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c704a77ca0
* dtypes alu test * those types don't exist in torch * floats * more tests * disable those * a couple unary tests * skip float16 tests in CI for GPU * fix LLVM bool add True+True=1+1=2 which truncates to False in native LLVM * remove hardcoded float for LLVM ALU fns * less sensitive atol for fp32, 1e-10 is flaky and sometimes failed even if you revert the merge commit for non-fp32 math, nothing has changed in our kernels for fp32. * return on overflows * fix CUDA exp2 * compute results of op regardless of bounds in a python backend * skip fp16 in GPU and CUDACPU * fuzz a smaller range in the float_midcast_int32 test I sampled this and we overflow ~70% of the time. because numpy behaves differently on different devices for overflows and Metal seems to do the same, I'm opting to eliminate the non-determinism here * remove CUDA exp2 overload it's already there now --------- Co-authored-by: George Hotz <geohot@gmail.com>
51 lines
721 B
Plaintext
51 lines
721 B
Plaintext
__pycache__
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.venv/
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.vscode
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.DS_Store
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notebooks
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*.txt
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build
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a.out
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boxes.jpg
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pandecode.dump
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vertex.bin
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recognize*
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disassemblers/applegpu
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disassemblers/cuda_ioctl_sniffer
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*.prof
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extra/datasets/cifar-10-python.tar.gz
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extra/datasets/librispeech/
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extra/datasets/imagenet/
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extra/datasets/kits19/
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extra/datasets/squad/
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extra/datasets/img_align_celeba*
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extra/datasets/open-images-v6-mlperf
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extra/datasets/kits/
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extra/datasets/COCO/
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extra/datasets/audio*
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extra/weights
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venv
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examples/**/net.*[js,json]
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examples/**/*.safetensors
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node_modules
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package.json
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package-lock.json
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temp
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coverage.xml
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wandb
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model.safetensors
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