CLANG=1 -> CPU=1 [pr]

.github/workflows/benchmark.yml

@@ -64,7 +64,7 @@ jobs:
       run: METAL=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - name: Test AMX tensor cores
       run: |
-        DEBUG=2 CLANG=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
+        DEBUG=2 CPU=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
         DEBUG=2 LLVM=1 AMX=1 python3.11 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded
     - name: Run Tensor Core GEMM (float)
       run: DEBUG=2 python3.11 extra/gemm/simple_matmul.py | tee matmul.txt

.github/workflows/test.yml

@@ -81,7 +81,7 @@ jobs:
       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
       run: |
-        CLANG=1 PYTHONPATH="." python examples/compile_efficientnet.py > recognize.c
+        CPU=1 PYTHONPATH="." python examples/compile_efficientnet.py > recognize.c
         clang -O2 recognize.c -lm -o recognize
         cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock
 
@@ -355,13 +355,13 @@ jobs:
           llvm: 'true'
       - name: Test ONNX (GPU)
         run: GPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
-      - name: Test ONNX (CLANG)
-        run: CLANG=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
+      - name: Test ONNX (CPU)
+        run: CPU=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
       - name: Test ONNX (LLVM)
         run: LLVM=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
       - name: Run CLOUD=1 Test
         run: |
-          CLOUDDEV=CLANG CLOUD=1 python3 test/test_tiny.py
+          CLOUDDEV=CPU CLOUD=1 python3 test/test_tiny.py
           CLOUDDEV=GPU CLOUD=1 python3 test/test_tiny.py
           CLOUDDEV=GPU IMAGE=2 CLOUD=1 python3 test/test_tiny.py
       - name: Test Optimization Helpers
@@ -378,7 +378,7 @@ jobs:
         uses: ./.github/actions/process-replay
 
   testmodels:
-    name: Models (llvm+clang+gpu)
+    name: Models (llvm+cpu+gpu)
     runs-on: ubuntu-22.04
     timeout-minutes: 10
     steps:
@@ -395,8 +395,8 @@ jobs:
         run: LLVM=1 python -m pytest -n=auto test/models --durations=20
       - name: Test models (gpu)
         run: GPU=1 python -m pytest -n=auto test/models --durations=20
-      - name: Test models (clang)
-        run: CLANG=1 python -m pytest -n=auto test/models --durations=20
+      - name: Test models (cpu)
+        run: CPU=1 python -m pytest -n=auto test/models --durations=20
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
@@ -431,8 +431,8 @@ jobs:
       run: PYTHONPATH="." DEBUG=2 DSP=1 python3 test/test_quantize_onnx.py
     - name: Test LLVM=1 DEVECTORIZE=0
       run: LLVM=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
-    - name: Test CLANG=1 DEVECTORIZE=0
-      run: CLANG=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+    - name: Test CPU=1 DEVECTORIZE=0
+      run: CPU=1 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
 
   testwebgpu:
     name: Linux (WebGPU)
@@ -464,7 +464,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        backend: [llvm, clang, gpu, ptx, amd, nv] #, triton]
+        backend: [llvm, cpu, gpu, ptx, amd, nv] #, triton]
 
     name: Linux (${{ matrix.backend }})
     runs-on: ubuntu-22.04
@@ -482,10 +482,10 @@ jobs:
           amd: ${{ matrix.backend == 'amd' && 'true' }}
           cuda: ${{ (matrix.backend == 'ptx' || matrix.backend == 'triton' || matrix.backend == 'nv') && 'true' }}
       - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'clang' && 'CLANG=1' || matrix.backend == 'gpu' && 'GPU=1' || matrix.backend == 'PTX' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nCUDA=1\nPTX=1\nMOCKGPU=1' || matrix.backend == 'triton' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nNV=1\nMOCKGPU=1\nTRITON=1\nTRITON_PTXAS_PATH=/usr/bin/ptxas' || matrix.backend == 'amd' && 'AMD=1\nMOCKGPU=1\nFORWARD_ONLY=1' || matrix.backend == 'nv' && 'NV=1\nMOCKGPU=1\nFORWARD_ONLY=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'gpu' && 'GPU=1' || matrix.backend == 'PTX' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nCUDA=1\nPTX=1\nMOCKGPU=1' || matrix.backend == 'triton' && 'FORWARD_ONLY=1\nJIT=1\nOPT=2\nNV=1\nMOCKGPU=1\nTRITON=1\nTRITON_PTXAS_PATH=/usr/bin/ptxas' || matrix.backend == 'amd' && 'AMD=1\nMOCKGPU=1\nFORWARD_ONLY=1' || matrix.backend == 'nv' && 'NV=1\nMOCKGPU=1\nFORWARD_ONLY=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','CLANG','CUDA','GPU','AMD','NV'], Device.DEFAULT"
+          PYTHONPATH=${{ github.workspace }} python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['LLVM','CPU','CUDA','GPU','AMD','NV'], Device.DEFAULT"
           DEBUG=5 PYTHONPATH=${{ github.workspace }} FORWARD_ONLY=1 python3 test/test_ops.py TestOps.test_add
       - name: Run pytest (not cuda or amd)
         if: matrix.backend!='ptx' && matrix.backend!='triton' && matrix.backend != 'amd' && matrix.backend != 'nv'
@@ -582,7 +582,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        backend: [metal, llvm, clang]
+        backend: [metal, llvm, cpu]
     name: MacOS (${{ matrix.backend }})
     runs-on: macos-15
     timeout-minutes: 10
@@ -596,7 +596,7 @@ jobs:
           deps: testing_minimal
           llvm: ${{ matrix.backend == 'llvm' && 'true' }}
       - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'clang' && 'CLANG=1' || matrix.backend == 'metal' && 'METAL=1\nJIT=2'}}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=1' || matrix.backend == 'metal' && 'METAL=1\nJIT=2'}}" >> $GITHUB_ENV
       - name: Check Device.DEFAULT and print some source
         run: |
           python -c "from tinygrad import Device; assert Device.DEFAULT == '${{ matrix.backend }}'.upper(), Device.DEFAULT"
@@ -612,7 +612,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        backend: [llvm, clang]
+        backend: [llvm, cpu]
 
     name: Windows (${{ matrix.backend }})
     runs-on: windows-latest
@@ -627,7 +627,7 @@ jobs:
           deps: testing_unit
       - name: Set env
         shell: bash
-        run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'clang' && 'CLANG=1'}}" >> $GITHUB_ENV
+        run:  printf "${{ matrix.backend == 'llvm' && 'LLVM=1' || matrix.backend == 'cpu' && 'CPU=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

README.md

@@ -81,7 +81,7 @@ See [examples/beautiful_mnist.py](examples/beautiful_mnist.py) for the full vers
 tinygrad already supports numerous accelerators, including:
 
 - [x] [GPU (OpenCL)](tinygrad/runtime/ops_gpu.py)
-- [x] [CLANG (C Code)](tinygrad/runtime/ops_clang.py)
+- [x] [CPU (C Code)](tinygrad/runtime/ops_cpu.py)
 - [x] [LLVM](tinygrad/runtime/ops_llvm.py)
 - [x] [METAL](tinygrad/runtime/ops_metal.py)
 - [x] [CUDA](tinygrad/runtime/ops_cuda.py)

docs/abstractions2.py

@@ -7,7 +7,7 @@
 
 print("******** first, the runtime ***********")
 
-from tinygrad.runtime.ops_clang import ClangJITCompiler, MallocAllocator, CPUProgram
+from tinygrad.runtime.ops_cpu import ClangJITCompiler, MallocAllocator, CPUProgram
 
 # allocate some buffers
 out = MallocAllocator.alloc(4)
@@ -34,7 +34,7 @@
 
 print("******** second, the Device ***********")
 
-DEVICE = "CLANG"   # NOTE: you can change this!
+DEVICE = "CPU"   # NOTE: you can change this!
 
 import struct
 from tinygrad.dtype import dtypes
@@ -90,7 +90,7 @@
 
 # schedule the computation as a list of kernels
 sched, _, becomes_map = create_schedule_with_vars(out.sink())
-for si in sched: print(si.ast.op)  # NOTE: the first two convert it to CLANG
+for si in sched: print(si.ast.op)  # NOTE: the first two convert it to CPU
 # NOTE: UOps are no longer mutable, the scheduler gives you a map to lookup which BUFFER the result was written to
 out = becomes_map[out]
 

docs/developer/runtime.md

@@ -38,7 +38,7 @@ The `Allocator` class is responsible for managing memory on the device. There is
 
 The `Program` class is created for each loaded program. It is responsible for executing the program on the device. As an example, here is a `CPUProgram` implementation which loads program and runs it.
 
-::: tinygrad.runtime.ops_clang.CPUProgram
+::: tinygrad.runtime.ops_cpu.CPUProgram
     options:
         members: true
 

docs/env_vars.md

@@ -37,7 +37,7 @@ AMD                 | [1]        | enable AMD backend
 NV                  | [1]        | enable NV backend
 METAL               | [1]        | enable Metal backend (for Mac M1 and after)
 METAL_XCODE         | [1]        | enable Metal using macOS Xcode SDK
-CLANG               | [1]        | enable Clang backend
+CPU                 | [1]        | enable CPU Clang backend
 LLVM                | [1]        | enable LLVM backend
 BEAM                | [#]        | number of beams in kernel beam search
 DEFAULT_FLOAT       | [HALF, ...]| specify the default float dtype (FLOAT32, HALF, BFLOAT16, FLOAT64, ...), default to FLOAT32

docs/mnist.md

@@ -17,7 +17,7 @@ from tinygrad import Device
 print(Device.DEFAULT)
 ```
 
-You will see `CUDA` here on a GPU instance, or `CLANG` here on a CPU instance.
+You will see `CUDA` here on a GPU instance, or `CPU` here on a CPU instance.
 
 ## A simple model
 

docs/runtime.md

@@ -1,6 +1,6 @@
 # Runtimes
 
-tinygrad supports various runtimes, enabling your code to scale across a wide range of devices. The default runtime can be automatically selected based on the available hardware, or you can force a specific runtime to be default using environment variables (e.g., `CLANG=1`).
+tinygrad supports various runtimes, enabling your code to scale across a wide range of devices. The default runtime can be automatically selected based on the available hardware, or you can force a specific runtime to be default using environment variables (e.g., `CPU=1`).
 
 | Runtime | Description | Requirements |
 |---------|-------------|--------------|
@@ -10,6 +10,6 @@ tinygrad supports various runtimes, enabling your code to scale across a wide ra
 | [METAL](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_metal.py) | Utilizes Metal for acceleration on Apple devices | M1+ Macs; Metal 3.0+ for `bfloat` support |
 | [CUDA](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cuda.py) | Utilizes CUDA for acceleration on NVIDIA GPUs | NVIDIA GPU with CUDA support |
 | [GPU (OpenCL)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_gpu.py) | Accelerates computations using OpenCL on GPUs | OpenCL 2.0 compatible device |
-| [CLANG (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_clang.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
+| [CPU (C Code)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_cpu.py) | Runs on CPU using the clang compiler | `clang` compiler in system `PATH` |
 | [LLVM (LLVM IR)](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_llvm.py) | Runs on CPU using the LLVM compiler infrastructure | llvm libraries installed and findable |
 | [WEBGPU](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/runtime/ops_webgpu.py) | Runs on GPU using the Dawn WebGPU engine (used in Google Chrome) | Dawn library installed and findable. Download binaries [here](https://github.com/wpmed92/pydawn/releases/tag/v0.1.6). |

examples/compile_efficientnet.py

@@ -15,9 +15,9 @@
   if getenv("WEBGPU"):
     safe_save(get_state_dict(model), (dirname / "net.safetensors").as_posix())
     load_state_dict(model, safe_load(str(dirname / "net.safetensors")))
-  mode = "clang" if getenv("CLANG", "") != "" else "webgpu" if getenv("WEBGPU", "") != "" else ""
+  mode = "clang" if getenv("CPU", "") != "" else "webgpu" if getenv("WEBGPU", "") != "" else ""
   prg, inp_sizes, out_sizes, state = export_model(model, mode, Tensor.randn(1,3,224,224))
-  if getenv("CLANG", "") == "":
+  if getenv("CPU", "") == "":
     ext = "js" if getenv("WEBGPU", "") != "" else "json"
     with open(dirname / f"net.{ext}", "w") as text_file:
       text_file.write(prg)
@@ -68,6 +68,6 @@
     else printf("%s\\n", lbls[best_idx]);
   }""")
 
-    # CLANG=1 python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/showcase/stable_diffusion_by_tinygrad.jpg
+    # CPU=1 python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/showcase/stable_diffusion_by_tinygrad.jpg
     # category : 281 (tabby, tabby cat) with 9.452788
     print('\n'.join(cprog))

examples/compile_tensorflow.py

@@ -1,7 +1,7 @@
 # An example to compile a small Tensorflow model to extremely portable C code
 
 import os, sys
-os.environ["CLANG"] = '1'
+os.environ["CPU"] = '1'
 os.environ["JIT"] = '2'
 
 import numpy as np

examples/llm.c/export.py

@@ -2,7 +2,7 @@
 import os
 if "NOOPT" not in os.environ: os.environ["NOOPT"] = "1"
 from tinygrad import Device, nn, Tensor, dtypes, Variable
-Device.DEFAULT = "CLANG"
+Device.DEFAULT = "CPU"
 from train_gpt2 import GPT, GPTConfig
 from tinygrad.helpers import dedup, to_function_name, flatten, getenv, GlobalCounters, ansilen, to_function_name
 from tinygrad.engine.realize import get_kernel, run_schedule
@@ -43,9 +43,9 @@
   ast_dedup = dedup([si.ast for si in sched if si.ast.op is Ops.SINK])
   srcs = {}
   for ast in ast_dedup:
-    k = get_kernel(Device["CLANG"].renderer, ast)
+    k = get_kernel(Device["CPU"].renderer, ast)
     k.linearize()
-    src = Device["CLANG"].renderer.render(to_function_name(k.name), k.uops)
+    src = Device["CPU"].renderer.render(to_function_name(k.name), k.uops)
     srcs[ast] = (k.name, src)
   print("functions:", len(srcs))
   used_buffers = dedup(flatten([si.bufs for si in sched]))

examples/mlperf/dataloader.py

@@ -170,13 +170,13 @@ def receive_batch():
 
 def process_batch_bert(data: List[dict]) -> dict[str, Tensor]:
   return {
-    "input_ids": Tensor(np.concatenate([s["input_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
-    "input_mask": Tensor(np.concatenate([s["input_mask"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
-    "segment_ids": Tensor(np.concatenate([s["segment_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_positions": Tensor(np.concatenate([s["masked_lm_positions"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_ids": Tensor(np.concatenate([s["masked_lm_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_weights": Tensor(np.concatenate([s["masked_lm_weights"] for s in data], axis=0), dtype=dtypes.float32, device="CLANG"),
-    "next_sentence_labels": Tensor(np.concatenate([s["next_sentence_labels"] for s in data], axis=0), dtype=dtypes.int32, device="CLANG"),
+    "input_ids": Tensor(np.concatenate([s["input_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "input_mask": Tensor(np.concatenate([s["input_mask"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "segment_ids": Tensor(np.concatenate([s["segment_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_positions": Tensor(np.concatenate([s["masked_lm_positions"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_ids": Tensor(np.concatenate([s["masked_lm_ids"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_weights": Tensor(np.concatenate([s["masked_lm_weights"] for s in data], axis=0), dtype=dtypes.float32, device="CPU"),
+    "next_sentence_labels": Tensor(np.concatenate([s["next_sentence_labels"] for s in data], axis=0), dtype=dtypes.int32, device="CPU"),
   }
 
 def load_file(file: str):

examples/mlperf/helpers.py

@@ -222,11 +222,11 @@ def get_mlperf_bert_model():
 
 def get_fake_data_bert(BS:int):
   return {
-    "input_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CLANG"),
-    "input_mask": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CLANG"),
-    "segment_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_positions": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_ids": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CLANG"),
-    "masked_lm_weights": Tensor.empty((BS, 76), dtype=dtypes.float32, device="CLANG"),
-    "next_sentence_labels": Tensor.empty((BS, 1), dtype=dtypes.int32, device="CLANG"),
+    "input_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "input_mask": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "segment_ids": Tensor.empty((BS, 512), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_positions": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_ids": Tensor.empty((BS, 76), dtype=dtypes.int32, device="CPU"),
+    "masked_lm_weights": Tensor.empty((BS, 76), dtype=dtypes.float32, device="CPU"),
+    "next_sentence_labels": Tensor.empty((BS, 1), dtype=dtypes.int32, device="CPU"),
   }

extra/backends/clang_graph.py

@@ -5,7 +5,7 @@
 from tinygrad.device import Buffer, Device
 from tinygrad.engine.realize import ExecItem, CompiledRunner
 from tinygrad.ops import Variable
-from tinygrad.runtime.ops_clang import ClangProgram
+from tinygrad.runtime.ops_cpu import ClangProgram
 from tinygrad.renderer.cstyle import ClangRenderer
 render_dtype = ClangRenderer().render_dtype
 
@@ -30,7 +30,7 @@ def __init__(self, jit_cache: List[ExecItem], input_rawbuffers: List[Buffer], va
       code.append(f"  {cast(CompiledRunner, ji.prg).p.function_name}({','.join(args)});")
     code.append("}")
     if DEBUG >= 4: print("\n".join(code))
-    compiler = Device["CLANG"].compiler
+    compiler = Device["CPU"].compiler
     assert compiler is not None
     self._prg = ClangProgram("batched", compiler.compile(prgs+"\n"+"\n".join(code))) # no point in caching the pointers
 

extra/export_model.py

@@ -8,7 +8,7 @@
 from tinygrad.dtype import dtypes
 import json
 
-EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CLANG", "CUDA", "GPU"]
+EXPORT_SUPPORTED_DEVICE = ["WEBGPU", "CPU", "CUDA", "GPU"]
 
 def compile_net(run:TinyJit, special_names:Dict[int,str]) -> Tuple[Dict[str,str],List[Tuple[str,List[str],List[int]]],Dict[str,Tuple[int,DType,int]],Dict[str,Tensor]]:
   functions, bufs, bufs_to_save, statements, bufnum = {}, {}, {}, [], 0
@@ -191,7 +191,7 @@ def export_model_webgpu(functions, statements, bufs, weight_names, input_names,
 """
 
 def export_model(model, target:str, *inputs, model_name: Optional[str] = None):
-  assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, "only WEBGPU, CLANG, CUDA, GPU, METAL are supported"
+  assert Device.DEFAULT in EXPORT_SUPPORTED_DEVICE, "only WEBGPU, CPU, CUDA, GPU, METAL are supported"
   with Context(JIT=2): run,special_names = jit_model(model, *inputs)
   functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
   state = get_state_dict(model)

extra/gemm/tvm_gemm.py

@@ -42,6 +42,6 @@
 lin = Kernel(sched[-1].ast, CompilerOptions(has_local=False, supports_float4=False))
 #lin.hand_coded_optimizations()
 lin.linearize()
-from tinygrad.runtime.ops_clang import renderer
+from tinygrad.runtime.ops_cpu import renderer
 src = renderer("mmult", lin.uops)
 print(src)