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Nicklas Boman
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### Welcome to the tinygrad documentation
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General instructions you will find in [README.md](https://github.com/geohot/tinygrad/blob/master/README.md)
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[abstraction.py](https://github.com/geohot/tinygrad/blob/master/docs/abstractions.py) is a well documented showcase of the abstraction stack.
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There are plenty of [tests](https://github.com/geohot/tinygrad/tree/master/test) you can read through
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[Examples](https://github.com/geohot/tinygrad/tree/master/examples) contains tinygrad implementations of popular models (vision and language) and neural networks. LLama, Stable diffusion, GANs and Yolo to name a few
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### Environment variables
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Here is a list of environment variables you can use with tinygrad.
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Most of these are self-explanatory, and used to enable an option at runtime.
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Example : `GPU=1 DEBUG=4 python3 -m pytest`
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The columns are: Variable, Value and Description
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They are also grouped into either general tinygrad or specific files
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##### General tinygrad
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DEBUG: [1-4], enable debugging output, with 4 you get operations, timings, speed, generated code and more
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GPU: [1], enable the GPU backend
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CPU: [1], enable CPU backend
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MPS: [1], emable MPS device (for Mac M1 and after)
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METAL: [1], enable Metal backend (for Mac M1 and after)
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METAL_XCODE: [1], enable Metal using MacOS Xcode sdk
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TORCH: [1], enable Torch backend
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CLANG: [1], enable Clang backend
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LLVM: [1], enable LLVM backend
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LLVMOPT: [1], enable LLVM optimization
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LAZY: [1], enable lazy operations
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OPT: [1-4], enable optimization
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OPTLOCAL: [1], enable local optimization
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JIT: [1], enable Jit
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GRAPH: [1], Create a graph of all operations
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GRAPHPATH: [/path/to], what path to generate the graph image
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PRUNEGRAPH, [1], prune movementops and loadops from the graph
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PRINT_PRG: [1], print program
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FLOAT16: [1], use float16 instead of float32
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ENABLE_METHOD_CACHE: [1], enable method cache
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EARLY_STOPPING: [1], stop early
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DISALLOW_ASSIGN: [1], enable not assigning the realized lazydata to the lazy output buffer
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##### tinygrad/codegen/cstyle.py
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NATIVE_EXPLOG: [1], enable using native explog
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##### accel/ane/2_compile/hwx_parse.py
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PRINTALL: [1], print all ane registers
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##### extra/onnx.py
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ONNXLIMIT: [ ], set a limit for Onnx
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DEBUGONNX: [1], enable Onnx debugging
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##### extra/thneed.py
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DEBUGCL: [1-4], enable Debugging for OpenCL
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PRINT_KERNEL: [1], Print OpenCL Kernels
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##### extra/kernel_search.py
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OP: [1-3], different operations
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NOTEST: [1], enable not testing ast
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DUMP: [1], enable dumping of intervention cache
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REDUCE: [1], enable reduce operations
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SIMPLE_REDUCE: [1], enable simpler reduce operations
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BC: [1], enable big conv operations
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CONVW: [1], enable convw operations
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FASTCONV: [1], enable faster conv operations
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GEMM: [1], enable general matrix multiply operations
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BROKEN: [1], enable a kind of operation
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BROKEN3: [1], enable a kind of operation
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##### examples/vit.py
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LARGE: [1], enable larger dimension model
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##### examples/llama.py
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WEIGHTS: [1], enable using weights
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##### examples/mlperf
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MODEL: [resnet,retinanet,unet3d,rnnt,bert,maskrcnn], what models to use
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##### examples/benchmark_train_efficientnet.py
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CNT: [10], the amount of times to loop the benchmark
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BACKWARD: [1], enable backward call
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TRAINING: [1], set Tensor.training
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CLCACHE: [1], enable Cache for OpenCL
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##### examples/hlb_cifar10.py
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TORCHWEIGHTS: [1], use torch to initialize weights
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DISABLE_BACKWARD: [1], dont use backward operations
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##### examples/benchmark_train_efficientnet.py & examples/hlb_cifar10.py
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ADAM: [1], enable Adam optimization
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##### examples/hlb_cifar10.py & xamples/hlb_cifar10_torch.py
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STEPS: [0-10], number of steps
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FAKEDATA: [1], enable to use random data
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##### examples/train_efficientnet.py
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STEPS: [1024 dividable], number of steps
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TINY: [1], use a tiny convolution network
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IMAGENET: [1], use imagenet for training
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##### examples/train_efficientnet.py & examples/train_resnet.py
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TRANSFER: [1], enable to use pretrained data
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##### examples & test/external/external_test_opt.py
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NUM: [18, 2], what ResNet[18] / EfficientNet[2] to train
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##### test/test_ops.py
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PRINT_TENSORS: [1], print tensors
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FORWARD_ONLY: [1], use forward operations only
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##### test/test_speed_v_torch.py
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TORCHCUDA: [1], enable the torch cuda backend
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##### test/external/external_test_gpu_ast.py
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KOPT: [1], enable kernel optimization
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KCACHE: [1], enable kernel cache
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##### test/external/external_test_opt.py
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ENET_NUM: [-2,-1], what EfficientNet to use
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##### test/test_dtype.py & test/extra/test_utils.py & extra/training.py
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CI: [1], enable to avoid some tests to run in CI
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##### examples & extra & test
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BS: [8, 16, 32, 64, 128], bytesize
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