Skip to contents

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

Source: R/autograd.R
with_detect_anomaly.Rd

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/pytorch/pytorch/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>>) + 81 (0x10404eca1 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&) + 98 (0x10404d342 in libc10.dylib)
#> frame #2: torch::autograd::SavedVariable::unpack(std::__1::shared_ptr<torch::autograd::Node>) const + 2815 (0x124574d0f in libtorch_cpu.dylib)
#> frame #3: torch::autograd::generated::PowBackward1::apply(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 168 (0x1236c7488 in libtorch_cpu.dylib)
#> frame #4: torch::autograd::Node::operator()(std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>>&&) + 99 (0x1245456c3 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&) + 3401 (0x12453ba89 in libtorch_cpu.dylib)
#> frame #6: torch::autograd::Engine::thread_main(std::__1::shared_ptr<torch::autograd::GraphTask> const&) + 954 (0x12453a6da 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&&) + 374 (0x124544646 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&) + 2605 (0x12454268d 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) + 2129 (0x124528df1 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&, c10::optional<bool>, bool, std::__1::vector<at::Tensor, std::__1::allocator<at::Tensor>> const&) + 104 (0x124529408 in libtorch_cpu.dylib)
#> frame #11: torch::autograd::VariableHooks::_backward(at::Tensor const&, c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 435 (0x12457a9f3 in libtorch_cpu.dylib)
#> frame #12: at::Tensor::_backward(c10::ArrayRef<at::Tensor>, c10::optional<at::Tensor> const&, c10::optional<bool>, bool) const + 75 (0x120ebbeab in libtorch_cpu.dylib)
#> frame #13: _lantern_Tensor__backward_tensor_tensorlist_tensor_bool_bool + 427 (0x1068a3b2b in liblantern.dylib)
#> frame #14: 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()() + 54 (0x10513fbf6 in torchpkg.so)
#> frame #15: std::__1::packaged_task<void ()>::operator()() + 72 (0x10513df88 in torchpkg.so)
#> frame #16: EventLoop<void>::run() + 379 (0x10513ddbb in torchpkg.so)
#> frame #17: 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 (0x10513db32 in torchpkg.so)
#> frame #18: _pthread_start + 125 (0x7ff805e541d3 in libsystem_pthread.dylib)
#> frame #19: thread_start + 15 (0x7ff805e4fbd3 in libsystem_pthread.dylib)
#>