I could reproduce it also on 0.5.0a0+2c7c12f.
Here is the backtrace:

#0  0x00007fffd42ce067 in THCTensor_nElement () from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libcaffe2_gpu.so
#1  0x00007fffd3cbaefa in bool THC_pointwiseApply3<float, float, float, THTensor, THTensor, THTensor, PReLUAccGradParametersShared >(THCState*, THTensor*, THTensor*, THTensor*, PReLUAccGradParametersShared const&, TensorArgType, TensorArgType, TensorArgType) ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libcaffe2_gpu.so
#2  0x00007fffd3c99692 in THNN_CudaPReLU_accGradParameters () from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libcaffe2_gpu.so
#3  0x00007fffd41f2018 in at::CUDAFloatType::prelu_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, std::array<bool, 2ul>) const ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libcaffe2_gpu.so
#4  0x00007fffd1f47b7d in torch::autograd::VariableType::prelu_backward(at::Tensor const&, at::Tensor const&, at::Tensor const&, std::array<bool, 2ul>) const ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#5  0x00007fffd1e4fa93 in torch::autograd::generated::PreluBackward::apply(std::vector<torch::autograd::Variable, std::allocatortorch::autograd::Variable >&&) ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#6  0x00007fffd1e0bfcb in torch::autograd::Function::operator()(std::vector<torch::autograd::Variable, std::allocatortorch::autograd::Variable >&&) ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#7  0x00007fffd1e07291 in torch::autograd::Engine::evaluate_function(torch::autograd::FunctionTask&) ()
from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#8  0x00007fffd1e07d8b in torch::autograd::Engine::thread_main(torch::autograd::GraphTask*) () from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#9  0x00007fffd1e045b4 in torch::autograd::Engine::thread_init(int) () from /home/pbialecki/libs/ptrblck/pytorch/torch/lib/libtorch.so.1
#10 0x00007fffe41c5a2a in torch::autograd::python::PythonEngine::thread_init (this=0x7fffe4a88200 , device=0) at torch/csrc/autograd/python_engine.cpp:39
#11 0x00007fffd1651c5c in std::execute_native_thread_routine_compat (__p=)
at /opt/conda/conda-bld/compilers_linux-64_1520532893746/work/.build/src/gcc-7.2.0/libstdc+±v3/src/c++11/thread.cc:110
#12 0x00007ffff7bc16ba in start_thread (arg=0x7fff8df82700) at pthread_create.c:333
#13 0x00007ffff78f741d in clone () at …/sysdeps/unix/sysv/linux/x86_64/clone.S:109

I tried to debug it a bit and it seems the error is thrown if the input does not require gradients.
Code to reproduce the bug:
act = nn.PReLU().to('cuda')
x = torch.randn(1, requires_grad=False, device='cuda')
output = act(x)
output.mean().backward()
print(x.grad)

Setting requires_grad=True for x works.
@tommm994 Could you open a gihub issue 8 and link to this thread?
If you are busy, let me know and I can do it.