torch/csrc/autograd/custom_function.cpp - platform/external/pytorch - Git at Google

 #include <c10/util/irange.h>
 #include <torch/csrc/autograd/custom_function.h>
 #include <torch/csrc/autograd/functions/accumulate_grad.h>
 #include <torch/csrc/autograd/autograd.h>

 namespace torch { namespace autograd {

 VariableInfo::VariableInfo(const Variable& var)
   : layout(var.layout())
   , device(var.device())
   , scalar_type(var.scalar_type())
   , size(var.sizes().vec())
   , requires_grad(var.requires_grad())
   , is_empty(false) {
 }

 VariableInfo::VariableInfo() : requires_grad(false), is_empty(true) {}

 Variable VariableInfo::zeros(at::OptionalDeviceGuard& device_guard) const {
   if (is_empty) {
     // Return undefined tensor.
     return at::Tensor();
   } else {
     return at::zeros(
         size, at::TensorOptions(scalar_type).device(device).layout(layout));
   }
 }

 optional_variable_list _process_backward_mode_ad(
   const std::unordered_set<at::TensorImpl*> &inputs_set,
   const std::unordered_set<at::TensorImpl*> &non_differentiable,
   const std::unordered_set<at::TensorImpl*> &dirty_inputs,
   const at::ArrayRef<c10::optional<Variable>> raw_outputs,
   const std::shared_ptr<Node> &cdata) {


   int num_outputs = raw_outputs.size();

   // Sets the grad_fn and output_nr of an output Variable.
   auto set_history = [&](Variable& var, uint32_t output_nr, bool is_input, bool is_modified,
                          bool is_differentiable) {
     if (!is_differentiable) {
       if (!var.requires_grad()) {
         return;
       }
       // Return detached aliases of inputs, instead of changing their requires_grad
       // property.
       if (is_input) {
         var = var.detach();
       } else if (!var.is_view()) {
         var.detach_();
       }
       // If var is a view of one of the inputs of the custom autograd Function,
       // we don't detach it in a no_grad block. This is so that we can mimic the
       // behavior of returning a view from a no_grad block:
       //   x = torch.randn(3, requires_grad=True)
       //   with torch.no_grad():
       //       y = x.view(-1)
       // Here, `y` requires_grad (!).
     } else if (is_modified) {
       if (var.is_leaf() && var.requires_grad()) {
         TORCH_CHECK(false, "a leaf Variable that requires grad has been used in an in-place operation.");
       }
       // No need to mark as modified Tensors that are not inputs.
       if (!is_input) {
         TORCH_WARN("Only input Tensors should be given to ctx.mark_dirty(). If a Tensor is not an input, there"
                    " is no need to pass it to mark_dirty().");
       }
       // If the input is a view, the rebase will need to rewrite the graph and this only works if we have a single
       // output to this Function.
       TORCH_CHECK(!(var.is_view() && num_outputs > 1), "If your Function modifies inplace an input that is a view"
                   " of another Tensor, your Function cannot return more than one Tensor. This is not supported"
                   " by the current autograd engine. You should either make sure the input is not a view (using"
                   " .clone() for example) or make your Function only return one Tensor (potentially splitting"
                   " it into two Functions: one doing the inplace that returns a single Tensor and a second one"
                   " that does the other operations). You can ask on the forum https://discuss.pytorch.org/ if"
                   " you need help to do this change.");

       // If the input was modified, transplant the grad_fn in the graph:
       // grad_fn <- variable <- self  ==>  grad_fn <- self <- variable
       var.mutable_grad().reset();
       impl::clear_hooks(var);
       if (auto grad_acc_fn = impl::try_get_grad_accumulator(var)) {
         auto grad_acc = dynamic_cast<AccumulateGrad*>(grad_acc_fn.get());
         grad_acc->variable.reset();
       }
       if (cdata) {
         impl::rebase_history(var, {cdata, output_nr});
       }
     } else if (is_input) {
       // An input has been returned, but it wasn't modified. Return it as a view
       // so that we can attach a new grad_fn to the Variable.
       // Run in no_grad mode to mimic the behavior of the forward.
       {
         AutoGradMode grad_mode(false);
         var = var.view_as(var);
       }
       impl::set_gradient_edge(var, {cdata, output_nr});
     } else if (cdata) {
       impl::set_gradient_edge(var, {cdata, output_nr});
     }
   };

   optional_variable_list outputs;
   std::unordered_set<at::TensorImpl*> outputs_impl; // For dirty_inputs check
   outputs.reserve(num_outputs);
   int num_diff_outputs = 0;


   for (const auto i : c10::irange(num_outputs)) {
     // For outputs that are not tensors, put a placeholder undefined input.
     if (!raw_outputs[i].has_value()) {
       if (cdata) {
         auto output_nr = cdata->add_input_metadata(Node::undefined_input());
         AT_ASSERT(i == (int)output_nr);
       }
       outputs.emplace_back();
       continue;
     }

     Variable var = raw_outputs[i].value();

     auto out_tensor_impl = var.unsafeGetTensorImpl();
     bool is_input = inputs_set.count(out_tensor_impl) > 0;
     bool is_modified = dirty_inputs.count(out_tensor_impl) > 0;
     bool is_differentiable = cdata && non_differentiable.count(out_tensor_impl) == 0
                               && isDifferentiableType(var.scalar_type());

     if (cdata) {
       auto output_nr = cdata->add_input_metadata(var);
       AT_ASSERT(i == (int)output_nr);
     }
     set_history(var, i, is_input, is_modified, is_differentiable);

     // For deprecation cycle. Can be removed after 1.6. In the case where we detected a view
     // in no grad mode during the forward, only warn the user (do not change the flag if we
     // return and input that is a view as is).
     // See NOTE [ View + Inplace detection ] for why we replace everything by a warning.
     if (!(is_input && is_modified) && var.is_view()) {
       // is_view() => diff_view_meta
       auto diff_view_meta = impl::get_view_autograd_meta(var);
       diff_view_meta->set_creation_meta(CreationMeta::IN_CUSTOM_FUNCTION);
     }

     if (is_differentiable) {
       ++num_diff_outputs;
     }

     outputs_impl.insert(out_tensor_impl);
     outputs.emplace_back(var);
   }

   // If multiple differentiable outputs are returned, we do not allow views to be modified inplace
   // See NOTE [ View + Inplace detection ] for more details
   if (num_diff_outputs > 1) {
     for (auto& var: outputs) {
       if (var.has_value()) {
         auto diff_view_meta = impl::get_view_autograd_meta(var.value());
         if (diff_view_meta && diff_view_meta->has_bw_view()) {
           diff_view_meta->set_creation_meta(CreationMeta::MULTI_OUTPUT_NODE);
         }
       }
     }
   }

   // All the modified Tensors must be returned as is for the rewrite to be valid.
   for (auto& dirty_input : dirty_inputs) {
     TORCH_CHECK(outputs_impl.count(dirty_input) > 0,
                 "Some elements marked as dirty during the forward method were not returned as output. The"
                 " inputs that are modified inplace must all be outputs of the Function.");
   }

   return outputs;
 }


 optional_variable_list _wrap_outputs(const variable_list &input_vars,
   const std::unordered_set<at::TensorImpl*> &non_differentiable,
   const std::unordered_set<at::TensorImpl*> &dirty_inputs,
   const at::ArrayRef<c10::optional<Variable>> raw_outputs,
   const std::shared_ptr<Node> &cdata) {

   std::unordered_set<at::TensorImpl*> inputs_set;
   inputs_set.reserve(input_vars.size());
   for (auto& var : input_vars) {
     inputs_set.emplace(var.unsafeGetTensorImpl());
   }

   auto outputs = _process_backward_mode_ad(inputs_set, non_differentiable, dirty_inputs, raw_outputs, cdata);


   return outputs;
 }

 void check_variable_result(const Variable& original, const Variable& result, std::string hook_name) {
   if (!original.options().type_equal(result.options())) {
     std::stringstream ss;
     ss << "hook '" << hook_name << "' has changed the type of value (";
     ss << "was " << original.toString() << " got ";
     ss << result.toString() << ")";
     throw std::runtime_error(ss.str());
   }

   if (original.is_cuda() != result.is_cuda()) {
     std::stringstream ss;
     ss << "hook '" << hook_name << "' has changed the type of value";
     if (original.is_cuda()) {
       ss << " (was CUDA tensor got CPU tensor)";
     } else {
       ss << " (was CPU tensor got CUDA tensor)";
     }
     throw std::runtime_error(ss.str());
   }

   if (original.sizes().vec() != result.sizes().vec()) {
     std::stringstream ss;
     ss << "hook '" << hook_name << "' has changed the size of value";
     throw std::runtime_error(ss.str());
   }
 }

 void AutogradContext::save_for_backward(variable_list to_save) {
   to_save_ = std::move(to_save);
 }

 // The logic for handling saved variables here is the same as python_function.cpp
 // See _save_variables() and unpack_saved_variables()
 void AutogradContext::save_variables() {
   saved_variables_.clear();
   auto ptr = grad_fn_.lock();

   for (const auto& var : to_save_) {
     // Allow empty variables to be saved
     if (var.defined()) {
       bool is_output = var.grad_fn().get() == ptr.get();
       saved_variables_.emplace_back(var, is_output);
     } else {
       saved_variables_.emplace_back();
     }
   }
   to_save_.clear();
 }

 variable_list AutogradContext::get_saved_variables() const {
   TORCH_CHECK(!has_freed_buffers_, ERR_BACKWARD_TWICE);
   variable_list saved;
   saved.reserve(saved_variables_.size());
   auto ptr = grad_fn_.lock();
   TORCH_INTERNAL_ASSERT(ptr);
   for (auto& var : saved_variables_) {
     saved.push_back(var.unpack(ptr));
   }
   return saved;
 }

 void AutogradContext::mark_dirty(const variable_list &inputs) {
   dirty_inputs_.clear();
   dirty_inputs_.reserve(inputs.size());
   for(auto& var : inputs) {
     dirty_inputs_.insert(var.unsafeGetTensorImpl());
   }
 }

 void AutogradContext::mark_non_differentiable(const variable_list &outputs) {
   non_differentiable_.clear();
   non_differentiable_.reserve(outputs.size());
   for(auto& var : outputs) {
     non_differentiable_.insert(var.unsafeGetTensorImpl());
   }
 }

 void AutogradContext::set_materialize_grads(bool value) {
   materialize_grads_ = value;
 }

 const std::unordered_set<at::TensorImpl*>& AutogradContext::get_and_bump_dirty() const {
   for (auto& var : dirty_inputs_) {
     var->bump_version();
   }
   return dirty_inputs_;
 }

 const std::unordered_set<at::TensorImpl*>& AutogradContext::get_non_differentiable() const {
   return non_differentiable_;
 }
 }} // namespace torch::autograd
	#include <c10/util/irange.h>
	#include <torch/csrc/autograd/custom_function.h>
	#include <torch/csrc/autograd/functions/accumulate_grad.h>
	#include <torch/csrc/autograd/autograd.h>

	namespace torch { namespace autograd {

	VariableInfo::VariableInfo(const Variable& var)
	: layout(var.layout())
	, device(var.device())
	, scalar_type(var.scalar_type())
	, size(var.sizes().vec())
	, requires_grad(var.requires_grad())
	, is_empty(false) {
	}

	VariableInfo::VariableInfo() : requires_grad(false), is_empty(true) {}

	Variable VariableInfo::zeros(at::OptionalDeviceGuard& device_guard) const {
	if (is_empty) {
	// Return undefined tensor.
	return at::Tensor();
	} else {
	return at::zeros(
	size, at::TensorOptions(scalar_type).device(device).layout(layout));
	}
	}

	optional_variable_list _process_backward_mode_ad(
	const std::unordered_set<at::TensorImpl*> &inputs_set,
	const std::unordered_set<at::TensorImpl*> &non_differentiable,
	const std::unordered_set<at::TensorImpl*> &dirty_inputs,
	const at::ArrayRef<c10::optional<Variable>> raw_outputs,
	const std::shared_ptr<Node> &cdata) {


	int num_outputs = raw_outputs.size();

	// Sets the grad_fn and output_nr of an output Variable.
	auto set_history = [&](Variable& var, uint32_t output_nr, bool is_input, bool is_modified,
	bool is_differentiable) {
	if (!is_differentiable) {
	if (!var.requires_grad()) {
	return;
	}
	// Return detached aliases of inputs, instead of changing their requires_grad
	// property.
	if (is_input) {
	var = var.detach();
	} else if (!var.is_view()) {
	var.detach_();
	}
	// If var is a view of one of the inputs of the custom autograd Function,
	// we don't detach it in a no_grad block. This is so that we can mimic the
	// behavior of returning a view from a no_grad block:
	// x = torch.randn(3, requires_grad=True)
	// with torch.no_grad():
	// y = x.view(-1)
	// Here, `y` requires_grad (!).
	} else if (is_modified) {
	if (var.is_leaf() && var.requires_grad()) {
	TORCH_CHECK(false, "a leaf Variable that requires grad has been used in an in-place operation.");
	}
	// No need to mark as modified Tensors that are not inputs.
	if (!is_input) {
	TORCH_WARN("Only input Tensors should be given to ctx.mark_dirty(). If a Tensor is not an input, there"
	" is no need to pass it to mark_dirty().");
	}
	// If the input is a view, the rebase will need to rewrite the graph and this only works if we have a single
	// output to this Function.
	TORCH_CHECK(!(var.is_view() && num_outputs > 1), "If your Function modifies inplace an input that is a view"
	" of another Tensor, your Function cannot return more than one Tensor. This is not supported"
	" by the current autograd engine. You should either make sure the input is not a view (using"
	" .clone() for example) or make your Function only return one Tensor (potentially splitting"
	" it into two Functions: one doing the inplace that returns a single Tensor and a second one"
	" that does the other operations). You can ask on the forum https://discuss.pytorch.org/ if"
	" you need help to do this change.");

	// If the input was modified, transplant the grad_fn in the graph:
	// grad_fn <- variable <- self ==> grad_fn <- self <- variable
	var.mutable_grad().reset();
	impl::clear_hooks(var);
	if (auto grad_acc_fn = impl::try_get_grad_accumulator(var)) {
	auto grad_acc = dynamic_cast<AccumulateGrad*>(grad_acc_fn.get());
	grad_acc->variable.reset();
	}
	if (cdata) {
	impl::rebase_history(var, {cdata, output_nr});
	}
	} else if (is_input) {
	// An input has been returned, but it wasn't modified. Return it as a view
	// so that we can attach a new grad_fn to the Variable.
	// Run in no_grad mode to mimic the behavior of the forward.
	{
	AutoGradMode grad_mode(false);
	var = var.view_as(var);
	}
	impl::set_gradient_edge(var, {cdata, output_nr});
	} else if (cdata) {
	impl::set_gradient_edge(var, {cdata, output_nr});
	}
	};

	optional_variable_list outputs;
	std::unordered_set<at::TensorImpl*> outputs_impl; // For dirty_inputs check
	outputs.reserve(num_outputs);
	int num_diff_outputs = 0;


	for (const auto i : c10::irange(num_outputs)) {
	// For outputs that are not tensors, put a placeholder undefined input.
	if (!raw_outputs[i].has_value()) {
	if (cdata) {
	auto output_nr = cdata->add_input_metadata(Node::undefined_input());
	AT_ASSERT(i == (int)output_nr);
	}
	outputs.emplace_back();
	continue;
	}

	Variable var = raw_outputs[i].value();

	auto out_tensor_impl = var.unsafeGetTensorImpl();
	bool is_input = inputs_set.count(out_tensor_impl) > 0;
	bool is_modified = dirty_inputs.count(out_tensor_impl) > 0;
	bool is_differentiable = cdata && non_differentiable.count(out_tensor_impl) == 0
	&& isDifferentiableType(var.scalar_type());

	if (cdata) {
	auto output_nr = cdata->add_input_metadata(var);
	AT_ASSERT(i == (int)output_nr);
	}
	set_history(var, i, is_input, is_modified, is_differentiable);

	// For deprecation cycle. Can be removed after 1.6. In the case where we detected a view
	// in no grad mode during the forward, only warn the user (do not change the flag if we
	// return and input that is a view as is).
	// See NOTE [ View + Inplace detection ] for why we replace everything by a warning.
	if (!(is_input && is_modified) && var.is_view()) {
	// is_view() => diff_view_meta
	auto diff_view_meta = impl::get_view_autograd_meta(var);
	diff_view_meta->set_creation_meta(CreationMeta::IN_CUSTOM_FUNCTION);
	}

	if (is_differentiable) {
	++num_diff_outputs;
	}

	outputs_impl.insert(out_tensor_impl);
	outputs.emplace_back(var);
	}

	// If multiple differentiable outputs are returned, we do not allow views to be modified inplace
	// See NOTE [ View + Inplace detection ] for more details
	if (num_diff_outputs > 1) {
	for (auto& var: outputs) {
	if (var.has_value()) {
	auto diff_view_meta = impl::get_view_autograd_meta(var.value());
	if (diff_view_meta && diff_view_meta->has_bw_view()) {
	diff_view_meta->set_creation_meta(CreationMeta::MULTI_OUTPUT_NODE);
	}
	}
	}
	}

	// All the modified Tensors must be returned as is for the rewrite to be valid.
	for (auto& dirty_input : dirty_inputs) {
	TORCH_CHECK(outputs_impl.count(dirty_input) > 0,
	"Some elements marked as dirty during the forward method were not returned as output. The"
	" inputs that are modified inplace must all be outputs of the Function.");
	}

	return outputs;
	}



	optional_variable_list _wrap_outputs(const variable_list &input_vars,
	const std::unordered_set<at::TensorImpl*> &non_differentiable,
	const std::unordered_set<at::TensorImpl*> &dirty_inputs,
	const at::ArrayRef<c10::optional<Variable>> raw_outputs,
	const std::shared_ptr<Node> &cdata) {

	std::unordered_set<at::TensorImpl*> inputs_set;
	inputs_set.reserve(input_vars.size());
	for (auto& var : input_vars) {
	inputs_set.emplace(var.unsafeGetTensorImpl());
	}

	auto outputs = _process_backward_mode_ad(inputs_set, non_differentiable, dirty_inputs, raw_outputs, cdata);


	return outputs;
	}

	void check_variable_result(const Variable& original, const Variable& result, std::string hook_name) {
	if (!original.options().type_equal(result.options())) {
	std::stringstream ss;
	ss << "hook '" << hook_name << "' has changed the type of value (";
	ss << "was " << original.toString() << " got ";
	ss << result.toString() << ")";
	throw std::runtime_error(ss.str());
	}

	if (original.is_cuda() != result.is_cuda()) {
	std::stringstream ss;
	ss << "hook '" << hook_name << "' has changed the type of value";
	if (original.is_cuda()) {
	ss << " (was CUDA tensor got CPU tensor)";
	} else {
	ss << " (was CPU tensor got CUDA tensor)";
	}
	throw std::runtime_error(ss.str());
	}

	if (original.sizes().vec() != result.sizes().vec()) {
	std::stringstream ss;
	ss << "hook '" << hook_name << "' has changed the size of value";
	throw std::runtime_error(ss.str());
	}
	}

	void AutogradContext::save_for_backward(variable_list to_save) {
	to_save_ = std::move(to_save);
	}

	// The logic for handling saved variables here is the same as python_function.cpp
	// See _save_variables() and unpack_saved_variables()
	void AutogradContext::save_variables() {
	saved_variables_.clear();
	auto ptr = grad_fn_.lock();

	for (const auto& var : to_save_) {
	// Allow empty variables to be saved
	if (var.defined()) {
	bool is_output = var.grad_fn().get() == ptr.get();
	saved_variables_.emplace_back(var, is_output);
	} else {
	saved_variables_.emplace_back();
	}
	}
	to_save_.clear();
	}

	variable_list AutogradContext::get_saved_variables() const {
	TORCH_CHECK(!has_freed_buffers_, ERR_BACKWARD_TWICE);
	variable_list saved;
	saved.reserve(saved_variables_.size());
	auto ptr = grad_fn_.lock();
	TORCH_INTERNAL_ASSERT(ptr);
	for (auto& var : saved_variables_) {
	saved.push_back(var.unpack(ptr));
	}
	return saved;
	}

	void AutogradContext::mark_dirty(const variable_list &inputs) {
	dirty_inputs_.clear();
	dirty_inputs_.reserve(inputs.size());
	for(auto& var : inputs) {
	dirty_inputs_.insert(var.unsafeGetTensorImpl());
	}
	}

	void AutogradContext::mark_non_differentiable(const variable_list &outputs) {
	non_differentiable_.clear();
	non_differentiable_.reserve(outputs.size());
	for(auto& var : outputs) {
	non_differentiable_.insert(var.unsafeGetTensorImpl());
	}
	}

	void AutogradContext::set_materialize_grads(bool value) {
	materialize_grads_ = value;
	}

	const std::unordered_set<at::TensorImpl*>& AutogradContext::get_and_bump_dirty() const {
	for (auto& var : dirty_inputs_) {
	var->bump_version();
	}
	return dirty_inputs_;
	}

	const std::unordered_set<at::TensorImpl*>& AutogradContext::get_non_differentiable() const {
	return non_differentiable_;
	}
	}} // namespace torch::autograd