Context-manager that enable anomaly detection for the autograd engine.

This does two things:

Usage

with_detect_anomaly(code)

Arguments

code: Code that will be executed in the detect anomaly context.

Details

Running the forward pass with detection enabled will allow the backward pass to print the traceback of the forward operation that created the failing backward function.
Any backward computation that generate "nan" value will raise an error.

Warning

This mode should be enabled only for debugging as the different tests will slow down your program execution.

Examples

if (torch_is_installed()) {
x <- torch_randn(2, requires_grad = TRUE)
y <- torch_randn(1)
b <- (x^y)$sum()
y$add_(1)

try({
  b$backward()

  with_detect_anomaly({
    b$backward()
  })
})
}
#> Error : one of the variables needed for gradient computation has been modified by an inplace operation: [CPUFloatType [1]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
#> Exception raised from unpack at /Users/runner/work/libtorch-mac-m1/libtorch-mac-m1/pytorch/torch/csrc/autograd/saved_variable.cpp:194 (most recent call first):
#> frame #0: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 56 (0x10cd42b74 in libc10.dylib)
#> frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 120 (0x10cd400e8 in libc10.dylib)
#> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 2300 (0x120893644 in libtorch_cpu.dylib)
#> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 60 (0x11f7d63b4 in libtorch_cpu.dylib)
#> frame #4: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 128 (0x120860fdc in libtorch_cpu.dylib)
#> frame #5: torch::autograd::Engine::evaluate_function(std::__1::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::__1::shared_ptr<torch::autograd::ReadyQueue> const&) + 3196 (0x1208591f0 in libtorch_cpu.dylib)
#> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 844 (0x120857e8c in libtorch_cpu.dylib)
#> frame #7: torch::autograd::Engine::execute_with_graph_task(std::__1::shared_ptr<torch::autograd::GraphTask> const&, std::__1::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&) + 660 (0x12085fb54 in libtorch_cpu.dylib)
#> frame #8: torch::autograd::Engine::execute(std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, bool, std::__1::vector<torch::autograd::Edge, std::__1::allocator<torch::autograd::Edge>> const&) + 2088 (0x12085e99c in libtorch_cpu.dylib)
#> frame #9: torch::autograd::run_backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, bool, bool) + 832 (0x120846694 in libtorch_cpu.dylib)
#> frame #10: torch::autograd::backward(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&, std::__1::optional<bool>, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&) + 88 (0x120845bbc in libtorch_cpu.dylib)
#> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, std::__1::optional<at::Tensor> const&, std::__1::optional<bool>, bool) const + 412 (0x1208984f4 in libtorch_cpu.dylib)
#> frame #12: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 184 (0x10f501c34 in liblantern.dylib)
#> frame #13: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_0, void ()>::operator()() + 64 (0x10ea49b00 in torchpkg.so)
#> frame #14: std::__1::packaged_task<void ()>::operator()() + 80 (0x10ea47d50 in torchpkg.so)
#> frame #15: EventLoop<void>::run() + 388 (0x10ea47b44 in torchpkg.so)
#> frame #16: void* std::__1::__thread_proxy[abi:nqe210106]<std::__1::tuple<std::__1::unique_ptr<std::__1::__thread_struct, std::__1::default_delete<std::__1::__thread_struct>>, ThreadPool<void>::ThreadPool(int)::'lambda'()>>(void*) + 52 (0x10ea478b4 in torchpkg.so)
#> frame #17: _pthread_start + 136 (0x188f5dc58 in libsystem_pthread.dylib)
#> frame #18: thread_start + 8 (0x188f58c1c in libsystem_pthread.dylib)
#>