Use a CUDAGuard when running Torch models #340
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| @@ -291,6 +295,16 @@ std::unique_ptr<NeuropodValueMap> TorchNeuropodBackend::infer_internal(const Neu | |||
| { | |||
| torch::NoGradGuard guard; | |||
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| #ifndef __APPLE__ | |||
| // Make sure we're running on the correct device | |||
| std::unique_ptr<at::cuda::CUDAGuard> device_guard; | |||
| const auto model_device = get_torch_device(DeviceType::GPU); | ||
| if (model_device.is_cuda()) | ||
| { | ||
| device_guard = stdx::make_unique<at::cuda::CUDAGuard>(model_device); |
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I guess we can use std:: here not stdx:: because Neuropod became C++14 recently, right?
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This PR ensures that we're running on the correct device even if something else calls
cudaSetDevicebefore running inference.This fixes a class of issues where another piece of code changes the current device for the current thread. For example, this can happen if TF and Torch run together on the same threadpool. TF will call
cudaSetDeviceand cause torch to break if it runs on the same thread in the future.This can cause some obscure cuDNN errors and generally hard-to-debug issues.