variable.cpp

#include <torch/csrc/autograd/variable.h>

#include <torch/csrc/autograd/autograd.h>
#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/InferenceMode.h>
#include <torch/csrc/autograd/functions/accumulate_grad.h>
#include <torch/csrc/autograd/functions/tensor.h>
#include <torch/csrc/autograd/generated/Functions.h>
#include <torch/csrc/autograd/utils/error_messages.h>

#include <ATen/core/VariableHooksInterface.h>

#include <ATen/ATen.h>
#include <ATen/MemoryOverlap.h>
#include <ATen/FuncTorchTLS.h>
#include <c10/util/Exception.h>

#include <list>
#include <memory>
#include <mutex>
#include <stdexcept>
#include <string>
#include <vector>
#include <typeinfo>
#include <iostream>

namespace torch {
namespace autograd {


DifferentiableViewMeta::DifferentiableViewMeta(at::TensorImpl* self_impl,
  c10::optional<ViewInfo> backward_info,
  c10::optional<ViewInfo> forward_info,
  bool shared_view_info,
  CreationMeta creation_meta)
    : AutogradMeta(self_impl),
      backward_info_(std::move(backward_info)),
      forward_info_(std::move(forward_info)),
      shared_view_info_(shared_view_info),
      creation_meta_(creation_meta) {
  is_view_ = true;
  if (backward_info_.has_value()) {
    self_impl->set_version_counter(impl::version_counter(backward_info_.value().base_));
    attr_version_ = self_impl->version_counter().current_version();
  }
  if (shared_view_info_) {
    TORCH_INTERNAL_ASSERT(backward_info_.has_value(), "Shared view info require a backward view info.");
    TORCH_INTERNAL_ASSERT(!forward_info_.has_value(), "Shared view info require forward view info to be empty")
  }
}

// Chain this view info with the new view op between base and tensor
ViewInfo ViewInfo::chain(const Variable & base, const Variable & tensor,
  std::function<Variable(const Variable&)> view_func) const {
  // Set `view_func` using the root base as input.
  // `view_func` is used to recover views in backward when either as_strided is not supported
  // or the view function changes the metadata which is not recorded by as_strided
  // See Note [View + Inplace update on base tensor] and [View + Inplace update on view tensor]
  // for more details how we use this function in backward.
  if (view_func) {
    // both current_view and it's parent have a view_func
    if (view_fn_) {
      // Copy parent view function to gain ownership
      auto prev_fn = view_fn_;
      view_func = [=](const at::Tensor& root_base) {
        auto temp = prev_fn(root_base);
        return view_func(temp);
      };
    } else {
      // current_view has a view_func and but it's parent doesn't have one
      if (base.unsafeGetTensorImpl()->support_as_strided()) {
        auto size = base.sizes().vec();
        auto stride = base.strides().vec();
        auto storage_offset = base.storage_offset();
        view_func = [=](const at::Tensor& root_base) {
          auto temp = root_base.as_strided(size, stride, storage_offset);
          return view_func(temp);
        };
      } else {
        // When base is a view but doesn't carry a view_fn in DifferentiableViewMeta, it's
        // a view that doesn't support inplace update, e.g. unbind.
        // In this case we should throw an error when inplace update happens in **forward**.
        // One would naturally think the following function will be first called in backward pass.
        // But the first call site is indeed in **forward** pass when we refresh `grad_fn`
        // triggered by inplace update.
        // Search Note [View + Inplace update for view tensor] to for the call site.
        view_func = [=](const at::Tensor& root_base) {
          TORCH_CHECK(false, "This view is the output of a function that returns multiple views."
                  "Such functions do not allow the output views to be modified inplace."
                  "You should replace the inplace operation by an out-of-place one");
          return root_base;
        };
      }
    }
  } else if(view_fn_) {
    // if current_view doesn't have a view_func but it's parent has one
    // Copy parent view function to gain ownership
    auto prev_view_fn = view_fn_;
    auto size = tensor.sizes().vec();
    auto stride = tensor.strides().vec();
    auto storage_offset = tensor.storage_offset();
    view_func = [=](const at::Tensor& root_base) {
      auto temp = prev_view_fn(root_base);
      return temp.as_strided(size, stride, storage_offset);
    };
  }

  return ViewInfo(base_, view_func);
}

namespace {

at::Tensor singleton_undefined_tensor;

struct ConcreteAutogradMetaFactory : public c10::impl::AutogradMetaFactory {
  std::unique_ptr<c10::AutogradMetaInterface> make() const override {
    return std::make_unique<AutogradMeta>();
  }
  const at::Tensor& undefined_tensor() const override {
    return singleton_undefined_tensor;
  }
};

ConcreteAutogradMetaFactory meta_factory;

static c10::impl::AutogradMetaFactoryRegisterer meta_factory_registerer(&meta_factory);

}

namespace impl {

  AutogradMeta* materialize_autograd_meta(const at::TensorBase& self) {
    TORCH_CHECK(self.defined(), "cannot call materialize_autograd_meta() on undefined tensor");
    auto p = self.unsafeGetTensorImpl();
    if (!p->autograd_meta()) {
      p->set_autograd_meta(std::make_unique<AutogradMeta>());
    }
    return get_autograd_meta(self);
  }

  void rebase_history(const Variable& self, Edge gradient_edge) {
    TORCH_INTERNAL_ASSERT(gradient_edge.function != nullptr);
    auto diff_view_meta = get_view_autograd_meta(self);
    if (diff_view_meta && diff_view_meta->has_bw_view()) {
      // See NOTE [ View + Inplace detection ]
      auto creation_meta = diff_view_meta->get_creation_meta();
      // Do not use handle_view_on_rebase here as check_inplace should have been called before this
      // and either throw an error
      TORCH_INTERNAL_ASSERT(creation_meta == CreationMeta::DEFAULT);
      TORCH_INTERNAL_ASSERT(gradient_edge.input_nr == 0);
      TORCH_INTERNAL_ASSERT(gradient_edge.function);
      TORCH_CHECK(
          gradient_edge.function->num_inputs() == 1,
          "Functions which modify views in-place must return a single Variable");
      auto view_info = diff_view_meta->get_backward_view();
      diff_view_meta->output_nr_ = gradient_edge.input_nr;
      auto copy_slices = std::make_shared<CopySlices>(
          view_info.base_, at::TensorGeometry(self), view_info.view_fn_, std::move(gradient_edge.function));
      set_gradient_edge(view_info.base_, {std::move(copy_slices), 0});
      self.grad_fn(); // trigger an update to the view's grad_fn
      return;
    }

    set_gradient_edge(self, std::move(gradient_edge));
  }

  void create_cpp_hook(const at::TensorBase& self) {
    auto &list = materialize_autograd_meta(self)->cpp_hooks_list_;
    // NOLINTNEXTLINE(modernize-make-shared)
    list.reset(new hooks_list());
    std::unique_ptr<FunctionPreHook> hook_ptr(new CppFunctionPreHook(list, self.output_nr()));
    clear_hooks(self);
    add_hook(self, std::make_shared<CppFunctionPreHook>(list, 0));
    const auto& fn = self.grad_fn();
    if (fn) {
      fn->add_pre_hook(std::move(hook_ptr));
    }
  }

  void set_grad_accumulator(const Variable& self,
      std::weak_ptr<Node> grad_accumulator) {
    materialize_autograd_meta(self)->grad_accumulator_ = std::move(grad_accumulator);
  }

  std::shared_ptr<Node> try_get_grad_accumulator(const Variable& self) {
    if (get_autograd_meta(self)) {
      return get_autograd_meta(self)->grad_accumulator_.lock();
    } else {
      return nullptr;
    }
  }

  std::shared_ptr<Node> grad_accumulator(const Variable& self) {
    auto autograd_meta = get_autograd_meta(self);
    if (!autograd_meta) {
      return nullptr;
    }
    if (autograd_meta->grad_fn_) {
      throw std::logic_error(
          "grad_accumulator() should be only called on leaf Variables");
    }
    if (!autograd_meta->requires_grad_) {
      return nullptr;
    }

    std::lock_guard<std::mutex> lock(autograd_meta->mutex_);

    auto result = autograd_meta->grad_accumulator_.lock();
    if (result)
      return result;

    c10::raw::intrusive_ptr::incref(self.unsafeGetTensorImpl());
    auto intrusive_from_this = c10::intrusive_ptr<at::TensorImpl>::reclaim(self.unsafeGetTensorImpl());
    result = std::make_shared<AccumulateGrad>(Variable(std::move(intrusive_from_this)));
    autograd_meta->grad_accumulator_ = result;
    return result;
  }

  Edge gradient_edge(const Variable& self) {
    // If grad_fn is null (as is the case for a leaf node), we instead
    // interpret the gradient function to be a gradient accumulator, which will
    // accumulate its inputs into the grad property of the variable. These
    // nodes get suppressed in some situations, see "suppress gradient
    // accumulation" below. Note that only variables which have `requires_grad =
    // True` can have gradient accumulators.
    if (const auto& gradient = self.grad_fn()) {
      return Edge(gradient, self.output_nr());
    } else {
      return Edge(grad_accumulator(self), 0);
    }
  }

  void set_gradient_edge(const Variable& self, Edge edge) {
    auto* meta = materialize_autograd_meta(self);
    meta->grad_fn_ = std::move(edge.function);
    meta->output_nr_ = edge.input_nr;
    // For views, make sure this new grad_fn_ is not overwritten unless it is necessary
    // in the VariableHooks::grad_fn below.
    // This logic is only relevant for custom autograd Functions for which multiple
    // operations can happen on a given Tensor before its gradient edge is set when
    // exiting the custom Function.
    auto diff_view_meta = get_view_autograd_meta(self);
    if (diff_view_meta && diff_view_meta->has_bw_view()) {
      diff_view_meta->set_attr_version(self._version());
    }
  }

  Node* grad_fn_unsafe(const Variable& self) {
    if (get_autograd_meta(self)) {
      return get_autograd_meta(self)->grad_fn_.get();
    } else {
      return nullptr;
    }
  }

  // Versions
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  void set_version_counter(
      const Variable& self,
      const c10::VariableVersion& version_counter) {
    TORCH_CHECK(self.defined(), "cannot call set_version_counter() on undefined tensor");
    self.unsafeGetTensorImpl()->set_version_counter(version_counter);
  }

  void bump_version(const Variable& self) {
    TORCH_CHECK(self.defined(), "cannot call bump_version() on undefined tensor");
    self.unsafeGetTensorImpl()->bump_version();
  }

  const c10::VariableVersion& version_counter(const Variable& self) {
    TORCH_CHECK(self.defined(), "cannot call version_counter() on undefined tensor");
    return self.unsafeGetTensorImpl()->version_counter();
  }

  // Hooks
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  void add_hook(const at::TensorBase& self, std::shared_ptr<FunctionPreHook> hook) {
    materialize_autograd_meta(self)->hooks_.push_back(std::move(hook));
  }

  namespace {
    std::vector<std::shared_ptr<FunctionPreHook>> empty_singleton;
  }

  // TODO: Return an ArrayRef instead (and delete the singleton while you're at
  // it
  const std::vector<std::shared_ptr<FunctionPreHook>>& hooks(const Variable& self)
      {
    if (get_autograd_meta(self)) {
      return get_autograd_meta(self)->hooks_;
    } else {
      return empty_singleton;
    }
  }

  void clear_hooks(const at::TensorBase& self) {
    // This is a little goofy, but usually this should be a no oop
    materialize_autograd_meta(self)->hooks_.clear();
  }

  void set_name(const Variable& self, const std::string& name) {
    materialize_autograd_meta(self)->name_ = name;
  }

  // Miscellaneous
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  AutogradMeta* get_autograd_meta(const at::TensorBase& self) {
    // NB: could return nullptr
    TORCH_CHECK(self.defined(), "cannot call get_autograd_meta() on undefined tensor");
    return static_cast<AutogradMeta*>(self.unsafeGetTensorImpl()->autograd_meta());
  }

  DifferentiableViewMeta* get_view_autograd_meta(const at::TensorBase& self) {
    // NB: return nullptr if self is not a view
    AutogradMeta* meta = get_autograd_meta(self);
    if (meta && meta->is_view_) {
      return static_cast<DifferentiableViewMeta*>(meta);
    } else {
      return nullptr;
    }
  }

} // namespace impl

using at::Tensor;

struct VariableHooks final : at::impl::VariableHooksInterface {
  at::TensorBase tensor_data(const at::TensorBase&) const override;
  at::TensorBase variable_data(const at::TensorBase&) const override;
  const std::shared_ptr<torch::autograd::Node>& grad_fn(const at::TensorBase&) const override;
  unsigned _register_hook(
      const at::TensorBase&, std::function<at::TensorBase(const at::TensorBase&)> hook) const override;
  void remove_hook(const at::TensorBase&, unsigned pos) const override;
  bool is_view(const at::TensorBase&) const override;
  const at::TensorBase& base(const at::TensorBase&) const override;
  const std::string& name(const at::TensorBase&) const override;
  bool is_leaf(const at::TensorBase&) const override;
  int64_t output_nr(const at::TensorBase&) const override;
  void set_data(const at::TensorBase & self, const at::TensorBase & new_data) const override;
  at::TensorBase data(const at::TensorBase & self) const override;
  int64_t _version(const at::TensorBase & self) const override;
  void retain_grad(const at::TensorBase& self) const override;
  bool retains_grad(const at::TensorBase& self) const override;
  void _backward(const Tensor& self, at::TensorList inputs,
    const c10::optional<Tensor>& gradient, c10::optional<bool> keep_graph,
    bool create_graph) const override;
  void requires_grad_(const at::TensorBase& self, bool _requires_grad) const override;
};

VariableHooks variableHooks;
at::impl::VariableHooksRegisterer registerVariableHooks(&variableHooks);

at::TensorBase VariableHooks::variable_data(const at::TensorBase& self) const {
  TORCH_CHECK(self.defined(), "cannot call variable_data() on undefined tensor");
  auto self_impl_copy = self.unsafeGetTensorImpl()->shallow_copy_and_detach(
    /*version_counter=*/0,
    /*allow_tensor_metadata_change=*/false);
  self_impl_copy->set_autograd_meta(nullptr);
  return at::Tensor(self_impl_copy);
}

at::TensorBase VariableHooks::tensor_data(const at::TensorBase& self) const {
  TORCH_CHECK(self.defined(), "cannot call tensor_data() on undefined tensor");
  auto self_impl_copy = self.unsafeGetTensorImpl()->shallow_copy_and_detach(
    /*version_counter=*/self.unsafeGetTensorImpl()->version_counter(),
    /*allow_tensor_metadata_change=*/self.unsafeGetTensorImpl()->allow_tensor_metadata_change());
  return at::Tensor(self_impl_copy);
}

bool VariableHooks::is_leaf(const at::TensorBase & self) const {
  if (impl::get_autograd_meta(self)) {
    return impl::get_autograd_meta(self)->grad_fn_ == nullptr;
  } else {
    return true;
  }
}

int64_t VariableHooks::output_nr(const at::TensorBase & self) const {
  if (impl::get_autograd_meta(self)) {
    return impl::get_autograd_meta(self)->output_nr_;
  } else {
    return 0;
  }
}

void VariableHooks::set_data(const at::TensorBase & self_base, const at::TensorBase & new_data_base) const {
  at::OptionalTensorRef self_ref(self_base);
  const Tensor &self = *self_ref;
  at::OptionalTensorRef new_data_ref(new_data_base);
  const Tensor &new_data = *new_data_ref;

  // `var.set_data(new_data)` shallow-copies all non-autograd TensorImpl fields
  // from `new_data` to `var`. It requires that `new_data` and `var` have compatible
  // tensor type.
  TORCH_CHECK(
    _has_compatible_shallow_copy_type(self, new_data),
    "Attempted to call `variable.set_data(tensor)`, but `variable` and `tensor` have incompatible tensor type.");

  // Resets gradient accumulator if metadata is out of date
  AutogradMeta* autograd_meta = impl::get_autograd_meta(self);
  if (autograd_meta) {
    std::lock_guard<std::mutex> lock(autograd_meta->mutex_);
    auto prior_accumulator = autograd_meta->grad_accumulator_.lock();
    if (prior_accumulator) {
      const auto prior_device = prior_accumulator->input_metadata(0).device();
      const auto new_device = new_data.device();

      if (!new_data.options().type_equal(self.options()) || prior_device != new_device) {
        autograd_meta->grad_accumulator_.reset();
      }
    }
  }

  // Version counter is not shared when we replace a `Variable`'s tensor data
  // by calling `set_data(...)`. The original version of the `Variable` is always preserved.
  // See NOTE [ Version Counter Sharing ] for details.
  //
  // `var.set_data(new_data)` always ignores `var`'s `allow_tensor_metadata_change_`, because
  // users need this API as an escape hatch for changing a tensor's metadata regardless of its
  // `allow_tensor_metadata_change_` value, and the users are responsible for ensuring this is
  // the behavior they want.
  self.unsafeGetTensorImpl()->shallow_copy_from(new_data.getIntrusivePtr());
}

at::TensorBase VariableHooks::data(const at::TensorBase & self) const {
  return self.variable_data();
}

int64_t VariableHooks::_version(const at::TensorBase & self) const {
  return self.unsafeGetTensorImpl()->version_counter().current_version();
}

void VariableHooks::retain_grad(const at::TensorBase& self) const {
  TORCH_CHECK(self.requires_grad(), "can't retain_grad on Tensor that has requires_grad=False");

  // temporary hack to improve functorch UX.
  const auto& functorch_tls = at::functorch::functorchTLSAccessor();
  if (functorch_tls) {
    functorch_tls->checkSupportsRetainGrad();
  }

  if (self.is_leaf()) {  // no-op for leaves
    return;
  }
  if (impl::get_autograd_meta(self)->retains_grad_) {
    return;
  }
  c10::weak_intrusive_ptr<c10::TensorImpl> weak_self(self.getIntrusivePtr());

  auto retain_grad_hook = [weak_self](const at::Tensor& grad) {
    if (weak_self.expired()) {
      return;
    } else {
      auto var = weak_self.lock();
      if (!var->grad().defined()) {
        if (grad.is_sparse()) {
          var->mutable_grad() = grad.clone();
        } else {
          var->mutable_grad() = grad.clone(at::MemoryFormat::Contiguous);
        }
      } else {
        var->mutable_grad() = var->grad() + grad;
      }
    }
  };

  at::OptionalTensorRef(self)->register_hook(retain_grad_hook);
  impl::get_autograd_meta(self)->retains_grad_ = true;
}

bool VariableHooks::retains_grad(const at::TensorBase& self) const {
  if (impl::get_autograd_meta(self)) {
    return impl::get_autograd_meta(self)->retains_grad_;
  } else {
    return false;
  }
}

void VariableHooks::_backward(
    const Tensor& self,
    at::TensorList inputs,
    const c10::optional<Tensor>& gradient,
    c10::optional<bool> keep_graph,
    bool create_graph) const {
  // TODO torch::autograd::backward should take the c10::optional<Tensor> gradient directly
  // instead of us having to unwrap it to Tensor _gradient here.
  Tensor _gradient = gradient.has_value() ? *gradient : Tensor();
  std::vector<torch::autograd::Variable> input_vars(inputs.begin(), inputs.end());
  torch::autograd::backward({self}, {_gradient}, keep_graph, create_graph, input_vars);
}

void VariableHooks::requires_grad_(const at::TensorBase& self, bool _requires_grad) const {
  if (!self.is_leaf() && !_requires_grad) {
    throw std::runtime_error(
      autograd::utils::requires_grad_leaf_error(_requires_grad)
    );
  }
  self.set_requires_grad(_requires_grad);
}

// Backward View Variables
//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

bool VariableHooks::is_view(const at::TensorBase& self) const {
  auto diff_view_meta = torch::autograd::impl::get_view_autograd_meta(self);
  if (diff_view_meta) {
    return diff_view_meta->has_bw_view();
  } else {
    return false;
  }
}

const at::TensorBase& VariableHooks::base(const at::TensorBase& self) const {
  auto diff_view_meta = torch::autograd::impl::get_view_autograd_meta(self);
  if (diff_view_meta) {
    TORCH_CHECK(diff_view_meta->has_bw_view(), "Can't get base of non-backward view Tensor");
    return diff_view_meta->get_backward_view().base_;
  } else {
    throw std::runtime_error("Can't get base of non-view Tensor");
  }
}

namespace {
  std::string singleton_string;
}

const std::string& VariableHooks::name(const at::TensorBase& self) const {
  TORCH_CHECK(self.defined(), "cannot call variable_data() on undefined tensor");
  if (torch::autograd::impl::get_autograd_meta(self)) {
    return torch::autograd::impl::get_autograd_meta(self)->name_;
  } else {
    return singleton_string;
  }
}

namespace {
  std::shared_ptr<torch::autograd::Node> singleton_shared_ptr;
}

const std::shared_ptr<torch::autograd::Node>& VariableHooks::grad_fn(const at::TensorBase& self) const {
  auto diff_view_meta = torch::autograd::impl::get_view_autograd_meta(self);
  if (diff_view_meta && diff_view_meta->has_bw_view()) {
    // See NOTE [ View + Inplace detection ]
    std::lock_guard<std::mutex> lock(diff_view_meta->mutex_);
    auto view_info = diff_view_meta->get_backward_view();
    if (!diff_view_meta->grad_fn_ && !view_info.base_.requires_grad()) {
      return diff_view_meta->grad_fn_;
    }
    auto current_version = self._version();
    if (diff_view_meta->get_attr_version() != current_version) {
      // This is an indirect rebase_history due to another view or the base being modified inplace
      handle_view_on_rebase(diff_view_meta, /* indirect */ true);
      TORCH_INTERNAL_ASSERT(diff_view_meta->output_nr_ == 0);
      // Note [View + Inplace update for view tensor]
      // An inplace update happened on Tensor `self` (which is a view).
      // For example:
      //   view_1 = view_op_1(diff_view_meta->base_)
      //   view_2 = view_op_2(view_1)
      //   ...
      //   self = view_op_n(view_n-1)
      //   self = inplace_op(self)
      //
      // For CPU/CUDA backends, we employ one AsStridedBackward0 Node to represent the chain of
      // view backward ops for effienciency.
      //
      // However in XLA backend we don't have full support of AsStridedBackward0, we instead run a full
      // forward pass with a tensor that requires gradient to get proper grad_fn setup,
      // then save it to DifferentiableViewMeta for future use.
      // This is fairly cheap for XLA lazy tensor approach (but would be really expensive for CPU/CUDA).
      // XLA Tensor only run thorugh VariableType dispatch and lower the forward pass to a XLA HLO graph,
      // then we take grad_fn and never materialize the tensor content.
      // So we only construct the graph but not execute it, which is a fairly cheap operation to do.
      //
      // See Note [View + Inplace update for base tensor] for what we do to base tensor when
      // an in-place operation happens.
      //
      // TODO: Potentially the following logic can be replaced by special logic in VariableType_x.cpp
      //       that would provide a way to recreate the grad_fn chain.
      if (view_info.has_view_fn()) {
        auto view_fn = view_info.view_fn();
        auto diff_view = view_fn(view_info.base_);
        diff_view_meta->grad_fn_ = diff_view.grad_fn();
      } else {
        auto fn = std::make_shared<torch::autograd::generated::AsStridedBackward0>();
        fn->self_geometry = at::TensorGeometry(view_info.base_);
        fn->size = self.sizes().vec();
        fn->stride = self.strides().vec();
        fn->storage_offset = self.storage_offset();
        fn->set_next_edges(torch::autograd::collect_next_edges(view_info.base_));
        fn->add_input_metadata(
          view_info.base_.options(),
          self.sizes(), // Note: sizes(), not base_.sizes(), is intentional
          self.unsafeGetTensorImpl()->is_python_dispatch());
        diff_view_meta->grad_fn_ = std::move(fn);
      }
      diff_view_meta->set_attr_version(current_version);
    }
    return diff_view_meta->grad_fn_;
  }

  if (torch::autograd::impl::get_autograd_meta(self)) {
    return torch::autograd::impl::get_autograd_meta(self)->grad_fn_;
  } else {
    return singleton_shared_ptr;
  }
}

void VariableHooks::remove_hook(const at::TensorBase& self, unsigned pos) const {
  auto &list = torch::autograd::impl::materialize_autograd_meta(self)->cpp_hooks_list_;
  TORCH_CHECK(list && pos < list->size() , "Invalid index, no hook at position ", pos);
  // Hook will be ignored
  (*list)[pos] = nullptr;
}

unsigned VariableHooks::_register_hook(
    const at::TensorBase& self, std::function<at::TensorBase(const at::TensorBase&)> hook) const {
  TORCH_CHECK(self.requires_grad(), "cannot register a hook on a variable that "
                           "doesn't require gradient");
  // NB: materialize_autograd_meta unnecessary due to requires grad check
  auto &list = torch::autograd::impl::get_autograd_meta(self)->cpp_hooks_list_;
  if(!list) {
    torch::autograd::impl::create_cpp_hook(self);
  }
  unsigned idx = list->size();
  list->push_back(hook);
  return idx;
}

void handle_view_on_rebase(DifferentiableViewMeta* diff_view_meta, bool indirect) {
  /// See NOTE [ View + Inplace detection ] for justification of the logic below
  auto creation_meta = diff_view_meta->get_creation_meta();
  if (creation_meta != CreationMeta::DEFAULT) {
    auto grad_fn = diff_view_meta->grad_fn_.get();
    std::string msg;
    std::string modified_obj;
    // Create the header for the error message.
    if (indirect) {
      modified_obj = "its base or another view of its base has been";
    } else {
      modified_obj = "is being";
    }
    if (grad_fn) {
      msg = c10::str("Output ", diff_view_meta->output_nr_, " of ", grad_fn->name(), " is a view and ",
                     modified_obj, " modified inplace.");
    } else if (creation_meta == CreationMeta::INFERENCE_MODE) {
      msg = c10::str("A view was created in inference mode and ", modified_obj, " modified inplace in normal mode.");
    } else {
      msg = c10::str("A view was created in no_grad mode and ", modified_obj, " modified inplace with grad mode enabled.");
    }

    if (creation_meta == CreationMeta::MULTI_OUTPUT_NODE) {
      msg = c10::str(msg, " This view is the output of a function that returns multiple views. Such functions do not"
                     " allow the output views to be modified inplace. You should replace the inplace operation by an"
                     " out-of-place one.");
    } else if (creation_meta == CreationMeta::NO_GRAD_MODE) {
      TORCH_INTERNAL_ASSERT(!grad_fn);
      msg = c10::str(msg, " Given that this use case is ambiguous and error-prone, it is forbidden."
                      " You can clarify your code by moving both the view and the inplace either both"
                      " inside the no_grad block (if you don't want the inplace to be tracked) or both outside (if you want"
                      " the inplace to be tracked).");
    } else if (creation_meta == CreationMeta::INFERENCE_MODE) {
      TORCH_INTERNAL_ASSERT(!grad_fn);
      msg = c10::str(msg, " Given that this use case is ambiguous and error-prone, it is forbidden."
                      " You can clarify your code by moving both the view and the inplace either both"
                      " inside the inference_mode block (if you don't want the inplace to be tracked) or both outside (if you want"
                      " the inplace to be tracked).");
      TORCH_CHECK(false, msg);
    } else if (creation_meta == CreationMeta::IN_CUSTOM_FUNCTION) {
      msg = c10::str(msg, " This view was created inside a custom Function (or because an input was returned as-is) and the"
                      " autograd logic to handle view+inplace would override the custom backward associated with the custom"
                      " Function, leading to incorrect gradients. This behavior is forbidden. You can fix this by"
                      " cloning the output of the custom Function.");
    } else {
      TORCH_INTERNAL_ASSERT(false, "Invalid CreationMeta state");
    }

    TORCH_CHECK(false, msg);
  }
}



}} // namespace torch::autograd