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 in (function (self, inputs, gradient, retain_graph, create_graph)  : 
#>   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:187 (most recent call first):
#> frame #0: std::__1::shared_ptr<c10::(anonymous namespace)::PyTorchStyleBacktrace> std::__1::make_shared[abi:ue170006]<c10::(anonymous namespace)::PyTorchStyleBacktrace, c10::SourceLocation&, void>(c10::SourceLocation&) + 121 (0x112338639 in libc10.dylib)
#> frame #1: c10::Error::Error(c10::SourceLocation, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>) + 54 (0x112338776 in libc10.dylib)
#> frame #2: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) + 149 (0x112335035 in libc10.dylib)
#> frame #3: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 1987 (0x1291dd6f3 in libtorch_cpu.dylib)
#> frame #4: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 84 (0x1280541a4 in libtorch_cpu.dylib)
#> frame #5: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 104 (0x1291a4108 in libtorch_cpu.dylib)
#> frame #6: 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&) + 3039 (0x12919cbdf in libtorch_cpu.dylib)
#> frame #7: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 1140 (0x12919b9d4 in libtorch_cpu.dylib)
#> frame #8: 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&&) + 415 (0x1291a355f in libtorch_cpu.dylib)
#> frame #9: 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&) + 1786 (0x1291a1dda in libtorch_cpu.dylib)
#> frame #10: 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) + 982 (0x129189186 in libtorch_cpu.dylib)
#> frame #11: 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&) + 107 (0x12918868b in libtorch_cpu.dylib)
#> frame #12: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, std::__1::optional<at::Tensor> const&, std::__1::optional<bool>, bool) const + 296 (0x1291e2918 in libtorch_cpu.dylib)
#> frame #13: at::Tensor::_backward(c10::ArrayRef<at::Tensor>, std::__1::optional<at::Tensor> const&, std::__1::optional<bool>, bool) const + 73 (0x1251a8419 in libtorch_cpu.dylib)
#> frame #14: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 211 (0x1161a9003 in liblantern.dylib)
#> frame #15: std::__1::__function::__func<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2, std::__1::allocator<cpp_torch_method__backward_self_Tensor_inputs_TensorList(XPtrTorchTensor, XPtrTorchTensorList, XPtrTorchOptionalTensor, XPtrTorchoptional_bool, XPtrTorchbool)::$_2>, void ()>::operator()() + 54 (0x114a31836 in torchpkg.so)
#> frame #16: std::__1::packaged_task<void ()>::operator()() + 72 (0x114a2f908 in torchpkg.so)
#> frame #17: EventLoop<void>::run() + 413 (0x114a2f75d in torchpkg.so)
#> frame #18: void* std::__1::__thread_proxy[abi:v160006]<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*) + 50 (0x114a2f4b2 in torchpkg.so)
#> frame #19: _pthread_start + 125 (0x7ff8084b01d3 in libsystem_pthread.dylib)
#> frame #20: thread_start + 15 (0x7ff8084abbd3 in libsystem_pthread.dylib)
#>