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usr
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local
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lib64
/
python3.6
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site-packages
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torch
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include
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torch
/
csrc
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utils
/
/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/utils
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auto_gil.h
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byte_order.h
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crash_handler.h
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cuda_enabled.h
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cuda_lazy_init.h
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disable_torch_function.h
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disallow_copy.h
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init.h
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invalid_arguments.h
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memory.h
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numpy_stub.h
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object_ptr.h
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out_types.h
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pybind.h
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pycfunction_helpers.h
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python_arg_parser.h
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python_compat.h
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python_dispatch.h
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python_numbers.h
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python_scalars.h
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python_strings.h
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python_stub.h
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python_tuples.h
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six.h
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structseq.h
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tensor_apply.h
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tensor_dtypes.h
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tensor_flatten.h
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tensor_layouts.h
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tensor_list.h
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tensor_memoryformats.h
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tensor_new.h
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tensor_numpy.h
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tensor_qschemes.h
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tensor_types.h
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throughput_benchmark-inl.h
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throughput_benchmark.h
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variadic.h
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Edit:
/usr/local/lib64/python3.6/site-packages/torch/include/torch/csrc/utils/cuda_lazy_init.h
(974B)
#pragma once #include <c10/core/TensorOptions.h> // cuda_lazy_init() is always compiled, even for CPU-only builds. // Thus, it does not live in the cuda/ folder. namespace torch { namespace utils { // The INVARIANT is that this function MUST be called before you attempt // to get a CUDA Type object from ATen, in any way. Here are some common // ways that a Type object may be retrieved: // // - You call getNonVariableType or getNonVariableTypeOpt // - You call toBackend() on a Type // // It's important to do this correctly, because if you forget to add it // you'll get an oblique error message about "Cannot initialize CUDA without // ATen_cuda library" if you try to use CUDA functionality from a CPU-only // build, which is not good UX. // void cuda_lazy_init(); void set_run_yet_variable_to_false(); static void maybe_initialize_cuda(const at::TensorOptions& options) { if (options.device().is_cuda()) { torch::utils::cuda_lazy_init(); } } } }
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