClass(cls): Device[Device.DEFAULT].synchronize()
@@ -54,22 +54,24 @@ class TestCopySpeed(unittest.TestCase):
     @TinyJit
     def _do_copy(t): return t.to('CPU').realize()
 
-    t = Tensor.randn(N, N, 4).contiguous().realize()
+    t = Tensor.randn(N, N).contiguous().realize()
+    Device[Device.DEFAULT].synchronize()
     for _ in range(5):
-      with Timing("sync:  ", on_exit=lambda ns: f" @ {t.nbytes()/ns:.2f} GB/s"):
+      with Timing(f"copy {Device.DEFAULT} -> CPU {t.nbytes()/(1024**2)}M:  ", on_exit=lambda ns: f" @ {t.nbytes()/ns:.2f} GB/s"):
         x = _do_copy(t)
         Device[Device.DEFAULT].synchronize()
       np.testing.assert_equal(t.numpy(), x.numpy())
 
-  def testCopytoCPUtoDefaultJit(self):
+  def testCopyCPUtoDefaultJit(self):
     if Device.DEFAULT == "CPU": return unittest.skip("CPU to CPU copy is a no-op")
 
     @TinyJit
-    def _do_copy(x): return t.to(Device.DEFAULT).realize()
+    def _do_copy(x): return x.to(Device.DEFAULT).realize()
 
     for _ in range(5):
-      t = Tensor.randn(N, N, 4, device="CPU").contiguous().realize()
-      with Timing("sync:  ", on_exit=lambda ns: f" @ {t.nbytes()/ns:.2f} GB/s"):
+      t = Tensor.randn(N, N, device="CPU").contiguous().realize()
+      Device["CPU"].synchronize()
+      with Timing(f"copy CPU -> {Device.DEFAULT} {t.nbytes()/(1024**2)}M:  ", on_exit=lambda ns: f" @ {t.nbytes()/ns:.2f} GB/s"):
         x = _do_copy(t)
         Device[Device.DEFAULT].synchronize()
       np.testing.assert_equal(t.numpy(), x.numpy())

test/test_arange.py

@@ -102,7 +102,6 @@ class TestIndexing(unittest.TestCase):
       run_schedule(sched)
     self.assertEqual(out.item(), 1337)
 
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_manual_index(self):
     dataset = Tensor.rand(DSET, DDIM).realize()
     idxs = Tensor([0,3,5,6]).realize()
@@ -172,7 +171,6 @@ class TestIndexing(unittest.TestCase):
       X = dataset[idxs]
       np.testing.assert_equal(X.numpy(), 0)
 
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_index_mnist(self, noopt=1, op_limit=512*784*13, split_reduceop=0):
     # WEBGPU generates more ops due to bitpacking of < 4-byte dtypes
     if Device.DEFAULT == "WEBGPU": op_limit *= 15
@@ -191,7 +189,6 @@ class TestIndexing(unittest.TestCase):
   def test_index_mnist_split(self): self.test_index_mnist(1, split_reduceop=1)
   def test_index_mnist_opt_split(self): self.test_index_mnist(0, split_reduceop=1)
 
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_llama_embedding(self, noopt=1, op_limit=65536):
     # llama3 is 128256
     vocab_size, embed_size = (10, 3) if CI else (32000, 4096)

test/test_dtype.py

@@ -4,9 +4,8 @@ import torch
 from typing import Any, List
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv, DEBUG, CI
-from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8
+from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8, _to_np_dtype, _to_torch_dtype
 from tinygrad import Device, Tensor, dtypes
-from tinygrad.tensor import _to_np_dtype
 from hypothesis import assume, given, settings, strategies as strat
 from test.helpers import rand_for_dtype
 from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX
@@ -24,6 +23,10 @@ def get_available_cast_dtypes(dtype: DType) -> List[DType]:
   # dont cast internal dtypes
   return [v for k, v in DTYPES_DICT.items() if v != dtype and is_dtype_supported(v) and not k.startswith("_")]
 
+def _to_torch_storage_type(dtype:DType):
+  if dtype == dtypes.bfloat16: return torch.float32
+  return _to_torch_dtype(dtype)
+
 def _test_to_np(a:Tensor, np_dtype, target):
   if DEBUG >= 2: print(a)
   na = a.numpy()
@@ -46,10 +49,10 @@ def _test_cast(a:Tensor, target_dtype:DType):
 
   _test_op(lambda: a.cast(target_dtype), target_dtype, list(a.numpy().astype(_to_np_dtype(target_dtype))))
 def _test_bitcast(a:Tensor, target_dtype:DType, target=None):
-  if target_dtype == dtypes.bfloat16: raise unittest.SkipTest("no test for bf16 bitcast yet")
   if getenv("PTX") and a.dtype == dtypes.int8 and target_dtype.itemsize != a.dtype.itemsize:
     raise unittest.SkipTest("shape changing bitcast of int8 broken on PTX")
-  _test_op(lambda: a.bitcast(target_dtype), target_dtype, target or a.numpy().view(_to_np_dtype(target_dtype)).tolist())
+  expected = torch.tensor(a.tolist(), dtype=_to_torch_storage_type(a.dtype)).view(_to_torch_dtype(target_dtype))
+  _test_op(lambda: a.bitcast(target_dtype), target_dtype, target or expected.tolist())
 
 class TestDType(unittest.TestCase):
   DTYPE: Any = None
@@ -126,7 +129,7 @@ class TestDType(unittest.TestCase):
 
   def test_finfo(self):
     if self.DTYPE not in [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]: return
-    info = np.finfo(_to_np_dtype(self.DTYPE))
+    info = ml_dtypes.finfo(ml_dtypes.bfloat16 if self.DTYPE is dtypes.bfloat16 else _to_np_dtype(self.DTYPE))
     assert info.bits == self.DTYPE.itemsize*8
     assert info.nexp == dtypes.finfo(self.DTYPE)[0]
     assert info.nmant == dtypes.finfo(self.DTYPE)[1]
@@ -299,10 +302,10 @@ class TestBitCast(unittest.TestCase):
   @given(strat.sampled_from(dtype_ints + dtype_floats), strat.sampled_from(dtype_ints + dtype_floats))
   def test_shape_change_bitcast(self, dt1, dt2):
     # NOTE: this has to be assume to prevent hypothesis from skipping all samples
-    assume(dt2 != dtypes.bfloat16 and dt1 != dtypes.bfloat16) # no test for bf16 bitcast yet
     assume(not (getenv("PTX") and dt1 == dtypes.int8)) # TODO: bitcasting int8 fails in PTX
     data = rand_for_dtype(dt1, 32).reshape(2, 2, 8)
-    _test_op(lambda: Tensor(data, dtype=dt1).bitcast(dt2), dt2, data.view(_to_np_dtype(dt2)).tolist())
+    expected = torch.tensor(data.tolist(), dtype=_to_torch_storage_type(dt1)).view(_to_torch_dtype(dt2))
+    _test_op(lambda: Tensor(data, dtype=dt1).bitcast(dt2), dt2, expected.tolist())
 
   def test_shape_change_bitcast_exceptions(self):
     with self.assertRaises(RuntimeError):
@@ -342,6 +345,9 @@ class TestUint64DType(TestDType):
 
 class TestBoolDType(TestDType): DTYPE = dtypes.bool
 
+@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
+class TestBFloat16Type(TestDType): DTYPE = dtypes.bfloat16
+
 class TestPtrDType(unittest.TestCase):
   def test_vec_double(self):
     dt1 = dtypes.float.vec(4).ptr().vec(4)
@@ -414,7 +420,7 @@ class TestDtypeUsage(unittest.TestCase):
         t = Tensor([[1, 2], [3, 4]], dtype=d)
         (t*t).max().item()
 
-@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16) or Device.DEFAULT == "PYTHON", f"no bfloat16 on {Device.DEFAULT}")
+@unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
 class TestOpsBFloat16(unittest.TestCase):
   def test_cast(self):
     # TODO: helper_test_op breaks in unrelated part
@@ -422,5 +428,10 @@ class TestOpsBFloat16(unittest.TestCase):
     data = [60000.0, 70000.0, 80000.0]
     np.testing.assert_allclose(Tensor(data).cast("bfloat16").numpy(), torch.tensor(data).type(torch.bfloat16).float().numpy())
 
+  def test_no_approximation(self):
+    data = [326.0, 339.0, 10603200512.0]
+    expected = torch.tensor(data, dtype=torch.bfloat16).sqrt().float().numpy()
+    np.testing.assert_allclose(Tensor(data, dtype=dtypes.bfloat16).sqrt().numpy(), expected)
+
 if __name__ == '__main__':
   unittest.main()

test/test_dtype_alu.py

@@ -1,9 +1,10 @@
 import unittest, operator, math
 from tinygrad import Tensor, dtypes, Device
 from tinygrad.dtype import DType
-from tinygrad.helpers import CI, getenv
+from tinygrad.helpers import CI, getenv, AMD_LLVM
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
+from tinygrad.runtime.ops_python import from_storage_scalar
 import numpy as np
 import pytest
 from hypothesis import given, strategies as strat, settings, HealthCheck
@@ -20,13 +21,13 @@ dtypes_bool = (dtypes.bool,)
 binary_operations = [operator.add, operator.sub, operator.mul, operator.lt, operator.eq]
 
 # TODO: LLVM comparing with nan is incorrect
-if Device.DEFAULT == "LLVM" or getenv("AMD_LLVM", 0):
+if (Device.DEFAULT == "LLVM") or (Device.DEFAULT == "AMD" and AMD_LLVM):
   binary_operations.remove(operator.lt)
 
 integer_binary_operations = binary_operations + [(Tensor.bitwise_xor, np.bitwise_xor), (Tensor.bitwise_and, np.bitwise_and),
                                                  (Tensor.bitwise_or, np.bitwise_or), operator.mod]
 unary_operations = [(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.sin),
-                    (Tensor.sqrt, np.sqrt), (Tensor.reciprocal, np.reciprocal)]
+                    (Tensor.sqrt, np.sqrt), (Tensor.reciprocal, np.reciprocal), (Tensor.cos, np.cos)]
 
 # TODO: enable this (this is a dtype issue)
 #binary_operations.append(operator.truediv)
@@ -35,13 +36,14 @@ unary_operations = [(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.
 #binary_operations += [(Tensor.maximum, np.maximum)]
 
 # TODO: CI CUDA segfaults on sin, WEBGPU sin is not precise enough for large numbers
-if (getenv("MOCKGPU") and Device.DEFAULT in {"NV", "CUDA"}) or Device.DEFAULT == "WEBGPU": unary_operations.remove((Tensor.sin, np.sin))
+if (getenv("MOCKGPU") and Device.DEFAULT in {"NV", "CUDA"}) or Device.DEFAULT == "WEBGPU":
+  unary_operations.remove((Tensor.sin, np.sin))
+  unary_operations.remove((Tensor.cos, np.cos))
 
 class ht:
   float64 = strat.floats(width=64, allow_subnormal=False)
   float32 = strat.floats(width=32, allow_subnormal=False)
   float16 = strat.floats(width=16, allow_subnormal=False)
-  bfloat16 = strat.floats(width=16, allow_subnormal=False)
   uint8 = strat.integers(0, 255)
   uint16 = strat.integers(0, 65535)
   uint32 = strat.integers(0, 2**32-1)
@@ -51,6 +53,7 @@ class ht:
   int32 = strat.integers(-2147483648, 2147483647)
   int64 = strat.integers(-9223372036854775808, 9223372036854775807)
   bool = strat.booleans()
+ht.bfloat16 = ht.uint16
 
 def universal_test(a, b, dtype, op):
   # The 'nan' cases only fail with Vulkan WebGPU backend (CI)
@@ -68,11 +71,14 @@ def universal_test(a, b, dtype, op):
 def universal_test_unary(a, dtype, op):
   if not isinstance(op, tuple): op = (op, op)
   ta = Tensor([a], dtype=dtype)
+  # TODO: cos does not match for large input
+  if op[0] == Tensor.cos and abs(a) > 100: return
+  if op[0] == Tensor.log and a <= 0: return
   out: Tensor = op[0](ta)
   tensor_value = out.numpy()
   numpy_value = op[1](ta.numpy())
   if dtype in dtypes.floats:
-    atol, rtol = {dtypes.float16:(1e-3, 1e-2), dtypes.bfloat16:(1e-3, 1e-2)}.get(dtype, (1e-6, 1e-5))
+    atol, rtol = {dtypes.float16:(1e-3, 1e-2), dtypes.bfloat16:(1e-3, 2e-2)}.get(dtype, (1e-6, 1e-5))
     np.testing.assert_allclose(tensor_value, numpy_value, atol=atol, rtol=rtol)
   else: np.testing.assert_equal(tensor_value, numpy_value)
 
@@ -105,7 +111,8 @@ class TestDTypeALU(unittest.TestCase):
 
   @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
   @given(ht.bfloat16, ht.bfloat16, strat.sampled_from(binary_operations))
-  def test_bfloat16(self, a, b, op): universal_test(a, b, dtypes.bfloat16, op)
+  def test_bfloat16(self, a, b, op):
+    universal_test(from_storage_scalar(a, dtypes.bfloat16), from_storage_scalar(a, dtypes.bfloat16), dtypes.bfloat16, op)
 
   @given(ht.float32, strat.sampled_from(unary_operations))
   def test_float32_unary(self, a, op): universal_test_unary(a, dtypes.float32, op)
@@ -116,7 +123,7 @@ class TestDTypeALU(unittest.TestCase):
 
   @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
   @given(ht.bfloat16, strat.sampled_from(unary_operations))
-  def test_bfloat16_unary(self, a, op): universal_test_unary(a, dtypes.bfloat16, op)
+  def test_bfloat16_unary(self, a, op): universal_test_unary(from_storage_scalar(a, dtypes.bfloat16), dtypes.bfloat16, op)
 
   @given(ht.uint8, ht.uint8, strat.sampled_from(integer_binary_operations))
   def test_uint8(self, a, b, op): universal_test(a, b, dtypes.uint8, op)

test/test_linearizer.py

@@ -11,8 +11,8 @@ from tinygrad.shape.view import View
 from tinygrad.tensor import Tensor, _to_np_dtype
 from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program
 from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM
-from tinygrad.dtype import DType, dtypes, AddrSpace
+from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM, TC_SELECT, TC_OPT
+from tinygrad.dtype import DType, dtypes, PtrDType, AddrSpace
 from tinygrad.codegen import apply_rewrites, rewrites_for_views
 
 def push_views(ast): return apply_rewrites(ast, rewrites_for_views)
@@ -33,11 +33,10 @@ def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axi
   r = a.matmul(b, dtype=dtype_out)
   if dtype_in == dtypes.bfloat16: r = r.float()
   realized_ast, bufs = helper_realized_ast(r)
-  k = Kernel(realized_ast)
-  k.apply_tensor_cores(use_tensor_cores, axis=axis, tc_select=tc_select, tc_opt=tc_opt)
-  prg = CompiledRunner(replace(get_program(k.get_optimized_ast(), k.opts), device=Device.DEFAULT))
+  opts = [Opt(op=OptOps.TC, axis=axis, arg=(tc_select, tc_opt, use_tensor_cores))]
+  prg = CompiledRunner(replace(get_program(realized_ast, opts=opts), device=Device.DEFAULT))
   if use_tensor_cores == 1: assert len([uop for uop in prg.p.uops if uop.op is Ops.WMMA]) > 0, "wmma not triggered"
-  assert len([x for x in k.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
+  assert len([x for x in prg.p.uops[-1].arg.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included"
   prg.exec(bufs)
   if dtype_in == dtypes.half: tc_atol, tc_rtol = 1e-2, 1e-3
   elif dtype_in == dtypes.bfloat16: tc_atol, tc_rtol = 1e-2, 1e-2
@@ -134,7 +133,7 @@ class TestLinearizer(unittest.TestCase):
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
-    # RANGE -> LOAD -> RANGE -> ASSIGN
+    # RANGE -> LOAD -> RANGE -> STORE
     #assert any(x.op is Ops.LOAD for x in uops[ranges[0]:ranges[1]])
 
   def test_three_nested_range(self):
@@ -144,7 +143,7 @@ class TestLinearizer(unittest.TestCase):
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
-    # RANGE -> RANGE -> LOAD -> RANGE -> ASSIGN
+    # RANGE -> RANGE -> LOAD -> RANGE -> STORE
     # NOTE: nothing should toposort between the first two ranges
     #assert ranges[0]+1 == ranges[1]
     #assert any(x.op is Ops.LOAD for x in uops[ranges[1]:ranges[2]])
@@ -155,7 +154,7 @@ class TestLinearizer(unittest.TestCase):
     lin = helper_linearizer_opt(out, wanna_output=[24])[0]
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
-    # RANGE -> ALU -> RANGE -> ALU + LOAD -> ASSIGN
+    # RANGE -> ALU -> RANGE -> ALU + LOAD -> STORE
     assert any(x.op in GroupOp.ALU for x in uops[ranges[0]:ranges[1]])
     assert not any(x.op is Ops.LOAD for x in uops[ranges[0]:ranges[1]])
     assert any(x.op in {*GroupOp.ALU, Ops.LOAD} for x in uops[ranges[1]:])
@@ -167,7 +166,7 @@ class TestLinearizer(unittest.TestCase):
     lin = helper_linearizer_opt(out, wanna_output=[(a.numpy()+b.numpy()[0]).sum()+b.numpy()])[0]
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
-    # LOAD -> RANGE -> LOAD -> ASSIGN
+    # LOAD -> RANGE -> LOAD -> STORE
     assert len([x for x in uops[:ranges[0]] if x.op is Ops.LOAD]) == 1
 
   def test_range_outer_op_before_phi_nested_range(self):
@@ -179,11 +178,11 @@ class TestLinearizer(unittest.TestCase):
     ranges = [i for i,u in enumerate(uops) if u.op is Ops.RANGE]
     assert len(ranges) == 1 # NOTE: it collapses now
     #if getenv("PTX"):
-    # LOAD -> RANGE -> CAST -> ALU -> ALU -> LOAD -> ALU -> RANGE -> ALU -> ASSIGN
+    # LOAD -> RANGE -> CAST -> ALU -> ALU -> LOAD -> ALU -> RANGE -> ALU -> STORE
     #  assert uops[ranges[0]-2].op is Ops.LOAD
     #  assert ranges[1] == ranges[0]+6
     #  assert [x.op for x in uops[ranges[1]-2:ranges[1]]] == [Ops.LOAD, Ops.ALU]
-    # LOAD -> RANGE -> LOAD -> ALU -> RANGE -> ASSIGN
+    # LOAD -> RANGE -> LOAD -> ALU -> RANGE -> STORE
     #else:
     #  assert uops[ranges[0]-2].op is Ops.LOAD
     #  assert ranges[1] == ranges[0]+3
@@ -195,7 +194,7 @@ class TestLinearizer(unittest.TestCase):
     out = a.sum() * a.sum()
     lin = helper_linearizer_opt(out, wanna_output=[a.numpy().sum()*a.numpy().sum()])[0]
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
-    # RANGE -> LOAD -> ASSIGN -> ALU
+    # RANGE -> LOAD -> STORE -> ALU
     end = max(i for i,u in enumerate(uops) if u.op is Ops.ENDRANGE)
     # the INDEX can be first
     assert uops[end+1].op in GroupOp.ALU or uops[end+2].op in GroupOp.ALU
@@ -206,7 +205,7 @@ class TestLinearizer(unittest.TestCase):
     out = a.reshape(2, 1).expand(2, 3).sum() + a.reshape(2, 1).expand(2, 3).sum()
     lin = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3))).sum()*2])[0]
     uops = get_program(lin.get_optimized_ast(), lin.opts).uops
-    # RANGE -> LOAD -> ASSIGN -> ALU
+    # RANGE -> LOAD -> STORE -> ALU
     end = max(i for i,u in enumerate(uops) if u.op is Ops.ENDRANGE)
     # the INDEX can be first
     assert uops[end+1].op in GroupOp.ALU or uops[end+2].op in GroupOp.ALU
@@ -328,11 +327,7 @@ class TestLinearizer(unittest.TestCase):
       n, m, k = tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2]
       a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in)
       r = a.matmul(b, dtype=tc.dtype_out)
-      sched = r.schedule()
-      realized_ast = push_views(sched[-1].ast)
-      kernel = Kernel(realized_ast)
-      kernel.apply_tensor_cores(1, axis=0, tc_select=-1, tc_opt=2)
-      prg = get_program(kernel.get_optimized_ast(), kernel.opts)
+      prg = get_program(r.schedule()[-1].ast, opts=[Opt(op=OptOps.TC, axis=0, arg=(-1, 2, 1))])
       if Device.DEFAULT == "LLVM":
         assert "0x201000" in prg.src
       elif Device.DEFAULT == "AMD" and AMD_LLVM:
@@ -342,7 +337,7 @@ class TestLinearizer(unittest.TestCase):
       else:
         assert "__WMMA_" in prg.src
 
-  @unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "broken for AMD")
+  @unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "broken for AMD")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
   def test_tensor_cores_padded(self):
     for tc in Device[Device.DEFAULT].renderer.tensor_cores:
@@ -351,9 +346,9 @@ class TestLinearizer(unittest.TestCase):
 
   # AMD compiler bug: AMD miscompiles non-zero padded tc kernels with -O3, producing wrong results, nans or hang (see #9606)
   # Internal bug: zero-stride dimensions combined with a mask may produce wrong index/valid for pad == 1 on AMD
-  @unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "test for AMD's tc")
+  @unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and Device.default.renderer.device == "AMD"), "test for AMD's tc")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.expectedFailure
+  @unittest.skip("warp elements not duplicated properly across lanes")
   def test_tensor_cores_padded_amd(self):
     for tc in Device[Device.DEFAULT].renderer.tensor_cores:
       if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
@@ -385,6 +380,7 @@ class TestLinearizer(unittest.TestCase):
   def test_tensor_cores_multi_reduce(self):
     for tc in Device[Device.DEFAULT].renderer.tensor_cores:
       if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue
+      if tc.dtype_in is dtypes.bfloat16: continue # <-- broken with numpy
       # this will be a M=G16, N=G32, M=G16, M=G16, K=R16, K=R16, K=R16 with 9 choices of TC MNK axes
       golden_result = None
       for axis in range(9):
@@ -423,9 +419,9 @@ class TestLinearizer(unittest.TestCase):
     x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
     r = x.matmul(y, dtype=tc.dtype_out)
     k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.get_optimized_ast(), k.opts).uops:
+    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
       if u.op is Ops.WMMA:
-        assert u.src[-1].src[0].op != Ops.ASSIGN
+        assert u.src[-1].src[0].op != Ops.STORE
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
   @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
@@ -434,45 +430,51 @@ class TestLinearizer(unittest.TestCase):
     x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
     r = x.matmul(y, dtype=tc.dtype_out)
     k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.get_optimized_ast(), k.opts).uops:
+    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
       if u.op is Ops.WMMA:
         #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
-        assert u.src[-1].src[0].op != Ops.ASSIGN
+        assert u.src[-1].src[0].op != Ops.STORE
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
   @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation")
   def test_tensor_cores_unroll_casted_phi_with_children(self):
-    # all ASSIGN children are outside the loop
+    # all STORE children are outside the loop
     tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0]
     x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in)
     r = x.matmul(y, dtype=tc.dtype_out).relu()
     k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1]
-    for u in get_program(k.get_optimized_ast(), k.opts).uops:
+    for u in get_program(k.ast, k.opts, k.applied_opts).uops:
       if u.op is Ops.WMMA:
         #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2]))
-        assert u.src[-1].src[0].op != Ops.ASSIGN
+        assert u.src[-1].src[0].op != Ops.STORE
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
   def test_simple_unroll_no_between_phi_dependencies(self):
     x, y = Tensor.rand(128, 128), Tensor.rand(128, 128)
     r = (x@y).relu()
     k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4)]])[-1]
-    # the uops graph is RANGE -> DEFINE_ACC -> 4x ALU -> 4x ASSIGN -> ENDRANGE
-    uops = get_program(k.get_optimized_ast(), k.opts).uops
+    # the uops graph is DEFINE_REG -> 4x STORE 0.0 -> RANGE -> 4x ALU -> 4x STORE -> ENDRANGE
+    uops = get_program(k.ast, k.opts, k.applied_opts).uops
+    begin_range = [i for i, x in enumerate(uops) if x.op is Ops.RANGE][-1]
+    end_range = [i for i, x in enumerate(uops) if x.op is Ops.ENDRANGE][0]
+    for i,u in enumerate(uops): print(i, u.op, [uops.index(s) for s in u.src], u.arg, u.dtype)
     for u in uops:
-      if u.op is Ops.ASSIGN:
-        assert u.src[1].op in GroupOp.ALU
-      # children of ASSIGN are placed after ENDRANGE
-      if any(x.op is Ops.ASSIGN for x in u.src):
-        end_range = [i for i, x in enumerate(uops) if x.op is Ops.ENDRANGE][0]
+      if u.op is Ops.STORE and isinstance(dt:=u.src[0].dtype, PtrDType) and dt.addrspace is AddrSpace.REG:
+        if uops.index(u) < begin_range:
+          assert u.src[1].op is Ops.CONST
+        else:
+          assert u.src[1].op in GroupOp.ALU
+          assert begin_range < uops.index(u) < end_range
+      # children of STORE are placed after ENDRANGE
+      if any(x.op is Ops.STORE and x.src[1].op in GroupOp.ALU for x in u.src):
         assert end_range < uops.index(u)
 
   def test_grouped_dims(self):
     def _assert_grouped_dims(prefix, dims, max_sizes, reverse_dims, expected_sizes, assert_same_length = True):
       idxs = get_grouped_dims(prefix, dims, max_sizes, reverse_dims)
       loop_idxs = dedup(flatten([[y for y in x.toposort() if y.op is Ops.SPECIAL] for x in idxs]))
-      loop_idxs = sorted(loop_idxs, key=lambda uop: uop.arg[0])
-      sizes = [x.arg[1] for x in loop_idxs]
+      loop_idxs = sorted(loop_idxs, key=lambda uop: uop.arg)
+      sizes = [x.src[0].arg for x in loop_idxs]
       assert len(idxs) == len(dims), f"expected idxs to have same length as dims {len(dims)}, got {len(idxs)}"
       if assert_same_length:
         assert len(loop_idxs) == min(len(sizes), len(dims)), f"expected idxs to have length {min(len(sizes), len(dims))}, got {len(loop_idxs)}"
@@ -543,12 +545,12 @@ class TestLinearizer(unittest.TestCase):
     # shrink so that the dims do not collapse
     t = Tensor.ones(5, 6, 7).contiguous().realize().shrink(((0, 4), (0, 5), (0, 6)))
     k = helper_linearizer_opt(t+1)[0]
-    uops = get_program(k.get_optimized_ast(), k.opts).uops
+    uops = get_program(k.ast, k.opts, k.applied_opts).uops
     idxs = dedup([uop for uop in uops if uop.op is Ops.SPECIAL])
-    idxs = sorted(idxs, key=lambda uop: uop.arg[0])
-    assert idxs[0].arg == ('gidx0', 6), idxs[0].arg
-    assert idxs[1].arg == ('gidx1', 5), idxs[1].arg
-    assert idxs[2].arg == ('gidx2', 4), idxs[2].arg
+    idxs = sorted(idxs, key=lambda uop: uop.arg)
+    assert (idxs[0].arg, idxs[0].src[0].arg) == ('gidx0', 6), idxs[0]
+    assert (idxs[1].arg, idxs[1].src[0].arg) == ('gidx1', 5), idxs[1].arg
+    assert (idxs[2].arg, idxs[2].src[0].arg) == ('gidx2', 4), idxs[2].arg
 
   def test_sum_collapse(self):
     t = Tensor([2]).reshape(1, 1).expand(256, 256).sum()
@@ -583,12 +585,13 @@ class TestLinearizer(unittest.TestCase):
   def test_phi_simplification(self):
     def helper(t, max_ops=0):
       k = helper_linearizer_opt(t)[-1]
-      uops = get_program(k.get_optimized_ast(), k.opts).uops
+      uops = get_program(k.ast, k.opts, k.applied_opts).uops
       # ignore kernel optimized IF statements for now
       if if_op:=next((u for u in uops if u.op is Ops.IF), None):
         uops = uops[:uops.index(if_op)]
       assert len(set([u.op for u in uops if u.op in {Ops.RANGE, Ops.SPECIAL}])) == 1, "has either specials or ranges, not both"
-      assert len([u for u in uops if u.op is Ops.ASSIGN]) == 0, "ASSIGN should have been simplified"
+      reg_stores = [u for u in uops if u.op is Ops.STORE and isinstance(dt:=u.src[0].dtype, PtrDType) and dt.addrspace == AddrSpace.REG]
+      assert len(reg_stores) == 0, "STORE to reg should have been simplified"
       # TODO: once uops track min/max this will be fixed
       #assert len([u for u in uops if u.op is Ops.MAX]) <= max_ops, "no unnecessary MAX ops"
 
@@ -613,8 +616,9 @@ class TestLinearizer(unittest.TestCase):
     """
     x, y = Tensor.randn(64,64), Tensor.randn(64,64)
     out = x.matmul(y)
-    k = helper_linearizer_opt(out)[-1]
-    uops = get_program(k.get_optimized_ast(), k.opts).uops
+    with Context(TC=0):
+      k = helper_linearizer_opt(out)[-1]
+    uops = get_program(k.ast, k.opts, k.applied_opts).uops
     # check that the float4 cast collapses
     store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
     for val in store_vals:
@@ -639,7 +643,7 @@ class TestLinearizer(unittest.TestCase):
     x = Tensor.randn((4,3,6,6)).realize()
     out = x.flip((0,1)).contiguous()
     k = helper_linearizer_opt(out)[-1]
-    store_val = [u.src[1] for u in get_program(k.get_optimized_ast(), k.opts).uops if u.op is Ops.STORE][0]
+    store_val = [u.src[1] for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
     assert store_val.dtype == dtypes.float.vec(4) and store_val.op is not Ops.VECTORIZE
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
@@ -652,7 +656,7 @@ class TestLinearizer(unittest.TestCase):
             Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 2)] # upcast accs in both reduces
     k = helper_linearizer_opt(out, opts=[opt])[-1]
     def get_recursive(uop): return set.union(set(uop.src), [uop], *[get_recursive(v) for v in uop.src])
-    uops = get_program(k.get_optimized_ast(), k.opts).uops
+    uops = get_program(k.ast, k.opts, k.applied_opts).uops
     local_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_LOCAL for x in get_recursive(u.src[0]))]
     global_stores = [u for u in uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_GLOBAL for x in get_recursive(u.src[0]))]
     barrier = [u for u in uops if u.op is Ops.BARRIER][0]
@@ -672,7 +676,7 @@ class TestLinearizer(unittest.TestCase):
     x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
     r = (x@y).relu()
     k = helper_linearizer_opt(r)[-1]
-    uops = get_program(k.get_optimized_ast(), k.opts).uops
+    uops = get_program(k.ast, k.opts, k.applied_opts).uops
     stores = [u for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
 
     # the float4 value stores directly in lds and we skip upcast
@@ -698,7 +702,7 @@ class TestLinearizer(unittest.TestCase):
         Opt(op=OptOps.LOCAL, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=3, arg=2)
     ]
     k = helper_linearizer_ast(ast, [Tensor.randn(240*40).realize()], opts=[opt])[-1]
-    out = [u for u in get_program(k.get_optimized_ast(), k.opts).uops if u.op is Ops.STORE][0]
+    out = [u for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
     assert out.src[1].op is Ops.VECTORIZE and out.src[1].dtype == dtypes.float.vec(4)
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
@@ -716,233 +720,9 @@ class TestLinearizer(unittest.TestCase):
             Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8),
             Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=2)]
     k = helper_linearizer_ast(ast, [Tensor.randn(8*32).realize()], opts=[opt])[-1]
-    out = [u for u in get_program(k.get_optimized_ast(), k.opts).uops if u.op is Ops.STORE][0]
+    out = [u for u in get_program(k.ast, k.opts, k.applied_opts).uops if u.op is Ops.STORE][0]
     assert out.src[1].op is Ops.VECTORIZE and out.src[1].dtype.count != 1
 
-@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4")
-class TestFloat4(unittest.TestCase):
-  @staticmethod
-  def count_float4(uops: list[UOp], n=4):
-    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]),
-            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float.vec(n)]))
-  @staticmethod
-  def count_half4(uops: list[UOp]):
-    return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]),
-            len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half.vec(4)]))
-
-  def test_float4_basic(self):
-    a = Tensor.empty(2, 8).realize()
-    b = Tensor.empty(2, 8).realize()
-    c = a + b
-
-    s = c.schedule()[0]
-    realized_ast = s.ast
-    opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
-    realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
-    program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
-
-    assert TestFloat4.count_float4(program.uops) == (2, 1)
-
-  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
-  def test_float4_multidim(self):
-    a = Tensor.empty(2, 8).realize()
-    b = Tensor.empty(2, 8).realize()
-    c = a + b
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]).uops
-    assert TestFloat4.count_float4(uops) == (4, 2)
-
-  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
-  def test_float4_multidim_amx(self):
-    def kernel_for_shape(size, shift):
-      a = Tensor.empty(2, size).realize()
-      b = Tensor.empty(2, size).realize()
-      c = a + b
-
-      s = c.schedule()[0]
-      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]).uops
-
-    sizes = [12, 8, 16]
-    shifts = [3, 2, 4]
-    expected_upcast_size = [4, 8, 16]
-    expected_output = [(6,3), (2,1), (2,1)]
-
-    for i in range(len(sizes)):
-      assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
-
-  def test_float4_unaligned_load(self):
-    a = Tensor.empty(9).realize().shrink(((1, 9),))
-    b = Tensor.empty(9).realize().shrink(((1, 9),))
-    c = a + b
-
-    s = c.schedule()[0]
-    realized_ast = s.ast
-    opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
-    realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
-    program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
-
-    assert TestFloat4.count_float4(program.uops) == (0, 1)
-
-  @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16")
-  def test_float4_multidim_unaligned_load(self):
-    a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
-    b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
-    c = a + b
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]).uops
-
-    assert TestFloat4.count_float4(uops) == (0, 2)
-
-  @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16")
-  def test_float4_multidim_unaligned_load_amx(self):
-    def kernel_for_shape(size, shift):
-      a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
-      b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
-      c = a + b
-
-      s = c.schedule()[0]
-      return get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]).uops
-
-    sizes = [13, 9, 17]
-    shifts = [3, 2, 4]
-    expected_upcast_size = [4, 8, 16]
-    expected_output = [(0,3), (0,1), (0,1)]
-
-    for i in range(len(sizes)):
-      assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
-
-  def test_float4_sometimes_unaligned(self):
-    a = Tensor.empty(1, 1, 8).realize()
-    b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
-    c = a.conv2d(b)
-    # only the first and last conv dot products are aligned in a, and b is never aligned, so no
-    # float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UNROLL, axis=0, arg=4)]).uops
-
-    assert TestFloat4.count_float4(uops) == (0, 0)
-
-  def test_float4_multidim_sometimes_unaligned(self):
-    a = Tensor.empty(1, 1, 7).realize()
-    b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
-    c = a.conv2d(b)
-    # the first conv dot product is aligned in a. If we upcast the output and reduce
-    # dimension, then we could do float4 for only that one set of loads, but we currently
-    # don't.
-    # UPDATE: now we do this fusion
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]).uops
-
-    assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
-
-  def test_float4_expand(self):
-    a = Tensor.empty(9).realize().shrink(((1, 9),))
-    b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
-    c = a + b
-
-    # we will upcast the top axis of sz 4. they should not be coalesced into float4,
-    # since the top axis is not contiguous.
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
-
-    assert TestFloat4.count_float4(uops) == (0, 1)
-
-  def test_float4_heterogeneous(self):
-    a = Tensor.empty(8).realize()
-    b = Tensor.empty(9).realize().shrink(((1, 9),))
-    c = a + b
-
-    # should float4 b but not a
-
-    s = c.schedule()[0]
-    uops = get_program(s.ast, opts=[Opt(op=OptOps.UPCAST, axis=0, arg=4)]).uops
-
-    assert TestFloat4.count_float4(uops) == (1, 1)
-
-  def test_half4_load_unrolled(self):
-    # from llama 7B shard 4 gpus
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=(
-          UOp(Ops.CAST, dtypes.float, arg=None, src=(
-            UOp(Ops.MUL, dtypes.half, arg=None, src=(
-              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)),
-              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),))
-
-    # TODO: fix this, expected might change but should be positive
-    for expected, opts in [
-      ((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
-      ((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
-      ((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]),
-    ]:
-      ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
-      program = get_program(ast, Device[Device.DEFAULT].renderer)
-
-      count = TestFloat4.count_half4(program.uops)
-      assert count == expected, f"{count=}, {expected=}"
-
-  @unittest.skip("this doesn't happen anymore")
-  def test_float4_acc(self):
-    # from float32 stable diffusion red tinybox
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)),
-        UOp(Ops.ADD, dtypes.float, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
-            UOp(Ops.MUL, dtypes.float, arg=None, src=(
-              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)),
-              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)),
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-            UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),))
-
-    for expected, opts in [
-      (1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]),
-      (4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]),
-    ]:
-      ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
-      program = get_program(ast, Device[Device.DEFAULT].renderer)
-      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)])
-      assert count == expected, f"{count=}, {expected=}"
-
-  @unittest.skip("this doesn't happen anymore")
-  def test_float2_acc(self):
-    # from resnet
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)),
-        UOp(Ops.CAST, dtypes.half, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=(
-            UOp(Ops.CAST, dtypes.float, arg=None, src=(
-              UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),))
-    for expected, opts in [
-      (16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]),  # noqa: E501
-      (4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]),
-    ]:
-      ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
-      program = get_program(ast, Device[Device.DEFAULT].renderer)
-      count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)])
-      assert count == expected, f"{count=}, {expected=}"
-
 class TestHandCodedOpts(unittest.TestCase):
   def test_masked_upcast(self):
     layer_1 = Tensor.cat(*[Tensor.empty(5) for _ in range(4)])
@@ -1047,15 +827,13 @@ def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[]
   outbufs = [real_bufs[x.src[0].base.arg] for x in realized_ast.src]
   device = real_bufs[0].device
 
-  def get_prg(k:Kernel): return CompiledRunner(replace(get_program(k.get_optimized_ast(), k.opts), device=device))
+  def get_prg(k:Kernel): return CompiledRunner(replace(get_program(k.ast, k.opts, k.applied_opts), device=device))
 
   def check_opt(opts, create_k, expected_color_size):
     k = create_k()
     lins.append(k)
-    if apply_tc:
-      assert k.apply_tensor_cores(1, extra_opts=opts), "no tensor core triggered"
-    else:
-      k.apply_opts(opts)
+    if apply_tc: k.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, 1)))
+    k.apply_opts(opts)
     if expected_color_size is not None:
       cs = list(zip(k.colors(), k.full_shape))
       assert cs == expected_color_size, f"expected={expected_color_size} got={cs}"
@@ -1086,339 +864,5 @@ def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[]
     check_opt(x, lambda: Kernel(realized_ast), color_sizes[i] if i < len(color_sizes) else None)
   return lins
 
-class TestKernelOpts(unittest.TestCase):
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  def test_local_and_grouped_reduce(self):
-    N = 128
-    Tensor.manual_seed(1882)
-    a = Tensor.rand(4, 4, N, N)
-    b = Tensor.rand(4, 4, N)
-    r = (b.sqrt() + ((a+1).sum(axis=3).exp()))
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.LOCAL, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 8)],
-      [Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals
-      [Opt(OptOps.GROUPTOP, 0, 2)],
-      [Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)],
-      [Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)],
-      # Checking how it works with locals + grouped reduce
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)],
-      # Checking how it works with locals + grouped reduce + upcasts
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)],
-      # many local + many group
-      [Opt(OptOps.GROUP, 0, 2)] * 4,
-      [Opt(OptOps.LOCAL, 0, 2)] * 4,
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)] * 4,
-    ])
-
-  def test_upcasts(self):
-    N = 16
-    Tensor.manual_seed(1772)
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    r = (a+b).sqrt() * ((a+1).exp())
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.UPCAST, 0, 2)],
-      [Opt(OptOps.UPCAST, 0, 4)],
-      [Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts
-    ])
-
-  def test_full_upcast(self):
-    Tensor.manual_seed(1772)
-    a = Tensor.rand(4)
-    b = Tensor.rand(4)
-    r = (a+b).sqrt() * ((a+1).exp())
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts
-    ])
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  def test_matmul(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    r = a@b
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.UPCAST, 0, 2)],
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts
-      [Opt(OptOps.LOCAL, 0, 2)],
-      [Opt(OptOps.LOCAL, 1, 32)],
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)],
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)],
-      [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals
-      [Opt(OptOps.GROUPTOP, 0, 2)],
-      [Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce
-      # Checking all together
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4),
-       Opt(OptOps.UPCAST, 1, 2)],
-      # Full global upcast + local
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)],
-    ])
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  def test_double_reduce(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a = Tensor.rand(8, N, 8, N)
-    r = a.sum(axis=(1,3))
-    helper_linearizer_opt(r, [
-      # openCL / GPU=1 is 256 max threads
-      [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)],
-      [Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce.
-      [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)],
-      [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)],
-      [Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces.
-      [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)],
-      [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts.
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)],
-      # Checking how it works with 2 grouped_reduces + upcasts + locals.
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)],
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
-       Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals.
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2),
-       Opt(OptOps.UPCAST, 0, 2)], # No globals
-    ])
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  def test_invalid_tensor_core_extra_opts(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    realized_ast, _ = helper_realized_ast(a@b)
-    invalid_opts = [
-      [Opt(OptOps.LOCAL, 2, 2)],
-      [Opt(OptOps.UPCAST, 2, 2)],
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 2, 2)],
-    ]
-    for x in invalid_opts:
-      k = Kernel(realized_ast)
-      with self.assertRaises(AssertionError):
-        assert k.apply_tensor_cores(use_tensor_cores=1, extra_opts=x), "no valid tensor core" # for METAL in runners
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
-                      "test requires tensor cores with accumulation in half") # testing with half suffices.
-  def test_tensor_core_opts(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
-    r = a.matmul(b, dtype=dtypes.half)
-    atol, rtol = 0.25, 0.01
-    helper_linearizer_opt(r, [
-      [],
-      [Opt(OptOps.UPCAST, 0, 4)],
-      [Opt(OptOps.UPCAST, 1, 4)],
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts
-      [Opt(OptOps.UNROLL, 0, 2)], # check unroll
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)],
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)],
-      [Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations
-      [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)],
-      [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)],
-    ], apply_tc=True, atol=atol, rtol=rtol)
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
-                      "test requires tensor cores with accumulation in half") # testing with half suffices.
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  def test_tensor_core_opts_locals(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
-    r = a.matmul(b, dtype=dtypes.half)
-    atol, rtol = 0.25, 0.01
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals
-      [Opt(OptOps.LOCAL, 0, 4)], # check local
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)],
-    ], apply_tc=True, atol=atol, rtol=rtol)
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared memory")
-  @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores),
-                      "test requires tensor cores with accumulation in half") # testing with half suffices.
-  # NOTE: the METAL test is broken, likely due to a compiler bug. passes on CI with -O0 and with default opt level locally on M3
-  @unittest.skipIf(Device.DEFAULT == "METAL", "broken for METAL")
-  @unittest.skip("feature was removed")
-  def test_tensor_core_opts_group(self):
-    N = 128
-    Tensor.manual_seed(1552)
-    a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half)
-    r = a.matmul(b, dtype=dtypes.half)
-    atol, rtol = 0.25, 0.01
-    helper_linearizer_opt(r, [
-      [Opt(OptOps.GROUP, 0, 2)],
-      [Opt(OptOps.GROUPTOP, 0, 4)],
-      [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.GROUP, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
-      [Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.GROUP, 0, 2)],
-      [Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)],
-      [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 2)],
-    ], apply_tc=True, atol=atol, rtol=rtol)
-
-  def test_padto_matmul(self):
-    if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]):
-      self.skipTest("super slow on CUDA and AMD because of the big grid dims")
-    N = 17 * 17
-    Tensor.manual_seed(289)
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    helper_linearizer_opt(a@b, [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 1, 32)],
-      [Opt(OptOps.PADTO, 2, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.PADTO, 2, 32)],
-      # can optimize further post PADTO
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),],
-    ])
-
-  def test_padto_upcasted_not_ok(self):
-    N = 4
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    helper_linearizer_opt(a@b, [
-      [Opt(OptOps.UPCAST, 0, 0)],
-      [Opt(OptOps.UPCAST, 1, 0)],
-      [Opt(OptOps.UNROLL, 0, 0)],
-      [Opt(OptOps.PADTO, 0, 8)],
-      [Opt(OptOps.PADTO, 1, 8)],
-      [Opt(OptOps.PADTO, 2, 8)],
-    ])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 0, 0), Opt(OptOps.PADTO, 1, 8)]])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 1, 0), Opt(OptOps.PADTO, 1, 8)]])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]])
-
-  def test_padto_sum_ok(self):
-    N = 18 * 18
-    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
-    a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100
-    b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17)))
-
-    helper_linearizer_opt(a.sum(0), [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-    helper_linearizer_opt(a.sum(1), [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-
-    # can pad sum reduce axis if there's no unsafe ops prior to sum
-    for axis in (0, 1):
-      helper_linearizer_opt(a.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
-      helper_linearizer_opt(a.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
-      helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, axis, 32)],])
-      helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, axis, 32)],])
-      helper_linearizer_opt(b.sum(dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
-      # TODO: why?
-      if Device.DEFAULT != "WEBGPU":
-        helper_linearizer_opt(b.sum(0, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
-        helper_linearizer_opt(b.sum(1, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],])
-
-    # having unsafe ops after sum is fine
-    helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],])
-    helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],])
-
-  def test_padto_sum_not_ok(self):
-    N = 18 * 18
-    # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension
-    a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp()
-    # exp is not safe to pad
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a.exp().sum(), [[Opt(OptOps.PADTO, 0, 32)],])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a.exp().sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
-
-    b = a < 1
-    # lt is not safe to pad
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, 0, 32)],])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],])
-
-  def test_padto_max(self):
-    N = 18 * 18
-    # NOTE: this setup prevents 17 * 17 contiguous merged into one axis
-    a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100
-
-    helper_linearizer_opt(a.max(0), [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-    helper_linearizer_opt(a.max(1), [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-
-    # cannot pad max kernel on reduce
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],])
-    with self.assertRaises(KernelOptError):
-      helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],])
-
-  def test_padto_where(self):
-    Tensor.manual_seed(0)
-    N = 17 * 17
-    a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0)
-    helper_linearizer_opt(a.max(0), [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-
-  def test_padto_where_multioutput(self):
-    Tensor.manual_seed(0)
-    N = 17 * 17
-    r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1
-    a0 = r.where(1, 0)
-    a1 = r.where(2, 0)
-    helper_linearizer_opt([a0.max(0), a1.max(0)], [
-      [Opt(OptOps.PADTO, 0, 32)],
-      [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),],
-    ])
-
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
-  def test_color_shapes_with_local(self):
-    N = 32
-    Tensor.manual_seed(1552)
-    a = Tensor.rand(N, N)
-    b = Tensor.rand(N, N)
-    r = a@b
-    opts_shapes = [
-      ([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]),
-      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]),
-      # check to ensure local_dims are stable for full UNROLL of the first reduce
-      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
-      ([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
-      # check behavior for full UNROLL on an existing GROUP
-      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]),
-      ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
-      ([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]),
-      ([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]),
-    ]
-    helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes])
-
 if __name__ == '__main__':
   unittest.main()

test/test_linearizer_dumb.py

@@ -9,36 +9,24 @@ from tinygrad.uop.ops import UOp, Ops
 from tinygrad.helpers import getenv
 from tinygrad.shape.shapetracker import ShapeTracker, View
 from tinygrad.codegen.opt.search import Opt, OptOps
-from tinygrad.codegen.opt.kernel import Kernel
 from tinygrad.engine.realize import get_program
 
 class TestLinearizerDumb(unittest.TestCase):
   @unittest.skipUnless(Device.DEFAULT == "METAL", "only tested on METAL")
   def test_unmerged_ifs(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.half.ptr(1605632), arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(25088, 0, 49, 7, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=0, src=()),)),
-        UOp(Ops.MAX, dtypes.half, arg=None, src=(
-          UOp(Ops.MUL, dtypes.half, arg=None, src=(
-            UOp(Ops.CAST, dtypes.half, arg=None, src=(
-              UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=(
-                UOp(Ops.CAST, dtypes.float, arg=None, src=(
-                  UOp(Ops.MUL, dtypes.half, arg=None, src=(
-                    UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                      UOp(Ops.VIEW, dtypes.half.ptr(1605632), arg=ShapeTracker(views=(View(shape=(1, 64, 1, 512, 4, 9, 4, 9), strides=(0, 25088, 0, 49, 0, 7, 0, 1), offset=-8, mask=((0, 1), (0, 64), (0, 1), (0, 512), (0, 4), (1, 8), (0, 4), (1, 8)), contiguous=False), View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(663552, 0, 0, 36, 1, 1296, 360, 10), offset=0, mask=None, contiguous=False))), src=(
-                        UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=1, src=()),)),)),
-                    UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                      UOp(Ops.VIEW, dtypes.half.ptr(2359296), arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(0, 0, 4608, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=(
-                        UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(2359296), arg=2, src=()),)),)),)),)),)),)),
-            UOp(Ops.CONST, dtypes.half, arg=0.9999950000374996, src=(
-              x16:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-          UOp(Ops.CONST, dtypes.half, arg=0.0, src=(
-             x16,)),)),)),))
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(25088, 0, 49, 7, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(1605632), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(1, 64, 1, 512, 4, 9, 4, 9), strides=(0, 25088, 0, 49, 0, 7, 0, 1), offset=-8, mask=((0, 1), (0, 64), (0, 1), (0, 512), (0, 4), (1, 8), (0, 4), (1, 8)), contiguous=False), View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(663552, 0, 0, 36, 1, 1296, 360, 10), offset=0, mask=None, contiguous=False))))
+    c4 = c3.load()
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(2359296), arg=2, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 512, 3, 3), strides=(0, 0, 4608, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(64, 1, 512, 7, 7, 1, 1, 1), strides=(0, 0, 0, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c9 = c1.store(((c4*c7).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (5, 6, 7))).cast(dtypes.half)*UOp.const(dtypes.half, 0.9999950000374996, src=c8)).alu(Ops.MAX, UOp.const(dtypes.half, 0.0, src=c8)))
+    ast = c9.sink()
     opts = [Opt(op=OptOps.TC, axis=2, arg=(-1, 2, 1)), Opt(op=OptOps.UPCAST, axis=2, arg=0), Opt(op=OptOps.UNROLL, axis=1, arg=0)]
-    k = Kernel(ast, opts=Device["METAL"].renderer)
-    k.apply_opts(opts)
-    prg = get_program(k.get_optimized_ast(), k.opts)
+    prg = get_program(ast, Device["METAL"].renderer, opts)
     print(prg.src)
     Device[Device.DEFAULT].compiler.compile_cached(prg.src)
     gate_count = len([x for x in prg.src.splitlines() if "if" in x])
@@ -47,151 +35,81 @@ class TestLinearizerDumb(unittest.TestCase):
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
   def test_max_simplify_and_cancel(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.int.ptr(1000), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=()),)),
-        UOp(Ops.MUL, dtypes.int, arg=None, src=(
-          UOp(Ops.CAST, dtypes.int, arg=None, src=(
-            UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-              UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-                UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.float.ptr(1000), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
-                    UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1000), arg=1, src=()),)),)),
-                UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.float.ptr(1), arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=(
-                    UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=2, src=()),)),)),)),
-              UOp(Ops.CONST, dtypes.bool, arg=True, src=(
-                x14:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),
-          UOp(Ops.ADD, dtypes.int, arg=None, src=(
-            UOp(Ops.REDUCE_AXIS, dtypes.int, arg=(Ops.ADD, (1,)), src=(
-              UOp(Ops.WHERE, dtypes.int, arg=None, src=(
-                UOp(Ops.VALID, dtypes.bool, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1001, 1999), strides=(0, 0), offset=0, mask=((0, 1001), (999, 1999)), contiguous=False), View(shape=(1000, 1000), strides=(1, 2000), offset=0, mask=None, contiguous=False))), src=()),)),
-                UOp(Ops.CONST, dtypes.int, arg=-1, src=(
-                  x21:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),
-                UOp(Ops.CONST, dtypes.int, arg=0, src=(
-                   x21,)),)),)),
-            UOp(Ops.CONST, dtypes.int, arg=1000, src=(
-               x14,)),)),)),)),))
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1000), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1000), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=2, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c9 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1001, 1999), strides=(0, 0), offset=0, mask=((0, 1001), (999, 1999)), contiguous=False), View(shape=(1000, 1000), strides=(1, 2000), offset=0, mask=None, contiguous=False))), src=())
+    c10 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1000, 1000), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c11 = c1.store((c4.alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c8)).cast(dtypes.int)*(c9.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, -1, src=c10), UOp.const(dtypes.int, 0, src=c10)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,)))+UOp.const(dtypes.int, 1000, src=c8))))
+    ast = c11.sink()
     opts = [Opt(op=OptOps.UNROLL, axis=0, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8)]
-    k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
-    k.apply_opts(opts)
-    prg = get_program(k.get_optimized_ast(), k.opts)
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
     print(prg.src)
     assert prg.uops is not None and not any(uop.op is Ops.MAX for uop in prg.uops), "leftover MAX"
 
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
-  @unittest.skip("not applicable")
-  def test_expander_new_srcs(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(25), arg=ShapeTracker(views=(View(shape=(25, 1), strides=(1, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(25), arg=0, src=()),)),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (1,)), src=(
-          UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-            UOp(Ops.VIEW, dtypes.float.ptr(25), arg=ShapeTracker(views=(View(shape=(26, 49), strides=(0, -1), offset=48, mask=((0, 26), (24, 49)), contiguous=False), View(shape=(25, 25), strides=(1, 50), offset=0, mask=None, contiguous=False))), src=(
-              UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(25), arg=1, src=()),)),)),)),)),))
-    opts = [Opt(op=OptOps.GROUP, axis=0, arg=0), Opt(op=OptOps.PADTO, axis=0, arg=32), Opt(op=OptOps.LOCAL, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=0)]
-    k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
-    k.apply_opts(opts)
-    prg = get_program(k.get_optimized_ast(), k.opts)
-    print(prg.src)
-    if_uops = [u for u in prg.uops if u.op is Ops.IF]
-    self.assertIn(len(if_uops), {1,2,3})
-    conditions = if_uops[0].src[0].toposort()
-    self.assertLessEqual(len(conditions), 9)
-
   # this was a bug in embedding, someday we should fold this anyway
   @unittest.skipUnless(is_dtype_supported(dtypes.half), f"half dtype not supported on {Device.DEFAULT}")
   def test_llama_embedding(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.half.ptr(4096), arg=ShapeTracker(views=(View(shape=(4096, 1, 1), strides=(1, 0, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(4096), arg=0, src=()),)),
-        UOp(Ops.CAST, dtypes.half, arg=None, src=(
-          UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (1,)), src=(
-            UOp(Ops.CAST, dtypes.float, arg=None, src=(
-              UOp(Ops.MUL, dtypes.half, arg=None, src=(
-                UOp(Ops.CAST, dtypes.half, arg=None, src=(
-                  UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-                    UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-                      UOp(Ops.ADD, dtypes.int, arg=None, src=(
-                        UOp(Ops.REDUCE_AXIS, dtypes.int, arg=(Ops.ADD, (2,)), src=(
-                          UOp(Ops.WHERE, dtypes.int, arg=None, src=(
-                            UOp(Ops.VALID, dtypes.bool, arg=None, src=(
-                              UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32001, 63999), strides=(0, 0), offset=0, mask=((0, 32001), (31999, 63999)), contiguous=False), View(shape=(4096, 32000, 32000), strides=(0, 1, 64000), offset=0, mask=None, contiguous=False))), src=()),)),
-                            UOp(Ops.CONST, dtypes.int, arg=1, src=(
-                              x16:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 32000), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),
-                            UOp(Ops.CONST, dtypes.int, arg=0, src=(
-                               x16,)),)),)),
-                        UOp(Ops.CONST, dtypes.int, arg=-1, src=(
-                          x19:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-                      UOp(Ops.LOAD, dtypes.int, arg=None, src=(
-                        UOp(Ops.VIEW, dtypes.int.ptr(1), arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=(
-                          UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=()),)),)),)),
-                    UOp(Ops.CONST, dtypes.bool, arg=True, src=(
-                       x19,)),)),)),
-                UOp(Ops.LOAD, dtypes.half, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.half.ptr(131072000), arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(1, 4096, 0), offset=0, mask=None, contiguous=False),)), src=(
-                    UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(131072000), arg=2, src=()),)),)),)),)),)),)),)),))
-    k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
-    prg = get_program(k.get_optimized_ast(), k.opts)
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(4096), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(4096, 1, 1), strides=(1, 0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(32001, 63999), strides=(0, 0), offset=0, mask=((0, 32001), (31999, 63999)), contiguous=False), View(shape=(4096, 32000, 32000), strides=(0, 1, 64000), offset=0, mask=None, contiguous=False))), src=())
+    c3 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 32000), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c4 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), arg=1, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(131072000), arg=2, src=())
+    c9 = c8.view(ShapeTracker(views=(View(shape=(4096, 32000, 1), strides=(1, 4096, 0), offset=0, mask=None, contiguous=False),)))
+    c10 = c9.load()
+    c11 = c1.store(((c2.f(Ops.VALID, dtype=dtypes.bool).where(UOp.const(dtypes.int, 1, src=c3), UOp.const(dtypes.int, 0, src=c3)).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (2,)))+UOp.const(dtypes.int, -1, src=c4)).alu(Ops.CMPNE, c7).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c4)).cast(dtypes.half)*c10).cast(dtypes.float).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (1,))).cast(dtypes.half))
+    ast = c11.sink()
+    prg = get_program(ast, Device[Device.DEFAULT].renderer)
     print(prg.src)
 
   @unittest.expectedFailure
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
   def test_unrolled_float4_align(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(1), arg=ShapeTracker(views=(View(shape=(1, 1), strides=(0, 0), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=0, src=()),)),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (0, 1)), src=(
-          UOp(Ops.WHERE, dtypes.float, arg=None, src=(
-            UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-              UOp(Ops.CMPNE, dtypes.bool, arg=None, src=(
-                UOp(Ops.LOAD, dtypes.long, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.long.ptr(18), arg=ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)), src=(
-                    UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(18), arg=1, src=()),)),)),
-                UOp(Ops.CONST, dtypes.long, arg=-1, src=(
-                  x11:=UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(3, 6), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),
-              UOp(Ops.CONST, dtypes.bool, arg=True, src=(
-                 x11,)),)),
-            UOp(Ops.CONST, dtypes.float, arg=0.0, src=(
-               x11,)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.VIEW, dtypes.float.ptr(18), arg=ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)), src=(
-                UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18), arg=2, src=()),)),)),)),)),)),))
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(1, 1), strides=(0, 0), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.long.ptr(18), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(3, 6), strides=(0, 0), offset=0, mask=None, contiguous=False),)), src=())
+    c6 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(18), arg=2, src=())
+    c7 = c6.view(ShapeTracker(views=(View(shape=(3, 6), strides=(6, 1), offset=0, mask=None, contiguous=True),)))
+    c8 = c7.load()
+    c9 = c1.store(c4.alu(Ops.CMPNE, UOp.const(dtypes.long, -1, src=c5)).alu(Ops.CMPNE, UOp.const(dtypes.bool, True, src=c5)).where(UOp.const(dtypes.float, 0.0, src=c5), c8).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (0, 1))))
+    ast = c9.sink()
     opts = [Opt(op=OptOps.UNROLL, axis=0, arg=0)]
-    k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
-    k.apply_opts(opts)
-    prg = get_program(k.get_optimized_ast(), k.opts)
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
     print(prg.src)
-    load_idxs = [x.src[1] for x in k.uops if x.op is Ops.LOAD and x.src[0].arg == 2]
+    load_idxs = [x.src[1] for x in prg.uops if x.op is Ops.LOAD and x.src[0].arg == 2]
     assert load_idxs[0] < load_idxs[1], f"first loaded idx {load_idxs[0].arg} then {load_idxs[1].arg}!"
 
   @unittest.expectedFailure
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need float4")
   @unittest.skipIf(getenv("PTX"), "this is somehow correct in PTX")
   def test_upcasted_stores_out_of_order(self):
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(9360), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)), src=(
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=()),)),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (6,)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.VIEW, dtypes.float.ptr(144), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(0, 0, 0, 0, 0, 0, 1, 0, 4, 48, 16), offset=0, mask=None, contiguous=False),)), src=(
-                UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), arg=1, src=()),)),)),
-            UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-              UOp(Ops.VIEW, dtypes.float.ptr(1040), arg=ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(260, 13, 1, 0, 0, 0, 65, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=(
-                UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1040), arg=2, src=()),)),)),)),)),)),))
+    c0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9360), arg=0, src=())
+    c1 = c0.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 1, 1, 4, 3, 3), strides=(2340, 468, 36, 0, 0, 0, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=True),)))
+    c2 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(144), arg=1, src=())
+    c3 = c2.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(0, 0, 0, 0, 0, 0, 1, 0, 4, 48, 16), offset=0, mask=None, contiguous=False),)))
+    c4 = c3.load()
+    c5 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(1040), arg=2, src=())
+    c6 = c5.view(ShapeTracker(views=(View(shape=(4, 5, 13, 1, 1, 1, 4, 1, 4, 3, 3), strides=(260, 13, 1, 0, 0, 0, 65, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)))
+    c7 = c6.load()
+    c8 = c1.store((c4*c7).f(Ops.REDUCE_AXIS, arg=(Ops.ADD, (6,))))
+    ast = c8.sink()
     opts = [Opt(op=OptOps.UPCAST, axis=3, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=0)]
-    k = Kernel(ast, opts=Device[Device.DEFAULT].renderer)
-    k.apply_opts(opts)
-    prg = get_program(k.get_optimized_ast(), k.opts)
+    prg = get_program(ast, Device[Device.DEFAULT].renderer, opts)
     print(prg.src)
-    store_idxs = [x.src[1] for x in k.uops if x.op is Ops.STORE]
+    store_idxs = [x.src[1] for x in prg.uops if x.op is Ops.STORE]
     for i in range(len(store_idxs) - 1):
       first_bounds = store_idxs[i].vmin+store_idxs[i].vmax
       next_bounds = store_idxs[i+1].vmin+store_idxs[i+1].vmax

test/test_nn.py

@@ -2,7 +2,7 @@
 import unittest
 import numpy as np
 import torch
-from tinygrad import Tensor, Device, TinyJit
+from tinygrad import Tensor, Device, TinyJit, dtypes
 from tinygrad.uop.ops import Ops
 from tinygrad.helpers import GlobalCounters, CI, Context
 from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
@@ -465,7 +465,7 @@ class TestNN(unittest.TestCase):
     # used to fail bounds check
     with Context(FUSE_ARANGE=1):
       embedding = Embedding(100, 1024)
-      input_ids = Tensor.empty(16, 16)
+      input_ids = Tensor.empty(16, 16, dtype=dtypes.int)
       embedding(input_ids).realize()
 
   def test_load_state_dict(self):

test/test_ops.py

@@ -1209,32 +1209,24 @@ class TestOps(unittest.TestCase):
     # match torch ellipsis handling
     helper_test_op([(32, 7, 24, 24, 24), (32, 7, 24, 24, 24)], lambda a, b: torch.einsum('ij...,ij...->ij', [a, b]),
                lambda a, b: Tensor.einsum('ij...,ij...->ij', [a, b]))
-    # multiple ellipsis in one operand are not allowed. This test shall raise an exception.
-    with self.assertRaises(RuntimeError):
-      helper_test_op([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('...ik..., ...jk ->', [a, b]),
-                       lambda a, b: Tensor.einsum('...ik..., ...jk ->', [a, b]))
-    # multiple ellipsis must broadcast together. This test shall raise an exception.
-    with self.assertRaises(RuntimeError):
-      helper_test_op([(2, 3, 4, 5), (5, 2, 7)], lambda a, b: torch.einsum('i...j,ji...->...', [a, b]),
-                       lambda a, b: Tensor.einsum('i...j,ji...->...', [a, b]))
+    # multiple ellipsis in one operand are not allowed
+    self.helper_test_exception([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('...ik..., ...jk ->', [a, b]),
+                lambda a, b: Tensor.einsum('...ik..., ...jk ->', [a, b]), expected=(RuntimeError, IndexError))
+    # multiple ellipsis must broadcast together
+    self.helper_test_exception([(2, 3, 4), (2, 3, 4)], lambda a, b: torch.einsum('i...j,ji...->...', [a, b]),
+                lambda a, b: Tensor.einsum('i...j,ji...->...', [a, b]), expected=RuntimeError)
 
   def test_einsum_shape_check(self):
-    a = Tensor.zeros(3,8,10,5)
-    b = Tensor.zeros(11,5,13,16,8)
-    with self.assertRaises(AssertionError):
-      Tensor.einsum('pqrs,tuqvr->pstuv',a,b)
+    self.helper_test_exception([(3,8,10,5), (11,5,13,16,8)], lambda a, b: torch.einsum('pqrs,tuqvr->pstuv', [a, b]),
+                lambda a, b: Tensor.einsum('pqrs,tuqvr->pstuv', [a, b]), expected=RuntimeError)
 
   def test_einsum_arity_check1(self):
-    a = Tensor.zeros(10,15)
-    b = Tensor.zeros(15,20)
-    c = Tensor.zeros(20,10)
-    with self.assertRaises(AssertionError):
-      Tensor.einsum('ij,jk->ij', a,b,c)
+    self.helper_test_exception([(10,15), (15,20), (20,10)], lambda a, b, c: torch.einsum('ij,jk->ij', [a, b, c]),
+                lambda a, b, c: Tensor.einsum('ij,jk->ij', [a, b, c]), expected=(ValueError, RuntimeError))
 
   def test_einsum_arity_check2(self):
-    a = Tensor.zeros(10,10)
-    with self.assertRaises(AssertionError):
-      Tensor.einsum('ij,jk->ij', a)
+    self.helper_test_exception([(10,10)], lambda a: torch.einsum('ij,jk->ij', a),
+                lambda a: Tensor.einsum('ij,jk->ij', a), expected=(ValueError, RuntimeError))
 
   @unittest.skipIf(IMAGE>0, "no 1d dot for images")
   def test_dot_1d(self):

test/test_opts.py

@@ -0,0 +1,22 @@
+import unittest
+from tinygrad import Tensor, Device
+from tinygrad.helpers import RANGEIFY
+from tinygrad.codegen.opt.kernel import Opt, OptOps
+from tinygrad.engine.realize import get_program
+
+@unittest.skipIf(RANGEIFY>0, "arg is partial contig in rangeify")
+class TestOpts(unittest.TestCase):
+  def test_opt_upcast(self):
+    opts = (Opt(OptOps.UPCAST, 0, 4),)
+    a = Tensor.empty(16)
+    b = Tensor.empty(16)
+    out = (a+b).contiguous(arg=opts)
+    s = out.schedule()
+    self.assertEqual(s[-1].ast.arg.opts_to_apply, opts)
+    if Device.DEFAULT in {"CPU", "GPU", "METAL"}:
+      prg = get_program(s[-1].ast)
+      self.assertIn('float4', prg.src)
+
+if __name__ == '__main__':
+  unittest.main()
+

test/test_outerworld_range.py

@@ -1,5 +1,5 @@
 import unittest
-from tinygrad import Tensor, nn, Variable, UOp, dtypes
+from tinygrad import Tensor, nn, Variable, UOp
 
 # outerworld range should support three things
 # 1. full optimizer steps (test_model_bound_range)
@@ -136,7 +136,7 @@ class TestOuterworldRange(unittest.TestCase):
   def test_model_bound_range(self):
     m, opt = get_model_and_opt()
     # TODO: should ranges be unique so you don't have to pass in the -1?
-    rng = UOp.range(dtypes.int, self.STEPS, -1)
+    rng = UOp.range(self.STEPS, -1)
     vib = Variable('i', 0, self.STEPS-1).bind(rng)
     loss = (m(self.X[vib]) - self.Y[vib]).square().mean()
     loss.backward()

test/test_rangeify.py

@@ -1,6 +1,7 @@
 import unittest
 from tinygrad import Tensor
 from tinygrad.helpers import RANGEIFY, Context, GlobalCounters
+from tinygrad.uop.ops import UOp
 
 N = 256
 
@@ -116,10 +117,10 @@ class TestRangeify(unittest.TestCase):
     out.realize()
 
   def test_flash_attention(self):
-    #BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
+    BS, HEADS, SEQLEN, EMB = 4, 2, 16, 8
 
     # bigger
-    BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
+    #BS, HEADS, SEQLEN, EMB = 4, 32, 1024, 64
 
     # llama 8B
     #BS, HEADS, SEQLEN, EMB = 4, 32, 2048, 128
@@ -141,9 +142,6 @@ class TestRangeify(unittest.TestCase):
     print(f"mse: {mse}")
     self.assertLessEqual(mse, 1e-6)
 
-from tinygrad import dtypes
-from tinygrad.uop.ops import UOp
-
 # contiguous + reduce can support ranges?
 
 @unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
@@ -152,7 +150,7 @@ class TestOuterworld(unittest.TestCase):
     t = Tensor.rand(10, 10).realize()
 
     # passthrough ranges
-    a = UOp.range(dtypes.int, 10, -1)
+    a = UOp.range(10, -1)
     sel = t[a]
     cpy = sel.contiguous(a).realize()
 
@@ -162,7 +160,7 @@ class TestOuterworld(unittest.TestCase):
     t = Tensor.rand(10, 10).realize()
 
     # passthrough ranges
-    a = UOp.range(dtypes.int, 10, -1)
+    a = UOp.range(10, -1)
     sel = t[9-a]
     cpy = sel.contiguous(a).realize()
 
@@ -174,7 +172,7 @@ class TestOuterworld(unittest.TestCase):
     x = Tensor.ones(3, 10, 2).contiguous()
 
     # vmap across axis 0
-    a = UOp.range(dtypes.int, 3, -1)
+    a = UOp.range(3, -1)
     out = f(x[a])
     out = out.contiguous(a)
 
@@ -182,17 +180,39 @@ class TestOuterworld(unittest.TestCase):
     out.realize()
     print(out.numpy())
 
+  @unittest.skip("opts don't work")
   def test_triple_gemm(self):
     x = Tensor.rand(1, 16).realize()
     W = Tensor.rand(3, 16, 16).realize()
 
     manual = (x @ W[0] @ W[1] @ W[2]).contiguous().realize()
 
-    a = UOp.range(dtypes.int, 3, -1)
+    a = UOp.range(3, -1)
     x = x.assign(x @ W[a])
     out = x.contiguous(a)[-1].contiguous().realize()
 
     self.assertTrue((manual==out).all().item())
 
+  def test_setitem_pyrange(self):
+    with Context(DEBUG=0):
+      t = Tensor.rand(10).realize()
+      o = Tensor.empty(10)
+    GlobalCounters.reset()
+    for i in range(10):
+      o[i] = t[i]
+    o.realize()
+    self.assertTrue((t==o).all().item())
+
+  @unittest.skip("TODO: fix this")
+  def test_setitem(self):
+    with Context(DEBUG=0):
+      t = Tensor.rand(10).realize()
+      o = Tensor.empty(10)
+    GlobalCounters.reset()
+    i = UOp.range(10, -1)
+    o[i] = t[i]
+    o.contiguous(i).realize()
+    self.assertTrue((t==o).all().item())
+
 if __name__ == '__main__':
   unittest.main()

test/test_renderer_failures.py

@@ -25,7 +25,8 @@ def _test_uop_result(inputs:List[Tensor], stores:List[UOp], local_size=None):
       initial_value=np.zeros(sz, dtype=_to_np_dtype(dtype)).data) for u in uops if u.op is Ops.STORE]
   inbufs = [cast(UOp,x.uop).base.buffer for x in inputs]
   src = Device[Device.DEFAULT].renderer.render(uops)
-  ei = CompiledRunner(ProgramSpec("test", src, Device.DEFAULT, uops[-1], uops=uops, local_size=local_size))
+  ei = CompiledRunner(ProgramSpec(uops[-1].arg.name if uops[-1].arg is not None else "test",
+                                  src, Device.DEFAULT, uops[-1], uops=uops, local_size=local_size))
   ei.exec(outbufs+inbufs)
   return [np.frombuffer(x.as_buffer(), _to_np_dtype(x.dtype)) for x in outbufs]
 
@@ -45,7 +46,7 @@ class TestRendererFailures(unittest.TestCase):
   @unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
   def test_gated_store_with_alu(self):
     a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
-    gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
+    gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
     gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0, gate_alu), UOp.const(dtypes.int, 1)))
     sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
     uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
@@ -55,8 +56,8 @@ class TestRendererFailures(unittest.TestCase):
   @unittest.skipIf(not isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, PythonRenderer)), "test is for ptx or python renderer")
   def test_gated_store_with_alu_2d(self):
     a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
-    gate_alu_0 = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
-    gate_alu_1 = (lidx1:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx1', 2))).ne(0)
+    gate_alu_0 = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
+    gate_alu_1 = (lidx1:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 2),), 'lidx1')).ne(0)
     gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0+lidx1*4, gate_alu_0&gate_alu_1), UOp.const(dtypes.int, 1)))
     sink = UOp(Ops.SINK, dtypes.void, (gated_alu_store,))
     uops = full_rewrite(sink, Device[Device.DEFAULT].renderer)
@@ -100,7 +101,7 @@ class TestPTXFailures(unittest.TestCase):
   @unittest.skip("INDEX can only have a gate ALU parent, not an IF")
   def test_gated_store_with_if(self):
     a = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
-    gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (), ('lidx0', 4))).ne(0)
+    gate_alu = (lidx0:=UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'lidx0')).ne(0)
     val = UOp.const(dtypes.int, 1)
     if_uop = UOp(Ops.IF, dtypes.void, (gate_alu,))
     gated_alu_store = UOp(Ops.STORE, dtypes.void, (a.index(lidx0, if_uop), val))

test/test_search.py

@@ -1,16 +1,10 @@
 import unittest
 
 from tinygrad.codegen.opt.kernel import Opt, OptOps, Kernel
-from tinygrad.uop.ops import UOp, Ops
 from tinygrad.codegen.opt.search import bufs_from_lin, actions, beam_search
-from tinygrad.device import Device
 from tinygrad.tensor import Tensor
-from tinygrad.dtype import dtypes
 from tinygrad.helpers import Context, GlobalCounters
 from tinygrad.engine.realize import capturing
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import View
-from extra.optimization.helpers import time_linearizer
 
 class TestBEAM(unittest.TestCase):
   def test_dynamic_beam(self):
@@ -58,24 +52,6 @@ class TestBEAM(unittest.TestCase):
     assert Opt(OptOps.GROUP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUP"
     assert Opt(OptOps.GROUPTOP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUPTOP"
 
-  @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
-  def test_search_over_shape(self):
-    from test.test_linearizer import helper_realized_ast
-    from tinygrad.codegen.opt.search import get_kernel_actions
-
-    dtype_pairs = [(tc.dtype_in, tc.dtype_out) for tc in Device[Device.DEFAULT].renderer.tensor_cores]
-    multi_shape_dtype_pairs = [dts for dts in dtype_pairs if dtype_pairs.count(dts) > 1]
-
-    if len(multi_shape_dtype_pairs) == 0: raise unittest.SkipTest("only one tc available per dtype pair to search over")
-
-    for (dtype_in, dtype_out) in multi_shape_dtype_pairs:
-      a = Tensor.rand(16, 16, dtype=dtype_in)
-      b = Tensor.rand(16, 16, dtype=dtype_in)
-      realized_ast, _ = helper_realized_ast(a.matmul(b, dtype=dtype_out))
-
-      lins = get_kernel_actions(Kernel(realized_ast)).values()
-      assert len(set(lin.tensor_core.dims for lin in lins if lin.tensor_core is not None)) > 1
-
   def test_get_kernel_actions_preserves_actions_state(self):
     from test.test_linearizer import helper_realized_ast
     from tinygrad.codegen.opt.search import get_kernel_actions
@@ -87,49 +63,6 @@ class TestBEAM(unittest.TestCase):
     actions_after = actions.copy()
     assert actions_after == actions_before, "actions state was not preserved"
 
-  @unittest.skip("invalid reduce now")
-  def test_filter_global_buffer(self):
-    # taken from https://github.com/tinygrad/tinygrad/issues/4612
-    ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
-      UOp(Ops.STORE, dtypes.void, arg=None, src=(
-        UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(1, 1, 256), strides=(0, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501
-          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=0, src=()),)),
-        UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.MAX, (1,)), src=(
-          UOp(Ops.MUL, dtypes.float, arg=None, src=(
-            UOp(Ops.ADD, dtypes.float, arg=None, src=(
-              UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                  UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                    UOp(Ops.ADD, dtypes.float, arg=None, src=(
-                      UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                        UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=0, mask=((0, 64128),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=1, src=()),)),)),
-                      UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                        UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-64128, mask=((64128, 128256),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                          UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=2, src=()),)),)),)),
-                    UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                      UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-128256, mask=((128256, 192384),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                        UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=3, src=()),)),)),)),
-                  UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                    UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-192384, mask=((192384, 256512),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                      UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=4, src=()),)),)),)),
-                UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                  UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-256512, mask=((256512, 320640),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                    UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=5, src=()),)),)),)),
-              UOp(Ops.LOAD, dtypes.float, arg=None, src=(
-                UOp(Ops.VIEW, dtypes.float.ptr(64128), arg=ShapeTracker(views=(View(shape=(384768,), strides=(1,), offset=-320640, mask=((320640, 384768),), contiguous=False), View(shape=(1, 501, 256), strides=(0, 1, 501), offset=256512, mask=None, contiguous=False))), src=( # noqa: E501
-                  UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(64128), arg=6, src=()),)),)),)),
-            UOp(Ops.CONST, dtypes.float, arg=1.4285714285714286, src=(
-              UOp(Ops.VIEW, dtypes.void, arg=ShapeTracker(views=(View(shape=(1, 501, 256), strides=(0, 0, 0), offset=0, mask=None, contiguous=False),)), src=()),)),)),)),)),)) # noqa: E501
-    lin = Kernel(ast)
-
-    bufs = bufs_from_lin(lin)
-    best_lin = beam_search(lin, bufs, 2)
-    assert best_lin
-    # need disable_cache to trigger.
-    tm = time_linearizer(best_lin, bufs, allow_test_size=False, cnt=2, disable_cache=True)
-    assert tm
-
   def test_beam_unnamed_kernels(self):
     from test.test_linearizer import push_views
     a = Tensor.rand(100)

test/test_symbolic_jit.py

@@ -2,50 +2,41 @@ import unittest
 
 from test.helpers import assert_jit_cache_len
 from tinygrad import Variable, Tensor, TinyJit
-from tinygrad.helpers import Context
 import numpy as np
 
 class TestSymbolicJit(unittest.TestCase):
-  def setUp(self):
-    # A lot of these test are out of bounds, so we ignore the bounds check
-    self.context = Context(IGNORE_OOB=1)
-    self.context.__enter__()
-
-  def tearDown(self):
-    self.context.__exit__(None, None, None)
-
   def test_plus1(self):
     def f(a): return (a+1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      symbolic = jf(a.reshape(3, vi)).reshape(3, i).numpy()
-      expected = f(a).numpy()
+      symbolic = jf(a[:, :vi]).reshape(3, i).numpy()
+      expected = f(a[:, :i]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_add(self):
     def f(a, b): return (a+b).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(3, i)
-      symbolic = jf(a.reshape(3, vi), b.reshape(3, vi)).reshape(3, i).numpy()
-      expected = f(a, b).numpy()
+      symbolic = jf(a[:, :vi], b[:, :vi]).reshape(3, i).numpy()
+      expected = f(a[:, :i], b[:, :i]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_matmul(self):
     def f(a, b): return (a@b).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(10, 5)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(i, 5)
-      symbolic = jf(a.reshape(3, vi), b.reshape(vi, 5)).numpy()
-      expected = f(a, b).numpy()
+      symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
+      expected = f(a[:, :i], b[:i, :]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
@@ -55,119 +46,119 @@ class TestSymbolicJit(unittest.TestCase):
       s = (s+s).realize() # this one does not have symbols in input
       return s
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(10, 5)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(i, 5)
-      symbolic = jf(a.reshape(3, vi), b.reshape(vi, 5)).numpy()
-      expected = f(a, b).numpy()
+      symbolic = jf(a[:, :vi], b[:vi, :]).numpy()
+      expected = f(a[:, :i], b[:i, :]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 2)
 
   def test_attention(self):
     def f(q, k, v): return Tensor.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).realize()
     jf = TinyJit(f)
+    q = Tensor.rand(2, 1, 4, 8)
+    k = Tensor.rand(2, 10, 4, 8)
+    v = Tensor.rand(2, 10, 4, 8)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      q = Tensor.rand(2, 1, 4, 8)
-      k = Tensor.rand(2, i, 4, 8)
-      v = Tensor.rand(2, i, 4, 8)
-      symbolic = jf(q, k.reshape(2, vi, 4, 8), v.reshape(2, vi, 4, 8)).reshape(2, 4, 1, 8).numpy()
-      expected = f(q, k, v).numpy()
+      symbolic = jf(q, k[:, :vi], v[:, :vi]).reshape(2, 4, 1, 8).numpy()
+      expected = f(q, k[:, :i], v[:, :i]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 5)
 
   def test_cat_dim0(self):
     def f(a, b): return a.cat(b, dim=0).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(2, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(i, 3)
-      b = Tensor.rand(2, 3)
-      symbolic = jf(a.reshape(vi, 3), b).reshape(i+2, 3).numpy()
-      expected = f(a, b).numpy()
+      symbolic = jf(a[:vi], b).reshape(i+2, 3).numpy()
+      expected = f(a[:i], b).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_cat_dim1(self):
     def f(a, b): return a.cat(b, dim=1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 2)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(3, 2)
-      symbolic = jf(a.reshape(3, vi), b).reshape(3, i+2).numpy()
-      expected = f(a, b).numpy()
+      symbolic = jf(a[:, :vi], b).reshape(3, i+2).numpy()
+      expected = f(a[:, :i], b).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_cat_dim0_two_vars(self):
     def f(a, b): return a.cat(b, dim=0).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(10, 3)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(i, 3)
-        b = Tensor.rand(j, 3)
-        symbolic = jf(a.reshape(vi, 3), b.reshape(vj, 3)).reshape(i+j, 3).numpy()
-        expected = f(a, b).numpy()
+        symbolic = jf(a[:vi], b[:vj]).reshape(i+j, 3).numpy()
+        expected = f(a[:i], b[:j]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_cat_dim1_two_vars(self):
     def f(a, b): return a.cat(b, dim=1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(3, i)
-        b = Tensor.rand(3, j)
-        symbolic = jf(a.reshape(3, vi), b.reshape(3, vj)).reshape(3, i+j).numpy()
-        expected = f(a, b).numpy()
+        symbolic = jf(a[:, :vi], b[:, :vj]).reshape(3, i+j).numpy()
+        expected = f(a[:, :i], b[:, :j]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_two_vars_plus1_ij(self):
     def f(a, b): return (a@b+1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(i, 3)
-        b = Tensor.rand(3, j)
-        symbolic = jf(a.reshape(vi, 3), b.reshape(3, vj)).reshape(i, j).numpy()
-        expected = f(a, b).numpy()
+        symbolic = jf(a[:vi, :], b[:, :vj]).reshape(i, j).numpy()
+        expected = f(a[:i, :], b[:, :j]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_two_vars_plus1_ji(self):
     def f(a, b): return (a@b+1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(j, 3)
-        b = Tensor.rand(3, i)
-        symbolic = jf(a.reshape(vj, 3), b.reshape(3, vi)).reshape(j, i).numpy()
-        expected = f(a, b).numpy()
+        symbolic = jf(a[:vj, :], b[:, :vi]).reshape(j, i).numpy()
+        expected = f(a[:j, :], b[:, :i]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
     assert_jit_cache_len(jf, 1)
 
   def test_jit_symbolic_shape_mismatch(self):
     @TinyJit
     def add(a, b): return (a+b).realize()
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i).reshape(3, vi)
-      b = Tensor.rand(3, i).reshape(3, vi)
-      add(a, b)
+      add(a[:, :vi], b[:, :vi])
     vi2 = Variable("i", 1, 10).bind(7)
-    a = Tensor.rand(3, 7).reshape(3, vi2)
-    bad = Tensor.rand(4, 7).reshape(4, vi2)
+    a = Tensor.rand(3, 7)[:, :vi2]
+    bad = Tensor.rand(4, 7)[:, :vi2]
     with self.assertRaises(AssertionError):
       add(a, bad)
 
@@ -175,9 +166,9 @@ class TestSymbolicJit(unittest.TestCase):
     # shrink is a movement, so we pair it with a simple function to test the JIT interaction
     def f(a): return (a+1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(7, 11)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(7, 11)
       symbolic = a.shrink(((3,5),(vi,vi+2)))
       symbolic = jf(symbolic).numpy()
       expected = f(a.shrink(((3,5),(i,i+2)))).numpy()
@@ -188,9 +179,9 @@ class TestSymbolicJit(unittest.TestCase):
     # slice is a movement, so we pair it with a simple function to test the JIT interaction
     def f(a): return (a+1).realize()
     jf = TinyJit(f)
+    a = Tensor.rand(7, 11)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(7, 11)
       symbolic = a[3:5, vi:vi+2]
       symbolic = jf(symbolic).numpy()
       expected = f(a[3:5, i:i+2]).numpy()
@@ -212,11 +203,11 @@ class TestSymbolicJit(unittest.TestCase):
   def test_ones_sum(self):
     def f(a): return a.sum().realize()
     jf = TinyJit(f)
+    t = Tensor.ones(10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      t = Tensor.ones(i)
-      symbolic = jf(t.reshape(vi)).item()
-      expected = f(t).item()
+      symbolic = jf(t[:vi]).item()
+      expected = f(t[:i]).item()
       np.testing.assert_equal(symbolic, expected)
 
   def test_mean(self):
@@ -226,22 +217,22 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     jf0 = TinyJit(f0)
     jf1 = TinyJit(f1)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(10, 3)
+    c = Tensor.rand(10, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      # aixs = None
-      a = Tensor.rand(i, 3)
-      symbolic = jf(a.reshape(vi, 3)).numpy()
-      expected = a.mean().numpy()
+      # axis = None
+      symbolic = jf(a[:vi]).numpy()
+      expected = a[:i].mean().numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 0
-      a = Tensor.rand(i, 3)
-      symbolic = jf0(a.reshape(vi, 3)).numpy()
-      expected = a.mean(0).numpy()
+      # axis = 0
+      symbolic = jf0(b[:vi]).numpy()
+      expected = b[:i].mean(0).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 1
-      a = Tensor.rand(i, 3)
-      symbolic = jf1(a.reshape(vi, 3)).reshape(i).numpy()
-      expected = a.mean(1).numpy()
+      # axis = 1
+      symbolic = jf1(c[:vi]).reshape(i).numpy()
+      expected = c[:i].mean(1).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_mean_2d(self):
@@ -251,24 +242,24 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     jf0 = TinyJit(f0)
     jf1 = TinyJit(f1)
+    a = Tensor.rand(10, 10)
+    b = Tensor.rand(10, 10)
+    c = Tensor.rand(10, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        # aixs = None
-        a = Tensor.rand(i, j)
-        symbolic = jf(a.reshape(vi, vj)).numpy()
-        expected = a.mean().numpy()
+        # axis = None
+        symbolic = jf(a[:vi, :vj]).numpy()
+        expected = a[:i, :j].mean().numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 0
-        a = Tensor.rand(i, j)
-        symbolic = jf0(a.reshape(vi, vj)).reshape(j).numpy()
-        expected = a.mean(0).numpy()
+        # axis = 0
+        symbolic = jf0(b[:vi, :vj]).reshape(j).numpy()
+        expected = b[:i, :j].mean(0).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 1
-        a = Tensor.rand(i, j)
-        symbolic = jf1(a.reshape(vi, vj)).reshape(i).numpy()
-        expected = a.mean(1).numpy()
+        # axis = 1
+        symbolic = jf1(c[:vi, :vj]).reshape(i).numpy()
+        expected = c[:i, :j].mean(1).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_var(self):
@@ -278,22 +269,22 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     jf0 = TinyJit(f0)
     jf1 = TinyJit(f1)
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(10, 3)
+    c = Tensor.rand(10, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      # aixs = None
-      a = Tensor.rand(i, 3)
-      symbolic = jf(a.reshape(vi, 3)).numpy()
-      expected = a.var().numpy()
+      # axis = None
+      symbolic = jf(a[:vi]).numpy()
+      expected = a[:i].var().numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 0
-      a = Tensor.rand(i, 3)
-      symbolic = jf0(a.reshape(vi, 3)).numpy()
-      expected = a.var(0).numpy()
+      # axis = 0
+      symbolic = jf0(b[:vi]).numpy()
+      expected = b[:i].var(0).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 1
-      a = Tensor.rand(i, 3)
-      symbolic = jf1(a.reshape(vi, 3)).reshape(i).numpy()
-      expected = a.var(1).numpy()
+      # axis = 1
+      symbolic = jf1(c[:vi]).reshape(i).numpy()
+      expected = c[:i].var(1).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_var_2d(self):
@@ -303,24 +294,24 @@ class TestSymbolicJit(unittest.TestCase):
     jf = TinyJit(f)
     jf0 = TinyJit(f0)
     jf1 = TinyJit(f1)
+    a = Tensor.rand(10, 10)
+    b = Tensor.rand(10, 10)
+    c = Tensor.rand(10, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        # aixs = None
-        a = Tensor.rand(i, j)
-        symbolic = jf(a.reshape(vi, vj)).numpy()
-        expected = a.var().numpy()
+        # axis = None
+        symbolic = jf(a[:vi, :vj]).numpy()
+        expected = a[:i, :j].var().numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 0
-        a = Tensor.rand(i, j)
-        symbolic = jf0(a.reshape(vi, vj)).reshape(j).numpy()
-        expected = a.var(0).numpy()
+        # axis = 0
+        symbolic = jf0(b[:vi, :vj]).reshape(j).numpy()
+        expected = b[:i, :j].var(0).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 1
-        a = Tensor.rand(i, j)
-        symbolic = jf1(a.reshape(vi, vj)).reshape(i).numpy()
-        expected = a.var(1).numpy()
+        # axis = 1
+        symbolic = jf1(c[:vi, :vj]).reshape(i).numpy()
+        expected = c[:i, :j].var(1).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
 if __name__ == '__main__':

test/test_symbolic_ops.py

@@ -1,62 +1,53 @@
 import unittest
-from tinygrad import Tensor, Variable
+from tinygrad import Tensor, Variable, GlobalCounters
 from tinygrad.shape.shapetracker import View
-from tinygrad.helpers import Context, GlobalCounters
 from tinygrad.uop.ops import sym_infer
 from tinygrad.dtype import dtypes
-from tinygrad.device import Device
+from tinygrad.device import is_dtype_supported
 from examples.gpt2 import Attention
 import numpy as np
 
 class TestSymbolicOps(unittest.TestCase):
-  def setUp(self):
-    # A lot of these test are out of bounds, so we ignore the bounds check
-    self.context = Context(IGNORE_OOB=1)
-    self.context.__enter__()
-
-  def tearDown(self):
-    self.context.__exit__(None, None, None)
-
   def test_plus1(self):
     def f(a): return (a+1).realize()
+    a = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      symbolic = f(a.reshape(3, vi)).reshape(3, i).numpy()
-      expected = f(a).numpy()
+      symbolic = f(a[:, :vi]).reshape(3, i).numpy()
+      expected = f(a[:, :i]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_add(self):
     def f(a, b): return (a+b).realize()
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(3, i)
-      symbolic = f(a.reshape(3, vi), b.reshape(3, vi)).reshape(3, i).numpy()
-      expected = f(a, b).numpy()
+      symbolic = f(a[:, :vi], b[:, :vi]).reshape(3, i).numpy()
+      expected = f(a[:, :i], b[:, :i]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_matmul(self):
     def f(a, b): return (a@b).realize()
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(10, 5)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
-      b = Tensor.rand(i, 5)
-      symbolic = f(a.reshape(3, vi), b.reshape(vi, 5)).numpy()
-      expected = f(a, b).numpy()
+      symbolic = f(a[:, :vi], b[:vi, :]).numpy()
+      expected = f(a[:, :i], b[:i, :]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_attention(self, dropout_p=0.0, imin=1, imax=5, use_symbolic=True):
     def f(q, k, v): return Tensor.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=dropout_p).realize()
+    q = Tensor.rand(2, 1, 4, 8)
+    k = Tensor.rand(2, 10, 4, 8)
+    v = Tensor.rand(2, 10, 4, 8)
     for i in range(imin, imax):
       vi = Variable("i", 1, 10).bind(i) if use_symbolic else i
-      q = Tensor.rand(2, 1, 4, 8)
-      k = Tensor.rand(2, i, 4, 8)
-      v = Tensor.rand(2, i, 4, 8)
       Tensor.realize(q, k, v)
       GlobalCounters.reset()
-      symbolic = f(q, k.reshape(2, vi, 4, 8), v.reshape(2, vi, 4, 8)).reshape(2, 4, 1, 8).numpy()
-      expected = f(q, k, v).numpy()
+      symbolic = f(q, k[:, :vi, :, :], v[:, :vi, :, :]).reshape(2, 4, 1, 8).numpy()
+      expected = f(q, k[:, :i, :, :], v[:, :i, :, :]).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_attention_cmp_symbolic(self):
@@ -90,73 +81,89 @@ class TestSymbolicOps(unittest.TestCase):
 
   def test_cat_dim0(self):
     def f(a, b): return a.cat(b, dim=0).realize()
+    a = Tensor.rand(10, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(i, 3)
       b = Tensor.rand(2, 3)
-      symbolic = f(a.reshape(vi, 3), b).reshape(i+2, 3).numpy()
-      expected = f(a, b).numpy()
+      symbolic = f(a[:vi, :], b).reshape(i+2, 3).numpy()
+      expected = f(a[:i, :], b).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_cat_dim1(self):
     def f(a, b): return a.cat(b, dim=1).realize()
+    a = Tensor.rand(3, 10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(3, i)
       b = Tensor.rand(3, 2)
-      symbolic = f(a.reshape(3, vi), b).reshape(3, i+2).numpy()
-      expected = f(a, b).numpy()
+      symbolic = f(a[:, :vi], b).reshape(3, i+2).numpy()
+      expected = f(a[:, :i], b).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_cat_dim0_two_vars(self):
     def f(a, b): return a.cat(b, dim=0).realize()
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(10, 3)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(i, 3)
-        b = Tensor.rand(j, 3)
-        symbolic = f(a.reshape(vi, 3), b.reshape(vj, 3)).reshape(i+j, 3).numpy()
-        expected = f(a, b).numpy()
+        symbolic = f(a[:vi, :], b[:vj, :]).reshape(i+j, 3).numpy()
+        expected = f(a[:i, :], b[:j, :]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_cat_dim1_two_vars(self):
     def f(a, b): return a.cat(b, dim=1).realize()
+    a = Tensor.rand(3, 10)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(3, i)
-        b = Tensor.rand(3, j)
-        symbolic = f(a.reshape(3, vi), b.reshape(3, vj)).reshape(3, i+j).numpy()
-        expected = f(a, b).numpy()
+        symbolic = f(a[:, :vi], b[:, :vj]).reshape(3, i+j).numpy()
+        expected = f(a[:, :i], b[:, :j]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_two_vars_plus1_ij(self):
     def f(a, b): return (a@b+1).realize()
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(i, 3)
-        b = Tensor.rand(3, j)
-        symbolic = f(a.reshape(vi, 3), b.reshape(3, vj)).reshape(i, j).numpy()
-        expected = f(a, b).numpy()
+        symbolic = f(a[:vi, :], b[:, :vj]).reshape(i, j).numpy()
+        expected = f(a[:i, :], b[:, :j]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_two_vars_plus1_ji(self):
     # reverse the order of variables
     def f(a, b): return (a@b+1).realize()
+    a = Tensor.rand(10, 3)
+    b = Tensor.rand(3, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        a = Tensor.rand(j, 3)
-        b = Tensor.rand(3, i)
-        symbolic = f(a.reshape(vj, 3), b.reshape(3, vi)).reshape(j, i).numpy()
-        expected = f(a, b).numpy()
+        symbolic = f(a[:vj, :], b[:, :vi]).reshape(j, i).numpy()
+        expected = f(a[:j, :], b[:, :i]).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
+  def test_reshape_from_symbolic(self):
+    a = Tensor.rand(30)
+    for i in range(3, 5):
+      vi = Variable("i", 3, 10).bind(i)
+      symbolic = a[:vi*3].reshape((3, 3)).numpy()
+      # To match symbolic reshape (potential implicit shrink), we need a shrink
+      expected = a[:i*3].shrink(((0, 9),)).reshape((3, 3)).numpy()
+      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+
+  def test_invalid_symbolic_reshape(self):
+    a = Tensor.rand(30)
+    for i in range(1, 5):
+      vi = Variable("i", 1, 10).bind(i)
+      # Cannot reshape into symbolic from non-symbolic
+      with self.assertRaises(AssertionError): a.reshape((3, vi))
+
   def test_shrink(self):
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
@@ -176,11 +183,10 @@ class TestSymbolicOps(unittest.TestCase):
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_slice_no_start(self):
+    a = Tensor.rand(7, 11)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(7, 11)
-      symbolic = a[3:5, :vi:1].reshape(2,i)
-      symbolic = symbolic.numpy()
+      symbolic = a[3:5, :vi:1].reshape(2, i).numpy()
       expected = a[3:5, :i:1].numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
@@ -201,31 +207,31 @@ class TestSymbolicOps(unittest.TestCase):
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_ones_sum(self):
+    t = Tensor.ones(10)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      t = Tensor.ones(i)
-      symbolic = t.reshape(vi).sum().item()
-      expected = t.sum().item()
+      symbolic = t[:vi].sum().item()
+      expected = t[:i].sum().item()
       np.testing.assert_equal(symbolic, expected)
 
   def test_mean(self):
+    a = Tensor.rand(10, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
       for axis in [None, 0, 1]:
-        a = Tensor.rand(i, 3)
-        expected = a.mean(axis).numpy()
-        symbolic = a.reshape(vi, 3).mean(axis).reshape(expected.shape).numpy()
+        expected = a[:i].mean(axis).numpy()
+        symbolic = a[:vi].mean(axis).reshape(expected.shape).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_mean_2d(self):
+    a = Tensor.rand(10, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         for axis in [None, 0, 1]:
-          a = Tensor.rand(i, j)
-          expected = a.mean(axis).numpy()
-          symbolic = a.reshape(vi, vj).mean(axis).reshape(expected.shape).numpy()
+          expected = a[:i, :j].mean(axis).numpy()
+          symbolic = a[:vi, :vj].mean(axis).reshape(expected.shape).numpy()
           np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_var(self):
@@ -233,43 +239,43 @@ class TestSymbolicOps(unittest.TestCase):
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
       for axis in [None, 0, 1]:
-        expected = a[:i, :].var(axis).numpy()
-        symbolic = a[:vi, :].var(axis).reshape(expected.shape).numpy()
+        expected = a[:i].var(axis).numpy()
+        symbolic = a[:vi].var(axis).reshape(expected.shape).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_var_2d(self):
+    a = Tensor.rand(10, 10)
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
         for axis in [None, 0, 1]:
-          a = Tensor.rand(i, j)
-          expected = a.var(axis).numpy()
-          symbolic = a.reshape(vi, vj).var(axis).reshape(expected.shape).numpy()
+          expected = a[:i, :j].var(axis).numpy()
+          symbolic = a[:vi, :vj].var(axis).reshape(expected.shape).numpy()
           np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_bitcast_down(self):
+    a = Tensor.rand(10, 3)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(i, 3)
-      expected = a.bitcast(dtypes.uint8).numpy()
-      symbolic = a.reshape(vi, 3).bitcast(dtypes.uint8).reshape(expected.shape).numpy()
+      expected = a[:i].bitcast(dtypes.uint8).numpy()
+      symbolic = a[:vi].bitcast(dtypes.uint8).reshape(expected.shape).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=0)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "no uint64")
+  @unittest.skipUnless(is_dtype_supported(dtypes.uint64), "no uint64")
   def test_bitcast_up(self):
+    a = Tensor.rand(10, 4)
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      a = Tensor.rand(i, 4)
-      expected = a.bitcast(dtypes.uint64).numpy()
-      symbolic = a.reshape(vi, 4).bitcast(dtypes.uint64).reshape(expected.shape).numpy()
+      expected = a[:i].bitcast(dtypes.uint64).numpy()
+      symbolic = a[:vi].bitcast(dtypes.uint64).reshape(expected.shape).numpy()
       np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=0)
 
   @unittest.expectedFailure
   def test_conv2d_ceildiv_edge_case(self):
     v = Variable('v', 11, 50_000)
     val = 39601
-    x = Tensor.randn(1, 22, 39601).reshape(1, 22, v.bind(val))
+    x = Tensor.randn(1, 22, 50_000)[:, :, :v.bind(val)]
     weight = Tensor.randn(256, 22, 12)
 
     result = x.conv2d(weight=weight, groups=1, stride=6, dilation=1, padding=(3, 3))

test/test_tensor_variable.py

@@ -1,7 +1,6 @@
 import unittest
 import numpy as np
 from tinygrad import Tensor, Variable
-from tinygrad.helpers import Context
 
 class TestTensorVariable(unittest.TestCase):
   def test_add_tvar(self):
@@ -23,43 +22,38 @@ class TestTensorVariable(unittest.TestCase):
     assert (Tensor(3) * (vv * 4)).item() == 24
 
   def test_symbolic_mean(self):
-    with Context(IGNORE_OOB=1):
-      vv = Variable("a", 1, 10).bind(2)
-      t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
-      ret = t.mean().item()
-      assert ret == 1
+    vv = Variable("a", 1, 10).bind(2)
+    t = Tensor.ones(2, 10).contiguous()[:, :vv]
+    ret = t.mean().item()
+    assert ret == 1
 
   def test_symbolic_mean_2d(self):
-    with Context(IGNORE_OOB=1):
-      vv = Variable("a", 1, 10).bind(2)
-      vv2 = Variable("b", 1, 10).bind(2)
-      t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
-      ret = t.mean().item()
-      assert ret == 1
+    vv = Variable("a", 1, 10).bind(2)
+    vv2 = Variable("b", 1, 10).bind(2)
+    t = Tensor.ones(10, 10).contiguous()[:vv2, :vv]
+    ret = t.mean().item()
+    assert ret == 1
 
   def test_symbolic_mean_2d_axis_1(self):
-    with Context(IGNORE_OOB=1):
-      vv = Variable("a", 1, 10).bind(2)
-      vv2 = Variable("b", 1, 10).bind(2)
-      t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
-      ret = t.mean(axis=1).reshape(2, 1).numpy()
-      assert np.all(ret == 1)
+    vv = Variable("a", 1, 10).bind(2)
+    vv2 = Variable("b", 1, 10).bind(2)
+    t = Tensor.ones(10, 10).contiguous()[:vv2, :vv]
+    ret = t.mean(axis=1).reshape(2, 1).numpy()
+    assert np.all(ret == 1)
 
   def test_symbolic_mean_2d_add(self):
-    with Context(IGNORE_OOB=1):
-      add_term = Variable("c", 0, 10).bind(1)
-      vv = Variable("a", 1, 10).bind(1)
-      vv2 = Variable("b", 1, 10).bind(1)
-      t = Tensor.ones(2, 2).contiguous().reshape(vv2+add_term, vv+add_term)
-      ret = t.mean().item()
-      assert ret == 1
+    add_term = Variable("c", 0, 10).bind(1)
+    vv = Variable("a", 1, 10).bind(1)
+    vv2 = Variable("b", 1, 10).bind(1)
+    t = Tensor.ones(20, 20).contiguous()[:vv2+add_term, :vv+add_term]
+    ret = t.mean().item()
+    assert ret == 1
 
   def test_symbolic_var(self):
-    with Context(IGNORE_OOB=1):
-      vv = Variable("a", 1, 10).bind(2)
-      t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
-      ret = t.var().item()
-      assert ret == 0
+    vv = Variable("a", 1, 10).bind(2)
+    t = Tensor.ones(2, 10).contiguous()[:, :vv]
+    ret = t.var().item()
+    assert ret == 0
 
   def test_symbolic_pad(self):
     vv = Variable("a", 1, 10).bind(2)
@@ -92,5 +86,15 @@ class TestTensorVariable(unittest.TestCase):
     ret = Tensor.arange(begin.bind(4), end.bind(7))
     self.assertListEqual(ret.reshape(3).tolist(), [4,5,6])
 
+  def test_variable_empty(self):
+    v = Variable("i", 1, 10)
+    # TODO: Tensor creation from unbound variable should assert
+    # with self.assertRaises(AssertionError): t = Tensor.empty(3, v)
+    vb = v.bind(3)
+    t = Tensor.empty(3, vb)
+    assert t.uop.base.buffer.size == 30
+    assert t.uop.st.shape == (3, vb)
+
+
 if __name__ == '__main__':
   unittest.main()

test/test_tiny.py

@@ -1,7 +1,7 @@
 # basic self-contained tests of the external functionality of tinygrad
 import unittest, random
 from tinygrad import Tensor, Context, Variable, TinyJit, dtypes, Device, nn
-from tinygrad.helpers import IMAGE, CI
+from tinygrad.helpers import IMAGE, CI, getenv
 
 class TestTiny(unittest.TestCase):
 
@@ -27,7 +27,7 @@ class TestTiny(unittest.TestCase):
     out = Tensor.ones(256).contiguous().sum()
     self.assertEqual(out.item(), 256)
 
-  def test_gemm(self, N=64, out_dtype=dtypes.float):
+  def test_gemm(self, N=getenv("GEMM_N", 64), out_dtype=dtypes.float):
     a = Tensor.ones(N,N).contiguous()
     b = Tensor.eye(N).contiguous()
     lst = (out:=a@b).tolist()
@@ -36,6 +36,14 @@ class TestTiny(unittest.TestCase):
         self.assertEqual(lst[y][x], 1.0, msg=f"mismatch at ({y},{x})")
     if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)
 
+  def test_gemv(self, N=getenv("GEMV_N", 64), out_dtype=dtypes.float):
+    a = Tensor.ones(1,N).contiguous()
+    b = Tensor.eye(N).contiguous()
+    lst = (out:=a@b).tolist()
+    for x in range(N):
+      self.assertEqual(lst[0][x], 1.0, msg=f"mismatch at {x}")
+    if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)
+
   # *** randomness ***
 
   def test_random(self):

test/test_uop_graph.py

@@ -458,8 +458,8 @@ class TestUOpGraph(unittest.TestCase):
       sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.uint.ptr(8, addrspace=AddrSpace.LOCAL), (), "temp0")
 
       # Define indices, valids and barrier
-      gidx = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 416))
-      lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 10))
+      gidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 416),), "gidx0")
+      lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "lidx0")
 
       gate = (gidx<400) & (lidx<8)
 
@@ -477,7 +477,7 @@ class TestUOpGraph(unittest.TestCase):
 
   def test_load_with_float_in_index(self):
     with Context(IGNORE_OOB=0):
-      ridx = UOp.range(dtypes.int, 20, 0)
+      ridx = UOp.range(20, 0)
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
       i = (ridx.cast(dtypes.float)*0.68).trunc().cast(dtypes.int)
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16))),))
@@ -490,7 +490,7 @@ class TestUOpGraph(unittest.TestCase):
   def test_load_cast_to_bool(self):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), (), 0)
-      ridx = UOp.range(dtypes.int, 20, 0)
+      ridx = UOp.range(20, 0)
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(ridx, ridx.cast(dtypes.bool).logical_not()),))
       to_uops_list([ld0])
 
@@ -499,7 +499,7 @@ class TestUOpGraph(unittest.TestCase):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
       mask = UOp(Ops.DEFINE_GLOBAL, dtypes.bool.ptr(16), (), 0)
-      ridx = UOp.range(dtypes.int, 20, 0)
+      ridx = UOp.range(20, 0)
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(ridx, ridx<16&mask),)))
       to_uops_list([ld0])
 
@@ -512,7 +512,7 @@ class TestUOpGraph(unittest.TestCase):
   def test_in_out_bounds_access_with_mask(self):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
-      gidx0 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 42))
+      gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, (5<gidx0)&(gidx0<16)),))
       ld1 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<16),))
       to_uops_list([ld0, ld1])
@@ -536,7 +536,7 @@ class TestUOpGraph(unittest.TestCase):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
       glbl1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(64), (), 0)
-      gidx0 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 42))
+      gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 42),), "gidx0")
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(gidx0, gidx0<8),))
       ld1 = UOp(Ops.LOAD, dtypes.int, (glbl1.index(ld0*2, (ld0>=0)&(ld0<32)),))
       to_uops_list([ld1])
@@ -559,7 +559,7 @@ class TestUOpGraph(unittest.TestCase):
   def test_fold_gated_load_local(self):
     glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
     smem = UOp(Ops.DEFINE_LOCAL, dtypes.int.ptr(size=18, addrspace=AddrSpace.LOCAL), (), "temp")
-    lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 16))
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
     st = UOp(Ops.STORE, dtypes.void, (smem.index(lidx), UOp.load(glbl0.index(lidx), dtype=dtypes.int)))
     barrier = UOp(Ops.BARRIER, dtypes.void, (st, ))
     ld0 = UOp(Ops.LOAD, dtypes.int, (smem.index(lidx+1, UOp.const(dtypes.bool, False)), barrier))
@@ -592,8 +592,8 @@ class TestUOpGraph(unittest.TestCase):
   def test_switched_range_order(self):
     glbl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
     cf = UOp.const(dtypes.float, 0.0)
-    r1 = UOp.range(dtypes.int, 2, 0)
-    r2 = UOp.range(dtypes.int, 2, 1)
+    r1 = UOp.range(2, 0)
+    r2 = UOp.range(2, 1)
     alu = UOp(Ops.MUL, dtypes.int, (r2, r1))
     store = UOp(Ops.STORE, dtypes.void, (glbl.index(alu), cf))
     uops = to_uops_list([store])
@@ -756,8 +756,8 @@ class TestIFUOps(unittest.TestCase):
   def test_create_ifs(self):
     gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=4, addrspace=AddrSpace.LOCAL), (), "smem")
-    valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 10))<5
-    lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 4))
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
     gate = valid&(lidx.ne(2))
     idx = UOp.const(dtypes.int, 0)
     st = UOp(Ops.STORE, dtypes.void, (sbuf.index(idx), UOp.const(dtypes.float, 42)))
@@ -775,8 +775,8 @@ class TestIFUOps(unittest.TestCase):
   def test_expand_ifs_one_gate(self):
     gbuf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     sbuf = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(size=16, addrspace=AddrSpace.LOCAL), (), "smem")
-    valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 4))<1
-    lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 16))
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "gidx0")<1
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 16),), "lidx0")
     gate = valid&(lidx.ne(2))
     st = UOp(Ops.STORE, dtypes.void, (sbuf, lidx, UOp.const(dtypes.float, 42)))
     barrier = UOp(Ops.BARRIER, dtypes.void, (st,))
@@ -794,8 +794,8 @@ class TestIFUOps(unittest.TestCase):
   @unittest.expectedFailure
   def test_expand_ifs_dumb(self):
     buf = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
-    valid = UOp(Ops.SPECIAL, dtypes.int, (), ("gidx0", 10))<5
-    lidx = UOp(Ops.SPECIAL, dtypes.int, (), ("lidx0", 4))
+    valid = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 10),), "gidx0")<5
+    lidx = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), "lidx0")
     gate = valid&(lidx.ne(2))
     stores = [UOp(Ops.STORE, dtypes.void, (buf, UOp.const(dtypes.int, i), UOp.const(dtypes.float, i), gate)) for i in range(4)]
     sink = UOp(Ops.SINK, dtypes.void, tuple(stores))

test/test_uops.py

@@ -22,7 +22,7 @@ def _uops_to_prg(uops_list):
   uops = full_rewrite(ast:=UOp.sink(*uops_list), opts=Device[Device.DEFAULT].renderer)
   src = Device[Device.DEFAULT].renderer.render(uops)
   has_local = Device[Device.DEFAULT].renderer.has_local
-  return CompiledRunner(ProgramSpec("test", src, Device.DEFAULT, ast, uops=uops,
+  return CompiledRunner(ProgramSpec(uops[-1].arg.name if uops[-1].arg is not None else "test", src, Device.DEFAULT, ast, uops=uops,
                                 global_size=[1,1,1] if has_local else None, local_size=[1,1,1] if has_local else None))
 
 def uop(uops:list[UOp], uop:Ops, dtype:Optional[DType], src:tuple[UOp, ...], arg:Any=None) -> UOp:
@@ -177,6 +177,32 @@ class TestBoolUOps(TestUOps):
   def test_cmplt_bool(self): self._test_bop_bool_fxn(Ops.CMPLT, lambda a,b: a < b)
   def test_where_bool(self): self._test_top_bool_fxn(Ops.WHERE, lambda a,b,c: b if a else c)
 
+class TestSafeCast(TestUOps):
+  def test_cast_folds(self):
+    a = UOp.variable("a", 1, 10, dtype=dtypes.int32)
+    self.assertEqual(a.cast(dtypes.int64).cast(dtypes.int32).simplify(), a)
+    self.assertEqual(a.cast(dtypes.double).cast(dtypes.int32).simplify(), a)
+    a = UOp.variable("a", 1, 10, dtype=dtypes.uint8)
+    self.assertEqual(a.cast(dtypes.int64).cast(dtypes.uint8).simplify(), a)
+    self.assertEqual(a.cast(dtypes.uint32).cast(dtypes.uint8).simplify(), a)
+
+  def test_remove_intermediate_cast(self):
+    a = UOp.variable("a", 0., 100., dtype=dtypes.half)
+    self.assertEqual(a.cast(dtypes.double).cast(dtypes.float).simplify(), a.cast(dtypes.float))
+    a = UOp.variable("a", 1, 10, dtype=dtypes.int32)
+    # TODO: double preserves certain int dtypes
+    self.assertEqual(a.cast(dtypes.double).cast(dtypes.float).simplify(), a.cast(dtypes.float))
+    self.assertEqual(a.cast(dtypes.int64).cast(dtypes.int16).simplify(), a.cast(dtypes.int16))
+    a = UOp.variable("a", 1, 10, dtype=dtypes.uint8)
+    self.assertEqual(a.cast(dtypes.int64).cast(dtypes.int32).simplify(), a.cast(dtypes.int32))
+
+  def test_safe_cast_using_bounds(self):
+    a = UOp.variable("a", 1, 10, dtype=dtypes.uint64)
+    self.assertEqual(a.cast(dtypes.int16).cast(dtypes.int).simplify(), a.cast(dtypes.int))
+    a = UOp.variable("a", -10, 10, dtype=dtypes.int32)
+    self.assertEqual(a.cast(dtypes.int8).cast(dtypes.int64).simplify(), a.cast(dtypes.int64))
+    self.assertEqual(a.cast(dtypes.int8).cast(dtypes.float).simplify(), a.cast(dtypes.float))
+
 class TestExecALU(TestUOps):
   def test_sqrt(self):
     self.assertEqual(exec_alu(Ops.SQRT, dtypes.float, (0.0,)), 0.0)
@@ -244,7 +270,7 @@ class TestConstantFolding(unittest.TestCase):
 class TestGatedStoreRewrite(unittest.TestCase):
   def test_tiny_gate_store(self):
     gmem = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
-    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
+    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
     gate = gidx0<UOp.const(dtypes.int, 1)
     idx = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem, gidx0 * UOp.const(dtypes.int, 2), gate))
     val = UOp.const(dtypes.float, 42.0)
@@ -261,7 +287,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
   def test_gate_some_stores(self):
     gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     gmem1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
-    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
+    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
     idx = gidx0 * UOp.const(dtypes.int, 2)
     idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx, gidx0<UOp.const(dtypes.int, 1)))
     idx1 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem1, idx))
@@ -280,7 +306,7 @@ class TestGatedStoreRewrite(unittest.TestCase):
   def test_merge_ifs_alt(self):
     gmem0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     gmem1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)
-    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 4))
+    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 4),), 'gidx0')
     idx = gidx0*UOp.const(dtypes.int, 2)
     gate = gidx0<UOp.const(dtypes.int, 1)
     idx0 = UOp(Ops.INDEX, dtypes.float.ptr(), (gmem0, idx, gate))
@@ -403,7 +429,7 @@ class TestAssembly(unittest.TestCase):
     self.assertNotIn(Ops.IDIV, ops)
 
   def test_fast_idiv_remove_powers_of_two(self):
-    ridx = UOp.range(dtypes.int, 2**20, 0)
+    ridx = UOp.range(2**20, 0)
     uops = to_uops_list([ridx//(7*64)], opts=Device[Device.DEFAULT].renderer)
     ops = [x.op for x in uops]
     # this requires shifting out the powers of two before doing fast_idiv
@@ -447,13 +473,13 @@ class TestUOpMethod(unittest.TestCase):
   def test_uop_variables(self):
     a = UOp.variable("a", 1, 10)
     uop_var = Tensor(a.bind(1))
-    st_var = Tensor.empty((2, 1)).reshape((2, a.bind(1)))
+    st_var = Tensor.empty((2, 10))[:, :a.bind(1)]
     _, var_vals = (uop_var+st_var).schedule_with_vars()
     self.assertEqual(len(var_vals), 1)
     self.assertEqual(list(var_vals)[0], a)
 
   def test_const_factor(self):
-    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (), ('gidx0', 8))
+    gidx0 = UOp(Ops.SPECIAL, dtypes.int, (UOp.const(dtypes.int, 8),), 'gidx0')
     self.assertEqual(UOp(Ops.CONST, dtypes.int, (), 17).const_factor(), 17)
     self.assertEqual(gidx0.const_factor(), 1)
     self.assertEqual((gidx0*3).const_factor(), 3)

test/test_uops_stats.py

@@ -98,7 +98,6 @@ class TestUOpsStatsMatmulHalf(unittest.TestCase):
     self.assertEqual(expected_ops, GlobalCounters.global_ops)
 
 class TestUOpsStats(unittest.TestCase):
-  @unittest.skipIf(getenv("PTX"), "wrong in PTX")
   def test_simple_add(self):
     a = Tensor.empty(100,100)
     b = Tensor.empty(100,100)
@@ -110,7 +109,6 @@ class TestUOpsStats(unittest.TestCase):
     # NOTE; ops also include indexing ops
     assert expected_ops <= ops and ops <= expected_ops * 2
 
-  @unittest.skipIf(getenv("PTX"), "wrong in PTX")
   def test_simple_add_sq(self):
     a = Tensor.empty(100,100)
     b = Tensor.empty(100,100)

test/unit/test_attention.py

@@ -1,5 +1,6 @@
 import unittest
-from tinygrad import Tensor, dtypes
+from tinygrad import Tensor, dtypes, TinyJit, UOp
+from tinygrad.apps.llm import apply_rope
 
 # TODO: test_scheduler, but just in uint
 class TestAttention(unittest.TestCase):
@@ -16,5 +17,29 @@ class TestAttention(unittest.TestCase):
     for si in softmax_inputs:
       assert all(b.dtype == dtypes.half for b in si.bufs), f"non half {si.bufs=}"
 
+  def test_apply_rope(self):
+    x = Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32)
+    result = apply_rope(x, 0)
+    self.assertEqual(result.shape, x.shape)
+    self.assertEqual(result.dtype, x.dtype)
+    self.assertGreater((result - apply_rope(x, 5)).abs().max().item(), 1e-6)
+    with self.assertRaises(AssertionError): apply_rope(Tensor.randn(1, 1, 4, 7, dtype=dtypes.float32), 0)
+
+  def test_apply_rope_jit_prune(self):
+    def rope_fn(x_in, pos): return apply_rope(x_in, pos)
+    rope_noprune = TinyJit(rope_fn)
+    rope_prune = TinyJit(rope_fn, prune=True)
+
+    v_pos = UOp.variable("start_pos", 0, 100)
+    for _ in range(3):
+      rope_noprune(Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32), v_pos.bind(1))
+      rope_prune(Tensor.randn(1, 2, 4, 8, dtype=dtypes.float32), v_pos.bind(1))
+    noprune_size = len(rope_noprune.captured.jit_cache)
+    prune_size = len(rope_prune.captured.jit_cache)
+
+    self.assertGreater(noprune_size, prune_size)
+    self.assertGreaterEqual(noprune_size, 3)
+    self.assertEqual(prune_size, 1)
+
 if __name__ == '__main__':
   unittest.main()

test/unit/test_block_reorder.py

@@ -37,8 +37,8 @@ class TestBlockReorder(unittest.TestCase):
     a = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=0)
     b = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=1)
     c = UOp(Ops.DEFINE_GLOBAL, dtype=dtypes.float.ptr(), arg=2)
-    v1 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx0", 4))
-    v2 = UOp(Ops.SPECIAL, dtype=dtypes.int, arg=("gidx1", 4))
+    v1 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx0")
+    v2 = UOp(Ops.SPECIAL, dtype=dtypes.int, src=(UOp.const(dtypes.int, 4),), arg="gidx1")
     v1 = v1*27
     v2 = v2*4
     loads = [

test/unit/test_dtype.py

@@ -21,6 +21,10 @@ class TestEqStrDType(unittest.TestCase):
   def test_ptr_eq(self):
     assert dtypes.float32.ptr() == dtypes.float32.ptr()
     assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
+  def test_ptr_nbytes(self):
+    assert dtypes.float16.ptr(32).nbytes() == 32 * dtypes.float16.itemsize
+  def test_ptr_nbytes_unlimited(self):
+    self.assertRaises(RuntimeError, lambda: dtypes.float32.ptr().nbytes())
   def test_strs(self):
     if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
     self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")

test/unit/test_dtype_spec.py

@@ -100,6 +100,11 @@ class TestHelpers(unittest.TestCase):
         np.testing.assert_equal(dt.min, False)
         np.testing.assert_equal(dt.max, True)
 
+  def test_dtype_range_vec(self):
+    for dt in core_dtypes:
+      self.assertEqual(dt.min, dt.vec(4).min)
+      self.assertEqual(dt.max, dt.vec(4).max)
+
   def test_truncate_fp16(self):
     self.assertEqual(truncate_fp16(1), 1)
     self.assertEqual(truncate_fp16(65504), 65504)
@@ -613,4 +618,4 @@ class TestAutoCastType(unittest.TestCase):
     np.testing.assert_allclose(out.numpy(), tt.log_softmax(0).numpy(), rtol=1e-3)
     out = t.log_softmax(0, dtype=dtypes.float)
     self.assertEqual(out.dtype, dtypes.float)
-    np.testing.assert_allclose(out.numpy(), tt.log_softmax(0, dtype=torch.float).numpy(), rtol=1e-3)
+    np.testing.assert_allclose(out.numpy(), tt.log_softmax(0, dtype=torch.float).numpy(), rtol=1e-3)

test/unit/test_graph_rewrite.py

@@ -65,21 +65,21 @@ class TestFoldingAndReduction(unittest.TestCase):
   def test_full_graph_rewrite_reduction_with_unused_range(self):
     const1 = UOp.const(dtypes.int32, 15)
     const2 = UOp.const(dtypes.int32, 25)
-    rng = UOp.range(dtypes.int32, 10, idx=0)
+    rng = UOp.range(10, idx=0)
     optimized_sink = apply_rewrite((const1 + const2).reduce(Ops.ADD, rng))
     expected_sum = 10 * (15 + 25)
     self.assertEqual(optimized_sink.arg, expected_sum)
 
   @unittest.skip("currently failing")
   def test_full_graph_rewrite_range_reduction(self):
-    simple_range = UOp.range(dtypes.int32, 5, idx=0)
+    simple_range = UOp.range(5, idx=0)
     optimized_sink = apply_rewrite(simple_range.reduce(Ops.ADD, simple_range))
     expected_sum = sum(range(5))
     self.assertEqual(optimized_sink.arg, expected_sum)
 
   @unittest.skip("currently failing")
   def test_full_graph_rewrite_simple_reduction_folding(self):
-    simple_range = UOp.range(dtypes.int32, 4, idx=0)
+    simple_range = UOp.range(4, idx=0)
     add_uop = simple_range + UOp.const(dtypes.int32, 1)
     optimized_sink = apply_rewrite(add_uop.reduce(Ops.ADD, simple_range))
     expected_sum = sum(i + 1 for i in range(4))
@@ -87,8 +87,8 @@ class TestFoldingAndReduction(unittest.TestCase):
 
   @unittest.skip("currently failing")
   def test_full_graph_rewrite_nested_loop_collapse(self):
-    outer_range = UOp.range(dtypes.int32, 8, 0)
-    inner_range = UOp.range(dtypes.int32, 4, 1)
+    outer_range = UOp.range(8, 0)
+    inner_range = UOp.range(4, 1)
     expr = (outer_range * 10) + inner_range
     optimized_reduce_uop = apply_rewrite(expr.reduce(Ops.ADD, outer_range, inner_range))
     self.assertEqual(optimized_reduce_uop.op, Ops.CONST)
@@ -116,7 +116,7 @@ class TestModuloAndDivisionFolding(unittest.TestCase):
 
   def test_graph_rewrite_div_folding_bug(self):
     lhs = UOp(Ops.ADD, dtypes.int.vec(4), src=(
-      UOp(Ops.VECTORIZE, dtypes.int.vec(4), arg=None, src=(UOp(Ops.SPECIAL, dtypes.int, arg=('lidx0', 32), src=()),)*4),
+      UOp(Ops.VECTORIZE, dtypes.int.vec(4), arg=None, src=(UOp(Ops.SPECIAL, dtypes.int, arg='lidx0', src=(UOp.const(dtypes.int, 32),)),)*4),
       UOp(Ops.VCONST, dtypes.int.vec(4), arg=(0, 256, 512, 768), src=())))
     rhs = UOp.const(dtypes.int.vec(4), 2)
     unopt = lhs<rhs

test/unit/test_helpers.py

@@ -93,7 +93,7 @@ class TestMergeDicts(unittest.TestCase):
     assert merge_dicts([a, b]) == {"a": 1, "b": 2, "c": 3}
     assert merge_dicts([a, c]) == a
     assert merge_dicts([a, b, c]) == {"a": 1, "b": 2, "c": 3}
-    with self.assertRaises(AssertionError):
+    with self.assertRaises(RuntimeError):
       merge_dicts([a, d])
 
 class TestStripParens(unittest.TestCase):

test/unit/test_linearizer_rewrite.py

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

test/unit/test_rewrite_bottom_up_gate.py

@@ -0,0 +1,28 @@
+import unittest
+from tinygrad.uop.ops import PatternMatcher, UOp, graph_rewrite, Ops, UPat, BottomUpGate
+
+def assert_not_reached(): assert False, "This function should not be reached"
+def gate(): raise BottomUpGate
+
+class TestBottomUpGate(unittest.TestCase):
+  def test_basic_bottom_up_gate(self):
+    """Test that BottomUpGate stops bottom-up"""
+    pm = PatternMatcher([
+      (UPat(Ops.ADD), gate),
+      (UPat(Ops.MUL), assert_not_reached)
+    ])
+
+    a,b,c = UOp.variable("a",0,10), UOp.variable("b",0,10), UOp.variable("c",0,10)
+    graph_rewrite((a*a)+(b*c), pm, bottom_up=True)
+
+  def test_bottom_up_gate_with_rewriting(self):
+    pm = PatternMatcher([
+      (UPat.var("a")+UPat.var("a"), lambda a: 2*a),
+      (UPat(Ops.MUL), gate),
+      (UPat(Ops.CONST), assert_not_reached)
+    ])
+    a = UOp.variable("a",0,10)
+    graph_rewrite(a+a, pm, bottom_up=True)
+
+if __name__ == "__main__":
+  unittest.main()

test/unit/test_shapetracker.py

@@ -839,25 +839,22 @@ class TestRender(unittest.TestCase):
     self.assertEqual(idx.render(), "((ridx0*3)+ridx1)")
     self.assertEqual(valid.render(), "(ridx0<2)")
 
-class TestVariableReshape(unittest.TestCase):
-  def test_reshape(self):
-    st = ShapeTracker.from_shape((3,))
-    st = st.reshape((Variable("i", 1, 10),))
+class TestVariableShrink(unittest.TestCase):
+  def test_shrink(self):
+    st = ShapeTracker.from_shape((10,))
+    st = st.shrink(((0, Variable("i", 1, 10)),))
     assert len(st.views) == 1
 
-  def test_reshape_stride_0(self):
-    st = ShapeTracker.from_shape((3,), (0,))
-    st = st.reshape((Variable("i", 1, 10).bind(3),))
-    assert len(st.views) == 1, f"multiview {st}"
-
-  def test_reshape_bound(self):
-    st = ShapeTracker.from_shape((3,))
-    st = st.reshape((Variable("i", 1, 10).bind(3),))
+  def test_shrink_bound(self):
+    st = ShapeTracker.from_shape((10,))
+    st = st.shrink(((0, Variable("i", 1, 10).bind(3)),))
     assert len(st.views) == 1
 
-  def test_add(self):
-    st1 = ShapeTracker.from_shape((3,))
-    st2 = ShapeTracker.from_shape((Variable("i", 1, 10),))
+class TestVariableMerge(unittest.TestCase):
+  def test_add_reshape(self):
+    vi = Variable("i", 1, 10)
+    st1 = ShapeTracker.from_shape((vi,))
+    st2 = ShapeTracker.from_shape((1, vi,))
     st = st1+st2
     assert len(st.views) == 1
 
@@ -867,15 +864,17 @@ class TestVariableReshape(unittest.TestCase):
     st = st1+st2
     assert len(st.views) == 1, f"multiview {st}"
 
-  def test_add_bound(self):
-    st1 = ShapeTracker.from_shape((3,))
-    st2 = ShapeTracker.from_shape((Variable("i", 1, 10).bind(3),))
+  def test_add_reshape_bound(self):
+    vi = Variable("i", 1, 10).bind(3)
+    st1 = ShapeTracker.from_shape((vi,))
+    st2 = ShapeTracker.from_shape((1, vi,))
     st = st1+st2
     assert len(st.views) == 1
 
   def test_simplify(self):
-    st1 = ShapeTracker.from_shape((3,))
-    st2 = ShapeTracker.from_shape((Variable("i", 1, 10).bind(3),))
+    vi = Variable("i", 1, 10).bind(3)
+    st1 = ShapeTracker.from_shape((vi,))
+    st2 = ShapeTracker.from_shape((1, vi,))
     st = ShapeTracker((st1.views[0], st2.views[0]))
     st = st.simplify()
     assert len(st.views) == 1

test/unit/test_shapetracker_math.py

@@ -87,20 +87,6 @@ class TestShapeTrackerAdd(unittest.TestCase):
     assert not (st_equal(st1, st2))
 
 class TestShapeTrackerAddVariable(unittest.TestCase):
-  def test_self_add(self):
-    j = Variable("j", 0, 20).bind(10)
-    a = ShapeTracker.from_shape((10,10))
-    x = a.reshape((10, j))
-    out = x + x
-    assert out == x
-
-  def test_self_add_reshape(self):
-    j = Variable("j", 0, 20).bind(10)
-    a = ShapeTracker.from_shape((10,10))
-    x = a.reshape((10, j))
-    out = x.reshape((5, 2, j)) + x
-    assert out == x
-
   def test_merge_symbolic_views(self):
     var_i = Variable('i', 1, 10)
     var_j = Variable('i', 1, 10)

test/unit/test_simplify_valid_idx.py

@@ -4,6 +4,7 @@ from tinygrad.codegen import full_rewrite_to_sink
 from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Ops
 from tinygrad.uop.symbolic import simplify_valid
+from tinygrad.helpers import Context
 
 def get_gated_load_uop(valid:UOp, idx:UOp):
   return UOp(Ops.LOAD, dtypes.float, (
@@ -17,9 +18,9 @@ def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UO
     UOp(Ops.VECTORIZE, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
   ))
 
-def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int, (), (expr, nmax))
+def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int32, (UOp.const(dtypes.int, nmax),), expr)
 def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
-def Range(n, nmax): return UOp.range(dtypes.int, nmax, n)
+def Range(n, nmax): return UOp.range(nmax, n)
 
 class TestHelpers(unittest.TestCase):
   def test_is_increasing(self):
@@ -45,7 +46,8 @@ class TestHelpers(unittest.TestCase):
 
 class TestValidIdxSimplification(unittest.TestCase):
   def check(self, load, sidx, svalid):
-    load = full_rewrite_to_sink(load.sink()).src[0]
+    with Context(NOOPT=1):
+      load = full_rewrite_to_sink(load.sink()).src[0]
     idx, valid = load.src[0].src[1], load.src[0].src[2]
     self.assertEqual(idx.render(simplify=False), sidx)
     self.assertEqual(valid.render(simplify=False), svalid)
@@ -195,9 +197,21 @@ class TestValidIdxSimplification(unittest.TestCase):
       "1",
       "((((ridx0+ridx1)<1)!=True)&(((ridx2+ridx3)<1)!=True))")
 
+  def test_valid_with_non_const_rhs(self):
+    ridx0 = Range(0, 2**16)
+    ridx1 = Range(1, 4)
+    ridx2 = Range(2, 4)
+    valid = (ridx0<(ridx1*4 + ridx2))&(ridx0<-1).ne(True)
+    idx = ridx0%1024
+    load = get_gated_load_uop(valid, idx)
+    self.check(load,
+      "ridx0",
+      "(ridx0<((ridx1*4)+ridx2))")
+
 class TestImageSimplification(unittest.TestCase):
   def check(self, load, svalid, sidx0, sidx1):
-    load = full_rewrite_to_sink(load.sink()).src[0]
+    with Context(NOOPT=1):
+      load = full_rewrite_to_sink(load.sink()).src[0]
     idx = load.src[0].src[1]
     self.assertEqual(idx.op, Ops.VECTORIZE)
     self.assertEqual(len(idx.src), 2)

test/unit/test_symbolic_shapetracker.py

@@ -48,11 +48,11 @@ class TestSymbolic(unittest.TestCase):
     i = Variable("i", 1, 5).bind(3)
     j = Variable("j", 1, 5).bind(3)
     k = Variable("k", 1, 5).bind(3)
-    t = Tensor.rand(3, 4).reshape(i, 4).cat(Tensor.rand(3, 4).reshape(j, 4), dim=0).cat(Tensor.rand(3, 4).reshape(k, 4), dim=0)
+    t = Tensor.rand(5, 4)[:i].cat(Tensor.rand(5, 4)[:j], dim=0).cat(Tensor.rand(5, 4)[:k], dim=0)
     st = t.uop.st
     self.assert_tuple_equal(st.shape, (i+j+k, 4))
     assert st.real_strides() == (4, 1)
-    t = Tensor.rand(3, 3).reshape(i, 3).cat(Tensor.rand(3, 3).reshape(i, 3), dim=0).cat(Tensor.rand(3, 3), dim=0)
+    t = Tensor.rand(5, 3)[:i].cat(Tensor.rand(5, 3)[:i], dim=0).cat(Tensor.rand(3, 3), dim=0)
     st = t.uop.st
     self.assert_tuple_equal(st.shape, (2*i+3, 3))
     assert st.real_strides() == (3, 1)
@@ -61,7 +61,7 @@ class TestSymbolic(unittest.TestCase):
     i = Variable("i", 1, 5).bind(4)
     j = Variable("j", 1, 5).bind(4)
     k = Variable("k", 1, 5).bind(4)
-    t = Tensor.rand(3, 4).reshape(3, i).cat(Tensor.rand(3, 4).reshape(3, j), dim=1).cat(Tensor.rand(3, 4).reshape(3, k), dim=1)
+    t = Tensor.rand(3, 5)[:, :i].cat(Tensor.rand(3, 5)[:, :j], dim=1).cat(Tensor.rand(3, 5)[:, :k], dim=1)
     st = t.uop.st
     self.assert_tuple_equal(st.shape, (3, i+j+k))
     self.assert_tuple_equal(st.real_strides(), (i+j+k, 1))
@@ -109,60 +109,44 @@ class TestShapeTrackerUnbind(unittest.TestCase):
     assert unbound_view == View.create(shape=(v, 4))
     assert var_val == {v: 3}
 
-  def test_reshape_unbind(self):
-    v = Variable("v", 1, 100)
-    bv = Variable("v", 1, 100).bind(3)
-    t = Tensor.rand(3, 4).reshape(bv, 4)
-    unbound_st, var_val = t.uop.st.unbind()
-    assert unbound_st == ShapeTracker((View.create(shape=(v, 4)),))
-    assert var_val == {v: 3}
-
   def test_shrink_unbind(self):
     v = Variable("v", 1, 100)
     bv = Variable("v", 1, 100).bind(2)
+    t = Tensor.rand(3, 4).shrink(((0,bv),(0,4)))
+    unbound_st, var_val = t.uop.st.unbind()
+    assert unbound_st == ShapeTracker((View.create(shape=(v, 4)),))
+    assert var_val == {v: 2}
     t = Tensor.rand(3, 4).shrink(((bv, bv+1), (0, 4)))
     unbound_st, var_val = t.uop.st.unbind()
     assert unbound_st == ShapeTracker((View.create(shape=(1, 4), offset=4*v),))
     assert var_val == {v: 2}
 
-class TestSymbolicReshapeFromContiguous(unittest.TestCase):
-  def test_reshape_into_symbols_simple(self):
+class TestSymbolicReshape(unittest.TestCase):
+  def test_reshape(self):
+    a = Tensor.rand(5, 4)
+    b = Tensor.rand(5, 6)
     for i in range(1, 6):
       vi = Variable("i", 1, 5).bind(i)
-      t = Tensor.rand(i, 4).reshape(vi, 4)
-      assert t.shape == (vi, 4)
-      t = Tensor.rand(i, 6).reshape(vi, 2, 3)
-      assert t.shape == (vi, 2, 3)
-
-  def test_reshape_symbols_reshape_ints(self):
-    for i in range(1, 6):
-      vi = Variable("i", 1, 5).bind(i)
-      t = Tensor.rand(i, 4).reshape(vi, 4)
-      assert t.shape == (vi, 4)
-      t = t.reshape(i, 4)
-      assert t.shape == (i, 4)
-
-  @unittest.skip("works now")
-  def test_reshape_into_symbols_bad_shape(self):
-    vi = Variable("i", 1, 10).bind(4)
-    # TODO: this never actually worked, it relied on lazy
-    #with self.assertRaises(ValueError):
-    #  Tensor.rand(4, 6).reshape(vi, 6).reshape(1, 77) # reshape to a different size new shape through symbolic shape
-    with self.assertRaises(AssertionError):
-      Tensor.rand(3, 4).reshape(3, (vi+1)) # reshape into non-Variable Node
+      ret = a[:vi]
+      ret = ret.reshape((vi, 4))
+      assert ret.shape == (vi, 4)
+      ret = b[:vi]
+      ret = ret.reshape((vi, 2, 3))
+      assert ret.shape == (vi, 2, 3)
 
   def test_two_symbol_reshape(self):
+    t = Tensor.rand(5, 5)
     for i in range(1, 6):
       for j in range(1, 6):
         vi = Variable("i", 1, 5).bind(i)
         vj = Variable("j", 1, 5).bind(j)
-        t = Tensor.rand(i, j).reshape(vi, vj)
-        assert t.shape == (vi, vj)
-        # NOTE: this is currently not allowed
-        # t = t.reshape(1, vi*vj)
-        # assert t.shape == (1, vi*vj)
-        t = t.reshape(vj, vi)
-        assert t.shape == (vj, vi)
+        ret = t[:vi, :vj]
+        ret = ret.reshape(vj, vi)
+        assert ret.shape == (vj, vi)
+        ret = ret.reshape(vi, vj)
+        assert ret.shape == (vi, vj)
+        ret = ret.reshape(1, vi*vj)
+        assert ret.shape == (1, vi*vj)
 
   def test_symbolic_mask(self):
     # taken from gpt2 single kvcache
@@ -175,41 +159,6 @@ class TestSymbolicReshapeFromContiguous(unittest.TestCase):
     new_shape = (2, (Variable('start_pos', 1, 128)+1), 16, 64)
     assert view.reshape(new_shape) is None
 
-class TestSymbolicReshapeFromNonContiguous(unittest.TestCase):
-  def test_reshape_from_const(self):
-    vi = Variable("i", 1, 5).bind(4)
-    t = Tensor.ones(3, 4).reshape(3, vi)
-    assert t.shape == (3, vi)
-    assert not t.uop.st.contiguous
-    assert len(t.uop.st.views) == 1
-
-  def test_reshape_not_allowed(self):
-    vi = Variable("i", 1, 5).bind(4)
-    with self.assertRaises(ValueError):
-      # different shape length  # TODO: cases where contractions matched might be fine
-      Tensor.ones(3, 4, 1).reshape(3, vi)
-    with self.assertRaises(ValueError):
-      # size matched, but dimensions do not match
-      Tensor.ones(4, 3).reshape(3, vi)
-
-  def test_reshape_from_padded(self):
-    vi = Variable("i", 1, 5).bind(4)
-    t = Tensor.ones(3, 4).contiguous().expand(2, 3, 4).pad(((1, 1), None, None)).shrink((None, None, (1, 3)))
-    st = t.uop.st
-    assert len(st.views) == 1
-    view = st.views[0]
-    assert view.shape == (4, 3, 2)
-    t = t.reshape(vi, 3, 2)
-    st2 = t.uop.st
-    assert len(st2.views) == 1
-    view2 = st2.views[0]
-    # check only shape changed. strides, offset, mask, contiguous remained the same
-    assert view2.shape == (vi, 3, 2)
-    assert view.strides == view2.strides == (0, 4, 1)
-    assert view.offset == view2.offset == 1
-    assert view.mask == view2.mask == ((1, 3), (0, 3), (0, 2))
-    assert not view.contiguous and not view2.contiguous
-
 class TestSymbolicExpand(unittest.TestCase):
   def test_expand_into_symbols(self):
     vi = Variable("i", 1, 5).bind(3)
@@ -220,11 +169,12 @@ class TestSymbolicExpand(unittest.TestCase):
     assert a.shape == (3, vi, vj)
 
   def test_plus_expands_constant(self):
+    a = Tensor.rand(3, 5)
     for i in range(1, 6):
       vi = Variable("i", 1, 5).bind(i)
-      a = Tensor.rand(3, i).reshape(3, vi)
-      a = a + 1
-      self.assertTupleEqual(a.shape, (3, vi))
+      ret = a[:, :vi]
+      ret = ret + 1
+      self.assertTupleEqual(ret.shape, (3, vi))
 
   def test_pad_then_expand_into_symbols(self):
     vi = Variable("i", 1, 10).bind(3)
@@ -234,6 +184,11 @@ class TestSymbolicExpand(unittest.TestCase):
     self.assertEqual(a.reshape(vi*25).shape, (vi*25,))
 
 class TestSymbolicShrink(unittest.TestCase):
+  def test_shrink_symbols_simple(self):
+    vi = Variable("i", 1, 5)
+    t = Tensor.rand(5, 5).shrink(((0, 5),(0,vi)))
+    assert t.shape == (5, vi)
+
   def test_shrink_symbols(self):
     vi = Variable("i", 1, 5)
     t = Tensor.rand(3, 5).shrink(((0, 2), (vi, vi+1)))
@@ -242,10 +197,10 @@ class TestSymbolicShrink(unittest.TestCase):
 class TestSymbolicPad(unittest.TestCase):
   def test_pad(self):
     v = Variable("v", 1, 100).bind(5)
-    t = Tensor.ones(5).reshape(v).pad(((4, 0),)).reshape(9)
-    assert t.shape == (9,)
-    st = t.uop.st
-    print(st)
+    t = Tensor.ones(100)[:v].pad(((4, 0),))
+    t = t.reshape(9)
+    assert t.tolist() == [0,0,0,0,1,1,1,1,1]
+
 
 if __name__ == '__main__':
   unittest.main()

test/unit/test_tensor_uop_representation.py

@@ -97,7 +97,7 @@ class TestTensorUopRepresentation(unittest.TestCase):
     is_pattern(a, UPat(Ops.RESHAPE, src=(UPat(Ops.BUFFER),)))
     vi = UOp.variable("i", 1, 3).bind(1)
     a = Tensor.empty(3, vi)
-    is_pattern(a, UPat(Ops.RESHAPE, src=(UPat(Ops.BUFFER),)))
+    is_pattern(a, UPat(Ops.RESHAPE, src=(UPat(Ops.SHRINK, src=(UPat(Ops.BUFFER),))),))
     self.assertEqual(a.uop.base.buffer.size, 9)
 
 if __name__ == '__main__':

test/unit/test_uop_symbolic.py

@@ -208,6 +208,16 @@ class TestSymbolic(unittest.TestCase):
     self.assertEqual((Variable("x", -10, 0)%Variable("y", -10, -1))._min_max, (-9, 0))
     self.assertEqual((Variable("x", -10, 0)%Variable("y", 1, 10))._min_max, (-9, 0))
 
+  def test_range_div_its_symbolic_bound(self):
+    a = Variable("a", 1, 10)
+    ridx0 = UOp.range(a+2, 0)
+    self.helper_test_variable(ridx0//(a+2), 0, 0, "0")
+
+  def test_range_mod_its_symbolic_bound(self):
+    a = Variable("a", 1, 10)
+    ridx = UOp.range(a+2, 0)
+    self.helper_test_variable(ridx%(a+2), 0, 11, "ridx0")
+
   def test_div_min_max(self):
     self.helper_test_variable(Variable("a", 2, 7) // 2, 1, 3, "(a//2)")
     self.helper_test_variable(Variable("a", 0, 6) // 2, 0, 3, "(a//2)")
@@ -722,6 +732,12 @@ class TestSymbolic(unittest.TestCase):
     a = Variable("a", 1, 10, dtypes.int)
     self.helper_test_variable(a.trunc(), 1, 10, "a", test_z3=False)
 
+  def test_do_math_in_int32(self):
+    a = Variable("a", 1, 10)
+    b = Variable("b", 1, 10)
+    self.helper_test_variable(a.cast(dtypes.long)+b.cast(dtypes.long), 2, 20, "(long)((a+b))")
+    self.helper_test_variable(a.cast(dtypes.long)*b.cast(dtypes.long), 1, 100, "(long)((a*b))")
+
 class TestSymbolicNumeric(unittest.TestCase):
   def helper_test_numeric(self, f):
     MIN, MAX = 0, 10

test/unit/test_uop_vmin_vmax.py

@@ -58,9 +58,9 @@ class TestVminVmaxProperties(unittest.TestCase):
     self.assertEqual(uop.vmax, 8)
 
   def test_vmin_vmax_variable_inside_special(self):
-    uop = UOp(Ops.SPECIAL, dtypes.int, arg=('gidx0', UOp(Ops.DEFINE_VAR, dtypes.int, arg=('i', 1, 10))))
+    uop = UOp(Ops.SPECIAL, dtypes.int, arg='gidx0', src=(UOp(Ops.DEFINE_VAR, dtypes.int, arg=('i', 1, 10)),))
     self.assertEqual(uop.vmin, 0)
-    self.assertEqual(uop.vmax, 10)
+    self.assertEqual(uop.vmax, 9)
 
   def test_vmin_vmax_multiplication_0_inf(self):
     # vmin and vmax for multiplication with a variable
@@ -251,6 +251,15 @@ class TestVminVmaxVConst(unittest.TestCase):
     self.assertIs(uop.vmin, False)
     self.assertIs(uop.vmax, True)
 
+  def test_vmin_vmax_vector_with_gep(self):
+    # vmin and vmax for a vector constant of bool values
+    d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 1)
+    idx = UOp.const(dtypes.int, 0)
+    val = UOp(Ops.LOAD, dtypes.int.vec(2), (d1.index(idx),))
+    uop = (val // 32).gep(0)
+    self.assertEqual(uop.vmin, -67108864)
+    self.assertEqual(uop.vmax, 67108863)
+
 class TestConstFactor(unittest.TestCase):
   def test_const_factor_constant(self):
     # const_factor for a constant

test/unit/test_viz.py

@@ -5,7 +5,7 @@ from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatch
 from tinygrad.uop.ops import graph_rewrite, track_rewrites, TRACK_MATCH_STATS
 from tinygrad.uop.symbolic import sym
 from tinygrad.dtype import dtypes
-from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context
+from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context, cpu_events, profile_marker
 from tinygrad.device import Buffer
 
 @track_rewrites(name=True)
@@ -16,10 +16,9 @@ def exec_rewrite(sink:UOp, pm_lst:list[PatternMatcher], names:None|list[str]=Non
 
 # real VIZ=1 pickles these tracked values
 from tinygrad.uop.ops import tracked_keys, tracked_ctxs, uop_fields, active_rewrites, _name_cnt
-from tinygrad.viz import serve
-serve.contexts = (tracked_keys, tracked_ctxs, uop_fields)
+traces = [(tracked_keys, tracked_ctxs, uop_fields)]
 from tinygrad.viz.serve import get_metadata, uop_to_json, get_details
-def get_viz_list(): return get_metadata(tracked_keys, tracked_ctxs)
+def get_viz_list(): return get_metadata(traces)
 
 class BaseTestViz(unittest.TestCase):
   def setUp(self):
@@ -142,6 +141,8 @@ class TestViz(BaseTestViz):
     z = UOp.const(dtypes.int, 0)
     alu = a*z
     exec_rewrite(alu, [sym])
+    lst = get_viz_list()
+    self.assertEqual(len(lst), 1)
     graphs = [x["graph"] for x in get_details(tracked_ctxs[0][0])]
     # embed const in the parent node when possible
     self.assertEqual(list(graphs[0]), [id(a), id(alu)])
@@ -265,27 +266,27 @@ def option(i:int) -> int|None: return None if i == 0 else i-1
 def load_profile(lst:list[ProfileEvent]) -> dict:
   ret = get_profile(lst)
   u = TinyUnpacker(ret)
-  dur, global_peak, index_len, layout_len = u("<IQII")
-  strings, dtypes = json.loads(ret[u.offset:u.offset+index_len]).values()
+  total_dur, global_peak, index_len, layout_len = u("<IQII")
+  strings, dtypes, markers = json.loads(ret[u.offset:u.offset+index_len]).values()
   u.offset += index_len
   layout:dict[str, dict] = {}
   for _ in range(layout_len):
     klen = u("<B")[0]
     k = ret[u.offset:u.offset+klen].decode()
     u.offset += klen
-    layout[k] = v = {"shapes":[]}
+    layout[k] = v = {"events":[]}
     event_type, event_count = u("<BI")
     if event_type == 0:
       for _ in range(event_count):
-        name, ref, st, dur, cat, _ = u("<IIIfBI")
-        v["shapes"].append({"name":strings[name], "ref":option(ref), "st":st, "dur":dur, "cat":option(cat)})
+        name, ref, st, dur, _ = u("<IIIfI")
+        v["events"].append({"name":strings[name], "ref":option(ref), "st":st, "dur":dur})
     else:
       v["peak"] = u("<Q")[0]
       for _ in range(event_count):
         alloc, ts, key = u("<BII")
-        if alloc: v["shapes"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
-        else: v["shapes"].append({"event":"free", "ts":ts, "key":key})
-  return {"dur":dur, "peak":global_peak, "layout":layout}
+        if alloc: v["events"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
+        else: v["events"].append({"event":"free", "ts":ts, "key":key})
+  return {"dur":total_dur, "peak":global_peak, "layout":layout, "markers":markers}
 
 class TestVizProfiler(unittest.TestCase):
   def test_perfetto_node(self):
@@ -294,7 +295,7 @@ class TestVizProfiler(unittest.TestCase):
 
     j = load_profile(prof)
 
-    dev_events = j['layout']['NV']['shapes']
+    dev_events = j['layout']['NV']['events']
     self.assertEqual(len(dev_events), 1)
     event = dev_events[0]
     self.assertEqual(event['name'], 'E_2')
@@ -310,14 +311,16 @@ class TestVizProfiler(unittest.TestCase):
 
     j = load_profile(prof)
 
-    event = j['layout']['NV']['shapes'][0]
+    event = j['layout']['NV']['events'][0]
     self.assertEqual(event['name'], 'COPYxx')
     self.assertEqual(event['st'], 0)   # first event
     self.assertEqual(event['dur'], 10)
 
-    event2 = j['layout']['NV:2']['shapes'][0]
+    event2 = j['layout']['NV:2']['events'][0]
     self.assertEqual(event2['st'], 20) # second event, diff clock
 
+    self.assertEqual(j["dur"], (event2["st"]+event2["dur"])-event["st"])
+
   def test_perfetto_graph(self):
     prof = [ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100)),
             ProfileDeviceEvent(device='NV:1', comp_tdiff=decimal.Decimal(-500), copy_tdiff=decimal.Decimal(-50)),
@@ -333,18 +336,18 @@ class TestVizProfiler(unittest.TestCase):
     self.assertEqual(tracks[1], 'NV')
     self.assertEqual(tracks[2], 'NV:1')
 
-    nv_events = j['layout']['NV']['shapes']
+    nv_events = j['layout']['NV']['events']
     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']['events']
     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']['events']
     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'])
 
@@ -364,6 +367,21 @@ class TestVizProfiler(unittest.TestCase):
     with self.assertRaises(struct.error):
       get_profile(prof)
 
+  def test_python_marker(self):
+    with Context(PROFILE=1):
+      a = Tensor.empty(1, device="NULL")
+      b = Tensor.empty(1, device="NULL")
+      (a+b).realize()
+      profile_marker("test 1")
+      (a*b).realize()
+      profile_marker("test 2")
+    profile_ret = load_profile(cpu_events)
+    markers = profile_ret["markers"]
+    kernels = profile_ret["layout"]["NULL"]["events"]
+    self.assertEqual(len(markers), 2)
+    assert kernels[0]["st"] <= markers[0]["ts"] <= kernels[1]["st"]
+    assert markers[1]["ts"] >= kernels[1]["st"]+kernels[1]["dur"]
+
 def _alloc(b:int):
   a = Tensor.empty(b, device="NULL", dtype=dtypes.char)
   a.uop.buffer.allocate()
@@ -376,7 +394,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{a.device} Memory"]
     self.assertEqual(ret["peak"], 2)
-    self.assertEqual(len(ret["shapes"]), 2)
+    self.assertEqual(len(ret["events"]), 2)
 
   def test_del_once(self):
     a = _alloc(1)
@@ -385,7 +403,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{b.device} Memory"]
     self.assertEqual(ret["peak"], 1)
-    self.assertEqual(len(ret["shapes"]), 3)
+    self.assertEqual(len(ret["events"]), 3)
 
   def test_alloc_free(self):
     a = _alloc(1)
@@ -395,7 +413,7 @@ class TestVizMemoryLayout(BaseTestViz):
     profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{c.device} Memory"]
     self.assertEqual(ret["peak"], 2)
-    self.assertEqual(len(ret["shapes"]), 4)
+    self.assertEqual(len(ret["events"]), 4)
 
 if __name__ == "__main__":
   unittest.main()

test/unit/test_winograd.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 from tinygrad import Tensor, GlobalCounters, dtypes, Context, nn
-from tinygrad.helpers import CI, Profiling, WINO, getenv
+from tinygrad.helpers import CI, Profiling, WINO
 
 class TestWinogradClose(unittest.TestCase):
   def test_close(self):
@@ -38,7 +38,6 @@ class TestWinograd(unittest.TestCase):
     assert GlobalCounters.kernel_count == 4
     out.numpy()
 
-  @unittest.skipIf(getenv("PTX"), "winograd uses too much in PTX")
   def test_counters(self):
     IC, OC, X, Y = 4,4,9,9
     #OC, IC, X, Y = 512, 256, 8, 8

tinygrad/apps/llm.py

@@ -53,17 +53,15 @@ class SimpleTokenizer:
     try: return [ self._normal_tokens[p] for p in parts ]
     except KeyError: raise RuntimeError("token not found")
 
-def apply_rope(x:Tensor, start_pos:int|UOp, base:int=10000):
+def apply_rope(x:Tensor, start_pos:int|UOp, base:float = 10000.0) -> Tensor:
   B, H, T, Hd = x.shape
-  # NOTE: this is usually in a RoPE cache, but tinygrad JIT should prune it outside the kernel
-  # TODO: make it do that
-  freq = base ** (-Tensor.arange(0, 1, 2/Hd, dtype='float32'))
-  angles = Tensor.arange(start_pos, start_pos+T, dtype='float32')[None, None, :, None] * freq
-  cos, sin = angles.cos(), angles.sin()
-  x = x.reshape(B, H, T, Hd // 2, 2)    # split into pairs
-  y1 = x[..., 0] * cos - x[..., 1] * sin
-  y2 = x[..., 0] * sin + x[..., 1] * cos
-  return Tensor.stack(y1, y2, dim=-1).reshape(B, H, T, Hd)
+  assert (Hd & 1) == 0, "RoPE requires an even head dimension"
+  half = Hd // 2
+  angles = (Tensor.arange(T, dtype="float32") + start_pos)[:, None] * (base ** (-(Tensor.arange(half, dtype="float32") / half)))[None, :]
+  cos, sin = angles.cos().reshape(1, 1, T, half).cast(x.dtype), angles.sin().reshape(1, 1, T, half).cast(x.dtype)
+  x_pairs = x.reshape(B, H, T, half, 2)
+  return Tensor.stack(x_pairs[..., 0] * cos - x_pairs[..., 1] * sin,
+                      x_pairs[..., 0] * sin + x_pairs[..., 1] * cos, dim=-1).reshape(B, H, T, Hd)
 
 class TransformerBlock:
   def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_kv_heads:int, norm_eps:float, max_context:int=0):

tinygrad/codegen/__init__.py

@@ -9,18 +9,17 @@ from tinygrad.renderer import Renderer
 # import all pattern matchers here
 from tinygrad.codegen.lowerer import pm_lowerer, get_index
 from tinygrad.codegen.quantize import pm_quant
-from tinygrad.codegen.gpudims import pm_add_gpudims, pm_tensor_cores, pm_group_for_reduce, pm_fix_locals, pm_bufferize_loop
+from tinygrad.codegen.gpudims import pm_add_gpudims
 from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing
 from tinygrad.uop.decompositions import get_late_rewrite_patterns
 from tinygrad.codegen.late.expander import migrate_indexing, expander, pm_pre_expander
 from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize, pm_reduce, \
   ReduceContext, correct_load_store, pm_render
 from tinygrad.codegen.late.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
-from tinygrad.codegen.opt import pm_get_optimization, pm_do_optimize, pm_postrange_opt
+from tinygrad.codegen.opt.kernel import pm_get_optimization, pm_do_optimize
 from tinygrad.codegen.opt.swizzler import view_left, view_right, fix_kernel_ops
 from tinygrad.codegen.opt.postrange import pm_postrange_opt
 from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen
-from tinygrad.codegen.opt.postrange import pm_flatten_range
 
 @dataclass
 class RewriteStep:
@@ -58,34 +57,23 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
   ret.extend(rewrites_for_views)
 
   # this is kernel.py
-  if not _RANGEIFY: ret.append(RewriteStep(pm_get_optimization, ctx=lambda _: opts, name="get optimization"))
-
+  if _POSTOPT <= 1 and not _RANGEIFY: ret.append(RewriteStep(pm_get_optimization, ctx=lambda _: opts, name="get optimization"))
   if not _POSTOPT and not _RANGEIFY: ret.append(RewriteStep(pm_do_optimize, ctx=lambda _: opts, name="optimize ast"))
 
   if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
   ret.append(RewriteStep(pm_lowerer, get_index, name="lowerer", bottom_up=True))
-  
-  # add tensor cores
-  if _RANGEIFY:
-    ret.append(RewriteStep(pm_bufferize_loop, name="bufferize loop"))
-    ret.append(RewriteStep(pm_tensor_cores, lambda _: ({}, opts), name="tensor cores", bottom_up=True))
 
   if _POSTOPT or _RANGEIFY: ret.append(RewriteStep(pm_postrange_opt, ctx=lambda _: opts, name="post optimize ast"))
 
   # ** expander (expand_rewrite) **
   ret.append(RewriteStep(sym+migrate_indexing, name="initial symbolic"))
 
-  ret.append(RewriteStep(pm_fix_locals, name="fix locals"))
-
-  # add locals
-  ret.append(RewriteStep(pm_flatten_range+pm_add_buffers_local+rangeify_codegen+pm_group_for_reduce, name="add local buffers"))
-
-  # add gpu dims (late). this also handles UNROLL range
-  ret.append(RewriteStep(pm_add_gpudims, lambda _: opts, name="add gpudims"))
-
   # expand
   ret.append(RewriteStep(sym+pm_pre_expander+expander, name="expander"))
 
+  # add locals
+  ret.append(RewriteStep(pm_add_buffers_local+rangeify_codegen, name="add local buffers"))
+
   # ** devectorizer (full_graph_rewrite) **
   # remove reduce
   ret.append(RewriteStep(pm_reduce+gep_pushing, lambda _: ReduceContext(), name="remove_reduce"))

tinygrad/codegen/gpudims.py

@@ -1,10 +1,9 @@
-import math, functools, operator
-from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, graph_rewrite
-from tinygrad.helpers import all_int, partition, flatten, prod, dedup, USE_TC, DEBUG
-from tinygrad.dtype import dtypes, AddrSpace
+import math
+from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
+from tinygrad.helpers import all_int, dedup
+from tinygrad.dtype import dtypes
 from tinygrad.shape.view import get_contraction
 from tinygrad.renderer import Renderer
-from tinygrad.codegen.opt.tc import TensorCore
 
 def _group_dims(dims:tuple[sint, ...], max_sizes:tuple[int, ...]):
   # TODO: symbolic shape
@@ -35,7 +34,7 @@ def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|No
   if max_sizes is not None and len(limited) > len(max_sizes): raise RuntimeError(f"cannot limit dim {dims=}, {max_sizes=}")
   # try to split up dims: (a,) -> (b, c)
   if limited == dims: limited = _split_dims(dims, max_sizes) if max_sizes is not None else dims
-  ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (), (f"{prefix}{i}", s)) for i,s in enumerate(limited)]
+  ret = raw_idxs = [UOp(Ops.SPECIAL, dtypes.int, (sint_to_uop(s),), (f"{prefix}{i}")) for i,s in enumerate(limited)]
   if len(limited) < len(dims):
     ret = []
     if (contraction:=get_contraction(dims, limited)) is None: raise AssertionError(f"get_contraction should not be None {dims=} {limited=}")
@@ -89,181 +88,7 @@ def add_gpudims(ctx:Renderer, s:UOp):
     except ValueError: continue
   return s.substitute(subs)
 
-def fix_reduce_unroll(x:UOp):
-  reduce_range, reduce_expand = partition(x.src[1:], lambda y: y.op is Ops.RANGE)
-  if len(reduce_expand) == 0: return None
-  reduce_expand = [x for x in reduce_expand if x.op is not Ops.CONST]
-  assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand}"
-  ret = x.src[0]
-  if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
-    ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis), tag=1)
-  # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
-  return x.replace(src=(ret,)+tuple(reduce_range))
-
-def fix_store_unroll(x:UOp):
-  store_expand, store_range = partition(x.src[2:], lambda y: y.op is Ops.UNROLL)
-  if len(store_expand) == 0: return None
-  return UOp(Ops.CONTRACT, dtypes.void, (x.replace(src=x.src[:2]+tuple(store_range)),), tuple(flatten(x.arg for x in store_expand)), tag=1)
-
-def fix_group_for_reduce(x:UOp):
-  reduce_gfr, reduce_r = partition(x.src[1:], lambda u: u.op is Ops.RANGE and u.arg[1] == AxisType.GROUP_REDUCE)
-  if len(reduce_gfr) == 0: return None
-
-  # NOTE: if there's other locals here, we need them in the buffer too
-  upstream_locals = [u for u in x.toposort() if u.op is Ops.RANGE and u.arg[1] == AxisType.LOCAL]
-
-  # do only the non grouped reduces early
-  ret = x.replace(src=(x.src[0],)+tuple(reduce_r))
-  reduce_loop = [x.replace(arg=(x.arg[0]+100, AxisType.REDUCE)) for x in reduce_gfr]
-  buf = ret.bufferize(*upstream_locals, *reduce_gfr, arg=AddrSpace.LOCAL).index(*upstream_locals, *reduce_loop)
-
-  # gate with an if on the store + do the final reduce
-  buf = UOp(Ops.IF, dtype=buf.dtype, src=(functools.reduce(operator.and_, [x.eq(0) for x in reduce_gfr]), buf))
-  return buf.reduce(*reduce_loop, arg=x.arg)
-
-pm_group_for_reduce = PatternMatcher([
-  # fix group for reduce
-  (UPat(Ops.REDUCE, name="x"), fix_group_for_reduce),
-])
-
 pm_add_gpudims = PatternMatcher([
   # add gpudims must be last
   (UPat(Ops.SINK, name="s"), add_gpudims),
-  # rewrite UPCAST/UNROLL range to something to be expanded
-  (UPat(Ops.RANGE, name="r"),
-   lambda r: UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s:=r.vmax+1), tuple(range(s))),), ((r.arg[0],s),)) \
-    if r.arg[1] in {AxisType.UNROLL, AxisType.UPCAST} else None),
-  # fix REDUCEs with UNROLLs
-  (UPat(Ops.REDUCE, name="x"), fix_reduce_unroll),
-  (UPat(Ops.STORE, name="x"), fix_store_unroll),
 ])
-
-def apply_tensor_cores(ctx:tuple[dict, Renderer], in0:UOp, in1:UOp, r_range:UOp, reduceop:UOp):
-  if not USE_TC: return None
-  # tensor cores have three ranges. X, Y, and REDUCE
-  in0_ranges = sorted([u for u in in0.ranges if u not in in1.ranges], key=lambda x: x.arg[0])
-  in1_ranges = sorted([u for u in in1.ranges if u not in in0.ranges], key=lambda x: x.arg[0])
-  #print(len(in0_ranges), len(in1_ranges))
-  if not len(in0_ranges) or not len(in1_ranges): return None
-  in0_range, in1_range = in0_ranges[0], in1_ranges[0]
-  if DEBUG >= 2: print('TC', in0_range.arg, in1_range.arg, r_range.arg)
-
-  # confirm the dtype and size is good
-  tc_opts: list[TensorCore] = []
-  for tc in ctx[1].tensor_cores:
-    if reduceop.dtype == tc.dtype_out and in0.dtype == tc.dtype_in and in1.dtype == tc.dtype_in:
-      if all(i <= j for i,j in zip(tc.dims, [in0_range.vmax+1, in1_range.vmax+1, r_range.vmax+1])):
-        tc_opts.append(tc)
-  if len(tc_opts) == 0: return None
-  tc = tc_opts[0]
-
-  # create the new ranges as speced by the tensor core
-  old_range = [in0_range, in1_range, r_range]
-  new_range = [r.replace(src=(r.src[0]//tc.dims[i],)) for i,r in enumerate(old_range)]
-  new_reduce_range = new_range[2]
-  tc_range = 9050 #+ r_range.arg[0]*100
-  red_ranges = []
-
-  ne: list[UOp] = []
-  for o in tc.opts:
-    lrange = UOp.range(dtypes.int, 2, tc_range, AxisType.UPCAST if o[0] == "u" else AxisType.LOCAL)
-    ne.append(lrange)
-    tc_range += 1
-    new_range[1-int(o[1])] = (2 * new_range[1-int(o[1])]) + lrange
-  for _, amt in tc.get_reduce_axes():
-    lrange = UOp.range(dtypes.int, amt, tc_range, AxisType.UNROLL)
-    ne.append(lrange)
-    red_ranges.append(lrange)
-    tc_range += 1
-    new_range[2] = (amt * new_range[2]) + lrange
-  tne = [x.replace(tag=1) for x in ne]
-
-  # replace ranges in other parts of the graph
-  for x,y in zip(old_range, new_range): ctx[0][x] = y
-
-  # apply the swizzled ranges to the srcs
-  srcs = [s.substitute(dict(zip(old_range, new_range))).substitute(dict(zip(ne, tne))) for s in (in0, in1)]
-  srcs = [x.substitute(dict(zip(tne, [ne[i] for i in p]))) for x,p in zip(srcs, tc.permutes_for_shape_str(tc.base_shape_str()))]
-
-  ned = dict(zip(tc.base_shape_str(), ne))
-  tc_reduce_axes = tuple([ned[f"r{i}"].arg[0] for i in range(len(tc.get_reduce_axes()))])
-  base_upcast_axes = tuple([(ned[s].arg[0], 2) for s in tc.base_upcast_axes()])
-  tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
-
-  # construct the op
-  # TODO: remove tc_upcast_axes from the arg
-  wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, ctx[1].device, tc.threads, tc_upcast_axes, tc_reduce_axes)
-  wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
-    UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0]),
-    UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1]),
-    UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg)
-  tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2])
-  ret = tc_uop.reduce(new_reduce_range, arg=Ops.ADD)
-  # confirm the UNROLLs aren't actually used, these need to be broadcast MUL
-  assert all(u not in red_ranges for u in ret.toposort()), "UNROLLs in TC"
-  return ret
-
-from tinygrad.codegen.opt.postrange import pm_flatten_range
-
-pm_tensor_cores = PatternMatcher([
-  ((UPat.var("in0")*UPat.var("in1")).reduce(UPat(Ops.RANGE, name="r_range"), name="reduceop", arg=Ops.ADD), apply_tensor_cores),
-
-  # replace range
-  #(UPat(Ops.RANGE, name="r"), lambda ctx,r: ctx[0].get(r, None)),
-  (UPat(Ops.SINK, name="s"), lambda ctx,s: graph_rewrite(s.substitute(ctx[0]), pm_flatten_range, name="flatten")),
-])
-
-def fix_bufferize(x:UOp):
-  if x.arg != AddrSpace.LOCAL: return None
-  locals_left = [r for r in x.ranges if r.arg[1] == AxisType.LOCAL]
-  if not len(locals_left): return None
-  acc = x.size
-  st = []
-  for l in locals_left:
-    st.append(l*acc)
-    acc *= l.vmax+1
-  return x.replace(src=(x.src[0],) + tuple(locals_left[::-1]) + x.src[1:]).index(sum(st))
-
-
-pm_double_index = PatternMatcher([
-  (UPat(Ops.INDEX, src=(UPat(Ops.INDEX, src=(UPat.var("b"), UPat.var("x"))), UPat.var("y"))), lambda b,x,y: b.index(x+y)),
-])
-
-pm_fix_locals = pm_double_index+PatternMatcher([
-  (UPat(Ops.BUFFERIZE, name="x"), fix_bufferize),
-])
-
-B = 8
-
-def loop_store(x:UOp):
-  r_maybe = [r for r in x.src[0].ranges if r.arg[0] == 2 and r.tag is None]
-  ur_maybe = [r for r in x.ranges if r.arg[0] < 0]
-  #print("store", len(r_maybe), len(ur_maybe))
-  if not len(r_maybe) or not len(ur_maybe): return None
-
-  r = r_maybe[0]
-  ur = ur_maybe[0]
-  rr = r.replace(src=(r.src[0]//B,), tag=1)
-  return x.substitute({r:rr*B+ur})
-
-def loop_bufferize(x:UOp):
-  if x.arg != AddrSpace.LOCAL: return None
-  r_maybe = [r for r in x.ranges if r.arg[0] == 2 and r.tag is None]
-  ur_maybe = [r for r in x.ranges if r.arg[0] < 0]
-
-  if len(ur_maybe):
-    ur = ur_maybe[0]
-  else:
-    if len(r_maybe) == 0: return None
-    r = r_maybe[0]
-    ur = UOp.range(dtypes.int, B, -2010)
-    rr = r.replace(src=(r.src[0]//B,), tag=1)
-    x = x.substitute({r:rr*B + ur})
-
-  ur1 = UOp.range(dtypes.int, B, ur.arg[0]+1)
-  return x.replace(src=(x.src[0],ur)+x.src[1:]).index(x.size*ur1)
-
-pm_bufferize_loop = pm_double_index+PatternMatcher([
-  (UPat(Ops.BUFFERIZE, name="x"), loop_bufferize),
-  (UPat(Ops.STORE, name="x"), loop_store),
-])

tinygrad/codegen/late/devectorizer.py

@@ -2,9 +2,9 @@ from typing import Any, cast
 import functools, operator, itertools
 from collections import defaultdict
 from dataclasses import dataclass
-from tinygrad.dtype import dtypes, ImageDType, PtrDType, DType, AddrSpace
+from tinygrad.dtype import dtypes, ImageDType, DType, AddrSpace
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, graph_rewrite, GroupOp, identity_element
-from tinygrad.uop.symbolic import split_uop, uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
+from tinygrad.uop.symbolic import uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
 from tinygrad.helpers import getenv, flatten, AMX, prod, partition
 from tinygrad.renderer import Renderer
 
@@ -19,13 +19,13 @@ def simplify_valid_load(buf:UOp, start_idx:UOp, valid:UOp) -> UOp|None:
 
   # can drop valid if idx is out of bound when valid is False
   drop_stmt = []
-  for stmt in split_uop(valid, Ops.AND):
+  for stmt in valid.split_uop(Ops.AND):
     try: X, is_upper_bound, c = parse_valid(stmt)
     except ValueError: return None
 
     # for X0 + X1 + ... >= 1, check if it's out of bound when Xi = 0 for all i
-    if not is_upper_bound and c == 1 and all(u.op in GroupOp.Irreducible and u.vmin == 0 for u in split_uop(X, Ops.ADD)):
-      testidx = functools.reduce(lambda nowidx,u: nowidx.substitute({u:u.const_like(0)}), split_uop(X, Ops.ADD), idx)
+    if not is_upper_bound and c == 1 and all(u.op in GroupOp.Irreducible and u.vmin == 0 for u in X.split_uop(Ops.ADD)):
+      testidx = functools.reduce(lambda nowidx,u: nowidx.substitute({u:u.const_like(0)}), X.split_uop(Ops.ADD), idx)
       testidx = testidx.simplify()
       if testidx.gep(0).vmax < 0 or testidx.gep(1).vmax < 0:
         drop_stmt.append(stmt)
@@ -42,7 +42,7 @@ def simplify_valid_load(buf:UOp, start_idx:UOp, valid:UOp) -> UOp|None:
           break
 
   if not drop_stmt and idx is start_idx: return None
-  new_valid = functools.reduce(operator.and_, ss) if (ss:=[s for s in split_uop(valid, Ops.AND) if s not in drop_stmt]) else None
+  new_valid = functools.reduce(operator.and_, ss) if (ss:=[s for s in valid.split_uop(Ops.AND) if s not in drop_stmt]) else None
   return buf.index(idx, new_valid)
 
 def delete_redundant_gates(store:UOp, buf:UOp, idx:UOp, val:UOp, store_gate:UOp, cast:UOp|None=None) -> UOp|None:
@@ -80,9 +80,6 @@ def expand_index(buf:UOp, vec:UOp, mask:UOp|None=None):
     if len(midx.src[i].src) == 3: root_src = (midx.src[i].src[2], root_src)
     offsets_rootsrc[root_src].setdefault(arg, []).append(i)
 
-  # the buf.dtype is always a pointer
-  ptrdtype = cast(PtrDType, buf.dtype)
-
   # then rewrite everything we can into groups
   ret = []
   idxs: list[int|None] = [None]*vec.dtype.count
@@ -92,7 +89,7 @@ def expand_index(buf:UOp, vec:UOp, mask:UOp|None=None):
     for grp in grouped_offsets:
       # get the index offset for this element. using [0] is okay, because they are the same
       lidx = midx.src[offsets[grp[0]][0]]
-      if len(grp) > 1: lidx = lidx.cast(ptrdtype.base.vec(len(grp)).ptr(size=ptrdtype.size, addrspace=ptrdtype.addrspace))
+      if len(grp) > 1: lidx = lidx.cast(buf.ptrdtype.base.vec(len(grp)).ptr(size=buf.ptrdtype.size, addrspace=buf.ptrdtype.addrspace))
       # set the idxs of the output
       for i,g in enumerate(grp):
         for oo in offsets[g]: idxs[oo] = global_offset+i
@@ -101,7 +98,7 @@ def expand_index(buf:UOp, vec:UOp, mask:UOp|None=None):
       global_offset += len(grp)
   assert None not in idxs, f"some idxs are missing {idxs}"
   # this base thing is for image, we want the CAT to be a normal pointer
-  post_cat = UOp(Ops.PTRCAT, ptrdtype.base.ptr(size=ptrdtype.size, addrspace=ptrdtype.addrspace).vec(vec.dtype.count), tuple(ret))
+  post_cat = UOp(Ops.PTRCAT, buf.ptrdtype.base.ptr(size=buf.ptrdtype.size, addrspace=buf.ptrdtype.addrspace).vec(vec.dtype.count), tuple(ret))
   return post_cat.gep(tuple(cast(list[int], idxs)))
 
 def cat_after_store(cat:UOp, data:UOp, sto:UOp):
@@ -154,7 +151,7 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
     must_divide = False
   elif buf.dtype.base != dtypes.float and buf.dtype.base != dtypes.half and not isinstance(buf.dtype, ImageDType):
     pass
-  elif cast(PtrDType, buf.dtype).addrspace == AddrSpace.REG:
+  elif buf.ptrdtype.addrspace == AddrSpace.REG:
     pass
   elif isinstance(buf.dtype, ImageDType):
     lengths = [4]
@@ -169,13 +166,12 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
   # split based on the fold lengths
   global_offset = 0
   ret = []
-  ptrdtype = cast(PtrDType, buf.dtype)
   while global_offset < sz:
     # with 1 at the end of the lengths list, this will always hit
     for fold_length in lengths:
       if global_offset+fold_length > sz: continue
       lidx = buf.index(idx.src[1] + global_offset, idx.src[2] if len(idx.src) > 2 else None)
-      if fold_length > 1: lidx = lidx.cast(ptrdtype.base.vec(fold_length).ptr(size=ptrdtype.size, addrspace=ptrdtype.addrspace))
+      if fold_length > 1: lidx = lidx.cast(buf.ptrdtype.base.vec(fold_length).ptr(size=buf.ptrdtype.size, addrspace=buf.ptrdtype.addrspace))
       if ls.op is Ops.STORE: ret.append(ls.replace(src=(lidx,ls.src[1].gep(tuple(range(global_offset, global_offset+fold_length))))+ls.src[2:]))
       else: ret.append(ls.replace(src=(lidx,)+ls.src[1:], dtype=ls.dtype.scalar().vec(fold_length)))
       global_offset += fold_length
@@ -233,8 +229,7 @@ def no_vectorized_alu(alu:UOp):
   return UOp(Ops.VECTORIZE, alu.dtype, alus)
 
 def no_vectorized_buf(buf:UOp):
-  dtype = cast(PtrDType, buf.dtype)
-  return buf.replace(dtype=dtype.base.scalar().ptr(dtype.size*dtype.count, dtype.addrspace)).cast(dtype)
+  return buf.replace(dtype=buf.ptrdtype.base.scalar().ptr(buf.ptrdtype.size*buf.ptrdtype.count, buf.ptrdtype.addrspace)).cast(buf.dtype)
 
 def no_vectorized_index(buf:UOp, cast:UOp, idx:UOp):
   cnt = cast.dtype.count

tinygrad/codegen/late/linearize.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 import heapq
 from collections import defaultdict
 from dataclasses import dataclass, replace
-from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp
+from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, BottomUpGate
 from tinygrad.helpers import dedup, all_same, flatten, BLOCK_REORDER
 
 # NOTE: any toposort should be valid here, unlike last time this isn't required, it's just for speed
@@ -76,12 +76,13 @@ class BlockContext:
   def from_sink(sink:UOp) -> BlockContext:
     # get children and all block contexts
     ctx = BlockContext({}, {}, {})
-    for u in sink.toposort():
+    for u in sink.toposort(gate=lambda u:u.op is not Ops.SPECIAL):
       this_block_ctx: list[UOp] = []
       ctx.child_count[u] = 0
 
       # get children and accumulate the last_ctx
       for s in u.src:
+        if s.op is Ops.SPECIAL: continue
         # NOTE: if a parent appears multiple times in the src, it counts multiple times as a child
         ctx.child_count[s] += 1
         this_block_ctx += ctx.last_ctx(s)
@@ -142,7 +143,7 @@ def make_block_bottom_up(ctx:BlockContext, x:UOp):
 
   # add unmergables to sources
   srcs = []
-  for u,cnt in unmergable.items(): srcs += [add_blockends(u, ctx.block_ctxs[u], current_ctx, cnt=cnt)]*cnt
+  for u,cnt in unmergable.items(): srcs += [add_blockends(u, ctx.block_ctxs.get(u,()), current_ctx, cnt=cnt)]*cnt
 
   # add blockseeds, with blockends as needed
   for (new_ctx, new_child_ctx), v in blockseeds.items():
@@ -154,8 +155,12 @@ def make_block_bottom_up(ctx:BlockContext, x:UOp):
   bb = BasicBlock(tuple(lst), ctx=current_ctx, cnt=child_count, child_ctx=child_ctx)
   return UOp(Ops.BLOCK, src=tuple(srcs), arg=bb)
 
+# we prevent the source of the SPECIAL from being linearized since its not part of the kernel
+def raise_bottom_up_gate(): raise BottomUpGate()
+
 block_create = PatternMatcher([
   (UPat(GroupOp.All-DONT_PLACE_IN_BLOCK.union({Ops.BLOCK, Ops.BLOCKEND}), name="x"), make_block_bottom_up),
+  (UPat(Ops.SPECIAL), raise_bottom_up_gate)
 ])
 
 # ***** blockend merging ****

tinygrad/codegen/lowerer.py

@@ -1,6 +1,5 @@
 # the job of the lowerer is to do indexing
 from dataclasses import dataclass
-from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType, graph_rewrite, resolve
 
 # ***** indexing *****
@@ -12,11 +11,11 @@ class IndexContext:
   start: int = 0
 
 def shape_to_idx(s, axis_types, start=0):
-  return [UOp.range(dtypes.int, sint_to_uop(s), start+i, at) for i, (s, at) in enumerate(zip(s, axis_types))]
+  return [UOp.range(sint_to_uop(s), start+i, at) for i, (s, at) in enumerate(zip(s, axis_types))]
 
 def get_index(ast:UOp) -> IndexContext:
   axis_types = ast.arg.axis_types if isinstance(ast.arg, KernelInfo) else ()
-  if len(ast.full_shape) != len(axis_types):
+  if len(ast.full_shape) != len(axis_types) and ast.st is not None:
     axis_types = tuple([AxisType.REDUCE if resolve(s != fs) else AxisType.LOOP for s,fs in zip(ast.shape, ast.full_shape)])
   return IndexContext(axis_types, [], 0)
 

tinygrad/codegen/opt/__init__.py

@@ -1,59 +1,26 @@
 # opt opinionatedly transforms an ast into an optimized ast using either heuristics or beam search
+from __future__ import annotations
+from enum import Enum, auto
+from dataclasses import dataclass
+from tinygrad.uop.ops import AxisType
 
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.postrange import RKernel, pm_flatten_range
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, KernelInfo
-from tinygrad.helpers import NOOPT, BEAM, USE_TC, getenv
-from tinygrad.renderer import Renderer
-from tinygrad.uop.spec import type_verify
+class OptOps(Enum):
+  TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
+  GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
+  def __lt__(self, x:OptOps): return self.value < x.value
 
-def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp:
-  """
-  Optimize an AST based on heuristics or BEAM search.
+@dataclass(frozen=True, order=True)
+class Opt:
+  op: OptOps
+  axis: int|None = None
+  arg: int|tuple|None = None
+  def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
 
-  Args:
-    ast: The Ops.SINK rooted AST
-    renderer: The renderer used to generate the code
+axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
+                AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
+axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
+               AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
 
-  Returns:
-    The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
-  """
-
-  assert ast.arg is None, "no opt if there's an arg"
-  k = Kernel(ast, opts=renderer)
-  if not NOOPT:
-    if not k.apply_tensor_cores(USE_TC.value): k.apply_opts(hand_coded_optimizations(k))
-    if BEAM >= 1:
-      from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
-      kb = Kernel(ast, opts=renderer)
-      rawbufs = bufs_from_lin(kb, allocate=False)
-      k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
-  return ast.replace(arg=KernelInfo(opts_to_apply=tuple(k.applied_opts)))
-
-pm_get_optimization = PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx) if ast.arg is None and ast.src[0].st is not None else None),
-])
-
-def apply_opt(ast:UOp, renderer:Renderer):
-  k = Kernel(ast, opts=renderer)
-  k.apply_opts(ast.arg.opts_to_apply)
-  ret = k.get_optimized_ast()
-  if __debug__: type_verify(list(ret.toposort()))
-  return ret
-
-pm_do_optimize = PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
-])
-
-# ** postrange **
-
-def apply_ropt(ast:UOp, renderer:Renderer):
-  k = RKernel(ast, opts=renderer)
-  if ast.arg is not None: k.apply_opts(ast.arg.opts_to_apply)
-  return k.get_optimized_ast()
-
-pm_postrange_opt = pm_flatten_range+PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_ropt(ast, ctx) if ast.arg is None or \
-   (ast.arg is not None and ast.arg.opts_to_apply is not None) else None),
-])
+class KernelOptError(Exception): pass
+def check(cond:bool, msg:str=""):
+  if not cond: raise KernelOptError(msg)

tinygrad/codegen/opt/heuristic.py

@@ -1,10 +1,50 @@
 import itertools
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError, AxisType
-from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS
+from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
+from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS, TC_OPT, TC_SELECT, USE_TC, AMX
 from tinygrad.dtype import ImageDType
-from tinygrad.uop.ops import Ops, resolve
+from tinygrad.uop.ops import Ops, resolve, AxisType
+
+# both versions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.postrange import Scheduler
+
+def hand_coded_optimizations(k:Kernel|Scheduler) -> list[Opt]:
+  # first try the tensor cores
+  """ Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
+  Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
+
+  Keyword arguments:
+  use_tensor_cores -- controls how tensor cores are applied (default 1)
+    0: will disable any tensor core matching
+    1: enable tensor cores
+    2: apply tensor core shape but don't use UOp.WMMA
+  extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
+  tc_select -- specifies which tensor core(s) to use for optimization (default -1)
+    -1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
+    [0-N]: uses only the n'th tensor core available; useful for search
+  tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
+    0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
+    1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
+    2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
+  """
+  if USE_TC > 0:
+    try: # check TC first and apply hand-coded opts if successful
+      tk = k.copy()
+      tk.apply_opt(Opt(OptOps.TC, 0, (TC_SELECT.value, TC_OPT.value, USE_TC.value)))
+
+      # skip hand-coded TC opts if AMX, upcasting will make kernel slower
+      if isinstance(k, Kernel) and (tc_opts:=tk.tensor_core_opts) is not None and not AMX:
+        # hand-coded TC opts
+        for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
+          szs = [sz for sz in [5,4,3,2] if tk.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
+          if szs: tk.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
+
+        if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if tk.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
+          tk.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
+      return tk.applied_opts
+    except KernelOptError:
+      pass
 
-def hand_coded_optimizations(k:Kernel) -> list[Opt]:
   # make a copy so it does not mutate the input
   k = k.copy()
 
@@ -13,19 +53,20 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
   if k.opts.has_local and getenv("MV",1) != 0 and (MV_BLOCKSIZE > 1 or MV_THREADS_PER_ROW > 1 or MV_ROWS_PER_THREAD > 1) and  \
     k.reduceop is not None and k.reduceop.arg[0] is Ops.ADD and len(k.full_shape) >= 2 and k.opts.has_shared and \
     (mulop:=k.reduceop.src[0]).op is Ops.MUL and mulop.src[0].op is Ops.LOAD and mulop.src[1].op is Ops.LOAD:
-    st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
-    strides0, strides1 = st0.real_strides(), st1.real_strides()
-    def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
-    if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
-      not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
-      for global_idx in k.axes_of(AxisType.GLOBAL):
-        if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
-          if DEBUG >= 3:
-            print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
-          if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
-          if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
-          if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
-          return k.applied_opts
+    if isinstance(k, Kernel):
+      st0, st1 = k.sts[k.bufs.index(mulop.src[0])], k.sts[k.bufs.index(mulop.src[1])]
+      strides0, strides1 = st0.real_strides(), st1.real_strides()
+      def has_expanded_axis(shape, strides): return any(resolve(s > 1) and not resolve(st != 0) for s,st in zip(shape,strides))
+      if strides0[first_reduce:=(k.axes_of(AxisType.REDUCE)[0])] == 1 and \
+        not (has_expanded_axis(st0.shape, strides0) and has_expanded_axis(st1.shape, strides1)):
+        for global_idx in k.axes_of(AxisType.GLOBAL):
+          if k.full_shape[first_reduce]%MV_THREADS_PER_ROW == 0 and k.full_shape[global_idx]%(MV_BLOCKSIZE*MV_ROWS_PER_THREAD) == 0:
+            if DEBUG >= 3:
+              print(f"MATVEC: {k.full_shape=} {first_reduce=} {strides0=} {MV_BLOCKSIZE=} {MV_THREADS_PER_ROW=} {MV_ROWS_PER_THREAD=}")
+            if MV_THREADS_PER_ROW > 1: k.apply_opt(Opt(OptOps.GROUP, 0, MV_THREADS_PER_ROW))
+            if MV_BLOCKSIZE > 1: k.apply_opt(Opt(OptOps.LOCAL, global_idx, MV_BLOCKSIZE))
+            if MV_ROWS_PER_THREAD > 1: k.apply_opt(Opt(OptOps.UPCAST, global_idx, MV_ROWS_PER_THREAD))
+            return k.applied_opts
 
   # are we grouping? (requires local shape support)
   if resolve(prod(k.output_shape[i] for i in k.upcastable_dims) <= 2048, False):
@@ -38,7 +79,12 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
   # upcast float4 images
   for buf_index,buf in enumerate(k.bufs):
     if isinstance(buf.src[0].dtype, ImageDType):
-      if (unit_stride_axes_mul_4 := [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]):
+      if hasattr(k, "sts"):
+        unit_stride_axes_mul_4 = [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]
+      else:
+        # part of real_strides
+        unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].split_uop(Ops.ADD) if c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
+      if len(unit_stride_axes_mul_4):
         if (axis:=unit_stride_axes_mul_4[0]) in k.upcastable_dims:
           k.apply_opt(Opt(OptOps.UPCAST, axis, 4))
         elif axis in k.unrollable_dims:
@@ -53,8 +99,9 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
   to_upcast: list[int] = []
   # upcast leading axes first (hack-ish for winograd; we actually want to upcast masked axes with low stride first)
   for axis in k.upcastable_dims:
-    if k.full_shape[axis] <= 7 and any(st.axis_is_masked(axis) for st in k.sts) and \
-      prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
+    if isinstance(k, Kernel): is_masked = any(st.axis_is_masked(axis) for st in k.sts)
+    else: is_masked = any(len(st.src) > 2 and k.rngs[axis] in st.src[2].parents for st in k.bufs)
+    if k.full_shape[axis] <= 7 and is_masked and prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
       if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
       to_upcast.append(axis)
   for axis in to_upcast[::-1]: k.apply_opt(Opt(OptOps.UPCAST, axis, 0))
@@ -68,10 +115,24 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
     for axis, upcast_amount in itertools.product(k.upcastable_dims, ([128] if not len(upcasted_axis) else []) if is_dsp else [3,4]):
       # if we haven't upcasted it, it mods, and buffer has stride 0 on axis while having no stride 0 in the upcasted axis already
       if axis in upcasted_axis or k.full_shape[axis]%upcast_amount != 0: continue
-      if any(st.views[-1].strides[axis] == 0 and \
-             all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
-        xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
-                           sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
+      if isinstance(k, Kernel):
+        # must have stride 0 on a view
+        # must have all non stride 0 on what's upcasted before
+        if any(st.views[-1].strides[axis] == 0 and \
+              all(x != 0 for t,x in zip(k.axis_types, st.real_strides()) if t in (AxisType.UPCAST, AxisType.UNROLL)) for st in k.sts):
+          xb_choices.append((sum(st.views[-1].strides[axis]>0 for st in k.sts),
+                            sum(st.views[-1].strides[axis] for st in k.sts), axis, upcast_amount))
+      else:
+        rng = k.rngs[axis]
+        if any(rng not in b.src[1].parents and all(r2 in b.src[1].parents for r2 in k.ranges_of(AxisType.UPCAST, AxisType.UNROLL)) for b in k.bufs):
+          num_strides, sum_strides = 0, 0
+          for b in k.bufs:
+            if rng in b.src[1].parents: num_strides += 1
+            for c in b.src[1].split_uop(Ops.ADD):
+              if c is rng: sum_strides += 1
+              if c.op is Ops.MUL and c.src[0] is rng and c.src[1].op is Ops.CONST: sum_strides += c.src[1].arg
+              if c.op is Ops.MUL and c.src[1] is rng and c.src[0].op is Ops.CONST: sum_strides += c.src[0].arg
+          xb_choices.append((num_strides, sum_strides, axis, upcast_amount))
     if xb_choices:
       xb_choices = sorted(xb_choices)
       if DEBUG >= 4: print(f"more upcast axis : {xb_choices}")
@@ -109,7 +170,11 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
       k.apply_opt(Opt(OptOps.NOLOCALS))
     else:
       # prioritize making expand axes local
-      local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
+      if isinstance(k, Kernel):
+        local_axis_ranking = [(any(st.views[-1].strides[axis] == 0 for st in k.sts), axis) for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP)]
+      else:
+        local_axis_ranking = [(any(k.rngs[axis] not in b.src[1].parents for b in k.bufs), axis) \
+                                for axis in k.axes_of(AxisType.GLOBAL, AxisType.LOOP) if k.rngs[axis].src[0].op is Ops.CONST]
       to_local: list[tuple[int, int]] = []
       for _, axis in sorted(local_axis_ranking, key=lambda x: (-x[0], -x[1])):
         local_size = prod(sz for _, sz in to_local)

tinygrad/codegen/opt/kernel.py

@@ -3,40 +3,18 @@ import itertools, functools, math
 from dataclasses import dataclass
 from collections import defaultdict
 from typing import cast, Final, Callable, Sequence
-from enum import Enum, auto
-
-from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType
+from tinygrad.codegen.opt import OptOps, Opt, KernelOptError, check, axis_letters, axis_colors
+from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType, PatternMatcher, UPat
 from tinygrad.uop.spec import type_verify, ast_spec
 from tinygrad.device import Device
 from tinygrad.codegen.opt.tc import TensorCore
 from tinygrad.renderer import Renderer
 from tinygrad.dtype import ImageDType
-from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, TC_SELECT, TC_OPT, AMX
+from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, NOOPT, BEAM, getenv, POSTOPT
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import strides_for_shape, get_contraction
 from tinygrad.codegen.opt.swizzler import view_left, view_left_through_load
 
-class OptOps(Enum):
-  TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
-  GROUP = auto(); GROUPTOP = auto(); NOLOCALS = auto(); PADTO = auto(); SWAP = auto() # noqa: E702
-  def __lt__(self, x:OptOps): return self.value < x.value
-
-@dataclass(frozen=True, order=True)
-class Opt:
-  op: OptOps
-  axis: int|None = None
-  arg: int|tuple|None = None
-  def __repr__(self): return f"Opt(op={self.op}, axis={self.axis}, arg={self.arg})"
-
-axis_letters = {AxisType.GLOBAL: "g", AxisType.LOCAL: "l", AxisType.LOOP: "L", AxisType.UPCAST: "u",
-                AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
-axis_colors = {AxisType.GLOBAL: "blue", AxisType.LOCAL: "cyan", AxisType.LOOP: "WHITE", AxisType.UPCAST: "yellow",
-               AxisType.GROUP_REDUCE: "green", AxisType.REDUCE: "red", AxisType.UNROLL: "magenta"}
-
-class KernelOptError(Exception): pass
-def check(cond:bool, msg:str=""):
-  if not cond: raise KernelOptError(msg)
-
 @dataclass
 class TensorCoreOptions:
   axes: tuple[int, ...] # the location of the original N and M axes if still in the shape
@@ -92,6 +70,10 @@ class Kernel:
     global_loops = AxisType.GLOBAL if self.opts.has_local else AxisType.LOOP
     self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.output_shape, self.full_shape)]
 
+    # confirm all reduce axes are at the end
+    if (final_reduces := [x for x in self.axis_types if x == AxisType.REDUCE]) and final_reduces != self.axis_types[-len(final_reduces):]:
+      raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
+
   def copy(self):
     ret = type(self).__new__(type(self))
 
@@ -395,45 +377,6 @@ class Kernel:
         return True
     return False
 
-  def apply_tensor_cores(self, use_tensor_cores=1, extra_opts:list[Opt]|None=None, axis:int=0, tc_select:int|None=None, tc_opt:int|None=None) -> bool:
-    """ Attempts to apply a tensor core optimization to the kernel. If one exists and applies properly, return true, otherwise return false.
-    Tensor cores are optimized instructions that matrix multiply-accumulate across a wave of threads: D(M, N) = A(M, K) * B(K, N) + C(M, N).
-
-    Keyword arguments:
-    use_tensor_cores -- controls how tensor cores are applied (default 1)
-      0: will disable any tensor core matching
-      1: enable tensor cores
-      2: apply tensor core shape but don't use UOp.WMMA
-    extra_opts -- additional Opt's to apply after the tensor core instead of the hand-coded additional Opt's (default None)
-    tc_select -- specifies which tensor core(s) to use for optimization (default -1)
-      -1: iterates through all available tensor cores in order and uses the first one that matches the requirements (dims and dtypes)
-      [0-N]: uses only the n'th tensor core available; useful for search
-    tc_opt -- controls which kinds of kernels may be eligible for tensor cores application (default 2 during BEAM, 0 otherwise)
-      0: applies to only kernels with a single reduce axis and direct Ops.LOAD into Ops.MUL
-      1: allows kernels with multiple reduce axes and also multiplication of Ops.CAST'd buffers
-      2: allows kernels with M, N, K axes that are not multiples of the tensor core dimensions by applying padding those axes as needed
-    """
-    if tc_select is None: tc_select = TC_SELECT.value
-    if tc_opt is None: tc_opt = TC_OPT.value
-    if not self.opts.tensor_cores: return False
-    try: # check TC first and apply hand-coded opts if successful
-      self.apply_opt(Opt(OptOps.TC, axis, (tc_select, tc_opt, use_tensor_cores)))
-
-      if (tc_opts:=self.tensor_core_opts) is not None:
-        if extra_opts is not None: self.apply_opts(extra_opts)
-        else:
-          if AMX: return True # skip hand-coded TC opts if AMX, upcasting will make kernel slower
-          # hand-coded TC opts
-          for tc_dim in [tc_dim for tc_dim in [1,0] if tc_opts.axes_exist[tc_dim]]: # attempt to upcast M and N
-            szs = [sz for sz in [5,4,3,2] if self.full_shape[tc_opts.axes[tc_dim]] % sz == 0]
-            if szs: self.apply_opt(Opt(OptOps.UPCAST, tc_opts.axes[tc_dim], szs[0]))
-
-          if tc_opts.axes_exist[0] and (szs := [sz for sz in [4,2] if self.full_shape[tc_opts.axes[0]] % sz == 0]): # attempt to local N
-            self.apply_opt(Opt(OptOps.LOCAL, tc_opts.axes[0], szs[0]))
-      return True
-    except KernelOptError:
-      return False
-
   # strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
   def shape_str(self) -> list[str]:
     ret: list[str] = []
@@ -490,3 +433,47 @@ class Kernel:
     fixed_ast = fixup_ast(self.ast)
     del fixup_ast
     return graph_rewrite(fixed_ast, view_left+view_left_through_load, name="fixup optimized AST")
+
+def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp|None:
+  """
+  Optimize an AST based on heuristics or BEAM search.
+
+  Args:
+    ast: The Ops.SINK rooted AST
+    renderer: The renderer used to generate the code
+
+  Returns:
+    The Ops.SINK rooted AST transformed to apply the opts and with a KernelInfo in the arg.
+  """
+
+  # no shape, no opt
+  if ast.src[0].st is None: return None
+  new_arg = ast.arg
+  if new_arg is None:
+    k = Kernel(ast, opts=renderer)
+    if not NOOPT:
+      from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+      k.apply_opts(hand_coded_optimizations(k))
+      if not POSTOPT and BEAM >= 1:
+        from tinygrad.codegen.opt.search import beam_search, bufs_from_lin
+        kb = Kernel(ast, opts=renderer)
+        rawbufs = bufs_from_lin(kb, allocate=False)
+        k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
+    new_arg = KernelInfo(opts_to_apply=tuple(k.applied_opts))
+  elif len(new_arg.applied_opts): return None
+  return Kernel(ast.replace(arg=None), opts=renderer).get_optimized_ast().replace(arg=new_arg)
+
+pm_get_optimization = PatternMatcher([
+  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: get_optimized_ast(ast, ctx)),
+])
+
+def apply_opt(ast:UOp, renderer:Renderer):
+  k = Kernel(ast, opts=renderer)
+  k.apply_opts(ast.arg.opts_to_apply)
+  ret = k.get_optimized_ast()
+  if __debug__: type_verify(list(ret.toposort()))
+  return ret
+
+pm_do_optimize = PatternMatcher([
+  (UPat(Ops.SINK, name="ast"), lambda ctx,ast: apply_opt(ast, ctx) if ast.arg is not None and ast.arg.opts_to_apply is not None else None),
+])

tinygrad/codegen/opt/postrange.py

@@ -1,10 +1,18 @@
-import math
-from tinygrad.dtype import AddrSpace
-from tinygrad.uop.ops import UOp, Ops, sint, ssimplify, AxisType, KernelInfo, PatternMatcher, UPat, graph_rewrite, _substitute
+import math, itertools
+from collections import defaultdict
+from typing import cast, Final
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo, graph_rewrite, _substitute, AxisType
 from tinygrad.uop.symbolic import symbolic
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps
+from tinygrad.device import Buffer
+from tinygrad.dtype import AddrSpace, dtypes
+from tinygrad.helpers import colored, BEAM, getenv, DEBUG, to_function_name, NOOPT, argsort, round_up
+from tinygrad.codegen.opt import axis_colors, Opt, OptOps, KernelOptError, check, axis_letters
 from tinygrad.renderer import Renderer
-from tinygrad.dtype import dtypes
+from tinygrad.schedule.rangeify import remove_tags
+
+# NOTE: LOCAL and GROUP_REDUCE have the same priority. the order here matters
+axis_to_pos = {AxisType.LOOP: -1, AxisType.GLOBAL: 0, AxisType.LOCAL: 1, AxisType.UPCAST: 2,
+               AxisType.GROUP_REDUCE: 1, AxisType.REDUCE: 3, AxisType.UNROLL: 4}
 
 def flatten_range(r:UOp):
   off = 2 if r.op is Ops.STORE else 1
@@ -20,41 +28,79 @@ pm_flatten_range = PatternMatcher([
 
 def count_divmod(x:UOp): return len([u for u in x.toposort() if u.op in {Ops.IDIV, Ops.MOD}])
 
-class RKernel(Kernel):
-  def __init__(self, ast:UOp, opts:Renderer|None=None):
-    self.rng = sorted([u for u in ast.toposort() if u.op is Ops.RANGE and u.vmax > 0], key=lambda x: x.arg)
-    super().__init__(ast, opts)
-    self.sts.clear()
+class Scheduler:
+  def __init__(self, ast:UOp, opts:Renderer):
+    self.ast, self.opts = ast, opts
+    self.dont_use_locals = self.ast.arg.dont_use_locals if self.ast.arg is not None else False
+    self.applied_opts = list(self.ast.arg.applied_opts) if self.ast.arg is not None else []
 
-    # convert LOOP to GLOBAL
-    self.replaces = {}
-    if self.opts.has_local:
-      store_rngs = self.ast.src[0].src[2:]
+  @property
+  def rngs(self):
+    # always in order by axistype
+    return sorted([u for u in self.ast.parents if u.op is Ops.RANGE and u.vmax > 0], key=lambda x: (axis_to_pos[x.arg[-1]],) + x.arg[0:-1])
+  @property
+  def shape_len(self): return len(self.rngs)
+  @property
+  def full_shape(self): return [x.vmax+1 for x in self.rngs]
+  @property
+  def axis_types(self): return [x.arg[-1] for x in self.rngs]
+  @property
+  def maxarg(self): return max([x.arg[0] for x in self.rngs], default=0)
 
-      # filter any not in local stores
-      local_store_rngs = [x.ranges for x in self.ast.toposort() if (x.op is Ops.STORE and x.src[0].dtype.addrspace == AddrSpace.LOCAL) \
-                          or (x.op is Ops.BUFFERIZE and x.arg == AddrSpace.LOCAL)]
-      for ls in local_store_rngs: store_rngs = [x for x in store_rngs if x in ls]
+  # strings like ['g0', 'g1', 'l0', 'l1', 'l2', 'l3', 'l4', 'l5', 'R0', 'r0', 'r1', 'r2', 'u0', 'u1', 'u2']
+  def shape_str(self) -> list[str]:
+    ret: list[str] = []
+    cnt: dict[AxisType, int] = {}
+    for x in self.axis_types:
+      cnt[x] = (cnt[x] + 1) if x in cnt else 0
+      ret.append(f"{axis_letters[x]}{cnt[x]}")
+    return ret
+  def shape_str_to_axis(self, nms:list[str]) -> tuple[int, ...]: return tuple([self.shape_str().index(x) for x in nms])
 
-      store_rng = [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE] if store_rngs else []
-      rng = [x.replace(arg=(x.arg[0], AxisType.GLOBAL)) if x.arg[1] == AxisType.LOOP and x in store_rng else x for x in self.rng]
-      self.replaces.update(dict(zip(self.rng, rng)))
-      self.rng = rng
-
-  def simplify_merge_adjacent(self):
-    return
-    # NOTE: this one is better than the one in kernel.py, which is kind of a problem
-    terminators = [u for u in self.ast.toposort() if u.op in {Ops.REDUCE, Ops.STORE}]
+  @property
+  def termination(self):
+    terminators = [u for u in self.ast.parents if u.op in {Ops.REDUCE, Ops.STORE}]
     termination = {}
     for t in terminators:
-      for u in t.src[1 if t.op is Ops.REDUCE else 2:]: termination[u] = t
+      # works without pm_flatten_range
+      for u in UOp.sink(*t.src[1 if t.op is Ops.REDUCE else 2:]).parents:
+        if u.op is Ops.RANGE: termination[u] = t
+    return termination
 
-    replaces = {}
+  def copy(self): return Scheduler(self.get_optimized_ast(), self.opts)
+
+  kernel_cnt: Final[defaultdict[str, int]] = defaultdict(int)
+  def get_optimized_ast(self, name_override:str|None=None):
+    if name_override is not None: name = name_override
+    else:
+      name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), color) for x,color in zip(self.rngs, self.colors())])
+      Scheduler.kernel_cnt[(function_name := to_function_name(name))] += 1
+      num = f"n{Scheduler.kernel_cnt[function_name]-1}" if Scheduler.kernel_cnt[function_name] > 1 else ""
+      name += colored(num, 'BLACK')
+    self.ast = graph_rewrite(self.ast, pm_flatten_range, name="flatten range")
+    return self.ast.replace(arg=KernelInfo(name=name, applied_opts=tuple(self.applied_opts), dont_use_locals=self.dont_use_locals), tag=1)
+
+  def convert_loop_to_global(self):
+    if not self.opts.has_local: return None
+    store_rngs = self.ast.src[0].src[2:]
+
+    # filter any not in local stores
+    local_store_rngs = [x.ranges for x in self.ast.toposort() if (x.op is Ops.STORE and x.src[0].ptrdtype.addrspace == AddrSpace.LOCAL) \
+                        or (x.op is Ops.BUFFERIZE and x.arg == AddrSpace.LOCAL)]
+    for ls in local_store_rngs: store_rngs = tuple([x for x in store_rngs if x in ls])
+
+    store_rng = [x for x in UOp.sink(*store_rngs).toposort() if x.op is Ops.RANGE] if store_rngs else []
+    rng = [x.replace(arg=(x.arg[0], AxisType.GLOBAL)) if x.arg[1] == AxisType.LOOP and x in store_rng else x for x in self.rngs]
+
+    self.ast = self.ast.substitute(dict(zip(self.rngs, rng)))
+
+  def simplify_merge_adjacent(self):
     i = 0
-    while i < len(self.rng)-1:
-      r0, r1 = self.rng[i], self.rng[i+1]
+    while i < len(self.rngs)-1:
+      r0, r1 = self.rngs[i], self.rngs[i+1]
       # same axistype and same termination
-      if r0.arg[1] == r1.arg[1] and termination[r0] == termination[r1]:
+      termination = self.termination
+      if r0.arg[1] == r1.arg[1] and r0 in termination and r1 in termination and termination[r0] == termination[r1]:
         s0, s1 = r0.src[0], r1.src[0]
         new_range = r0.replace(src=(s0*s1,)).simplify()
         # this checks the legality of a merge
@@ -65,119 +111,222 @@ class RKernel(Kernel):
           # it is correct
           midx = graph_rewrite(nidx, _substitute+symbolic+pm_flatten_range, ctx={new_range:r0*s1+r1}, name=f"correct_merge_{i}_{i+1}")
           if oidx is midx:
-            termination[new_range] = termination[r0]
-            replaces[r0] = new_range//s1
-            replaces[r1] = new_range%s1
-            self.rng[i] = new_range
-            del self.rng[i+1]
+            self.ast = nidx
             continue
       i += 1
-    self.ast = self.ast.substitute(replaces, name="simplify_merge_adjacent")
 
-  def shift_to(self, axis:int, amount:int, new_type:AxisType, top:bool=False, insert_at:int|None=None):
-    old_sz = self.rng[axis].src[0].arg // amount
-    assert old_sz > 0, f"bad old_sz on {axis} {amount} {self.rng[axis]}"
+  def colors(self) -> list[str]: return [axis_colors[x] if not self.dont_use_locals or not x == AxisType.GLOBAL else "BLUE" for x in self.axis_types]
+  def colored_shape(self) -> str: return ' '.join([colored(f'{x.src[0].render():4s}', color) for x,color in zip(self.rngs, self.colors())])
 
-    maxarg = max([x.arg[0] for x in self.rng])
-    new_rng = UOp.range(dtypes.int, amount, maxarg+1, new_type)
+  def shift_to(self, rng:UOp, amount:int, new_type:AxisType, top:bool=False):
+    if (old_sz:=rng.src[0].divides(amount)) is None:
+      raise KernelOptError(f"{amount} can't divide {rng.src[0]} in {self.colored_shape()}")
+    new_rng = UOp.range(amount, self.maxarg+1, new_type)
+    replaced_rng = rng.replace(src=(UOp.const(dtypes.int, old_sz),))
+    sub_axis = (new_rng * old_sz + replaced_rng) if top else (replaced_rng * amount + new_rng)
+    self.ast = self.ast.substitute({rng:sub_axis}, name=f"shift {rng.arg[0]} {amount}")
+    return replaced_rng, new_rng
 
-    if old_sz == 1:
-      self.replaces[self.rng[axis]] = new_rng
-      self.rng.insert(insert_at if insert_at is not None else len(self.rng), new_rng)
-      del self.rng[axis]
+  def ranges_of(self, *axis_type:AxisType) -> list[UOp]: return [r for r in self.rngs if r.arg[-1] in axis_type]
+  def axes_of(self, *axis_type:AxisType) -> list[int]: return [i for i,t in enumerate(self.axis_types) if t in axis_type]
+  @property
+  def upcastable_dims(self): return self.axes_of(AxisType.GLOBAL, AxisType.LOCAL)
+  @property
+  def unrollable_dims(self): return self.axes_of(AxisType.REDUCE, AxisType.GROUP_REDUCE)
+
+  def real_axis(self, op:OptOps, axis:int|None):
+    try:
+      if axis is None: return -1
+      if op is OptOps.UNROLL: return self.unrollable_dims[axis]
+      if op in {OptOps.GROUP, OptOps.GROUPTOP}: return self.axes_of(AxisType.REDUCE)[axis]
+      check(axis < self.shape_len, f"invalid axis on {axis=} {op=} {self.shape_len=}")
+      return axis
+    except IndexError as e: raise KernelOptError from e
+
+  def apply_opt(self, opt:Opt, append_opt:bool=True):
+    if opt.op is OptOps.NOLOCALS:
+      check(all(x not in {AxisType.LOCAL, AxisType.GROUP_REDUCE} for x in self.axis_types), "no locals can't have locals")
+      self.dont_use_locals = True
+      self.applied_opts.append(opt)
+      return
+
+    if opt.op in {OptOps.LOCAL, OptOps.GROUP, OptOps.GROUPTOP}:
+      check(self.opts.has_local, "locals needed for opt")
+
+    rng = self.rngs[self.real_axis(opt.op, opt.axis)]
+
+    opt_to_at = {
+      OptOps.LOCAL: AxisType.LOCAL, OptOps.UPCAST: AxisType.UPCAST,
+      OptOps.UNROLL: AxisType.UNROLL, OptOps.GROUP: AxisType.GROUP_REDUCE,
+      OptOps.GROUPTOP: AxisType.GROUP_REDUCE}
+
+    if opt.op in opt_to_at:
+      amt:int = (rng.vmax+1) if opt.arg == 0 else cast(int, opt.arg)
+      if opt.op is OptOps.UNROLL:
+        check(amt <= 32, "don't unroll more than 32")
+        check(rng.arg[-1] in {AxisType.GROUP_REDUCE, AxisType.REDUCE}, "unroll is for GROUP_REDUCE/REDUCE")
+      if opt.op is OptOps.UPCAST:
+        check((self.opts is not None and self.opts.device == "DSP") or amt <= 16, "don't upcast more than 16")
+        check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOCAL, AxisType.LOOP}, "upcast is for GLOBAL/LOCAL/LOOP")
+      if opt.op is OptOps.LOCAL:
+        check(not self.dont_use_locals, "can't use locals")
+        check(rng.arg[-1] in {AxisType.GLOBAL, AxisType.LOOP}, "local is for globals")
+      if opt.op in {OptOps.GROUP, OptOps.GROUPTOP}:
+        check(not self.dont_use_locals, "can't use locals")
+        check(rng.arg[-1] == AxisType.REDUCE, "group is for reduce")
+      self.shift_to(rng, amt, opt_to_at[opt.op], top=opt.op==OptOps.GROUPTOP)
+    elif opt.op is OptOps.TC:
+      check(len(self.applied_opts) == 0, "tensor core opts must be first") # TODO: remove the need for this by having warps
+      check(opt.axis is not None, "tensor core opts must have an axis")
+      check(opt.arg is not None and isinstance(opt.arg, tuple) and len(opt.arg) == 3, "tensor core opts must have valid arg")
+      check(-1 <= (tc_select:=cast(tuple, opt.arg)[0]) < len(self.opts.tensor_cores), "tensor core opts must have valid tc_select")
+      check(0 <= (tc_opt:=cast(tuple, opt.arg)[1]) <= 2, "tensor core opts must have valid tc_opt")
+      check(0 < (use_tensor_cores:=cast(tuple, opt.arg)[2]) <= 2, "use_tensor_cores value is not valid")
+      check(self._apply_tc_opt(use_tensor_cores, cast(int, opt.axis), tc_select, tc_opt), "no tensor core available")
+    elif opt.op is OptOps.PADTO:
+      check(rng.src[0].op is Ops.CONST, "only pad const")
+      replaced_rng = UOp.range(round_up(rng.vmax+1, cast(int, opt.arg)), *rng.arg)
+      replaces = {rng:replaced_rng}
+      for b in self.bufs:
+        if rng in b.src[1].sparents:
+          valid = replaced_rng < rng.vmax+1
+          if len(b.src) > 2: valid = b.src[2] & valid
+          replaces[b] = b.replace(src=b.src[0:2]+(valid,))
+      self.ast = self.ast.substitute(replaces, f"padto {rng.arg[:-1]} {opt.arg}")
+    elif opt.op is OptOps.SWAP:
+      try:
+        altrng = self.rngs[opt.arg]
+      except IndexError:
+        raise KernelOptError
+      check(rng.arg[-1] == AxisType.GLOBAL and altrng.arg[-1] == AxisType.GLOBAL, "swap only for globals")
+      self.ast = self.ast.substitute({rng:rng.replace(arg=(*altrng.arg[0:-1], rng.arg[-1]), tag=1),
+                                      altrng:altrng.replace(arg=(*rng.arg[0:-1], altrng.arg[-1]), tag=1)})
+      self.ast = graph_rewrite(self.ast, remove_tags)
     else:
-      replaced_rng = self.rng[axis].replace(src=(UOp.const(dtypes.int, old_sz),))
-      self.replaces[self.rng[axis]] = (new_rng * old_sz + replaced_rng) if top else (replaced_rng * amount + new_rng)
-      self.rng[axis] = replaced_rng
-      self.rng.insert(insert_at if insert_at is not None else len(self.rng), new_rng)
-    return new_rng
-
-  @property
-  def axis_types(self) -> list[AxisType]: return [x.arg[1] for x in self.rng]
-  @property
-  def shape_len(self): return len(self.rng)
-
-  @property
-  def full_shape(self) -> tuple[sint, ...]: return tuple([ssimplify(x.src[0]) for x in self.rng])
-  @property
-  def output_shape(self) -> tuple[sint, ...]: return tuple([ssimplify(x.src[0]) for x in self.ast.src[0].src[2:]])
-
-  def get_optimized_ast(self, name_override:str|None=None) -> UOp:
-    ret = self.ast
-    kernel_name = ret.arg.name if ret.arg is not None and ret.arg.name != "test" else self.name if name_override is None else name_override
-    rarg = KernelInfo(kernel_name, tuple(self.axis_types), self.dont_use_locals, tuple(self.applied_opts))
-    return ret.substitute(self.replaces).replace(arg=rarg)
-
-  # does nothing
-  @axis_types.setter
-  def axis_types(self, value): pass
+      raise KernelOptError(f"unsupported opt {opt.op}")
+    if append_opt:
+      self.applied_opts.append(opt)
 
   def _apply_tc_opt(self, use_tensor_cores:int, axis:int, tc_select:int, opt_level:int) -> bool:
-    reduceop = [x for x in self.ast.toposort() if x.op is Ops.REDUCE][0]
+    reduceops = [x for x in self.ast.toposort() if x.op is Ops.REDUCE]
+    if not len(reduceops): raise KernelOptError("no reduce ops for TensorCore")
+    reduceop = reduceops[0]
     if use_tensor_cores and reduceop is not None and reduceop.arg is Ops.ADD:
-      tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
+      mul = reduceop.src[0] if reduceop.src[0].op is not Ops.CAST else reduceop.src[0].src[0]
+      if mul.op is not Ops.MUL: return False
+      in0, in1 = mul.src
+      try:
+        tensor_cores = self.opts.tensor_cores if tc_select == -1 else [self.opts.tensor_cores[tc_select]]
+      except IndexError:
+        raise KernelOptError(f"invalid tensor core choice {tc_select}")
       for tc in tensor_cores:
-        if tc.dtype_in == dtypes.float and tc.dtype_out == dtypes.float:
-          axes = [1,0]
+        if tc.dtype_in == in0.dtype.scalar() and tc.dtype_in == in1.dtype.scalar() and tc.dtype_out == reduceop.dtype.scalar():
+          # tensor cores have three ranges. X, Y, and REDUCE
+          in0_ranges = sorted([u for u in in0.ranges if u not in in1.ranges], key=lambda x: x.arg[0])
+          in1_ranges = sorted([u for u in in1.ranges if u not in in0.ranges], key=lambda x: x.arg[0])
+          red_ranges = sorted(reduceop.src[1:], key=lambda x: x.arg[0])
+          if DEBUG >= 3:
+            print(f"TC({axis}): {[(x.arg[0],x.vmax+1) for x in in0_ranges]}",
+                              f"{[(x.arg[0],x.vmax+1) for x in in1_ranges]} {[(x.arg[0],x.vmax+1) for x in red_ranges]}")
+          if not len(in0_ranges) or not len(in1_ranges) or not len(red_ranges): continue
+
+          # pick ranges
+          # NOTE: why are in1 and in0 switched?
+          axis_choices = list(itertools.product(in1_ranges, in0_ranges, red_ranges))
+          if not (axis < len(axis_choices)): continue
+          axes = list(axis_choices[axis])
 
           # do optimizations and save the ranges
+          try:
+            for i,a in enumerate(axes):
+              # apply_opt should return the updated range?
+              idx = self.rngs.index(a)
+              self.apply_opt(Opt(OptOps.PADTO, idx, tc.dims[i]), append_opt=False) # PADTO might fail
+              axes[i] = self.rngs[idx]
+          except KernelOptError: continue
+
           ne: list[UOp] = []
           for opt in tc.opts:
-            ne.append(self.apply_opt(Opt({"u":OptOps.UPCAST, "l":OptOps.LOCAL}[opt[0]], axes[int(opt[1])], 2), append_opt=False))
+            axes[int(opt[1])], new_range = self.shift_to(axes[int(opt[1])], 2, {"u":AxisType.UPCAST, "l":AxisType.LOCAL}[opt[0]])
+            ne.append(new_range)
           for _, amt in tc.get_reduce_axes():
-            ne.append(self.apply_opt(Opt(OptOps.UNROLL, 0, amt), append_opt=False)) # TODO: this should be the reduce, not 0
+            axes[2], new_range = self.shift_to(axes[2], amt, AxisType.UNROLL)
+            ne.append(new_range)
 
-          # early realize for TC
-          self.ast = self.ast.substitute(self.replaces)
-          self.replaces = {}
+          if use_tensor_cores != 2:
+            # fix the srcs
+            reduceop = [x for x in self.ast.toposort() if x.op is Ops.REDUCE][0]
+            tne = [x.replace(tag=1) for x in ne]
+            ret = reduceop.substitute(dict(zip(ne, tne)))
+            srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src)
+            srcs = [x.substitute(dict(zip(tne, [ne[i] for i in argsort(p)]))) for x,p in zip(srcs, tc.permutes_for_shape_str(tc.base_shape_str()))]
 
-          # fix the srcs
-          reduceop = [x for x in self.ast.toposort() if x.op is Ops.REDUCE][0]
-          tne = [x.replace(tag=1) for x in ne]
-          ret = reduceop.substitute(dict(zip(ne, tne)))
-          srcs = list((ret.src[0] if ret.src[0].op is not Ops.CAST else ret.src[0].src[0]).src)
-          srcs = [x.substitute(dict(zip(tne, [ne[i] for i in p]))) for x,p in zip(srcs, tc.permutes_for_shape_str(tc.base_shape_str()))]
+            # get reduce/upcast axes for the tensor cores
+            tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
+            base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
+            tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
 
-          # get reduce/upcast axes for the tensor cores
-          tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
-          base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
-          tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
+            # axes to range number (was done in lowerer)
+            tc_upcast_axes = tuple([tuple([(self.rngs[a].arg[0], sz) for a,sz in v]) for v in tc_upcast_axes])
+            tc_reduce_axes = tuple([self.rngs[a].arg[0] for a in tc_reduce_axes])
 
-          # axes to range number (was done in lowerer)
-          tc_upcast_axes = tuple([tuple([(self.rng[a].arg[0], sz) for a,sz in v]) for v in tc_upcast_axes])
-          tc_reduce_axes = tuple([self.rng[a].arg[0] for a in tc_reduce_axes])
+            # construct the op
+            # TODO: remove tc_upcast_axes from the arg
+            # do the reduce_axes always disappear? i think they don't
+            # they need to be moved into the WMMA srcs
+            wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, ()) #, tc_reduce_axes)
+            wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
+              UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0], tag=1),
+              UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1], tag=1),
+              UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg, tag=1)
+            tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2], tag=1)
 
-          # construct the op
-          # TODO: remove tc_upcast_axes from the arg
-          wmma_arg = (str(tc), tc.dims, tc.dtype_in, tc.dtype_out, self.opts.device, tc.threads, tc_upcast_axes, tc_reduce_axes)
-          wmma = UOp(Ops.WMMA, dtype=tc.dtype_out.vec(tc.elements_per_thread[2]), src=(
-            UOp(Ops.CONTRACT, dtype=srcs[0].dtype.vec(tc.elements_per_thread[0]), src=(srcs[0],), arg=tc_upcast_axes[0]),
-            UOp(Ops.CONTRACT, dtype=srcs[1].dtype.vec(tc.elements_per_thread[1]), src=(srcs[1],), arg=tc_upcast_axes[1]),
-            UOp.const(tc.dtype_out.vec(tc.elements_per_thread[2]), 0.0)), arg=wmma_arg)
-          tc_uop = UOp(Ops.UNROLL, tc.dtype_out, (wmma,), arg=tc_upcast_axes[2])
-
-          # preserve extra reduces
-          reduce_ranges = [x for x in UOp.sink(*reduceop.src[1:]).toposort() if x.op is Ops.RANGE and x.arg[0] not in tc_reduce_axes]
-          if len(reduce_ranges): tc_uop = UOp(Ops.REDUCE, tc_uop.dtype, (tc_uop,)+tuple(reduce_ranges), Ops.ADD)
-          self.ast = self.ast.substitute({reduceop: tc_uop})
+            # preserve extra reduces
+            reduce_ranges = [x for x in UOp.sink(*reduceop.src[1:]).toposort() if x.op is Ops.RANGE and x.arg[0] not in tc_reduce_axes]
+            if len(reduce_ranges): tc_uop = UOp(Ops.REDUCE, tc_uop.dtype, (tc_uop,)+tuple(reduce_ranges), Ops.ADD)
+            self.ast = self.ast.substitute({reduceop: tc_uop})
           return True
     return False
 
-from dataclasses import replace
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, KernelInfo
-from tinygrad.helpers import colored
-from tinygrad.codegen.opt.kernel import axis_colors
+  # helpers for hand_coded_optimizations
+  @property
+  def reduceop(self) -> UOp|None:
+    red = [x for x in self.ast.parents if x.op is Ops.REDUCE]
+    if not len(red): return None
+    return UOp(Ops.REDUCE_AXIS, red[0].dtype, red[0].src, (red[0].arg, ()))
+  @property
+  def bufs(self) -> list[UOp]: return [x for x in self.ast.toposort() if x.op is Ops.INDEX][::-1]
+  @property
+  def output_shape(self):
+    return [s if at not in {AxisType.REDUCE, AxisType.UNROLL, AxisType.GROUP_REDUCE} else 1 for s,at in zip(self.full_shape, self.axis_types)]
+  @property
+  def upcasted(self) -> int: return len(self.axes_of(AxisType.UPCAST, AxisType.UNROLL))
+  @property
+  def group_for_reduces(self) -> int: return len(self.axes_of(AxisType.GROUP_REDUCE))
 
-def rename_sink(s:UOp):
-  if s.arg is not None and s.arg.name != "test": return None
+def bufs_from_ast(ast:UOp, dname:str) -> list[Buffer]:
+  glbls = sorted([x for x in ast.parents if x.op is Ops.DEFINE_GLOBAL], key=lambda x: x.arg)
+  return [Buffer(dname, x.ptrdtype.size, x.dtype.base) for x in glbls]
 
-  # get all ranges (sorted)
-  rngs = sorted([u for u in s.parents if u.op is Ops.RANGE], key=lambda x: x.arg[0:-1])
-
-  # add name to kernel
-  name = "k" + colored('_', 'BLACK').join(['']+[colored(x.src[0].render(), axis_colors[x.arg[-1]]) for x in rngs])
-  return s.replace(arg=KernelInfo(name=name) if s.arg is None else replace(s.arg, name=name))
+def apply_opts(ctx:Renderer, ast:UOp):
+  if ast.tag is not None: return None
+  k = Scheduler(ast, ctx)
+  k.convert_loop_to_global()
+  if BEAM >= 1:
+    k.simplify_merge_adjacent()
+    from tinygrad.codegen.opt.search import beam_search
+    rawbufs = bufs_from_ast(ast, ctx.device)
+    k = beam_search(k, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
+  elif ast.arg is not None and ast.arg.opts_to_apply is not None:
+    for opt in ast.arg.opts_to_apply: k.apply_opt(opt)
+  elif not NOOPT:
+    k.simplify_merge_adjacent()
+    from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
+    # NOTE: hand_coded_optimizations doesn't support multiblock opts yet
+    if all(len(u.src) == 1 for u in ast.parents if u.op is Ops.LOAD):
+      for opt in hand_coded_optimizations(k): k.apply_opt(opt)
+  return k.get_optimized_ast(name_override=ast.arg.name if ast.arg is not None and ast.arg.name != "test" else None)
 
 pm_postrange_opt = PatternMatcher([
-  (UPat(Ops.SINK, name="s"), rename_sink),
+  (UPat(Ops.SINK, name="ast"), apply_opts),
 ])

tinygrad/codegen/opt/search.py

@@ -1,17 +1,21 @@
-from typing import cast, Callable
-import itertools, functools, random, math, time, multiprocessing, traceback, signal, atexit
+from typing import cast
+import functools, math, time, multiprocessing, traceback, signal, atexit
 from collections import defaultdict
 from dataclasses import replace
-from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType
+from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType, pyrender
 from tinygrad.device import Device, Buffer, Compiler
 from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
-from tinygrad.helpers import IGNORE_BEAM_CACHE, TC_SEARCH_OVER_SHAPE
+from tinygrad.helpers import IGNORE_BEAM_CACHE
 from tinygrad.dtype import ImageDType, PtrDType
-from tinygrad.codegen.opt.kernel import Kernel, Opt, OptOps, KernelOptError
+from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.tensor import Tensor
 from tinygrad.engine.realize import CompiledRunner, get_program
 from tinygrad.renderer import ProgramSpec
 
+# both versions
+from tinygrad.codegen.opt.kernel import Kernel
+from tinygrad.codegen.opt.postrange import Scheduler
+
 actions = [Opt(op=OptOps.UPCAST, axis=axis, arg=amt) for amt in [0,2,3,4,5,7] for axis in range(8)]
 actions += [Opt(op=OptOps.UNROLL, axis=axis, arg=amt) for amt in [0,4,7] for axis in range(5)]
 actions += [Opt(op=OptOps.LOCAL, axis=axis, arg=amt) for amt in [2,3,4,8,13,16,29] for axis in range(6)]
@@ -55,7 +59,9 @@ def _time_program(p:ProgramSpec, lib:bytes, var_vals:dict[Variable, int], rawbuf
   return tms
 
 class TimeoutException(Exception): pass
-def timeout_handler(signum, frame): raise TimeoutException()
+def timeout_handler(signum, frame):
+  if DEBUG >= 2: print("*** BEAM COMPILE TIMEOUT")
+  raise TimeoutException()
 
 def _try_compile_linearized_w_idx(x:tuple[int,Kernel], compiler:Compiler) -> tuple[int, tuple[ProgramSpec, bytes, float]|None]:
   if hasattr(signal, "alarm"):
@@ -91,6 +97,7 @@ def _ensure_buffer_alloc(bufs:list[Buffer]) -> list[Buffer]: return [buf.ensure_
 # *** external API ***
 
 # get (scrap) buffers for timing the linearizer
+# NOTE: there's also bufs_from_ast in postrange
 def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
   bufsts: defaultdict[int, list[UOp]] = defaultdict(list)
   for x in lin.bufs:
@@ -108,17 +115,10 @@ def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
   return cast(list[Buffer], rawbufs)
 
 # get dictionary of all possible actions
-def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel]:
+def get_kernel_actions(lin:Kernel|Scheduler, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel|Scheduler]:
   acted_lins, max_up, max_lcl = {0:lin} if include_0 else {}, getenv("BEAM_UPCAST_MAX", 256), getenv("BEAM_LOCAL_MAX", 1024)
   kernel_actions = (actions if candidates is None else candidates).copy()
 
-  if TC_SEARCH_OVER_SHAPE and len(lin.applied_opts) == 0: # tensor core opts must be first
-    for i, action in enumerate(kernel_actions):
-      if action.op == OptOps.TC and (tc_arg := cast(tuple, action.arg))[0] == -1:
-        # replace every tc_action with default tc with one tc_action for each available tc
-        kernel_actions[i:i+1] = \
-          [Opt(op=OptOps.TC, axis=action.axis, arg=(tc_select, tc_arg[1], tc_arg[2])) for tc_select,_ in enumerate(lin.opts.tensor_cores)]
-
   for i,a in enumerate(kernel_actions):
     if a.axis is not None and a.op is not OptOps.TC:
       try: ax = lin.real_axis(a.op, a.axis)
@@ -127,7 +127,7 @@ def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=Non
     lin2 = lin.copy()
     try:
       lin2.apply_opt(a)
-      up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if (tc:=lin2.tensor_core) else 1
+      up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if hasattr(lin2, 'tensor_core') and (tc:=lin2.tensor_core) else 1
       for s,c in zip(lin2.full_shape, lin2.axis_types):
         if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
         elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
@@ -139,7 +139,7 @@ def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=Non
   return acted_lins
 
 beam_pool, BEAM_DEBUG = None, getenv("BEAM_DEBUG")
-def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value) -> Kernel:
+def beam_search(lin:Kernel|Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=True, disable_cache=IGNORE_BEAM_CACHE.value):
   global beam_pool
   key = {"ast": lin.ast.key, "amt": amt, "allow_test_size": allow_test_size, "device": lin.opts.device, "suffix": lin.opts.suffix}
   if not disable_cache and CACHELEVEL >= 1 and (val:=diskcache_get("beam_search", key)) is not None:
@@ -147,7 +147,7 @@ def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True,
     for o in val[len(lin.applied_opts):]: ret.apply_opt(o)
     return ret
 
-  beam: list[tuple[Kernel, float]] = [(lin, float("inf"))]
+  beam: list[tuple[Kernel|Scheduler, float]] = [(lin, float("inf"))]
   seen_libs = set()
 
   default_parallel = multiprocessing.cpu_count() if lin.opts.device in {"CUDA", "AMD", "NV", "METAL", "HIP"} else 0
@@ -157,7 +157,9 @@ def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True,
     def close_pool(): beam_pool.close()
 
   min_progress = getenv("BEAM_MIN_PROGRESS", 0.01)/1e6
-  if BEAM_DEBUG: print(f"BEAM_SEARCH:\n{lin.ast}")
+  if BEAM_DEBUG:
+    print("BEAM_SEARCH:")
+    print('\n'.join(pyrender(lin.ast.replace(arg=None))))
   if DEBUG >= 2: print(f"   0.00s:                from   1 ->   1 actions {lin.colored_shape()}")
 
   try:
@@ -166,8 +168,8 @@ def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True,
     exiting, st = False, time.perf_counter()
     dev = Device[lin.opts.device]
     while not exiting:
-      acted_lins: list[Kernel] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
-      timed_lins: list[tuple[Kernel, float]] = []
+      acted_lins: list[Kernel|Scheduler] = flatten([get_kernel_actions(lin, include_0=False).values() for lin,_ in beam])
+      timed_lins: list[tuple[Kernel|Scheduler, float]] = []
       _compile_fn = functools.partial(_try_compile_linearized_w_idx, compiler=dev.compiler)
       least_compute_ops = math.inf
       for i,proc in (map(_compile_fn, enumerate(acted_lins)) if beam_pool is None else beam_pool.imap_unordered(_compile_fn, enumerate(acted_lins))):
@@ -201,15 +203,3 @@ def beam_search(lin:Kernel, rawbufs:list[Buffer], amt:int, allow_test_size=True,
   if CACHELEVEL >= 1: diskcache_put("beam_search", key, beam[0][0].applied_opts)
   if BEAM_DEBUG: print(f"BEAM_SEARCH: final tm={time_to_str(beam[0][1], w=0)}, applied_opts={beam[0][0].applied_opts}")
   return beam[0][0]
-
-def optimize_local_size(_prg:Callable, global_size:list[int], rawbufs:list[Buffer]) -> list[int]:
-  test_rawbuffers = [Buffer(rawbufs[0].device, rawbufs[0].size, rawbufs[0].dtype).allocate(), *rawbufs[1:]] if rawbufs[0] in rawbufs[1:] else rawbufs
-  MAX_WORKGROUP = 1024
-  local_dims = [[x for x in set([sz, 1, 2, 4, 8, 16, 32, 64, 128, 256, MAX_WORKGROUP]) if x<=sz] for sz in global_size]
-  local_sizes = [list(x) for x in itertools.product(*local_dims) if prod(x) <= MAX_WORKGROUP] * 2  # try each valid size twice
-  def try_exec(local_size):
-    try: return _prg(*[x._buf for x in test_rawbuffers], global_size=[g//l if g%l == 0 else g/l for g,l in zip(global_size, local_size)], local_size=local_size, wait=True)  # noqa: E501
-    except Exception: return float('inf')
-  ret = min([(try_exec(local_size), local_size) for local_size in random.sample(local_sizes, len(local_sizes))])
-  assert not math.isinf(ret[0]), "all optimize_local_size exec failed"
-  return ret[1]

tinygrad/device.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 from dataclasses import dataclass, replace
 from collections import defaultdict
 from typing import Any, Generic, TypeVar, Iterator
-import importlib, inspect, functools, pathlib, os, platform, contextlib, sys, re, atexit, pickle, decimal, time
+import importlib, inspect, functools, pathlib, os, platform, contextlib, sys, re, atexit, pickle, decimal
 from tinygrad.helpers import CI, OSX, LRU, getenv, diskcache_get, diskcache_put, DEBUG, GlobalCounters, flat_mv, PROFILE, temp, colored, \
                              Context, DISABLE_COMPILER_CACHE, ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, cpu_events, ProfileEvent, ProfilePointEvent, dedup
 from tinygrad.dtype import DType, ImageDType, PtrDType, dtypes, _to_np_dtype
@@ -138,15 +138,14 @@ class Buffer:
       if not self.device.startswith("DISK"): GlobalCounters.mem_used += self.nbytes
       if PROFILE:
         self._prof_num = num = len(Buffer.profile_events)
-        ts = decimal.Decimal(time.perf_counter_ns())/1000
-        Buffer.profile_events.append(ProfilePointEvent(self.device, "alloc", ts, num, {"dtype":self.dtype, "sz":self.size}))
+        Buffer.profile_events.append(ProfilePointEvent(self.device, "alloc", num, {"dtype":self.dtype, "sz":self.size}))
     return self
   def deallocate(self):
     assert hasattr(self, '_buf'), "buffer must be allocated to deallocate"
     if DEBUG is not None and DEBUG >= 7: print(f"buffer: deallocate {self.nbytes} bytes on {self.device}")
     if self._base is None and (self.options is None or self.options.external_ptr is None):
       if GlobalCounters is not None and not self.device.startswith("DISK"): GlobalCounters.mem_used -= self.nbytes
-      if PROFILE: Buffer.profile_events.append(ProfilePointEvent(self.device, "free", decimal.Decimal(time.perf_counter_ns())/1000, self._prof_num))
+      if PROFILE: Buffer.profile_events.append(ProfilePointEvent(self.device, "free", self._prof_num))
       self.allocator.free(self._buf, self.nbytes, self.options)
     elif self._base is not None: self._base.allocated_views -= 1
     del self._buf
@@ -304,7 +303,7 @@ def is_dtype_supported(dtype:DType, device:str|None=None) -> bool:
     if device == "METAL": return not CI
     if device in {"CUDA", "NV"}: return not CI and not getenv("PTX")
     if device in {"CPU", "LLVM"}: return not CI and platform.machine() in {"arm", "arm64", "aarch64", "x86_64", "amd64"}
-    return device == "AMD"
+    return device in {"AMD", "PYTHON"}
   if dtype in dtypes.fp8s:
     # not supported yet - in progress
     return False

tinygrad/dtype.py

@@ -67,7 +67,7 @@ class PtrDType(DType):
     return type(self)(self.priority, self.itemsize, self.name, self.fmt, self.count, self, self._base, self.addrspace, sz, self.size)
   def ptr(self, size=-1, addrspace=AddrSpace.GLOBAL): raise RuntimeError("can't make a pointer from a pointer")
   def nbytes(self) -> int:
-    if self.size == -1: return 0  # TODO: this should be an exception
+    if self.size == -1: raise RuntimeError("can't get nbytes of a pointer with unlimited size")
     return self.size*self.itemsize
   @property
   def vcount(self): return self.v
@@ -112,12 +112,12 @@ class dtypes:
   @staticmethod
   @functools.cache
   def min(dtype:DType):
-    if dtypes.is_int(dtype): return 0 if dtypes.is_unsigned(dtype) else -2**(dtype.itemsize*8-1)
+    if dtypes.is_int(dtype): return 0 if dtypes.is_unsigned(dtype) else -2**(dtype.scalar().itemsize*8-1)
     return -float("inf") if dtypes.is_float(dtype) else False
   @staticmethod
   @functools.cache
   def max(dtype:DType):
-    if dtypes.is_int(dtype): return 2**(dtype.itemsize*8)-1+dtypes.min(dtype)
+    if dtypes.is_int(dtype): return 2**(dtype.scalar().itemsize*8)-1+dtypes.min(dtype)
     return float("inf") if dtypes.is_float(dtype) else True
   @staticmethod
   def finfo(dtype:DType) -> tuple[int, int]:
@@ -198,8 +198,9 @@ def can_safe_cast(dt0:DType, dt1:DType) -> bool:
   # https://numpy.org/doc/stable/reference/generated/numpy.can_cast.html
   if dt0 == dt1 or dt0 == dtypes.bool: return True
   match dt1:
-    case dtypes.double: return dt0 in (dtypes.float, dtypes.half, dtypes.bfloat16)
-    case dtypes.float: return dt0 in (dtypes.half, dtypes.bfloat16)
+    case dtypes.double: return dt0 in (dtypes.float, dtypes.half, dtypes.bfloat16,
+      dtypes.uint32, dtypes.uint16, dtypes.uint8, dtypes.int32, dtypes.int16, dtypes.int8)
+    case dtypes.float: return dt0 in (dtypes.half, dtypes.bfloat16, dtypes.uint16, dtypes.uint8, dtypes.int16, dtypes.int8)
     case dtypes.uint64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8)
     case dtypes.uint32: return dt0 in (dtypes.uint16, dtypes.uint8)
     case dtypes.int64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8, dtypes.int32, dtypes.int16, dtypes.int8)
@@ -298,6 +299,7 @@ truncate: dict[DType, Callable] = {dtypes.bool: bool,
 
 def _to_np_dtype(dtype:DType) -> type|None:
   import numpy as np
+  if dtype == dtypes.bfloat16: return np.float32
   return np.dtype(dtype.fmt).type if dtype.fmt is not None else None
 def _from_np_dtype(npdtype:'np.dtype') -> DType: # type: ignore [name-defined] # noqa: F821
   import numpy as np
@@ -306,9 +308,11 @@ def _from_np_dtype(npdtype:'np.dtype') -> DType: # type: ignore [name-defined] #
 @functools.cache
 def _to_torch_dtype(dtype:DType) -> 'torch.dtype'|None:  # type: ignore [name-defined] # noqa: F821
   import numpy as np, torch
+  if dtype == dtypes.uint64: return torch.uint64
+  if dtype == dtypes.bfloat16: return torch.bfloat16
   # NOTE: torch doesn't expose this mapping with a stable API
   try: return torch.from_numpy(np.array([], dtype=_to_np_dtype(dtype))).dtype
   except TypeError: return None
 @functools.cache
 def _from_torch_dtype(torchdtype:'torch.dtype') -> DType: # type: ignore [name-defined] # noqa: F821
-  return {v:k for k in dtypes.all if (v:=_to_torch_dtype(k)) is not None}[torchdtype]
+  return {v:k for k in dtypes.all if (v:=_to_torch_dtype(k)) is not None}[torchdtype]

tinygrad/engine/realize.py

@@ -1,9 +1,9 @@
-from typing import cast, Generator
-import time, pprint, decimal
+from typing import cast, Generator, Callable
+import time, pprint, random, itertools, math
 from dataclasses import dataclass, replace, field
 from tinygrad.helpers import all_same, colored, DEBUG, GlobalCounters, ansilen, BEAM, NOOPT, all_int, CAPTURING, Metadata, TRACEMETA, TracingKey
-from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile, PROFILE, ProfilePointEvent, cpu_events
-from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo
+from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile, PROFILE, ProfilePointEvent, cpu_events, prod
+from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites, KernelInfo, pyrender
 from tinygrad.device import Device, Buffer
 from tinygrad.renderer import Renderer, ProgramSpec, Estimates
 from tinygrad.engine.schedule import ScheduleItem
@@ -26,6 +26,7 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
   """
 
   if getenv("VIZ"): graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
+  if DEBUG >= 5: print('\n'.join(pyrender(ast)))
 
   # linearize
   if renderer is None: renderer = Device.default.renderer
@@ -34,9 +35,10 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
     ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts)))
   try:
     uops = full_rewrite(ast, renderer)
-  except RuntimeError:
+  except RuntimeError as e:
     print("***** LINEARIZE FAILURE *****")
-    print(f"ast = {ast}")
+    print(e)
+    print('\n'.join(pyrender(ast)))
     raise
   assert uops[-1].op is Ops.SINK, "last uop must be sink"
 
@@ -59,13 +61,27 @@ class Runner:
   def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False) -> float|None:
     raise NotImplementedError("override this")
 
+def optimize_local_size(_prg:Callable, global_size:list[int], rawbufs:list[Buffer]) -> list[int]:
+  test_rawbuffers = [Buffer(rawbufs[0].device, rawbufs[0].size, rawbufs[0].dtype).allocate(), *rawbufs[1:]] if rawbufs[0] in rawbufs[1:] else rawbufs
+  MAX_WORKGROUP = 1024
+  local_dims = [[x for x in set([sz, 1, 2, 4, 8, 16, 32, 64, 128, 256, MAX_WORKGROUP]) if x<=sz] for sz in global_size]
+  local_sizes = [list(x) for x in itertools.product(*local_dims) if prod(x) <= MAX_WORKGROUP] * 2  # try each valid size twice
+  def try_exec(local_size):
+    try:
+      return _prg(*[x._buf for x in test_rawbuffers],global_size=[g//l if g%l == 0 else g/l for g,l in zip(global_size, local_size)],
+                  local_size=local_size, wait=True)
+    except Exception: return float('inf')
+  ret = min([(try_exec(local_size), local_size) for local_size in random.sample(local_sizes, len(local_sizes))])
+  assert not math.isinf(ret[0]), "all optimize_local_size exec failed"
+  return ret[1]
+
 class CompiledRunner(Runner):
   def __init__(self, p:ProgramSpec, precompiled:bytes|None=None, prg=None):
     if DEBUG >= 4: print(p.src)
     self.p:ProgramSpec = p
     if precompiled is not None: self.lib = precompiled
     else:
-      with cpu_profile(TracingKey(f"compile {p.name}", (p.function_name,), cat="compiler"), "TINY"):
+      with cpu_profile(TracingKey(f"compile {p.name}", (p.function_name,)), "TINY"):
         self.lib = Device[p.device].compiler.compile_cached(p.src)
     if DEBUG >= 7: Device[p.device].compiler.disassemble(self.lib)
     self._prg = Device[p.device].runtime(p.function_name, self.lib) if prg is None else prg
@@ -76,8 +92,6 @@ class CompiledRunner(Runner):
   def __call__(self, rawbufs:list[Buffer], var_vals:dict[Variable, int], wait=False) -> float|None:
     global_size, local_size = self.p.launch_dims(var_vals)
     if global_size is not None and local_size is None and all_int(self.p.global_size): # type: ignore[arg-type]
-      # TODO: this is copied from get_program
-      from tinygrad.codegen.opt.search import optimize_local_size
       local_size = optimize_local_size(self._prg, global_size, rawbufs)
       global_size = [g//l if g%l == 0 else g/l for g,l in zip(global_size, local_size)]
       self.p = replace(self.p, global_size=global_size, local_size=local_size)
@@ -149,8 +163,7 @@ class ExecItem:
   def run(self, _var_vals:dict[Variable, int]|None=None, wait=False, jit=False, do_update_stats=True) -> float|None:
     var_vals = self.fixedvars if _var_vals is None else (_var_vals|self.fixedvars)
     bufs = [cast(Buffer, x) for x in self.bufs] if jit else [cast(Buffer, x).ensure_allocated() for x in self.bufs]
-    if PROFILE: cpu_events.append(ProfilePointEvent(self.prg.device, "exec", decimal.Decimal(time.perf_counter_ns())/1000, self.prg.display_name,
-                                                    {"metadata":self.metadata, "var_vals":var_vals}))
+    if PROFILE: cpu_events.append(ProfilePointEvent(self.prg.device, "exec", self.prg.display_name, {"metadata":self.metadata, "var_vals":var_vals}))
     et = self.prg(bufs, var_vals, wait=wait or DEBUG >= 2)
     if do_update_stats:
       GlobalCounters.kernel_count += 1

tinygrad/helpers.py

@@ -56,7 +56,7 @@ def i2u(bits: int, value: int): return value if value >= 0 else (1<<bits)+value
 def is_numpy_ndarray(x) -> bool: return str(type(x)) == "<class 'numpy.ndarray'>"
 def merge_dicts(ds:Iterable[dict[T,U]]) -> dict[T,U]:
   kvs = set([(k,v) for d in ds for k,v in d.items()])
-  assert len(kvs) == len(set(kv[0] for kv in kvs)), f"cannot merge, {kvs} contains different values for the same key"
+  if len(kvs) != len(set(kv[0] for kv in kvs)): raise RuntimeError(f"{kvs} contains different values for the same key")
   return {k:v for d in ds for k,v in d.items()}
 def partition(itr:Iterable[T], fxn:Callable[[T],bool]) -> tuple[list[T], list[T]]:
   ret:tuple[list[T], list[T]] = ([], [])
@@ -130,7 +130,7 @@ JIT = ContextVar("JIT", 2 if platform.system() == 'Darwin' and ('Intel' in platf
 JIT_BATCH_SIZE = ContextVar("JIT_BATCH_SIZE", 32)
 WINO, CAPTURING, TRACEMETA = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1)
 USE_TC, TC_SELECT, TC_OPT, AMX = ContextVar("TC", 1), ContextVar("TC_SELECT", -1), ContextVar("TC_OPT", 0), ContextVar("AMX", 0)
-TRANSCENDENTAL, TC_SEARCH_OVER_SHAPE, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("TC_SEARCH_OVER_SHAPE", 1), ContextVar("NOLOCALS", 0)
+TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS", 0)
 FUSE_ARANGE, FUSE_CONV_BW = ContextVar("FUSE_ARANGE", 1), ContextVar("FUSE_CONV_BW", 0)
 SPLIT_REDUCEOP, NO_MEMORY_PLANNER, RING = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("RING", 1)
 PICKLE_BUFFERS, PROFILE, LRU = ContextVar("PICKLE_BUFFERS", 1), ContextVar("PROFILE", getenv("VIZ")), ContextVar("LRU", 1)
@@ -141,6 +141,7 @@ QUANTIZE, VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("QUANTIZE", 0), Cont
 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)
 RANGEIFY, POSTOPT, FUSE_ATTENTION = ContextVar("RANGEIFY", 0), ContextVar("POSTOPT", 0), ContextVar("FUSE_ATTENTION", 0)
+EMULATE = ContextVar("EMULATE", "")
 
 @dataclass(frozen=True)
 class Metadata:
@@ -192,12 +193,12 @@ class Profiling(contextlib.ContextDecorator):
               colored(_format_fcn(fcn).ljust(50), "yellow"),
               colored(f"<- {(scallers[0][1][2]/tottime)*100:3.0f}% {_format_fcn(scallers[0][0])}", "BLACK") if scallers else '')
 
+def perf_counter_us() -> decimal.Decimal: return decimal.Decimal(time.perf_counter_ns())/1000
 
 @dataclass(frozen=True)
 class TracingKey:
   display_name:str                       # display name of this trace event
   keys:tuple[Any, ...]=()                # optional keys to search for related traces
-  cat:str|None=None                      # optional category to color this by
   ret:Any=None
 
 class ProfileEvent: pass
@@ -206,17 +207,21 @@ class ProfileEvent: pass
 class ProfileRangeEvent(ProfileEvent): device:str; name:str|TracingKey; st:decimal.Decimal; en:decimal.Decimal|None=None; is_copy:bool=False # noqa: E702
 
 @dataclass(frozen=True)
-class ProfilePointEvent(ProfileEvent): device:str; name:str; ts:decimal.Decimal; key:Any; arg:dict=field(default_factory=dict) # noqa: E702
+class ProfilePointEvent(ProfileEvent): device:str; name:str; key:Any; arg:dict=field(default_factory=dict); \
+    ts:decimal.Decimal=field(default_factory=perf_counter_us) # noqa: E702
 
 cpu_events:list[ProfileEvent] = []
 @contextlib.contextmanager
 def cpu_profile(name:str|TracingKey, device="CPU", is_copy=False, display=True) -> Generator[ProfileRangeEvent, None, None]:
-  res = ProfileRangeEvent(device, name, decimal.Decimal(time.perf_counter_ns()) / 1000, is_copy=is_copy)
+  res = ProfileRangeEvent(device, name, perf_counter_us(), is_copy=is_copy)
   try: yield res
   finally:
-    res.en = decimal.Decimal(time.perf_counter_ns()) / 1000
+    res.en = perf_counter_us()
     if PROFILE and display: cpu_events.append(res)
 
+def profile_marker(name:str, color="gray") -> None:
+  cpu_events.append(ProfilePointEvent("TINY", "marker", None, {"name":name, "color":color}))
+
 # *** universal database cache ***
 
 cache_dir: str = os.path.join(getenv("XDG_CACHE_HOME", os.path.expanduser("~/Library/Caches" if OSX else "~/.cache")), "tinygrad")

tinygrad/nn/__init__.py

@@ -320,6 +320,7 @@ class Embedding:
 
   def __call__(self, idx:Tensor) -> Tensor:
     if not hasattr(self, 'arange'): self.arange = Tensor.arange(self.vocab_sz, requires_grad=False, device=self.weight.device).unsqueeze(-1)
+    if not dtypes.is_int(idx.dtype): raise TypeError(f"Expected integer dtype for index in embedding, got {idx.dtype}")
     big_shp = idx.shape+(self.vocab_sz, self.embed_sz)
     arange, idx, vals = self.arange.expand(big_shp), idx.reshape(idx.shape+(1, 1)).expand(big_shp), self.weight.expand(big_shp)
     return (arange == idx).mul(vals).sum(-2, dtype=vals.dtype)

tinygrad/renderer/__init__.py

@@ -39,14 +39,14 @@ class Estimates:
         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
+          mem[(buf, u.op)] = buf.ptrdtype.size * buf.dtype.itemsize
       if u.op is Ops.RANGE:
         mult_stack.append(mults)
         mults *= cast(sint, u.src[0].ssimplify())
         # SPECIAL are already counted in mults
         mults = mults.substitute({x:x.const_like(0) for x in mults.toposort() if x.op is Ops.SPECIAL}) if isinstance(mults, UOp) else mults
       elif u.op is Ops.ENDRANGE: mults = mult_stack.pop(-1)
-      elif u.op is Ops.SPECIAL: mults *= u.arg[1] # NOTE: we don't push to the mult_stack here, you can't end these
+      elif u.op is Ops.SPECIAL: mults *= cast(sint, u.src[0].ssimplify()) # NOTE: we don't push to the mult_stack here, you can't end these
       elif u.op is Ops.LOAD and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
         lds += u.dtype.itemsize * mults
       elif u.op is Ops.STORE and (not isinstance(u.src[0].dtype, PtrDType) or u.src[0].dtype.addrspace != AddrSpace.REG):
@@ -82,9 +82,9 @@ class ProgramSpec:
         if u.op is Ops.LOAD: self.ins.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
         if u.op is Ops.SPECIAL:
           # NOTE: you have to set local_size and global_size to the base [1,1,1] outside this
-          if u.arg[0][0] == 'i': self.local_size = None
-          special_size = self.local_size if u.arg[0][0] == 'l' else self.global_size
-          if special_size is not None: special_size[int(u.arg[0][-1])] = u.arg[1]
+          special_size = self.local_size if u.arg[0] == 'l' else self.global_size
+          assert special_size is not None, f"special_size is None but found SPECIAL in uops {u}"
+          special_size[int(u.arg[-1])] = cast(int, u.src[0].ssimplify())  # TODO: the type here should be sint
       self.vars = sorted(self.vars, key=lambda v: v.arg)
       self.outs = sorted(dedup(self.outs))
       self.ins = sorted(dedup(self.ins))

tinygrad/renderer/cstyle.py

@@ -2,7 +2,7 @@ from typing import Literal, Callable, cast
 import os, math, sys
 from collections import defaultdict, Counter
 from tinygrad.codegen.opt import tc
-from tinygrad.uop.ops import GroupOp, Ops, UOp, PatternMatcher, UPat
+from tinygrad.uop.ops import GroupOp, Ops, UOp, PatternMatcher, UPat, sint_to_uop
 from tinygrad.helpers import strip_parens, getenv, prod, dedup, AMX
 from tinygrad.dtype import ImageDType, dtypes, DType, PtrDType, AddrSpace, truncate
 from tinygrad.renderer import Renderer
@@ -26,7 +26,7 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.DEFINE_LOCAL, name="x"), lambda ctx,x: f"{ctx.smem_align}{ctx.smem_prefix}{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
   (UPat(Ops.BARRIER), lambda ctx: ctx.barrier),
   (UPat(Ops.PRECAST, name="x"), lambda ctx,x: ctx[x.src[0]]),
-  (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; /* {x.arg[1]} */"),
+  (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0]](x.arg[-1])}; /* {(x.src[0]).render()} */"),
   # const
   (UPat(Ops.CONST, arg=math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, ctx.infinity)})"),
   (UPat(Ops.CONST, arg=-math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, f'-{ctx.infinity}')})"),
@@ -111,7 +111,8 @@ class CStyleLanguage(Renderer):
     tmp = "const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;\n" if any(isinstance(dtype, ImageDType) for _,(dtype,_) in bufs) else ""  # noqa: E501
     buftypes = [(name, self.render_dtype(dtype, mutable)+self.buffer_suffix if isinstance(dtype, (ImageDType, PtrDType)) else
                 self.arg_int_prefix if dtype == dtypes.int else None) for name,(dtype,mutable) in bufs]
-    launch_bounds = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
+    local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
+    launch_bounds = sint_to_uop(prod(local_dims)).vmax
     prg = ''.join([f"{self.kernel_typedef.format(launch_bounds=launch_bounds)} {function_name}(",] +
     [', '.join([f'{t} {name}' for name,t in buftypes] + self.extra_args)] +
     [") {\n" + tmp] + ['\n'.join(kernel), "\n}"])
@@ -145,7 +146,7 @@ class CStyleLanguage(Renderer):
         if u.arg is not None: name = u.arg.function_name
         continue
       if u.op in (Ops.DEFINE_GLOBAL, Ops.DEFINE_VAR):
-        r[u] = (f"data{u.arg}_{sz}" if (sz:=cast(PtrDType, u.dtype).size) > 0 else f"data{u.arg}") if u.op is Ops.DEFINE_GLOBAL else u.arg[0]
+        r[u] = (f"data{u.arg}_{sz}" if (sz:=u.ptrdtype.size) > 0 else f"data{u.arg}") if u.op is Ops.DEFINE_GLOBAL else u.arg[0]
         bufs[u] = (r[u], (u.dtype, False))
         continue
 
@@ -156,7 +157,7 @@ class CStyleLanguage(Renderer):
 
       # naming
       prefix = None
-      if u.op is Ops.SPECIAL: r[u] = u.arg[0]
+      if u.op is Ops.SPECIAL: r[u] = u.arg
       elif u.op is Ops.RANGE: r[u] = "ridx"+'_'.join([str(x) if x >= 0 else "m"+str(-x) for x in u.arg[0:-1]])
       else:
         prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
@@ -169,7 +170,7 @@ class CStyleLanguage(Renderer):
 
       if u.op in {Ops.ENDIF, Ops.ENDRANGE}: depth -= 1
       if (u.op is not Ops.CAST or u.dtype.vcount == 1) and (u.op in {Ops.CONST, Ops.GEP, Ops.INDEX, Ops.CUSTOMI} or \
-        (u.op is Ops.LOAD and cast(PtrDType, u.src[0].dtype).addrspace == AddrSpace.REG) or \
+        (u.op is Ops.LOAD and u.src[0].ptrdtype.addrspace == AddrSpace.REG) or \
         (u.op is Ops.CAST and isinstance(u.dtype, PtrDType)) or \
         (u.op in {Ops.VECTORIZE, *(GroupOp.ALU-{Ops.WHERE}), Ops.CAST, Ops.BITCAST} and child_count[u] == 1 and not getenv("EXPAND_SSA"))):
         r[u] = l
@@ -316,9 +317,7 @@ class MetalRenderer(CStyleLanguage):
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None):
     prefix = ["#include <metal_stdlib>","using namespace metal;"]
-    wargs = wmma_args(uops)
-    if len(wargs) > 0: wargs = wargs[0:1]
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wargs: prefix.append(
+    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): prefix.append(
   f"""{(dstr_out:=self.render_dtype(dtype_out.vec(2)))} __{name}({(dstr_in:=self.render_dtype(dtype_in.vec(2)))} a, {dstr_in} b, {dstr_out} c){{
   simdgroup_{self.render_dtype(dtype_in)}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(dtype_out)}8x8 mat_c;
   mat_a.thread_elements()[0] = a[0]; mat_b.thread_elements()[0] = b[0]; mat_c.thread_elements()[0] = c[0];

tinygrad/renderer/llvmir.py

@@ -3,7 +3,7 @@ import math, struct, sys
 from tinygrad.codegen.opt import tc
 from tinygrad.renderer import Renderer
 from tinygrad.renderer.cstyle import AMDRenderer
-from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, GroupOp
+from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, GroupOp, sint_to_uop
 from tinygrad.dtype import dtypes, DType, PtrDType, truncate
 from tinygrad.helpers import prod, AMX
 
@@ -196,14 +196,20 @@ class LLVMRenderer(Renderer):
 barrier = 'fence syncscope("workgroup") release\ntail call void @llvm.amdgcn.s.barrier()\nfence syncscope("workgroup") acquire\n'
 code_for_workitem = {"g": lambda x: f"tail call i32 @llvm.amdgcn.workgroup.id.{chr(120+int(x))}()",
                      "l": lambda x: f"tail call i32 @llvm.amdgcn.workitem.id.{chr(120+int(x))}()"}
+# https://rocm.docs.amd.com/projects/llvm-project/en/latest/LLVM/llvm/html/AMDGPUUsage.html#llvm-ir-intrinsics
+# llvm.log2/llvm.exp2 don't support double
+llvm_intrinsics = {Ops.SQRT: "sqrt"}
 class AMDLLVMRenderer(LLVMRenderer):
   device = "AMD"
   has_local = True
   shared_max = AMDRenderer.shared_max
   global_max = AMDRenderer.global_max
   abi = "amdgpu_kernel"
+  code_for_op = {**LLVMRenderer.code_for_op, **{op: lambda: None for op in llvm_intrinsics}}
   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.SPECIAL, name="x"), lambda ctx, x: f"  {ctx[x]} = " + f"{ code_for_workitem[x.arg[0]](x.arg[-1])}; "),
+    (UPat(tuple(llvm_intrinsics), name="x"),
+    lambda ctx, x: f"  {ctx[x]} = call {ldt(x.dtype)} @llvm.{llvm_intrinsics[x.op]}.{ldt(x.dtype.scalar())}({ldt(x.src[0].dtype)} {ctx[x.src[0]]})"),
     (UPat(Ops.BARRIER), lambda ctx: barrier),
   ]) + base_rewrite
   extra_matcher = LLVMRenderer.extra_matcher + PatternMatcher([
@@ -214,7 +220,8 @@ class AMDLLVMRenderer(LLVMRenderer):
   ])
   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")
+    local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
+    requiredMaxThreadsPerBlock = sint_to_uop(prod(local_dims)).vmax
     attributes = ["alwaysinline", "nounwind", '"no-builtins"',
                   f'"amdgpu-flat-work-group-size"="1,{requiredMaxThreadsPerBlock}"', '"no-trapping-math"="true"']
     return 'attributes #0 = { ' + ' '.join(attributes) + ' }'

tinygrad/renderer/ptx.py

@@ -2,7 +2,7 @@ from typing import cast, Callable
 import struct
 from collections import defaultdict
 from tinygrad.codegen.opt import tc
-from tinygrad.uop.ops import Ops, UOp, PatternMatcher, UPat, GroupOp
+from tinygrad.uop.ops import Ops, UOp, PatternMatcher, UPat, GroupOp, sint_to_uop
 from tinygrad.dtype import dtypes, DType, PtrDType, AddrSpace
 from tinygrad.renderer import Renderer
 from tinygrad.renderer.cstyle import CUDARenderer
@@ -91,7 +91,7 @@ string_rewrite = PatternMatcher([
   (UPat(Ops.STORE, name="x", src=(UPat.var('bidx'), UPat.var("var")), allow_any_len=True), lambda ctx, x, bidx, var: f"st.{mem_type(bidx)}" + \
     f"{f'.v{cnt}' if ((cnt:=var.dtype.count)>1) else ''}.{ctx.mem_types[var.dtype.scalar()]} " + \
     f"[{ctx.r[bidx]}+0], {('{' + ', '.join(ctx.r[var]) + '}') if var.dtype.count > 1 else ctx.r[var]};"),
-  (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"mov.u32 %{x.arg[0]}, %{'ctaid' if x.arg[0][0] == 'g' else 'tid'}.{chr(120+int(x.arg[0][-1]))};"),
+  (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"mov.u32 %{x.arg}, %{'ctaid' if x.arg[0] == 'g' else 'tid'}.{chr(120+int(x.arg[-1]))};"),
   (UPat(Ops.DEFINE_GLOBAL, name="x"), lambda ctx, x: f"ld.param.{ctx.types[dtypes.ulong]} {ctx.r[x]}, [data{x.arg}+0];"),
   (UPat((Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ), name="x", allow_any_len=True, src=(UPat.var("src0"),)),
     lambda ctx, x, src0: ctx.code_for_op[x.op](ctx.r[x], *[ctx.r[v] for v in x.src], src0.dtype, ctx.types[src0.dtype])),
@@ -155,7 +155,8 @@ class PTXRenderer(Renderer):
   def render_kernel(self, kernel, function_name, bufs, regs, uops) -> str:
     def fmt(line): return line if line[0]=="$" else "\t" + line.replace(" ", "\t" if len(line.split(" ")[0]) > 7 else "\t\t", 1)
     kernel = '\n'.join(map(fmt, [f".reg .{reg.split('_')[-2]} %{reg}<{cnt}>;" for reg,cnt in regs] + kernel + ["ret;"]))
-    launch_bounds = prod(u.arg[1] for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == "l")
+    local_dims = [u.src[0] for u in uops if u.op is Ops.SPECIAL and u.arg[0] == "l"]
+    launch_bounds = sint_to_uop(prod(local_dims)).vmax
     params = ',\n\t'.join([f".param .{'u64' if dtype.__class__ == PtrDType else self.types[dtype]} {name}" for name,dtype in bufs])
     return f"{self.kernel_prefix.format(launch_bounds=launch_bounds)} {function_name} (\n\t{params}\n)\n.maxntid {launch_bounds}\n{{\n{kernel}\n}}"
 
@@ -190,7 +191,7 @@ class PTXRenderer(Renderer):
         r[u] = r[u.src[0]]
         continue
       if u.op is Ops.DEFINE_REG:
-        r[u] = [ssa("reg", u, self.types[u.dtype.base.scalar()]) for _ in range(cast(PtrDType, u.dtype).size)]
+        r[u] = [ssa("reg", u, self.types[u.dtype.base.scalar()]) for _ in range(u.ptrdtype.size)]
         continue
       if u.op in {Ops.INDEX, Ops.LOAD, Ops.STORE} and isinstance(u.src[0].dtype, PtrDType) and u.src[0].dtype.addrspace == AddrSpace.REG:
         if u.op is Ops.INDEX:
@@ -202,7 +203,7 @@ class PTXRenderer(Renderer):
             typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
             kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
         continue
-      if u.op is Ops.SPECIAL: r[u] = "%" + u.arg[0]
+      if u.op is Ops.SPECIAL: r[u] = "%" + u.arg
       elif u.op is Ops.DEFINE_VAR: bufs.append((u.arg[0], u.dtype))
       elif u.op is Ops.LOAD:
         assert u.src[0].dtype == dtypes.int64, "load isn't int64"
@@ -223,5 +224,5 @@ class PTXRenderer(Renderer):
         raise RuntimeError(f"failed to render {u.op} with {u.dtype} srcs {[x.dtype for x in u.src]}")
       kernel.extend([l] if isinstance(l, str) else l)
 
-      if u.op is Ops.SPECIAL: kernel = [f".reg .u32 %{u.arg[0]};"] + kernel
+      if u.op is Ops.SPECIAL: kernel = [f".reg .u32 %{u.arg};"] + kernel
     return self.render_kernel(kernel, name, bufs, c.items(), uops)

tinygrad/renderer/wgsl.py

@@ -84,7 +84,7 @@ class WGSLRenderer(CStyleLanguage):
   def render_load(self, x:str, dt:DType) -> str: return f"atomicLoad(&{x})" if is_packed(dt) else x
   def buf_map(self, dt:DType) -> str: return "atomic<u32>" if is_packed(dt) else self.type_map[dt.base]
   def render_kernel(self, function_name:str, kernel:list[str], bufs:list[tuple[str,tuple[DType,bool]]], uops:list[UOp], prefix=None) -> str:
-    local_size = [num for _, num in sorted([u.arg for u in uops if u.op is Ops.SPECIAL and u.arg[0][0] == 'l'], key=lambda x: x[0])]
+    local_size = [u.src[0].ssimplify() for u in sorted([u for u in uops if u.op is Ops.SPECIAL and u.arg[0] == 'l'], key=lambda u: u.arg)]
     if not local_size: local_size = [1]
     bind_it = iter(range(len(bufs)))
     external_local_bufs = [line.lstrip() for line in kernel if "var<workgroup>" in line]

tinygrad/runtime/ops_amd.py

@@ -7,8 +7,7 @@ from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, H
 from tinygrad.runtime.support.hcq import MMIOInterface, BumpAllocator
 from tinygrad.uop.ops import sint
 from tinygrad.device import Compiled, DMAFdRef, BufferSpec
-from tinygrad.helpers import getenv, to_mv, round_up, data64_le, all_same, flatten, DEBUG, AMD_LLVM, PROFILE, ProfileEvent, suppress_finalizing
-from tinygrad.helpers import lo32, hi32
+from tinygrad.helpers import getenv, to_mv, round_up, data64_le, DEBUG, AMD_LLVM, PROFILE, ProfileEvent, suppress_finalizing, lo32, hi32
 from tinygrad.renderer.cstyle import AMDRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
 from tinygrad.runtime.autogen import kfd, hsa, pci, sqtt
@@ -109,17 +108,6 @@ class AMDComputeQueue(HWQueue):
 
     self.pkt3(self.pm4.PACKET3_RELEASE_MEM, event_dw | cache_flags_dw, memsel_dw, *data64_le(address), *data64_le(value), ctxid)
 
-  def xcc_barrier(self):
-    if self.dev.xcc_sync is None: return self
-    assert self.dev.xccs == 8, 'only 8 XCCs supported'
-    a, b = self.dev.xcc_sync
-    mem_eq = self.pm4.WAIT_REG_MEM_FUNCTION(WAIT_REG_MEM_FUNCTION_EQ) | self.pm4.WAIT_REG_MEM_MEM_SPACE(1)
-    self.pkt3(self.pm4.PACKET3_ATOMIC_MEM, self.soc.TC_OP_ATOMIC_ADD_RTN_32, *data64_le(a.value_addr), *data64_le(1), *data64_le(0), 0x10) # a += 1
-    self.pkt3(self.pm4.PACKET3_WAIT_REG_MEM, mem_eq, *data64_le(a.value_addr), 0, 0b111, 0x80) # a == 0 (mod 8) via bitmask
-    self.pkt3(self.pm4.PACKET3_ATOMIC_MEM, self.soc.TC_OP_ATOMIC_ADD_RTN_32, *data64_le(b.value_addr), *data64_le(1), *data64_le(0), 0x10) # b += 1
-    self.pkt3(self.pm4.PACKET3_WAIT_REG_MEM, mem_eq, *data64_le(b.value_addr), 0, 0b111, 0x80) # b == 0 (mod 8) via bitmask
-    return self
-
   def memory_barrier(self):
     pf = '' if self.nbio.version[0] == 2 else '0' if self.nbio.version[:2] != (7, 11) else '1'
     self.wait_reg_mem(reg_req=getattr(self.nbio, f'regBIF_BX_PF{pf}_GPU_HDP_FLUSH_REQ').addr[0],
@@ -127,13 +115,6 @@ class AMDComputeQueue(HWQueue):
     self.acquire_mem()
     return self
 
-  def xcc_config(self):
-    self.wreg(self.gc.regCOMPUTE_TG_CHUNK_SIZE, 1)
-    for xcc_id in range(self.dev.xccs):
-      with self.pred_exec(xcc_mask=1 << xcc_id):
-        self.wreg(self.dev.regCOMPUTE_CURRENT_LOGIC_XCC_ID, xcc_id)
-    return self
-
   def spi_config(self, tracing:bool):
     self.wreg(self.gc.regSPI_CONFIG_CNTL, ps_pkr_priority_cntl=3, exp_priority_order=3, gpr_write_priority=0x2c688,
               enable_sqg_bop_events=int(tracing), enable_sqg_top_events=int(tracing))
@@ -278,16 +259,10 @@ class AMDComputeQueue(HWQueue):
 
     if prg.dev.sqtt_enabled: self.pkt3(self.pm4.PACKET3_EVENT_WRITE, self.pm4.EVENT_TYPE(self.soc.THREAD_TRACE_MARKER) | self.pm4.EVENT_INDEX(0))
     self.pkt3(self.pm4.PACKET3_EVENT_WRITE, self.pm4.EVENT_TYPE(self.soc.CS_PARTIAL_FLUSH) | self.pm4.EVENT_INDEX(EVENT_INDEX_PARTIAL_FLUSH))
-
-    if self.dev.xccs > 1:
-      self.release_mem(cache_flush=True)
-      self.acquire_mem(gli=0)
-      self.xcc_barrier()
     return self
 
   def wait(self, signal:AMDSignal, value:sint=0):
     self.wait_reg_mem(mem=signal.value_addr, value=value, mask=0xffffffff)
-    if self.dev.xccs > 1 and not self.dev.is_aql: self.xcc_barrier()
     return self
 
   def timestamp(self, signal:AMDSignal):
@@ -538,12 +513,6 @@ class AMDQueueDesc:
   @property
   def read_ptr(self): return min(p[0] for p in self.read_ptrs)
 
-  @classmethod
-  def multi(cls, *queues: AMDQueueDesc):
-    assert all_same([(q.ring.addr, q.put_value) for q in queues]), f"All queues must have the same ring and put_value: {queues}"
-    return cls(ring=queues[0].ring, put_value=queues[0].put_value, doorbells=flatten(q.doorbells for q in queues),
-               read_ptrs=flatten(q.read_ptrs for q in queues), write_ptrs=flatten(q.write_ptrs for q in queues))
-
   def signal_doorbell(self, dev, doorbell_value:int|None=None):
     for write_ptr in self.write_ptrs: write_ptr[0] = self.put_value
 
@@ -707,14 +676,14 @@ class PCIIface(PCIIfaceBase):
 
   def create_queue(self, queue_type, ring, gart, rptr, wptr, eop_buffer=None, cwsr_buffer=None, ctl_stack_size=0, ctx_save_restore_size=0, xcc_id=0):
     assert cwsr_buffer is None, "no cwsr buffer for am"
-    assert queue_type != kfd.KFD_IOC_QUEUE_TYPE_COMPUTE_AQL, "no AQL queues for am"
 
     if queue_type == kfd.KFD_IOC_QUEUE_TYPE_SDMA:
       self.dev_impl.sdma.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr+rptr, wptr_addr=gart.va_addr+wptr,
                                     doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_sDMA_ENGINE0), pipe=0, queue=0)
     else:
       self.dev_impl.gfx.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr+rptr, wptr_addr=gart.va_addr+wptr,
-        eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0)
+        eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0,
+        aql=(queue_type==kfd.KFD_IOC_QUEUE_TYPE_COMPUTE_AQL))
 
     return AMDQueueDesc(ring=ring.cpu_view().view(fmt='I'), doorbells=[self.dev_impl.doorbell64.view(doorbell_index * 8, 8, fmt='Q')],
       read_ptrs=[gart.cpu_view().view(offset=rptr, size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=wptr, size=8, fmt='Q')])
@@ -800,14 +769,11 @@ class AMDDevice(HCQCompiled):
     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])
 
-    # Define the regCOMPUTE_CURRENT_LOGIC_XCC_ID register, which is missing from the asic_regs files.
-    if self.target[:2] in {(9,4),(9,5)}: self.regCOMPUTE_CURRENT_LOGIC_XCC_ID = AMDReg("regCOMPUTE_CURRENT_LOGIC_XCC_ID", 0xe25, 0, {}, self.gc.bases)
-
     nbio_name = 'nbio' if self.target[0] < 12 else 'nbif'
     nbio_pad = (0,) if self.target[0] == 9 else ()
     self.nbio = AMDIP(nbio_name, self.iface.ip_versions[am.NBIF_HWIP], {i:nbio_pad+x for i,x in self.iface.ip_offsets[am.NBIF_HWIP].items()})
 
-    self.is_aql = getenv("AMD_AQL", self.xccs > 1)
+    self.is_aql = getenv("AMD_AQL", int(self.xccs > 1))
     if self.is_aql:
       self.pm4_ibs = self.iface.alloc(0x2000 if self.is_usb() else (16 << 20), uncached=True, cpu_access=True)
       self.pm4_ib_alloc = BumpAllocator(self.pm4_ibs.size, wrap=True)
@@ -829,13 +795,6 @@ class AMDDevice(HCQCompiled):
     self.max_private_segment_size = 0
     self._ensure_has_local_memory(128) # set default scratch size to 128 bytes per thread
 
-    # XCC setup
-    self.xcc_sync: tuple[AMDSignal, AMDSignal]|None = None
-    if self.xccs > 1 and not self.is_aql:
-      self.xcc_sync_area = self.allocator.alloc(0x1000, BufferSpec(nolru=True, cpu_access=True))
-      self.xcc_sync = (AMDSignal(base_buf=self.xcc_sync_area), AMDSignal(base_buf=self.xcc_sync_area.offset(256)))
-      cast(AMDComputeQueue, self.hw_compute_queue_t()).xcc_config().submit(self)
-
     # SQTT is disabled by default because of runtime overhead and big file sizes (~200mb to Tensor.full() two 4096x4096 tensors and matmul them)
     self.sqtt_enabled = PROFILE and bool(getenv("SQTT", 0))
     if self.sqtt_enabled:
@@ -866,10 +825,9 @@ class AMDDevice(HCQCompiled):
     cwsr_buffer = self.iface.alloc(cwsr_buffer_size) if ctx_save_restore_size else None
     eop_buffer = self.iface.alloc(eop_buffer_size) if eop_buffer_size else None
 
-    return AMDQueueDesc.multi(*(self.iface.create_queue(queue_type, ring, gart, rptr=getattr(hsa.amd_queue_t, 'read_dispatch_id').offset,
-                                wptr=getattr(hsa.amd_queue_t, 'write_dispatch_id').offset, eop_buffer=eop_buffer, cwsr_buffer=cwsr_buffer,
-                                xcc_id=xcc_id, ctx_save_restore_size=ctx_save_restore_size, ctl_stack_size=ctl_stack_size)
-                                for xcc_id in range(self.xccs if queue_type == kfd.KFD_IOC_QUEUE_TYPE_COMPUTE else 1)))
+    return (self.iface.create_queue(queue_type, ring, gart, rptr=getattr(hsa.amd_queue_t, 'read_dispatch_id').offset,
+            wptr=getattr(hsa.amd_queue_t, 'write_dispatch_id').offset, eop_buffer=eop_buffer, cwsr_buffer=cwsr_buffer,
+            ctx_save_restore_size=ctx_save_restore_size, ctl_stack_size=ctl_stack_size))
 
   def _ensure_has_local_memory(self, required):
     if self.max_private_segment_size >= required: return

tinygrad/runtime/ops_cpu.py

@@ -28,9 +28,9 @@ class ClangJITCompiler(Compiler):
   def disassemble(self, lib:bytes): return capstone_flatdump(lib)
 
 class CPUWorker(threading.Thread):
-  def __init__(self, dev):
+  def __init__(self, dev, tasks, thread_id):
     super().__init__()
-    self.dev, self.tasks, self.daemon = dev, dev.tasks, True
+    self.dev, self.tasks, self.thread_id, self.daemon = dev, tasks, thread_id, True
 
   def run(self):
     while True:
@@ -121,5 +121,5 @@ class CPUAllocator(HCQAllocatorBase):
 class CPUDevice(HCQCompiled):
   def __init__(self, device:str=""):
     self.tasks:queue.Queue = queue.Queue()
-    CPUWorker(self).start()
+    CPUWorker(self, self.tasks, thread_id=0).start()
     super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), CPUSignal, CPUComputeQueue)

tinygrad/runtime/ops_llvm.py

@@ -74,5 +74,5 @@ class HostLLVMCompiler(LLVMCompiler):
 class LLVMDevice(HCQCompiled):
   def __init__(self, device:str=""):
     self.tasks:queue.Queue = queue.Queue()
-    CPUWorker(self).start()
+    CPUWorker(self, self.tasks, thread_id=0).start()
     super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue)

tinygrad/runtime/ops_null.py

@@ -1,7 +1,9 @@
+import functools
 from tinygrad.device import Compiled, Compiler, Allocator
 from tinygrad.engine.jit import MultiGraphRunner
 from tinygrad.renderer.cstyle import CStyleLanguage
 from tinygrad.uop.ops import Ops
+from tinygrad.helpers import cpu_profile
 
 class NullRenderer(CStyleLanguage):
   device = "NULL"
@@ -11,19 +13,21 @@ class NullRenderer(CStyleLanguage):
   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})"}
 
 class NullProgram:
-  def __init__(self, name:str, lib:bytes): pass
+  def __init__(self, device:str, name:str, lib:bytes): self.device, self.name = device, name
   def __call__(self, *bufs, global_size:tuple[int,int,int]=(1,1,1), local_size:tuple[int,int,int]=(1,1,1), vals:tuple[int, ...]=(), wait=False):
-    return 1e-4
+    with cpu_profile(self.name, self.device): return 1e-4
 
 class NullAllocator(Allocator['NullDevice']):
   def _alloc(self, size, options): pass
   def _copyin(self, dest, src:memoryview): pass
   def _copyout(self, dest:memoryview, src): pass
-  def _transfer(self, dest, src, sz:int, src_dev, dest_dev): pass
+  def _transfer(self, dest, src, sz:int, src_dev, dest_dev):
+    with cpu_profile(f"{src_dev.device} -> {dest_dev.device}", self.dev.device): pass
   def _offset(self, buf, offset:int, size:int): pass
 
 class NullGraph(MultiGraphRunner):
   def __call__(self, input_rawbuffers, var_vals, wait=False) -> float|None: return 1e-3
 
 class NullDevice(Compiled):
-  def __init__(self, device:str): super().__init__(device, NullAllocator(self), NullRenderer(), Compiler(), NullProgram, NullGraph)
+  def __init__(self, device:str): super().__init__(device, NullAllocator(self), NullRenderer(), Compiler(), functools.partial(NullProgram, device),
+                                                   NullGraph)

tinygrad/runtime/ops_python.py

@@ -2,27 +2,37 @@
 # a python uops emulator
 # works to test the tensor cores, and all the uops in general
 # this is the (living) definition of uops
-from typing import Any, TYPE_CHECKING
+from typing import Any, TYPE_CHECKING, cast
 import pickle, base64, itertools, time, struct, sys
-from tinygrad.dtype import DType, dtypes, ImageDType, PtrDType, truncate
-from tinygrad.helpers import all_same, getenv, flatten, get_single_element
+from tinygrad.dtype import DType, dtypes, ImageDType, PtrDType, truncate, float_to_bf16
+from tinygrad.helpers import all_same, getenv, flatten, get_single_element, EMULATE
 from tinygrad.device import Compiled, Compiler, Allocator
 from tinygrad.codegen.opt import tc
-from tinygrad.uop.ops import exec_alu, Ops, UOp, GroupOp
+from tinygrad.uop.ops import exec_alu, python_alu, Ops, UOp, GroupOp
 from tinygrad.renderer import Renderer
 
-def _load(m, i):
+def storage_fmt_for_dtype(dtype: DType): return 'H' if dtype == dtypes.bfloat16 else dtype.fmt
+
+def to_storage_scalar(x, dtype: DType):
+  if dtype == dtypes.bfloat16: return (struct.unpack('I', struct.pack('f', float_to_bf16(x)))[0] >> 16) & 0xFFFF
+  return x
+
+def from_storage_scalar(x, dtype: DType):
+  if dtype == dtypes.bfloat16: return struct.unpack('f', struct.pack('I', (x & 0xFFFF) << 16))[0]
+  return x
+
+def _load(m, i, dtype: DType):
   if i is None: return 0.0
   if i < 0 or i >= len(m): raise IndexError(f"load out of bounds, size is {len(m)} and access is {i}")
-  return m[i]
+  return from_storage_scalar(m[i], dtype)
 
-def load(inp, j=0):
-  if len(inp) == 2: return [_load(m, x+j if x is not None else None) if gate else default for (m,x,gate),default in zip(*inp)]
-  return [_load(m, x+j if x is not None else None) for m,x,_ in inp[0]]
+def load(inp, j, dtype: DType):
+  if len(inp) == 2: return [_load(m, x+j if x is not None else None, dtype) if gate else default for (m,x,gate),default in zip(*inp)]
+  return [_load(m, x+j if x is not None else None, dtype) for m,x,_ in inp[0]]
 
-def _store(m, i, v):
+def _store(m, i, v, dtype: DType):
   if i < 0 or i >= len(m): raise IndexError(f"store out of bounds, size is {len(m)}, access is {i}, value is {v}")
-  m[i] = v
+  m[i] = to_storage_scalar(v, dtype)
 
 class PythonProgram:
   def __init__(self, name:str, lib:bytes):
@@ -57,24 +67,25 @@ class PythonProgram:
         if uop is Ops.STORE:
           for j,val in enumerate(inp[1] if dtp[1].count > 1 else [inp[1]]):
             for (m,o,g),v in zip(inp[0], val):
-              if g: _store(m, o+j, v)
+              if g: _store(m, o+j, v, dtp[1].scalar())
           i += 1
           continue
         if uop in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
           assert isinstance(dtype, PtrDType), dtype
-          if dtype.fmt is None: raise RuntimeError(f"{dtype=} is not supported")
-          if TYPE_CHECKING or sys.version_info < (3, 12): assert dtype.fmt != "e"
+          storage_fmt = storage_fmt_for_dtype(dtype.base.scalar())
+          if storage_fmt is None: raise RuntimeError(f"{dtype=} is not supported")
+          if TYPE_CHECKING or sys.version_info < (3, 12): assert storage_fmt != "e"
           if uop is Ops.DEFINE_REG:
             # REGs are per thread
-            ul[i] = [memoryview(bytearray(dtype.size*dtype.itemsize)).cast(dtype.fmt) for _ in range(warp_size)]
+            ul[i] = [memoryview(bytearray(dtype.size*dtype.itemsize)).cast(storage_fmt) for _ in range(warp_size)]
           else:
             buf = memoryview(bytearray(dtype.size*dtype.itemsize)) if uop is not Ops.DEFINE_GLOBAL else pbufs.pop(0)
-            ul[i] = [buf.cast(dtype.fmt)] * warp_size
+            ul[i] = [buf.cast(storage_fmt)] * warp_size
         elif uop is Ops.DEFINE_VAR:
           ul[i] = [pvals.pop(0)] * warp_size
         elif uop is Ops.SPECIAL:
-          if arg[0][0] == 'g': ul[i] = [idxs[2-int(arg[0][-1])]] * warp_size
-          elif arg[0][0] == 'l': ul[i] = [x[2-int(arg[0][-1])] for x in warp]
+          if arg[0] == 'g': ul[i] = [idxs[2-int(arg[-1])]] * warp_size
+          elif arg[0] == 'l': ul[i] = [x[2-int(arg[-1])] for x in warp]
         elif uop is Ops.CONST: ul[i] = [arg] * warp_size
         elif uop is Ops.INDEX:
           ret:list = []
@@ -98,16 +109,17 @@ class PythonProgram:
               continue
         elif uop is Ops.VECTORIZE: ul[i] = inp
         elif uop is Ops.BITCAST:
-          assert dtp[0].fmt and dtype.fmt
-          pack_format, unpack_format = str(warp_size) + dtp[0].fmt, str(warp_size) + dtype.fmt
-          ul[i] = list(struct.unpack(unpack_format, struct.pack(pack_format, *inp[0])))
+          packed = struct.pack(str(warp_size) + storage_fmt_for_dtype(dtp[0].scalar()), *[to_storage_scalar(x, dtp[0].scalar()) for x in inp[0]])
+          ul[i] = list(struct.unpack(str(warp_size) +  storage_fmt_for_dtype(dtype.scalar()), packed))
+          ul[i] = [from_storage_scalar(x, dtype.scalar()) for x in ul[i]]
         elif uop is Ops.CAST:
           ul[i] = [truncate.get(dtype, lambda dt: dt)(dtypes.as_const(x, dtype)) for x in inp[0]]
         elif uop is Ops.LOAD:
           if dtype.count > 1:
-            ul[i] = [load([inp[i][j] if i != 0 and dtp[i].count > 1 else inp[i] for i in range(len(inp))], j) for j in range(dtype.count)]
+            ul[i] = [load([inp[i][j] if i != 0 and dtp[i].count > 1 else inp[i] for i in range(len(inp))], j, dtype.scalar()) \
+              for j in range(dtype.count)]
           else:
-            ul[i] = load(inp)
+            ul[i] = load(inp, 0, dtype)
         elif uop is Ops.GEP: ul[i] = inp[0][get_single_element(arg)]
         elif uop is Ops.WMMA:
           # here are the models for the WMMA instruction on the different hardware
@@ -188,7 +200,7 @@ class PythonProgram:
           else: raise NotImplementedError(f"unimplemented tensor core {arg}")
         elif uop in GroupOp.ALU:
           assert all_same([len(x) for x in inp]), f"{[len(x) for x in inp]} doesn't match on {uop}"
-          assert all_same([dtype] + dtp) or uop in {Ops.CMPNE, Ops.CMPLT, Ops.WHERE}, f"dtype mismatch on {uop}"
+          assert all_same([dtype] + dtp) or uop in {*GroupOp.Comparison, Ops.WHERE}, f"dtype mismatch on {uop}"
           ul[i] = [exec_alu(uop, dtype, p) for p in zip(*inp)]
         assert i in ul, (uop, dtype, idp, arg)
         i += 1
@@ -196,18 +208,23 @@ class PythonProgram:
 
 class PythonRenderer(Renderer):
   device = "PYTHON"
+  code_for_op = python_alu
   def __init__(self):
-    if getenv("EMULATE_METAL"): self.device, self.tensor_cores = "METAL", tc.metal
-    if getenv("EMULATE_AMD"): self.device, self.tensor_cores = "AMD", tc.amd_rdna3
-    if getenv("EMULATE_AMD_MFMA"): self.device, self.tensor_cores = "AMD", tc.amd_cdna
-    if getenv("EMULATE_AMD_RDNA4"): self.device, self.tensor_cores = "AMD", tc.amd_rdna4
-    if getenv("EMULATE_CUDA"): self.device, self.tensor_cores = "CUDA", tc.cuda_sm80
-    if getenv("EMULATE_CUDA_SM75"): self.device, self.tensor_cores = "CUDA", tc.cuda_sm75
-    if getenv("EMULATE_INTEL"): self.device, self.suffix, self.tensor_cores = "INTEL", "INTEL", tc.intel
-    if getenv("EMULATE_AMX"): self.device, self.tensor_cores = "CPU", tc.amx
+    match cast(str, EMULATE.value):
+      case "METAL": self.device, self.tensor_cores = "METAL", tc.metal
+      case "AMD": self.device, self.tensor_cores = "AMD", tc.amd_rdna3
+      case "AMD_MFMA": self.device, self.tensor_cores = "AMD", tc.amd_cdna
+      case "AMD_RDNA4": self.device, self.tensor_cores = "AMD", tc.amd_rdna4
+      case "CUDA": self.device, self.tensor_cores = "CUDA", tc.cuda_sm80
+      case "CUDA_SM75": self.device, self.tensor_cores = "CUDA", tc.cuda_sm75
+      case "INTEL": self.device, self.suffix, self.tensor_cores = "INTEL", "INTEL", tc.intel
+      case "AMX": self.device, self.tensor_cores = "CPU", tc.amx
+      case "": pass
+      case _: raise RuntimeError(f"can't EMULATE device: {EMULATE.value}")
 
   def render(self, uops:list[UOp]) -> str:
-    lops = [(u.op, u.dtype, [uops.index(v) for v in u.src], u.arg) for u in uops]
+    # the value of SPECIAL comes from local/global_size, not form its source
+    lops = [(u.op, u.dtype, [uops.index(v) for v in u.src if u.op is not Ops.SPECIAL], u.arg) for u in uops]
     return base64.b64encode(pickle.dumps(lops)).decode()
 
 class PythonCompiler(Compiler):

tinygrad/runtime/support/am/ip.py

@@ -224,7 +224,8 @@ class AM_GFX(AM_IP):
     self._grbm_select()
     self.adev.regGCVM_CONTEXT0_CNTL.write(0)
 
-  def setup_ring(self, ring_addr:int, ring_size:int, rptr_addr:int, wptr_addr:int, eop_addr:int, eop_size:int, doorbell:int, pipe:int, queue:int):
+  def setup_ring(self, ring_addr:int, ring_size:int, rptr_addr:int, wptr_addr:int, eop_addr:int, eop_size:int, doorbell:int, pipe:int, queue:int,
+                 aql:bool):
     mqd = self.adev.mm.valloc(0x1000, uncached=True, contiguous=True)
 
     struct_t = getattr(am, f"struct_v{self.adev.ip_ver[am.GC_HWIP][0]}_compute_mqd")
@@ -235,9 +236,10 @@ class AM_GFX(AM_IP):
       cp_hqd_pq_rptr_report_addr_lo=lo32(rptr_addr), cp_hqd_pq_rptr_report_addr_hi=hi32(rptr_addr),
       cp_hqd_pq_wptr_poll_addr_lo=lo32(wptr_addr), cp_hqd_pq_wptr_poll_addr_hi=hi32(wptr_addr),
       cp_hqd_pq_doorbell_control=self.adev.regCP_HQD_PQ_DOORBELL_CONTROL.encode(doorbell_offset=doorbell*2, doorbell_en=1),
-      cp_hqd_pq_control=self.adev.regCP_HQD_PQ_CONTROL.encode(rptr_block_size=5, unord_dispatch=0, queue_size=(ring_size//4).bit_length()-2),
+      cp_hqd_pq_control=self.adev.regCP_HQD_PQ_CONTROL.encode(rptr_block_size=5, unord_dispatch=0, queue_size=(ring_size//4).bit_length()-2,
+        **({'queue_full_en':1, 'slot_based_wptr':2, 'no_update_rptr':1} if aql else {})),
       cp_hqd_ib_control=self.adev.regCP_HQD_IB_CONTROL.encode(min_ib_avail_size=0x3), cp_hqd_hq_status0=0x20004000,
-      cp_mqd_control=self.adev.regCP_MQD_CONTROL.encode(priv_state=1), cp_hqd_vmid=0,
+      cp_mqd_control=self.adev.regCP_MQD_CONTROL.encode(priv_state=1), cp_hqd_vmid=0, cp_hqd_aql_control=int(aql),
       cp_hqd_eop_base_addr_lo=lo32(eop_addr>>8), cp_hqd_eop_base_addr_hi=hi32(eop_addr>>8),
       cp_hqd_eop_control=self.adev.regCP_HQD_EOP_CONTROL.encode(eop_size=(eop_size//4).bit_length()-2))
 

tinygrad/runtime/support/hcq.py

@@ -383,15 +383,20 @@ class HCQCompiled(Compiled, Generic[SignalType]):
     self.kernargs_buf:HCQBuffer = self.allocator.alloc(kernargs_size, BufferSpec(cpu_access=True))
     self.kernargs_offset_allocator:BumpAllocator = BumpAllocator(self.kernargs_buf.size, wrap=True)
 
+    self.error_state:Exception|None = None # Exception if error is unrecoverable and sync will always fail
+
     if self._is_cpu(): HCQCompiled.cpu_devices.append(self)
 
   def synchronize(self):
+    if self.error_state is not None: raise self.error_state
+
     # If we have any work on CPU devices, need to synchronize them. This is just an optimization to release GIL allowing to finish faster.
     if not self._is_cpu():
       for dev in HCQCompiled.cpu_devices: dev.synchronize()
 
     try: self.timeline_signal.wait(self.timeline_value - 1)
     except RuntimeError as e:
+      self.error_state = e
       if hasattr(self, 'on_device_hang'): self.on_device_hang()
       else: raise e
 

tinygrad/runtime/support/nv/nvdev.py

@@ -118,7 +118,7 @@ class NVDev(PCIDevImplBase):
 
     self.include("src/common/inc/swref/published/turing/tu102/dev_fb.h")
     if self.reg("NV_PFB_PRI_MMU_WPR2_ADDR_HI").read() != 0:
-      if DEBUG >= 2: print(f"nv {self.devfmt}: WPR2 is up. Issuing a full reset.")
+      if DEBUG >= 2: print(f"nv {self.devfmt}: WPR2 is up. Issuing a full reset.", flush=True)
       System.pci_reset(self.devfmt)
       time.sleep(0.5)
 

tinygrad/schedule/kernelize.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
 from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve
-from tinygrad.uop.ops import track_rewrites, _substitute
+from tinygrad.uop.ops import track_rewrites, _substitute, KernelInfo
 from tinygrad.uop.spec import type_verify, tensor_uop_spec
 from tinygrad.uop.symbolic import symbolic_simple
 from tinygrad.helpers import Metadata, all_int, all_same, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP
@@ -8,6 +8,7 @@ from tinygrad.dtype import ImageDType
 from tinygrad.schedule.multi import multi_pm
 from tinygrad.schedule.grouper import group_realizes, ALWAYS_CONTIGUOUS
 from tinygrad.codegen.opt.swizzler import merge_views, apply_swizzle, swizzle_reduceop
+from tinygrad.codegen.opt import Opt
 
 # creation can recurse a lot
 import sys
@@ -119,7 +120,7 @@ def create_kernel(x:UOp, b:UOp|None=None):
   if b is None: b = UOp.new_buffer(x.device, x.size, x.dtype)
   kernel = UOp(Ops.KERNEL, src=(b,)+x.src, arg=Kernel(x.sink(), m if (m:=x.metadata) else ()))
   buffer = b.base if b.size == b.base.size else UOp(Ops.BUFFER_VIEW, b.dtype, (b.base,), (b.size, b.arg.views[0].offset))
-  return buffer.assign(kernel).reshape(x.shape)
+  return buffer.assign(kernel).shrink(((0, prod(x.shape)),)).reshape(x.shape)
 
 DONT_PLACE_IN_KERNEL = {Ops.KERNEL, Ops.ASSIGN, Ops.BUFFER, Ops.MSELECT, Ops.MSTACK, Ops.MULTI, Ops.BIND}
 def append_to_kernel(x:UOp):
@@ -154,6 +155,10 @@ def unbind_view(x:UOp):
   return None
 
 replace_buffers = PatternMatcher([
+  # sink on contig creates a KernelInfo
+  (UPat(Ops.CONTIGUOUS, name="c").sink(name="s"),
+   lambda s,c: s.replace(src=(c.replace(arg=None),), arg=KernelInfo(opts_to_apply=c.arg)) \
+     if s.arg is None and c.arg is not None and isinstance(c.arg[0], Opt) else None),
   # replace ASSIGN with the target BUFFER
   (UPat(Ops.ASSIGN, src=(UPat((Ops.BUFFER, Ops.LOAD)), UPat(Ops.KERNEL)), name="assign", allow_any_len=True), lambda assign: assign.src[0]),
   # HACK: select the 0 branch of MSTACK (the device is wrong after this, is that okay?)

tinygrad/schedule/rangeify.py

@@ -109,7 +109,7 @@ class RangeifyContext:
   # create ranges
   range_idx: int = 0
   def new_range(self, s:sint, axistype:AxisType=AxisType.LOOP):
-    ret = UOp.range(dtypes.int, s, self.range_idx, axistype)
+    ret = UOp.range(s, self.range_idx, axistype)
     self.range_idx += 1
     return ret
 
@@ -189,7 +189,6 @@ def map_partial_contiguous(ctx:RangeifyContext, x:UOp, idx:UOp):
     ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.int, 0))
     new_ranges.append(ranges[-1])
   ret = x.src[0].index(*ranges).bufferize(*[x for x in new_ranges if x.op is not Ops.CONST], arg=x.device)
-  if len(ret.ranges): ret = ret.replace(arg=AddrSpace.LOCAL) # if some ranges are still open, this has to be LOCAL
   return ret.index(*passthrough_idx)
 
 def map_contiguous(ctx:RangeifyContext, x:UOp):
@@ -197,7 +196,8 @@ def map_contiguous(ctx:RangeifyContext, x:UOp):
   ranges = []
   for s in x.shape[len(x.src)-1:]:
     ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.int, 0))
-  return x.src[0].index(*ranges).bufferize(*x.src[1:], *[x for x in ranges if x.op is not Ops.CONST], arg=x.device).forced_reshape(x.shape)
+  ret = x.src[0].index(*ranges).bufferize(*x.src[1:], *[x for x in ranges if x.op is not Ops.CONST], arg=x.device)
+  return ret.shrink(((0, prod(x.shape)),)).forced_reshape(x.shape)
 
 def map_reduce(ctx:RangeifyContext, idx:UOp, red:UOp):
   rngs = list(idx.src[1:])
@@ -239,10 +239,7 @@ def index_child(ctx:RangeifyContext, c:UOp, x:UOp, idx:UOp):
   # index based on the shared ranges
   ret = c.index(*out_rngs)
   # if all ranges aren't the same between children, we have to bufferize
-  if len(idx_ranges) > 0:
-    ret = ret.bufferize(*end_ranges, arg=x.device)
-    if len(ret.ranges): ret = ret.replace(arg=AddrSpace.LOCAL) # if some ranges are still open, this has to be LOCAL
-    ret = ret.index(*[idx.src[1+i] for i in idx_ranges])
+  if len(idx_ranges) > 0: ret = ret.bufferize(*end_ranges, arg=x.device).index(*[idx.src[1+i] for i in idx_ranges])
   return ret
 
 def children_gate(ctx:RangeifyContext, idx:UOp, c:UOp):
@@ -338,7 +335,7 @@ def bufferize_to_store(x:UOp, locals_allowed=False):
   shape = tuple([int(r.vmax+1) for r in rngs])
   size = prod(shape)
   assert size > 0, f"no zero sized buffers {shape}"
-  sdtype = x.dtype.ptr(size=size, addrspace=AddrSpace.GLOBAL if not isinstance(x.arg, AddrSpace) else x.arg)
+  sdtype = x.dtype.ptr(size=size, addrspace=AddrSpace.GLOBAL if not isinstance(x.arg, tuple) else x.arg[0])
   if x.src[0].op is Ops.ASSIGN:
     assign_target, assign_src = x.src[0].src
     assert assign_target.op is Ops.INDEX
@@ -348,7 +345,7 @@ def bufferize_to_store(x:UOp, locals_allowed=False):
     buf = UOp.new_buffer(x.arg, size, x.dtype)
   else:
     if not locals_allowed: return None
-    buf = UOp(Ops.DEFINE_LOCAL, sdtype, arg=0) #UOp.unique().arg)
+    buf = UOp(Ops.DEFINE_LOCAL, sdtype, arg=x.arg[1])
   return buf.reshape(shape).index(*rngs, dtype=sdtype).store(x.src[0], *rngs, dtype=sdtype).forced_reshape(shape, dtype=x.dtype)
 
 pm_add_buffers_local = pm_mops+PatternMatcher([

tinygrad/shape/shapetracker.py

@@ -7,7 +7,7 @@ from tinygrad.helpers import merge_dicts, getenv
 from tinygrad.shape.view import View, unravel
 from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context, PatternMatcher, UPat, GroupOp
-from tinygrad.uop.symbolic import split_uop, symbolic_flat, uop_given_valid, simplify_valid
+from tinygrad.uop.symbolic import symbolic_flat, uop_given_valid, simplify_valid
 
 # If a node overflow, its srcs need to be checked to see if this overflow is the result of an ALU operation,
 # or that the node simply inherits the dtype from srcs. Upcast is either `Ops.CAST`+`replace` or just `replace`.
@@ -43,7 +43,7 @@ def views_to_real_strides(views: tuple[View, ...], ignore_valid=False) -> tuple[
   if len(views) == 1 and views[-1].mask is None: return views[-1].strides
   ret: list[sint|None] = [None] * len(views[-1].shape)
   idx, valid = views_to_indexed_uops(views)
-  for c in split_uop(idx, Ops.ADD):
+  for c in idx.split_uop(Ops.ADD):
     if c.op is Ops.RANGE: ret[c.arg[0]] = 1
     if c.op is Ops.MUL and c.src[0].op is Ops.RANGE and c.src[1].op is Ops.CONST: ret[c.src[0].arg[0]] = c.src[1].arg
     if c.op is Ops.MUL and c.src[1].op is Ops.RANGE and c.src[0].op is Ops.CONST: ret[c.src[1].arg[0]] = c.src[0].arg

tinygrad/shape/view.py

@@ -3,7 +3,7 @@ import functools, operator, itertools
 from dataclasses import dataclass
 from typing import cast, Sequence
 from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import resolve, UOp, Variable, sint, sym_infer, smax, smin, sint_to_uop, Ops, ssimplify
+from tinygrad.uop.ops import resolve, UOp, Variable, sint, smax, smin, sint_to_uop, Ops, ssimplify
 from tinygrad.helpers import prod, all_int, argsort, flatten, ceildiv
 
 # returns the axes to create new_shape if new_shape can be created by combining axis from old_shape
@@ -114,7 +114,7 @@ class View:
 
   def to_indexed_uops(self:View, idxs:Sequence[UOp]|None=None, vexpr:UOp=UOp.const(dtypes.bool, True)) -> tuple[UOp, UOp]:
     """(idx, valid)"""
-    if idxs is None: idxs = [UOp.range(dtypes.int, s, i) for i,s in enumerate(self.shape)]
+    if idxs is None: idxs = [UOp.range(s, i) for i,s in enumerate(self.shape)]
     iexpr = sint_to_uop(self.offset)
     for idx,sh,st,m in zip(idxs, self.shape, self.strides, self.mask if self.mask is not None else itertools.repeat(None)):
       if resolve(sh != 1) and resolve(st != 0): iexpr = iexpr + idx*st
@@ -311,9 +311,10 @@ class View:
 
     if not all(x >= 0 for x in new_shape): raise ValueError(f"shape can't contain negative numbers {new_shape}")
     # check for the same size
-    if (self_all_int := all_int(self.shape)):
-      assert all(isinstance(s, (int, UOp)) for s in new_shape), f"{self.shape=} -> {new_shape=} contains non (int, Variable) dim"
-      if resolve(prod(self.shape) != prod(new_shape), False): raise ValueError(f"size mismatched, can't reshape {self.shape=} -> {new_shape=}")
+    if all_int(self.shape):
+      # reshapes cannot introduce symbolic shape
+      assert all_int(new_shape), f"{self.shape=} -> {new_shape=} contains non int dims"
+      if prod(self.shape) != prod(new_shape): raise ValueError(f"size mismatched, can't reshape {self.shape=} -> {new_shape=}")
 
     if 0 in self.shape: return View.create(new_shape)
     if new_shape == () and self.mask and any(mx==my for (mx,my) in self.mask): return None
@@ -321,15 +322,6 @@ class View:
     # after the asserts, it's okay to check contiguous
     if self.contiguous: return View.create(new_shape)
 
-    # if it's not contiguous and new shape is symbolic, check if it's directly replaceable
-    if self_all_int and not all_int(new_shape):
-      if len(self.shape) != len(new_shape): raise ValueError(f"cannot symbolic reshape non-contiguous {self} -> {new_shape}")
-      for si, so in zip(self.shape, new_shape):
-        if not isinstance(so, int): so = sym_infer(so, dict([v.unbind() for v in so.vars()]))
-        if si != so: raise ValueError(f"cannot symbolic reshape non-contiguous {self} -> {new_shape}")
-      # all dimensions matched, return the new view directly
-      return View(new_shape, self.strides, self.offset, self.mask, self.contiguous)
-
     r_strides, r_new_shape = [], reversed(new_shape)
     for merged_size, new_stride, real_size in reversed(merge_dims(self.shape, self.strides, self.mask)):
       # TODO: write with get_contraction

tinygrad/tensor.py

@@ -442,7 +442,7 @@ class Tensor(MathTrait):
     if not isinstance(size:=prod([x.vmax if isinstance(x, UOp) else x for x in shape]), int): raise ValueError(f"size must be int {size}")
     # TODO: add test for multidevice tensor
     device = tuple(Device.canonicalize(d) for d in device) if isinstance(device, tuple) else Device.canonicalize(device)
-    return Tensor(UOp.new_buffer(device, size, dtype), device, dtype, **kwargs).reshape(shape)
+    return Tensor(UOp.new_buffer(device, size, dtype), device, dtype, **kwargs).shrink(((0,prod(shape)),)).reshape(shape)
 
   @staticmethod
   def from_blob(ptr:int, shape:tuple[int, ...], **kwargs) -> Tensor:
@@ -2255,7 +2255,7 @@ class Tensor(MathTrait):
     xs:tuple[Tensor, ...] = argfix(*operands)
     inputs_str, output = parse_formula(formula, *xs)
     inputs = inputs_str.split(",")
-    assert len(xs) == len(inputs), f"number of inputs doesn't match number of operands in formula, expected {len(inputs)}, got {len(xs)}"
+    if len(xs)!=len(inputs): raise ValueError(f"number of inputs doesn't match number of operands in formula, expected {len(inputs)}, got {len(xs)}")
 
     # map the value of each letter in the formula
     letter_val = sorted(merge_dicts([dict(zip(letters, tensor.shape)) for letters, tensor in zip(inputs, xs)]).items())
@@ -3099,6 +3099,7 @@ class Tensor(MathTrait):
     print(Tensor([0., math.pi/2, math.pi, 3*math.pi/2, 2*math.pi]).cos().numpy())
     ```
     """
+    if self.is_floating_point(): return ((math.pi/2)-self.cast(least_upper_dtype(self.dtype, dtypes.float32))).sin().cast(self.dtype)
     return ((math.pi/2)-self).sin()
 
   def tan(self) -> Tensor:

tinygrad/uop/ops.py

@@ -102,6 +102,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def argstr(self): return f'({", ".join(map(str, self.arg))})' if self.op is Ops.REDUCE_AXIS else repr(self.arg)
   def tagstr(self): return f", tag={self.tag}" if self.tag is not None else ""
 
+  def f(self, op, **kwargs): return UOp(op, dtype=kwargs.pop("dtype", self.dtype), src=(self,), **kwargs)
+
   @functools.cached_property
   def parents(self:UOp) -> dict[UOp, None]:
     ret = {s:None for s in self.src}
@@ -135,6 +137,11 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def tuplize(self:UOp) -> tuple:
     return (self.op.value, self.arg, self.dtype,)+tuple([x.tuplize for x in self.src])
 
+  @property
+  def ptrdtype(self) -> PtrDType:
+    if not isinstance(self.dtype, PtrDType): raise RuntimeError("ptrdtype called on UOp without PtrDType")
+    return self.dtype
+
   # *** uop shape stuff ***
 
   @functools.cached_property
@@ -163,7 +170,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op in {Ops.BUFFER, Ops.BUFFER_VIEW}: return ShapeTracker.from_shape((self.size,))
     if self.op is Ops.KERNEL: return ShapeTracker.from_shape((self.arg.ast.size,))
     if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
-      sz = cast(PtrDType, self.dtype).size
+      sz = self.ptrdtype.size
       return ShapeTracker.from_shape((sz,)) if sz > 0 else None
 
     # CONTIGUOUS with RANGE
@@ -207,10 +214,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     ret: dict[UOp, None] = {}
     if self.op in range_start.keys():
       for s in self.src[:range_start[self.op]]: ret.update(s.ranges)
-      delete_ranges = self.src[range_start[self.op]:]
-      if len(delete_ranges):
-        for s in UOp.sink(*delete_ranges).ranges:
-          if s in ret: del ret[s]
+      for s in UOp.sink(*self.src[range_start[self.op]:]).ranges:
+        if s in ret: del ret[s]
     else:
       for s in self.src: ret.update(s.ranges)
     return ret
@@ -287,10 +292,10 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if op in {Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
     return UOp(op, out_dtype, (self,)+src, **kwargs)
   @staticmethod
-  def const(dtype:DType, b:ConstLike, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None):
+  def const(dtype:DType, b:ConstLike, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None, src=None):
     if isinstance(b, UOp): return b.unbind()[0] if b.op is Ops.BIND else b
     if isinstance(b, tuple) and all_same(b): b = b[0]  # doesn't have to be a VCONST if they are all the same
-    ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype))
+    ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype), src=() if src is None else (src,))
     if shape is not None:
       from tinygrad.shape.shapetracker import ShapeTracker
       ret = ret.replace(src=(UOp(Ops.VIEW, dtypes.void, (), ShapeTracker.from_shape(shape, (0,)*len(shape))),))
@@ -299,10 +304,10 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       else: ret = ret.replace(src=(UOp(Ops.DEVICE, arg=device),))
     return ret
   @staticmethod
-  def range(dtype:DType, end:sint, *arg):
+  def range(end:sint, *arg):
     if len(arg) == 0: raise RuntimeError("range needs an arg")
     if len(arg) == 1: arg = arg+(AxisType.LOOP,)
-    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=arg)
+    return UOp(Ops.RANGE, dtype=dtypes.int, src=(sint_to_uop(end),), arg=arg)
   def r(self, op:Ops, axis:tuple[int, ...]):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
     if len(axis) == 0: return self
@@ -322,6 +327,14 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def allreduce(self, op, device:str|tuple[str, ...]|UOp):
     assert isinstance(self.device, tuple), f"allreduce must be on tuple {self.device} isn't"
     return UOp(Ops.ALLREDUCE, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), op)
+  def overflows(self, dtype:DType) -> bool: return self.vmin < dtype.min or dtype.max < self.vmax
+
+  # *** ShapeTracker helpers ***
+
+  def split_uop(self:UOp, sep:Ops):
+    if self.op is sep:
+      for s in self.src: yield from s.split_uop(sep)
+    else: yield self
 
   # *** from MultiLazyBuffer ***
 
@@ -553,13 +566,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)
     # NOTE: returned UOp is assumed to be CONST
     if self.op is Ops.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]
-    if self.op is Ops.RANGE: return 0, (self.src[0]-1).vmax
+    if self.op in (Ops.RANGE, Ops.SPECIAL): return 0, (self.src[0]-1).vmax
     if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value
     if self.op in {Ops.UNROLL, Ops.VECTORIZE}: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)
-    # TODO: Ops.SPECIAL is Ops.DEFINE_VAR
-    if self.op is Ops.SPECIAL: return 0, self.arg[1]-1 if isinstance(self.arg[1], int) else self.arg[1].vmax
     if self.op is Ops.CONST: return self.arg, self.arg
     if self.op is Ops.VCONST: return (min(self.arg), max(self.arg))
+    if self.op is Ops.GEP: return self.src[0]._min_max
     # TODO: CAST to bool/unsigned is not monotone, still some case can be simplified
     if self.op is Ops.CAST and self.dtype in (dtypes.floats+dtypes.sints):
       return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))
@@ -703,7 +715,8 @@ class UPat(MathTrait):
   def assign(self, x:UPat, **kwargs): return UPat(Ops.ASSIGN, self.dtype, (self,x), **kwargs)
   def reduce(self, *src:UPat, **kwargs): return UPat(Ops.REDUCE, self.dtype, src=(self,)+src, **kwargs)
   def fuse(self): return self.alu(Ops.FUSE)
-  def or_broadcasted(self, **kwargs): return UPat.any(self, UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs))
+  def broadcast(self, **kwargs): return UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs)
+  def or_broadcasted(self, **kwargs): return UPat.any(self, self.broadcast(**kwargs))
   def contiguous(self, *args, **kwargs): return UPat(Ops.CONTIGUOUS, dtype=self.dtype, src=(self,)+args, **kwargs)
 
   def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))
@@ -834,7 +847,7 @@ def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=Fal
     def __wrapper(*args, **kwargs):
       fn = key = func.__name__
       if TRACK_MATCH_STATS >= 2:
-        tracked_keys.append(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,), cat=fn))
+        tracked_keys.append(key:=TracingKey(n:=f"{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}", (n,)))
         tracked_ctxs.append([])
       with cpu_profile(key, "TINY") as e:
         ret = func(*args, **kwargs)
@@ -842,7 +855,7 @@ def track_rewrites(name:Callable[..., str|TracingKey]|bool=True, replay:bool=Fal
         name_ret = name(*args, **kwargs, ret=ret)
         assert isinstance(name_ret, (TracingKey, str)), f"name function returned {type(name_ret)}"
         tracked_keys[-1] = k = TracingKey(n:=tracked_keys[-1].display_name.replace(fn, name_ret), (n,)) if isinstance(name_ret, str) else name_ret
-        e.name = TracingKey(k.display_name if isinstance(name_ret, str) else f"{fn} for {k.display_name}", k.keys, cat=fn)
+        e.name = TracingKey(k.display_name if isinstance(name_ret, str) else f"{fn} for {k.display_name}", k.keys)
       if getenv("CAPTURE_PROCESS_REPLAY") and replay:
         # find the unittest frame we're capturing in
         frm = sys._getframe(1)
@@ -904,7 +917,7 @@ if TRACK_MATCH_STATS or PROFILE:
     if TRACK_MATCH_STATS >= 2:
       with open(fn:=temp("rewrites.pkl", append_user=True), "wb") as f:
         print(f"rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}")
-        pickle.dump((tracked_keys, tracked_ctxs, uop_fields), f)
+        pickle.dump([(tracked_keys, tracked_ctxs, uop_fields)], f)
     if VIZ: launch_viz(VIZ, temp("rewrites.pkl", append_user=True))
     if getenv("PRINT_MATCH_STATS", TRACK_MATCH_STATS.value):
       ret = [0,0,0.0,0.0]
@@ -927,6 +940,7 @@ if TRACK_MATCH_STATS or PROFILE:
 # *** simple graph rewrite engine ***
 
 class RewriteNotReady(Exception): pass
+class BottomUpGate(Exception): pass
 class RewriteContext:
   def __init__(self, pm, bpm, ctx=None):
     self.pm: PatternMatcher|None = pm
@@ -954,20 +968,23 @@ class RewriteContext:
       if n in self.replace: continue  # skip any nodes we have seen
       try:
         if stage == 0:
-          # if bottom up, we rewrite this node early. in both cases, we add its parents to the stack
-          if self.bpm is not None:
-            # apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
-            test_n: UOp|None = n
-            seen = set()
-            while test_n is not None:
-              if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
-              seen.add(test_n)
-              new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
-          stack.append((n, 1, new_n))
-          for x in reversed(new_n.src): stack.append((x, 0, x))
+          try:
+            # if bottom up, we rewrite this node early. in both cases, we add its parents to the stack
+            if self.bpm is not None:
+              # apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match
+              test_n: UOp|None = n
+              seen = set()
+              while test_n is not None:
+                if test_n in seen: raise RuntimeError("infinite loop in fixed_point_rewrite")
+                seen.add(test_n)
+                new_n, test_n = test_n, self.cached_bpm_rewrite(test_n)
+            stack.append((n, 1, new_n))
+            for x in reversed(new_n.src): stack.append((x, 0, x))
+          # if the bpm matching raised a gate, we are done with this node and dont continue down the srcs
+          except BottomUpGate: self.replace[n] = new_n
         elif stage == 1:
           try: new_src = tuple([self.replace[x] for x in new_n.src])
-          except KeyError: raise RewriteNotReady  # pylint: disable=raise-missing-from
+          except KeyError: raise RewriteNotReady
           if new_src == new_n.src:
             # if top down, do the rewrite. if no rewrite or bottom up, we are done rewriting this node so we add it to the dict
             if self.pm is None or (new_src_n:=self.cached_pm_rewrite(new_n)) is None:
@@ -982,7 +999,7 @@ class RewriteContext:
         else:
           # in stage 2, we link the result of new_n to the result of n
           try: self.replace[n] = self.replace[new_n]
-          except KeyError: raise RewriteNotReady  # pylint: disable=raise-missing-from
+          except KeyError: raise RewriteNotReady
       except RewriteNotReady:
         # retry this later
         stack.insert(0, (n, stage, new_n))
@@ -1013,12 +1030,12 @@ _substitute = PatternMatcher([(UPat(tuple(Ops), name="x"), lambda ctx,x: ctx.get
 syms = { Ops.ADD: "+", Ops.SUB: "-", Ops.IDIV: "//", Ops.MOD: "%", Ops.SHL: "<<", Ops.SHR: ">>",
          Ops.MUL: "*", Ops.CMPLT: "<", Ops.CMPNE: "!=", Ops.AND: "&", Ops.OR: "|", Ops.XOR: "^"}
 renderer = PatternMatcher([
-  (UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
+  (UPat((Ops.DEFINE_VAR,), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
+  (UPat((Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg)),
   (UPat(Ops.RANGE, name="x"), lambda x: UOp(Ops.NOOP, arg=f"ridx{x.arg[0]}" if x.arg[0] >= 0 else f"ridxm{-x.arg[0]}")),
   (UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),
   (UPat(Ops.UNROLL, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UNROLL({x.src[0].arg}, {x.arg})")),
   (UPat(Ops.CAST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"({str(x.dtype)[7:]})({x.src[0].arg})")),
-  (UPat(Ops.LOAD), lambda: UOp(Ops.NOOP, arg="load")),
   (UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
   #(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}[={x.src[1].arg}]")),
   (UPat(Ops.NEG, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(-{x.src[0].arg})")),
@@ -1026,7 +1043,8 @@ renderer = PatternMatcher([
   (UPat(Ops.MAX, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"max({x.src[0].arg}, {x.src[1].arg})")),
   (UPat(Ops.MULACC, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})")),
   (UPat(Ops.WHERE, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})")),
-  (UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}{syms[x.op]}{x.src[1].arg})")),
+  (UPat(set(syms.keys()), src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}{syms[x.op]}{x.src[1].arg})")),
+  (UPat(Ops.VIEW, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.view({x.arg})")),
 ])
 renderer_infer = PatternMatcher([
   (UPat(Ops.MOD, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"cmod({x.src[0].arg}, {x.src[1].arg})")),
@@ -1034,6 +1052,42 @@ renderer_infer = PatternMatcher([
   *renderer.patterns
 ])
 
+sugar = { Ops.SINK: "sink", Ops.STORE: "store", Ops.LOAD: "load", Ops.SQRT: "sqrt", Ops.INDEX: "index", Ops.REDUCE: "reduce",
+          Ops.WHERE: "where", Ops.RECIP: "reciprocal", Ops.EXP2: "exp2", Ops.LOG2: "log2"}
+pm_pyrender = PatternMatcher([
+  (UPat(Ops.CONST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg}, src={x.src[0].arg})")),
+  (UPat(Ops.CONST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg})")),
+  (UPat(Ops.CAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.cast({x.dtype})")),
+  (UPat(Ops.BITCAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.bitcast({x.dtype})")),
+  (UPat({Ops.MAX, Ops.THREEFRY, Ops.CMPLT, Ops.CMPNE}, src=UPat(Ops.NOOP), name="x"),
+   lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.alu({x.op}, {x.src[1].arg})")),
+  (UPat(Ops.RANGE, src=(UPat(Ops.NOOP),), name="x"), lambda x:
+    UOp(Ops.NOOP, arg=f"UOp.range({x.src[0].arg}, {str(x.arg[0])}, {str(x.arg[1])})")),
+  (UPat(set(sugar.keys()), src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP,
+    arg=f"{x.src[0].arg}.{sugar[x.op]}({', '.join([y.arg for y in x.src[1:]] + ([f'arg={str(x.arg)}'] if x.arg is not None else []))})")),
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.NOOP),), name="x"),
+   lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.f({x.op}, arg=({', '.join([str(y) for y in x.arg])}))")),
+  (UPat(Ops.VALID, src=(UPat(Ops.NOOP),), name="x"),
+   lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.f({x.op}, dtype=dtypes.bool)")),
+])
+
+def pyrender(ast:UOp) -> list[str]:
+  cmap = ast.get_children_map()
+  to_render = set()
+  for u in ast.toposort():
+    if u.op is Ops.STORE: to_render.add(u.src[1])
+    if len(cmap[u]) == 1 and u.op not in {Ops.DEFINE_GLOBAL, Ops.VIEW, Ops.LOAD} or u.op in {Ops.CONST}: continue
+    if u.op in {Ops.SINK, Ops.VIEW}:
+      for s in u.src: to_render.add(s)
+    to_render.add(u)
+  ret: list[str] = []
+  rep: dict[UOp, UOp] = {}
+  for u in ast.toposort():
+    if u not in to_render: continue
+    ret.append(f"c{len(ret)} = {u.substitute(rep).render(simplify=False, pm=pm_pyrender+renderer)}")
+    rep[u] = UOp(Ops.NOOP, arg=f"c{len(ret)-1}")
+  return ret[0:-1] + ["ast ="+ret[-1].split("=", 1)[1]]
+
 # *** what was symbolic.py ***
 
 sint = int|UOp