#include <torch/csrc/jit/runtime/autodiff.h>

#include <ATen/core/functional.h>
#include <c10/util/Exception.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
#include <torch/csrc/jit/passes/constant_pooling.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/passes/lower_tuples.h>
#include <torch/csrc/jit/passes/update_differentiable_graph_requires_grad.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/symbolic_script.h>
#include <algorithm>
#include <memory>

namespace torch {
namespace jit {

using value_map = std::unordered_map<Value*, Value*>;
using value_set = std::unordered_set<Value*>;

void wrapDim(int64_t& dim, const std::vector<int64_t>& sizes) {
  if (dim < 0) {
    dim += sizes.size();
  }
}

// need_trim_grad_ops contains functions that return multiple outputs in
// forward, but only the first one requires grad.
// Example:
// kthvalue returns (kthvalue, index of kthvalue), currently autodiff only
// supports at most one output that requires grad. Thus we need to remove
// the grad for index that doesn't require grad.
bool needTrimGrad(Node* n) {
  static OperatorSet need_trim_grad_ops = {
      "aten::kthvalue(Tensor self, int k, int dim, bool keepdim) -> (Tensor, Tensor)",
      "aten::topk(Tensor self, int k, int dim, bool largest, bool sorted) -> (Tensor, Tensor)",
      "aten::max_pool2d(Tensor self, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> Tensor",
      "aten::max_pool2d_with_indices(Tensor self, int[] kernel_size, int[] stride, int[] padding, int[] dilation, bool ceil_mode) -> (Tensor, Tensor)"};
  if (n->isMemberOf(need_trim_grad_ops)) {
    return true;
  }
  return false;
}

bool isDifferentiable(const Node* n) {
  // TODO: scalar-tensor ops should be canonicalized
  static OperatorSet differentiable_ops = {
      "aten::thnn_conv2d_forward(Tensor self, Tensor weight, int[] kernel_size, Tensor? bias, int[] stride, int[] padding) -> (Tensor, Tensor, Tensor)",
      "aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)",
  };

  // TODO: add support for the following fusible operators.
  // They're a little tricky to implement; max/min require mutability for best
  // perf "aten::atan2(Tensor self) -> Tensor", "aten::max(Tensor self) ->
  // Tensor", "aten::min(Tensor self) -> Tensor"

  if (n->kind() == prim::Constant || n->kind() == prim::AutogradZero ||
      n->kind() == prim::AutogradAdd || n->kind() == prim::ConstantChunk ||
      n->kind() == prim::profile)
    return true;

  if (n->isMemberOf(differentiable_ops))
    return true;

  if (n->matches(
          "aten::dropout(Tensor input, float p, bool train) -> Tensor",
          attr::train)) {
    return n->get<bool>(attr::train).value();
  }

  if (n->matches(
          "aten::expand(Tensor self, int[] size, *, bool implicit) -> Tensor")) {
    return n->get<c10::List<int64_t>>(attr::size) &&
        n->is_constant(attr::implicit);
  }

  auto schema = n->maybeSchema();
  if (schema && hasGradientInfoForSchema(*schema)) {
    return true;
  }

  // linear blocks may appear as inputs to graph executors, but they are removed
  // before differentiation occurs
  if (n->kind() == prim::GradOf) {
    auto body = n->blocks().at(0);
    return std::all_of(
        body->nodes().begin(),
        body->nodes().end(),
        static_cast<bool (*)(const Node*)>(isDifferentiable));
  }

  // formulas are only defined with floating point scalars,
  // so we fallback to autograd for other cases.
  for (const Value* input : n->inputs()) {
    if (input->type() == NumberType::get()) {
      return false;
    }
  }

  return false;
}

bool isDifferentiable(Graph& g) {
  return std::all_of(
      g.nodes().begin(),
      g.nodes().end(),
      static_cast<bool (*)(const Node*)>(isDifferentiable));
}

// NB: Write gradient using torchscript
// For example, node aten::mul() should be defined as follows
// def forward(x, y):
//     return x*y, (x, y)
// def backward(ctx, grad_output):
//     x, y = ctx
//     return (y * grad_output).sum_to_size(x), (x * grad_output).sum_to_size(y)
//
// Here ctx is a tuple that carries all input/intermediate results needed in
// backward from forward pass.
//
// This python code is compiled into a GradientPair which includes a forward
// graph and a backward graph. Forward graph will be used to replace the node in
// grad_desc.f, and backward graph will be used to construct GradOf(node) in
// reverse_block. Grad_values(a.k.a gradOutputs) propagated through
// node->owningGraph() in **reversed** order, thus GradientPair.forward should
// be inserted **after** the node being replaced, so that we don't traverse the
// graph infinite times.
//
// The output of compiled forward graph is [real_outputs, ctx]
// The input of compiled backward graph is [ctx, grad_values]
// We run LowerSimpleTuples afterwards to elmininate all tuples generated in
// this process. The original node and TupleConstruct nodes in forward graph
// will be cleaned up later using EliminateDeadCode(block). TupleUnPack node in
// backward graph will be removed in eliminateDeadcode(ReverseDetails) defined
// in this file.
static c10::optional<std::vector<Value*>> build_script_grad(
    Node* node,
    const ArrayRef<Value*>& grads) {
  auto graph = node->owningGraph();
  auto maybe_schema = node->maybeSchema();
  if (!maybe_schema) {
    return c10::nullopt;
  }
  auto compiled_graphs = gradientInfoForSchema(*maybe_schema);
  if (!compiled_graphs) {
    return c10::nullopt;
  }
  // Use forward graph to replace node in grad_desc.f
  value_list new_outputs;
  {
    WithInsertPoint guard(node->next());
    auto fw_graph = compiled_graphs->forward;
    new_outputs = insertGraph(*graph, *fw_graph, node->inputs());
    new_outputs = unpackOutputs(new_outputs);
    auto outputs = node->outputs();
    AT_ASSERT(new_outputs.size() == outputs.size() + 1);
    for (size_t i = 0; i < outputs.size(); ++i) {
      new_outputs.at(i)->setType(outputs[i]->type());
      outputs[i]->replaceAllUsesWith(new_outputs.at(i));
    }
  }

  // Use backward graph to construct reverse_block
  auto bw_graph = compiled_graphs->backward;
  auto grad_vec = grads.vec();
  if (needTrimGrad(node)) {
    grad_vec.erase(grad_vec.begin() + 1, grad_vec.end());
  }
  auto it = grad_vec.begin();
  grad_vec.insert(it, new_outputs.back());
  ArrayRef<Value*> grad(grad_vec);
  auto grad_inputs = insertGraph(*graph, *bw_graph, grad);
  grad_inputs = unpackOutputs(grad_inputs);
  return grad_inputs;
};

namespace {
class GradientHelper {
 public:
  GradientHelper(Node* n) : node(n) {}

  std::vector<Value*> gradient(ArrayRef<Value*> grad_values) {
    if (!isDifferentiable(node)) {
      throw std::runtime_error(
          std::string("differentiation of ") + node->kind().toDisplayString() +
          " is not supported, or it is missing necessary type information");
    }
    // If AD is defined using torchscript, use it instead of symbolic
    auto script_grads = build_script_grad(node, grad_values);
    if (script_grads)
      return *script_grads;

    // Definition not found in torchscript, look up in the buildSymbolicGradient
    // TODO: migrate all to using torchscript
    return buildSymbolicGradient(grad_values);
  }

 private:
  Node* node;

  std::vector<Value*> buildSymbolicGradient(
      const ArrayRef<Value*>& grad_values) {
    auto inputs = node->inputs();
    auto outputs = node->outputs();

    if (node->kind() == prim::AutogradAdd) {
      // NB: AutogradAdds don't broadcast
      return {grad_values.at(0), grad_values.at(0)};
    } else if (node->kind() == prim::profile) {
      return {grad_values.at(0)};
    } else if (node->kind() == prim::ConstantChunk) {
      auto* g = node->owningGraph();

      Value* input_list;
      if (grad_values.size() == 1 &&
          grad_values[0]->type()->isSubtypeOf(ListType::ofTensors())) {
        input_list = grad_values[0];
      } else {
        input_list =
            g->insertNode(g->createList(TensorType::get(), grad_values))
                ->output();
      }

      auto* cDim = g->insertConstant(node->i(attr::dim));
      auto* cat_node = g->insertNode(g->create(aten::cat, 1));
      cat_node->addInput(input_list);
      cat_node->addInput(cDim);
      return {cat_node->output()};
    } else if (
        node->kind() == prim::Constant || node->kind() == prim::AutogradZero) {
      return {};
    } else if (
        node->matches(
            "aten::thnn_conv2d_forward(Tensor self, Tensor weight, int[] kernel_size, Tensor? bias, int[] stride, int[] padding) -> (Tensor, Tensor, Tensor)")) {
      auto graph = node->owningGraph();
      auto backward_value = graph->insert(
          aten::thnn_conv2d_backward,
          {grad_values.at(0),
           inputs.at(0),
           inputs.at(1),
           node->namedInput(attr::kernel_size),
           node->namedInput(attr::stride),
           node->namedInput(attr::padding),
           outputs.at(1),
           outputs.at(2),
           graph->insertConstant(c10::List<bool>({true, true, true}))});
      // graph->insert returns a tuple automatically if multiple outputs are
      // returned. So unpack them again.
      Node* tuple_unpack_node =
          graph->insertNode(graph->createTupleUnpack(backward_value));
      auto tuple_outputs = tuple_unpack_node->outputs();
      AT_ASSERT(tuple_outputs.size() == size_t(3));
      return {
          tuple_outputs[0],
          tuple_outputs[1],
          nullptr,
          tuple_outputs[2],
          nullptr,
          nullptr};

    } else if (
        node->matches(
            "aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)")) {
      auto graph = node->owningGraph();
      auto backward_value = graph->insert(
          aten::native_batch_norm_backward,
          {grad_values.at(0),
           inputs.at(0),
           inputs.at(1),
           inputs.at(3),
           inputs.at(4),
           outputs.at(1),
           outputs.at(2),
           inputs.at(5),
           inputs.at(7),
           graph->insertConstant(c10::List<bool>({true, true, true}))});
      // graph->insert returns a tuple automatically if multiple outputs are
      // returned. So unpack them again.
      Node* tuple_unpack_node =
          graph->insertNode(graph->createTupleUnpack(backward_value));
      auto tuple_outputs = tuple_unpack_node->outputs();
      AT_ASSERT(tuple_outputs.size() == size_t(3));
      return {
          tuple_outputs[0],
          tuple_outputs[1],
          tuple_outputs[2],
          nullptr,
          nullptr,
          nullptr,
          nullptr,
          nullptr};
    }

    throw std::runtime_error(
        std::string("failed to differentiate `") +
        node->kind().toDisplayString() + "`");
  }
};
} // namespace

// If we have a function y = f(x) with jacobian J, the backwards of f is dx =
// J^t dy. Note that because the backwards always implements this matrix
// multiply, we know that it maps an input vector of zeros to an output vector
// of zero regardless of what operations it choses to do inside to actually
// implement the matrix multiply (most use some optimized form and never
// generate J^t). More generally, we know that all of the backward computations
// are linear and can use this property to do more aggressive optimizations
// later. It is ok to replace any backward function with known-zero inputs with
// something that produces known-zero outputs. This function encloses each
// know-linear backward function in a 'GradOf' sub-block so that we can perform
// optimizations using this information. In particular, specializeAutogradZero
// will observe if all the inputs to the linear block are AutogradZeroTensor,
// which the autograd uses to represent zeros, and then propagate the zeros to
// the outputs of the block.
static std::vector<Value*> linearGradientForNode(
    Node* node,
    ArrayRef<Value*> grad_values) {
  auto& graph = *node->owningGraph();

  // FIXME: In case forward has multi outputs, we only support one requires grad
  if (needTrimGrad(node)) {
    grad_values = grad_values.at(0);
  }
  auto linear = graph.insertNode(graph.create(prim::GradOf, {grad_values}, 0));
  // to make reading gradient graphs easier, remember the name of the forward op
  linear->s_(attr::name, node->kind().toDisplayString());
  auto block = linear->addBlock();
  WithInsertPoint guard(block);
  auto results = GradientHelper(node).gradient(grad_values);
  return fmap(results, [block, linear](Value* grad) -> Value* {
    if (!grad || grad->mustBeNone())
      return nullptr;
    block->registerOutput(grad);
    return linear->addOutput()->copyMetadata(grad);
  });
}

struct ReverseDetails {
  ReverseDetails(value_map&& grad_map, Block* reverse_block)
      : grad_map(std::move(grad_map)), reverse_block(reverse_block) {}

  value_map grad_map;
  Block* reverse_block;
};

// AutogradAdd is a special addition function that handles Undef
// AutogradAdd(a, b) == a + b if defined(a) and defined(b)
// AutogradAdd(Undef, b) == b
// AutogradAdd(a, Undef) == a
// AutogradAdd(Undef, Undef) == Undef
static Value* createAutogradAdd(Value* a, Value* b) {
  auto graph = a->owningGraph();
  return graph->insertNode(graph->create(prim::AutogradAdd, {a, b}))->output();
}

// Before:
//   - grad_desc has field f initialized to the original 0-stage graph
// After:
//   - the last node of f (f->nodes().reverse()[0]) is a gradient node
//     whose block has vjp inputs for all outputs that require_grad
//     and vjp outputs for all primal inputs that require_grad
//   - grad_desc has df_input_vjps and df_output_vjps set
//     (but df_input_vjps will be modified later as well)
static ReverseDetails addReverseInline(Gradient& grad_desc) {
  auto& graph = *grad_desc.f;
  // note: reverse_node is intentionally not inserted to avoid
  // accidentally acting on it (e.g. in elminate dead code),
  // std::cout << *reverse_node << to view its state.
  auto reverse_node = graph.create(prim::Reverse, 0);
  auto reverse_block = reverse_node->addBlock();
  WithInsertPoint guard(reverse_block);

  value_map grad_map; // x -> dx mapping
  const auto get_grad = [&](Value* v) -> Value* {
    auto it = grad_map.find(v);
    if (it == grad_map.end()) {
      auto autograd_zero = graph.insertNode(graph.createAutogradZero());
      std::tie(it, std::ignore) = grad_map.emplace(v, autograd_zero->output());
    }
    return it->second;
  };
  const auto set_grad = [&](Value* x, Value* dx) {
    if (Value* prev_grad = grad_map[x]) {
      GRAPH_DEBUG("grad_map[", x->debugName(), "] = ", *grad_map[x]->node());
      grad_map[x] = createAutogradAdd(prev_grad, dx);
    } else {
      GRAPH_DEBUG("grad_map[", x->debugName(), "] = ", dx->debugName());
      grad_map[x] = dx;
    }
  };

  auto outputs = graph.outputs();
  for (size_t i = 0, num_outputs = outputs.size(); i < num_outputs; ++i) {
    Value* output = outputs[i];
    if (!output->requires_grad())
      continue;
    Value* output_grad = reverse_block->addInput()->setType(output->type());
    GRAPH_DEBUG(
        "Adding output_grad ",
        output_grad->debugName(),
        " for ",
        output->debugName());
    set_grad(output, output_grad);
    grad_desc.df_input_vjps.push_back(i);
  }

  for (auto it = graph.nodes().rbegin(), end = graph.nodes().rend(); it != end;
       ++it) {
    Node* node = *it;
    auto inputs = node->inputs();
    auto outputs = node->outputs();
    if (std::all_of(outputs.begin(), outputs.end(), [](Value* v) {
          return !v->requires_grad();
        })) {
      continue;
    }

    value_list grad_inputs =
        linearGradientForNode(node, fmap(node->outputs(), get_grad));
    LowerSimpleTuples(reverse_block);

    AT_ASSERT(grad_inputs.size() == node->inputs().size());
    for (size_t i = 0, num_inputs = grad_inputs.size(); i < num_inputs; ++i) {
      if (!inputs[i]->requires_grad())
        continue;
      // NB: Not returning a gradient w.r.t. a value that requires grad is
      // normal if the input is non-differentiable. This happens e.g. in the
      // aten::type_as case.
      if (!grad_inputs[i])
        continue;
      set_grad(inputs[i], grad_inputs[i]);
    }
  }

  auto inputs = graph.inputs();
  for (size_t i = 0, num_inputs = inputs.size(); i < num_inputs; ++i) {
    Value* input = inputs[i];
    if (!input->requires_grad())
      continue;
    // NB: Not having a gradient defined w.r.t. an input to the graph which
    // requires grad can happen and is not an error. It might have been used
    // only in non-differentiable contexts (e.g. as second input to
    // aten::type_as). In that case we simply ignore it as an output, because it
    // won't ever produce any meaningful values.
    if (grad_map.count(input) == 0)
      continue;
    reverse_block->registerOutput(get_grad(input));
    grad_desc.df_output_vjps.push_back(i);
  }

  Inline(graph);
  return ReverseDetails(std::move(grad_map), reverse_block);
}

// Returns a topologically-sorted list of values produced in f, and used in its
// reverse program.
static value_list getReverseCaptures(Gradient& grad_desc) {
  auto& graph = *grad_desc.f;
  auto primal_block = graph.block();

  value_set reverse_captures_set;
  value_list reverse_captures; // Invariant: topo sorted
  auto check_uses = [&](Value* v) {
    for (auto use : v->uses()) {
      if (use.user->owningBlock() == primal_block)
        continue;
      if (/* bool unseen = */ reverse_captures_set.emplace(v).second) {
        reverse_captures.push_back(v);
      }
    }
  };
  for (Value* input : graph.inputs()) {
    check_uses(input);
  }
  for (Node* node : graph.nodes()) {
    for (Value* output : node->outputs())
      check_uses(output);
  }
  return reverse_captures;
}

// Any temporary value from the primal graphs needs to be captured for later use
// in the reverse graph, to avoid costly recomputations. However, a lot of the
// nodes we have in our graphs are simply constants, which are cheap to execute
// and replicate, and so it's better to just copy them into the reverse graph,
// without polluting the output lists unnecessarily.
static void liftConstants(Block* block, Block* move_to_this_block);

// is node defined inside container?
static bool inBlock(Node* node, Block* container) {
  Block* b = node->owningBlock();
  while (b) {
    if (b == container) {
      return true;
    }
    b = b->owningNode() ? b->owningNode()->owningBlock() : nullptr;
  }
  return false;
}

static void liftConstants(Node* node, Block* move_to_this_block) {
  static const auto err = [](Value*) -> Value* {
    throw std::runtime_error("unexpected input");
  };
  auto& graph = *node->owningGraph();
  for (Value* input : node->inputs()) {
    if (input->node()->kind() != prim::Constant)
      continue;
    // if this constant is _already_ defined in the backward pass
    // block, we do not need to duplicate and move it because
    // it already won't be part of the capture set
    if (inBlock(input->node(), move_to_this_block))
      continue;
    Node* lifted_constant = graph.createClone(input->node(), err);
    move_to_this_block->prependNode(lifted_constant);
    GRAPH_DEBUG(
        "Lifting constant ",
        input->debugName(),
        " from GradOf's block and adding ",
        lifted_constant->output()->debugName(),
        " to the backprop block");
    node->replaceInputWith(input, lifted_constant->output());
  }
  for (Block* sub : node->blocks()) {
    liftConstants(sub, move_to_this_block);
  }
}

static void liftConstants(Block* block, Block* move_to_this_block) {
  for (Node* node : block->nodes()) {
    liftConstants(node, move_to_this_block);
  }
  liftConstants(block->return_node(), move_to_this_block);
}

// we need to fold aten::_size_if_not_equal at the differentiation time
// while we know the shapes of aten::_size_if_not_equal's arguments
// Otherwise, they will become inputs to a reverse Graph, and we will
// lose this information and we don't profile Scalars, or Lists yet.
static void foldSizeIfNotEqual(Block* node);

static void foldSizeIfNotEqual(Node* node) {
  for (Value* input : node->inputs()) {
    if (input->node()->kind() != aten::_size_if_not_equal) {
      continue;
    }

    auto ptt_input =
        input->node()->input(0)->node()->input()->type()->expect<TensorType>();
    auto ptt_output =
        input->node()->input(1)->node()->input()->type()->expect<TensorType>();

    auto input_size = ptt_input->sizes().concrete_sizes();
    auto output_size = ptt_output->sizes().concrete_sizes();

    if (!input_size || !output_size) {
      continue;
    }
    // insert in front of _grad_sum_to_size
    WithInsertPoint guard(node);
    IValue ival{};
    Value* size;
    if (input_size != output_size) {
      size = node->owningGraph()->insertConstant(*input_size);
    } else {
      size = node->owningGraph()->insertConstant(IValue());
    }
    node->replaceInputWith(input, size);
  }

  for (auto ib : node->blocks()) {
    foldSizeIfNotEqual(ib);
  }
}

// we need to fold aten::_size_if_not_equal at the differentiation time
// while we know the shapes of aten::_size_if_not_equal's arguments
// Otherwise, they will become inputs to a reverse Graph, and we will
// lose this information and we don't profile Scalars, or Lists yet.
static void foldSizeIfNotEqual(Block* reverse_block) {
  for (auto n : reverse_block->nodes()) {
    foldSizeIfNotEqual(n);
  }
  foldSizeIfNotEqual(reverse_block->return_node());
}

static void deduplicateSizeCaptures(
    Gradient& grad_desc,
    ReverseDetails& rev_info) {
  Block* primal_block = grad_desc.f->block();
  const auto usedOnlyInReverse = [primal_block](Value* v) {
    const auto& uses = v->uses();
    return std::all_of(uses.begin(), uses.end(), [primal_block](const Use& u) {
      return u.user->owningBlock() != primal_block;
    });
  };
  auto captures = getReverseCaptures(grad_desc);
  value_set capture_set(captures.begin(), captures.end());
  for (Value* capture : captures) {
    Node* node = capture->node();
    if (!node->matches("aten::size(Tensor self) -> int[]")) {
      continue;
    }
    if (usedOnlyInReverse(capture) && capture_set.count(node->input())) {
      WithInsertPoint insert_guard{*rev_info.reverse_block->nodes().begin()};
      auto* size =
          node->input()->owningGraph()->insert(aten::size, {node->input()});
      GRAPH_DEBUG(
          "deduplicateSizeCaptures: Replacing ",
          capture->debugName(),
          " with ",
          size->debugName());
      capture->replaceAllUsesWith(size);
      node->destroy();
    }
  }
}

static void eliminateDeadCode(ReverseDetails& rev_info) {
  // addReverseInline has to call gradientForNode if *any* of the inputs
  // require grad, but it will emit vjps for *all* inputs. Use DCE to remove
  // unnecessary nodes. Additionally, requires_grad() on intermediates is an
  // overapproximation of the real state, so we might have emitted some
  // gradients, only to realize that they were unnecessary once we reach a
  // point that doesn't require grad.
  // Of course, we need to filter out corresponding entries of grad_map, because
  // we don't want to accidentally access freed pointers later.
  std::function<void(const std::unordered_set<const Value*>&)> cb =
      [&](const std::unordered_set<const Value*>& live_values) {
        std::vector<Value*> to_erase;
        for (auto& entry : rev_info.grad_map) {
          if (!live_values.count(entry.second)) {
            to_erase.push_back(entry.first);
          }
        }
        for (Value* v : to_erase) {
          GRAPH_DEBUG(
              "Erasing unused value ", v->debugName(), " from grad_map");
          rev_info.grad_map.erase(v);
        }
      };
  EliminateDeadCode(rev_info.reverse_block, std::move(cb));
}

static void Optimize(Gradient& grad_desc, ReverseDetails& rev_info) {
  // TODO: we are sometimes emitting expressions like
  // _grad_sum_to_size(_grad_sum_so_size(x, s1), s2), which are equivalent to
  // _grad_sum_to_size(x, s2), and could save us some
  // captures, but I'm not 100% sure how to optimize this at this stage, since
  // we don't know which GradOf blocks will be stitched together to form the
  // derivative. I guess a smart analysis could implement this, but I didn't
  // have time before the 1.0 release, so I put this only as a peephole
  // optimization.
  liftConstants(rev_info.reverse_block, rev_info.reverse_block);
  // TODO: see if this pass can be replaced with peephole pass
  foldSizeIfNotEqual(rev_info.reverse_block);
  // We generally add a lot of aten::size calls (for derivatives of broadcasting
  // operators), and they often end up duplicated, and would get captured
  // multiple times. Make sure we deduplicate them before lifting.
  EliminateCommonSubexpression(grad_desc.f);
  deduplicateSizeCaptures(grad_desc, rev_info);
  eliminateDeadCode(rev_info);
}

// Takes a grad_desc.f returned from `addReverseInline` and splits off the
// reverse_block into its own graph, storing it in df.
// All intermediates needed in the second stage are added to
// outputs of f, and taken as inputs in df. For a more
// detailed description see Note [Gradient graphs] in autodiff.h.
// This function also initializes the fields in grad_desc that were undefined
// after `addReverseInline` (and extends `df_input_vjps` with vjps for captured
// temporaries).
static void lambdaLiftReverse(Gradient& grad_desc, ReverseDetails& rev_info) {
  auto& graph = *grad_desc.f;
  auto reverse_block = rev_info.reverse_block;

  // --------------------------------------------------------------------------
  // 1. Find values of f that need to be captured.
  // --------------------------------------------------------------------------
  // First, we need to find all values that are produced in f,
  // and used in df. They will need to be added as inputs of the df
  // and some of them may also need to be appended as outputs of f if
  // they are not already an input or an output of f
  // Invariant: topo sorted
  value_list reverse_captures = getReverseCaptures(grad_desc);

  // --------------------------------------------------------------------------
  // 2. Prepare input/outputs lists for f and df
  // --------------------------------------------------------------------------
  // It's simple to construct primal_inputs/reverse_outputs,
  // but primal_outputs/reverse_inputs are much more subtle.
  // Here's a summary of how they are supposed to look like:
  //
  // Primal outputs:
  //   [original outputs], [temporaries]
  //
  // Reverse inputs:
  //   [output vjps (aka grad_outputs)], [temporary vjps]
  //   [captured primal values, in topological order],

  // -- Construct primal_outputs, df_input_captures, f_real_outputs ----
  grad_desc.f_real_outputs = graph.outputs().size();

  std::unordered_map<Value*, size_t> orig_primal_outputs_idx;
  std::unordered_map<Value*, size_t> orig_primal_inputs_idx;
  // NOTE: we use emplace to avoid replacing an existing index if an output is
  // repeated
  for (size_t i = 0, num_outputs = graph.outputs().size(); i < num_outputs; ++i)
    orig_primal_outputs_idx.emplace(graph.outputs()[i], i);
  for (size_t i = 0, num_inputs = graph.inputs().size(); i < num_inputs; ++i)
    orig_primal_inputs_idx[graph.inputs()[i]] = i;

  // NB: reverse_captures are already deduplicated, and in topo order
  for (Value* capture_val : reverse_captures) {
    // If it's already an output we don't have to add anything,
    // but register the fact that it needs to be captured.
    if (orig_primal_outputs_idx.count(capture_val) > 0) {
      grad_desc.df_input_captured_outputs.push_back(
          orig_primal_outputs_idx[capture_val]);
      // If it's an input, we could add it as an output but in fact it's
      // more efficient to use a special kind of capture.
    } else if (orig_primal_inputs_idx.count(capture_val) > 0) {
      grad_desc.df_input_captured_inputs.push_back(
          orig_primal_inputs_idx.at(capture_val));
      // Otherwise it's just a regular intermediate value that we need to add as
      // an output
    } else {
      // we need to create a new temporary output for this capture because it
      // wasn't availiable.

      auto out_index = graph.registerOutput(capture_val);
      GRAPH_DEBUG(
          "Capturing a temporary ",
          capture_val->debugName(),
          " as ",
          graph.outputs()[out_index]->debugName(),
          " for forward graph");
      grad_desc.df_input_captured_outputs.emplace_back(
          graph.outputs().size() - 1);
    }
  }

  // -- Add VJPs for temporaries, adjust df_input_vjps -------------------------
  // NB [possible optimization]: use the newly added vjp input as soon as the
  // first vjp for that value is generated, to reduce the lifespan of this input
  // (currently we add it to the final vjp after all adds).
  for (size_t i = grad_desc.f_real_outputs; i < graph.outputs().size(); ++i) {
    Value* tmp = graph.outputs().at(i);
    // Add VJP inputs only for intermediates that actually required grad.
    // Note that we check the contents of the grad_map instead of
    // tmp->requires_grad(), because it's actually a more faithful source.
    // tmp->requires_grad() is really an overapproximation (i.e. it can have
    // false positives), while the gradients we will emit for this value can get
    // DCE-d in the optimization pass (because it has no influence on the real
    // f's outputs that we differentiate).
    if (rev_info.grad_map.count(tmp) == 0)
      continue;

    Value* tmp_vjp_in = reverse_block->addInput()->setType(tmp->type());
    Value* tmp_vjp_prev = rev_info.grad_map.at(tmp);
    // This is quite weird because we can't first make a sum and then replace
    // all uses of tmp_vjp_prev (that would replace its use in the sum too!), so
    // we create an incorrect sum that doesn't use prev vjp, replace uses, and
    // fix the sum.
    Value* new_vjp = createAutogradAdd(tmp_vjp_in, tmp_vjp_in);
    if (tmp_vjp_prev->node()->kind() == prim::Param) {
      // can't move a node after a block param node
      new_vjp->node()->moveBefore(
          *tmp_vjp_prev->node()->owningBlock()->nodes().begin());
    } else {
      new_vjp->node()->moveAfter(tmp_vjp_prev->node());
    }

    tmp_vjp_prev->replaceAllUsesWith(new_vjp);
    new_vjp->node()->replaceInput(1, tmp_vjp_prev);
    GRAPH_DEBUG("grad_map[", tmp->debugName(), "] = ", *new_vjp->node());
    grad_desc.df_input_vjps.emplace_back(i);
  }

  // add the captures as formal arguments to the reverse_block
  // afterward inputs: [output vjps][temporary vjps][captures]
  // construct a map from captured 'value' to the index in the input list
  // used to extract this block into its own function
  std::unordered_map<Value*, size_t> capture_to_formal_index;
  const auto& add_capture = [&](Value* captured) {
    capture_to_formal_index[captured] = reverse_block->inputs().size();
    auto new_input = reverse_block->addInput()->copyMetadata(captured);
    GRAPH_DEBUG(
        "Capturing ",
        captured->debugName(),
        " as ",
        new_input->debugName(),
        " for an embedded backward block");
  };
  for (auto& offset : grad_desc.df_input_captured_inputs)
    add_capture(graph.inputs()[offset]);
  for (auto& offset : grad_desc.df_input_captured_outputs)
    add_capture(graph.outputs()[offset]);

  grad_desc.df = std::make_shared<Graph>();
  grad_desc.df->block()->cloneFrom(reverse_block, [&](Value* v) {
    return grad_desc.df->inputs()[capture_to_formal_index.at(v)];
  });

  GRAPH_DUMP(" forward graph: ", &graph);
  GRAPH_DEBUG(" backward graph: ", *(reverse_block->owningNode()));
  // reverse_node was just to hold onto reverse_block in a debuggable way
  // we can remove it now.
  reverse_block->owningNode()->destroy();
}

void packReturnValuesIntoTuple(const std::shared_ptr<Graph>& graph) {
  auto returnNode = graph->block()->return_node();
  WithInsertPoint wip(returnNode);
  auto tuple = graph->insertNode(graph->createTuple(returnNode->inputs()));
  returnNode->removeAllInputs();
  returnNode->addInput(tuple->output());
}

Gradient differentiate(std::shared_ptr<Graph>& graph) {
  Gradient grad_desc;
  // Take ownership of the graph
  TORCH_CHECK(
      graph.use_count() == 1,
      "differentiate will mutate and destroy the graph, so it requires "
      "graph.use_count() == 1, but found %d",
      graph.use_count());
  std::swap(graph, grad_desc.f);
  // XXX: Take care when handling outputs - they can be duplicated!

  GRAPH_DUMP("grad_desc.f: ", grad_desc.f);
  WithInsertPoint guard(grad_desc.f->block());
  // Fills in df_input_vjps and df_output_vjps
  auto rev_info = addReverseInline(grad_desc);
  Optimize(grad_desc, rev_info);
  // Clean up old nodes which has been replaced by forward graphs in torchscript
  EliminateDeadCode(grad_desc.f->block());

  // Fills in f, df, f_real_outputs, df_input_captures,
  // modifies df_input_vjps (new vjps are added for temporaries)
  lambdaLiftReverse(grad_desc, rev_info);
  packReturnValuesIntoTuple(grad_desc.df);

  // we have created a differentiable forward graph
  // which will be run with tensors that have their gradients detached,
  // so profiled types will have outdated requires_grad=True, update the
  // requires_grad property
  UpdateDifferentiableGraphRequiresGrad(grad_desc.f, false);
  return grad_desc;
}
} // namespace jit
} // namespace torch
