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 (0x104772b74 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 (0x1047700e8 in libc10.dylib)
#> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 2300 (0x118347644 in libtorch_cpu.dylib)
#> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 60 (0x11728a3b4 in libtorch_cpu.dylib)
#> frame #4: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 128 (0x118314fdc 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 (0x11830d1f0 in libtorch_cpu.dylib)
#> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 844 (0x11830be8c 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 (0x118313b54 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 (0x11831299c 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 (0x1182fa694 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 (0x1182f9bbc 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 (0x11834c4f4 in libtorch_cpu.dylib)
#> frame #12: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 184 (0x106fb5c34 in liblantern.dylib)
#> frame #13: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1, std::__1::allocator<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_1>, void ()>::operator()() + 64 (0x106516bc0 in torchpkg.so)
#> frame #14: std::__1::packaged_task<void ()>::operator()() + 80 (0x106514d90 in torchpkg.so)
#> frame #15: EventLoop<void>::run() + 384 (0x106514b40 in torchpkg.so)
#> frame #16: void* std::__1::__thread_proxy[abi:ne190102]<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 (0x1065148b4 in torchpkg.so)
#> frame #17: _pthread_start + 136 (0x18148fbc8 in libsystem_pthread.dylib)
#> frame #18: thread_start + 8 (0x18148ab80 in libsystem_pthread.dylib)
#>