#include <torch/csrc/distributed/c10d/reducer.h>

#include <functional>

#include <c10/core/DeviceGuard.h>
#include <c10/util/Exception.h>
#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/function_hook.h>
#include <torch/csrc/autograd/functions/accumulate_grad.h>
#include <torch/csrc/autograd/profiler.h>
#include <torch/csrc/utils/hash.h>
#include <torch/csrc/utils/memory.h>

namespace c10d {
namespace {

// Turns lambda without input/output into a torch::autograd::FunctionPostHook.
class LambdaPostHook : public torch::autograd::FunctionPostHook {
  using variable_list = std::vector<torch::autograd::Variable>;

 public:
  /* implicit */ LambdaPostHook(std::function<void(void)> fn)
      : fn_(std::move(fn)) {}

  variable_list operator()(
      const variable_list& outputs,
      const variable_list& /* unused */) override {
    fn_();
    return outputs;
  }

 protected:
  std::function<void(void)> fn_;
};

inline int64_t current_time_in_nanos() {
  return torch::autograd::profiler::getTime();
}

} // namespace

Reducer::Reducer(
    std::vector<std::vector<torch::autograd::Variable>> replicas,
    std::vector<std::vector<size_t>> bucket_indices,
    std::shared_ptr<c10d::ProcessGroup> process_group,
    std::vector<std::vector<bool>> expect_sparse_gradients)
    : replicas_(std::move(replicas)),
      process_group_(std::move(process_group)),
      expect_sparse_gradients_(std::move(expect_sparse_gradients)),
      expect_autograd_hooks_(false),
      require_finalize_(false),
      next_bucket_(0),
      has_marked_unused_parameters_(false),
      local_used_maps_reduced_(false),
      backward_stats_base_(0) {
  TORCH_CHECK(replicas_.size() >= 1, "Expected at least one model replica.");
  TORCH_CHECK(replicas_[0].size() >= 1, "Expected at least one parameter.");

  // If `expect_sparse_gradients` is not specified, initialize it such that
  // we do not expect sparse gradients for any parameter.
  if (expect_sparse_gradients_.empty()) {
    expect_sparse_gradients_ = std::vector<std::vector<bool>>(
        replicas_.size(), std::vector<bool>(replicas_[0].size(), false));
  }
  TORCH_INTERNAL_ASSERT(expect_sparse_gradients_.size() == replicas_.size());

  // Verify that all specified variables require gradients,
  // and that they have the same size across replicas.
  {
    const auto replica_count = replicas_.size();
    for (size_t replica_index = 0; replica_index < replica_count;
         replica_index++) {
      const auto variable_count = replicas_[replica_index].size();
      TORCH_CHECK(
          replicas_[replica_index].size() == replicas_[0].size(),
          "Model replicas must have an equal number of parameters.");
      TORCH_CHECK(
          expect_sparse_gradients_[replica_index].size() ==
              expect_sparse_gradients_[0].size(),
          "Expected number of entries in expect_sparse_gradients ",
          "to be equal across replicas.");
      for (size_t variable_index = 0; variable_index < variable_count;
           variable_index++) {
        TORCH_CHECK(
            replicas_[replica_index][variable_index].requires_grad(),
            "Variables must require gradients (have `requires_grad` set).");
        TORCH_CHECK(
            replicas_[replica_index][variable_index].sizes() ==
                replicas_[0][variable_index].sizes(),
            "Variables across model replicas must have identical sizes.");
        TORCH_CHECK(
            replicas_[replica_index][variable_index].dtype() ==
                replicas_[0][variable_index].dtype(),
            "Variables across model replicas must have identical dtype.");
        TORCH_CHECK(
            expect_sparse_gradients_[replica_index][variable_index] ==
                expect_sparse_gradients_[0][variable_index],
            "Expected the same variables across replicas to either both ",
            "or neither expect a sparse gradient.");
      }
    }
  }

  // Initialize variable bucketing.
  // This can be reinitialized later after capturing runtime information.
  initialize_buckets(std::move(bucket_indices));

  // All variables are expected to have their `grad_fn` set to the gradient
  // accumulation function (since they are leafs in the autograd graph).
  // We store pointers to these functions such that we can check if they are
  // used in an autograd pass. If they are not, we know their grad tensors
  // can be marked as ready for reduction.
  {
    const auto replica_count = replicas_.size();
    grad_accumulators_.resize(replica_count);
    for (size_t replica_index = 0; replica_index < replica_count;
         replica_index++) {
      const auto variable_count = replicas_[replica_index].size();
      grad_accumulators_[replica_index].resize(variable_count);
      for (size_t variable_index = 0; variable_index < variable_count;
           variable_index++) {
        auto& variable = replicas_[replica_index][variable_index];
        const auto index = VariableIndex{
            .replica_index = replica_index,
            .variable_index = variable_index,
        };

        // The gradient accumulator function is lazily initialized once.
        // Therefore we can use its presence in the autograd graph as
        // evidence that the parameter has participated in an iteration.
        auto grad_accumulator =
            torch::autograd::impl::grad_accumulator(variable);

        // Hook to execute after the gradient accumulator has executed.
        hooks_.emplace_back(
            grad_accumulator->add_post_hook(torch::make_unique<LambdaPostHook>(
                [=] { this->autograd_hook(index); })),
            grad_accumulator);

        // Map raw function pointer to replica index and parameter index.
        // This is used later on when the autograd graph is traversed
        // to check for parameters for which no gradient is computed.
        func_[grad_accumulator.get()] = index;

        // The gradient accumulator is stored as weak_ptr in the autograd
        // metadata of the variable, so we have to keep it alive here for
        // the raw pointer to be valid.
        grad_accumulators_[replica_index][variable_index] =
            std::move(grad_accumulator);
      }
    }
  }

  // Initialize backward stats vector.
  {
    const auto replica_count = replicas_.size();
    backward_stats_.resize(replica_count);
    const auto variable_count = replicas_[0].size();
    std::for_each(
        backward_stats_.begin(),
        backward_stats_.end(),
        [=](std::vector<int64_t>& v) { v.resize(variable_count); });
  }

  // Initialize locally used parameter maps
  {
    const auto replica_count = replicas_.size();
    const auto variable_count = replicas_[0].size();
    local_used_maps_.resize(replica_count);
    local_used_maps_dev_.resize(replica_count);

    for (size_t i = 0; i < replica_count; i++) {
      at::TensorOptions options, options_host;
      options = options.dtype(at::kInt);

      if (replicas_[i][0].is_cuda()) {
        at::DeviceGuard g(replicas_[i][0].device());
        local_used_maps_[i] = at::zeros(
            {static_cast<long>(variable_count)}, options.pinned_memory(true));
      } else {
        local_used_maps_[i] =
            at::zeros({static_cast<long>(variable_count)}, options);
      }

      // This tensor needs to be on the same device as replica because backend
      // such as NCCL may not support CPU tensors, and hence it might not work
      // if we always put it on CPU.
      options = options.device(replicas_[i][0].device());
      local_used_maps_dev_[i] =
          at::empty({static_cast<long>(variable_count)}, options);
    }
  }
}

Reducer::~Reducer() noexcept(false) {
  // Remove all hooks on variables registered by this Reducer. This is necessary
  // to make DDP failure recoverable. Otherwise, multiple Reducer instances
  // (from recoveries) will add their hooks to the original model, and those
  // hooks will try to invoke methods on a deleted Reducer objects.
  for (auto& hook : hooks_) {
    auto& key = hook.first;
    auto& grad_accumulator = hook.second;
    TORCH_CHECK(
        grad_accumulator->del_post_hook(key),
        "Reducer attempts to delete a non-existing hook.");
  }
}

void Reducer::mark_variable_ready_dense(VariableIndex index) {
  const auto replica_index = index.replica_index;
  const auto variable_index = index.variable_index;
  const auto& bucket_index = variable_locators_[variable_index];
  auto& bucket = buckets_[bucket_index.bucket_index];
  auto& replica = bucket.replicas[replica_index];
  auto& variable = replica.variables[bucket_index.intra_bucket_index];
  const auto offset = replica.offsets[bucket_index.intra_bucket_index];
  const auto length = replica.lengths[bucket_index.intra_bucket_index];

  // Copy contents of gradient tensor to bucket tensor.
  // If the gradient is not set, we assume it wasn't computed
  // as part of the current backwards pass, and zero the part
  // of the bucket it would otherwise hold.
  auto bucket_view = replica.contents.narrow(0, offset, length);
  auto& grad = variable.grad();
  if (grad.defined()) {
    // Ensure that the gradient type matches the bucket type.
    TORCH_CHECK(
        grad.options().type_equal(bucket_view.options()),
        "Expected ",
        bucket_view.toString(),
        ", got ",
        grad.toString());
    // Assert that the grad tensor and the bucket don't share storage.
    // If they did, we could avoid the copy altogether.
    // The reason for not doing this is that existing code calls
    // `detach_` from `zero_grad`, which is incompatible with views.
    TORCH_INTERNAL_ASSERT(!grad.is_alias_of(bucket_view));
    TORCH_INTERNAL_ASSERT(grad.device() == bucket_view.device());
    TORCH_INTERNAL_ASSERT(grad.numel() == bucket_view.numel());
    bucket_view.copy_(grad.view({-1}), /* non_blocking */ true);
  } else {
    bucket_view.zero_();
  }
}

void Reducer::mark_variable_ready_sparse(VariableIndex index) {
  const auto replica_index = index.replica_index;
  const auto variable_index = index.variable_index;
  const auto& bucket_index = variable_locators_[variable_index];
  auto& bucket = buckets_[bucket_index.bucket_index];
  auto& replica = bucket.replicas[replica_index];
  auto& variable = replica.variables[bucket_index.intra_bucket_index];
  auto& grad = variable.grad();
  TORCH_CHECK(grad.defined(), "Expected sparse gradient to be defined.");
  TORCH_CHECK(
      grad.options().layout() == c10::kSparse,
      "Expected variable to have sparse gradient.");

  // Sparse tensors cannot be grouped together with other sparse tensors
  // in a single reduction operation like we can for dense tensors.
  // Therefore, the `offsets` and `lengths` vectors in the bucket replica
  // struct are empty, and there is no pre-existing accumulation tensor.
  // Directly assign the sparse tensor to the `contents` field.
  replica.contents = grad;
}

// The function `autograd_hook` is called after the gradient for a
// model parameter has been accumulated into its gradient tensor.
// This function is only to be called from the autograd thread.
void Reducer::autograd_hook(VariableIndex index) {
  std::lock_guard<std::mutex> lock(this->mutex_);
  // Since it gets here, this param has been used for this iteration. We want
  // to mark it in local_used_maps_. During no_sync session, the same var can
  // be set multiple times, which is OK as does not affect correctness. As long
  // as it is used once during no_sync session, it is marked as used.
  local_used_maps_[index.replica_index][index.variable_index] = 1;

  // Ignore if we don't expect to be called.
  // This may be the case if the user wants to accumulate gradients
  // for number of iterations before reducing them.
  if (!expect_autograd_hooks_) {
    return;
  }

  // If there are model parameters that went unused when computing the model
  // output, they won't be part of the autograd graph, and won't receive
  // gradients. These parameters are discovered in the `prepare_for_backward`
  // function and their indexes stored in the `unused_parameters_` vector.
  if (!has_marked_unused_parameters_ && !unused_parameters_.empty()) {
    has_marked_unused_parameters_ = true;
    for (const auto& unused_index : unused_parameters_) {
      mark_variable_ready(unused_index);
    }
  }

  // Finally mark variable for which this function was originally called.
  mark_variable_ready(index);
}

void Reducer::mark_variable_ready(VariableIndex index) {
  const auto replica_index = index.replica_index;
  const auto variable_index = index.variable_index;
  TORCH_CHECK(replica_index < replicas_.size(), "Out of range replica index.");
  TORCH_CHECK(
      variable_index < variable_locators_.size(),
      "Out of range variable index.");
  backward_stats_[replica_index][variable_index] =
      current_time_in_nanos() - backward_stats_base_;

  // Any time we mark a variable ready (be it in line due to unused parameters,
  // or via an autograd hook), we require a call to the finalize function. If
  // this doesn't happen before the next iteration (or call to
  // `prepare_for_backwards`), we know something is wrong.
  require_finalize_ = true;

  const auto& bucket_index = variable_locators_[variable_index];
  auto& bucket = buckets_[bucket_index.bucket_index];
  auto& replica = bucket.replicas[replica_index];

  // Something is wrong if all variables contained in this bucket replica have
  // already been marked as ready.
  if (replica.pending == 0) {
    // Receiving a call to `mark_variable_ready` twice for the same variable
    // is only possible if the variable was initially deemed unused, and was
    // marked ready from the `prepare_for_backward` function, only to become
    // part of the autograd graph at a later point in time.
    TORCH_INTERNAL_ASSERT(has_marked_unused_parameters_);
    TORCH_CHECK(
        false,
        "Expected to mark a variable ready only once. ",
        "",
        "This error is caused by use of a module parameter outside the ",
        "`forward` function. The return value of the `forward` function ",
        "is inspected by the distributed data parallel wrapper to figure ",
        "out if any of the module's parameters went unused. If this is the ",
        "case, it knows they won't receive gradients in a backward pass. ",
        "If any of those parameters are then used outside `forward`, this ",
        "error condition is triggered. ",
        "",
        "You can disable unused parameter detection by passing the keyword "
        "argument `find_unused_parameters=False` to ",
        "`torch.nn.parallel.DistributedDataParallel`.");
  }

  if (bucket.expect_sparse_gradient) {
    mark_variable_ready_sparse(index);
  } else {
    mark_variable_ready_dense(index);
  }

  // TODO(@pietern): Make this work for both CPU/CUDA tensors.
  // When using CPU tensors we don't need to do this.
  // // Record event so that we can wait for all of them.
  // auto& event = replica.events[bucket_index.intra_bucket_index];
  // event.record();

  // Check if this was the final gradient for this bucket.
  if (--replica.pending == 0) {
    // Prescale bucket contents to turn the global sum into the global average.
    replica.contents.div_(process_group_->getSize());
    // Kick off reduction if all replicas for this bucket are ready.
    if (--bucket.pending == 0) {
      mark_bucket_ready(bucket_index.bucket_index);
    }
  }

  // Run finalizer function and kick off reduction for local_used_maps once the
  // final bucket was marked ready.
  if (next_bucket_ == buckets_.size()) {
    // H2D from local_used_maps_ to local_used_maps_dev_
    for (size_t i = 0; i < local_used_maps_.size(); i++) {
      // We do async H2D to avoid the blocking overhead. The async copy and
      // allreduce respect the current stream, so will be sequenced correctly.
      local_used_maps_dev_[i].copy_(local_used_maps_[i], true);
    }
    local_used_work_ = process_group_->allreduce(local_used_maps_dev_);

    torch::autograd::Engine::get_default_engine().queue_callback([=] {
      std::lock_guard<std::mutex> lock(this->mutex_);
      this->finalize_backward();
    });
  }
}

// Called when the bucket at the specified index is ready to be reduced.
void Reducer::mark_bucket_ready(size_t bucket_index) {
  TORCH_INTERNAL_ASSERT(bucket_index >= next_bucket_);

  // Buckets are reduced in sequence. Ignore this bucket if
  // it's not its turn to be reduced.
  if (bucket_index > next_bucket_) {
    return;
  }

  // Keep going, until we either:
  // - have kicked off reduction for all buckets, or
  // - found a bucket that's not yet ready for reduction.
  for (; next_bucket_ < buckets_.size() && buckets_[next_bucket_].pending == 0;
       next_bucket_++) {
    auto& bucket = buckets_[next_bucket_];
    std::vector<at::Tensor> tensors;
    tensors.reserve(bucket.replicas.size());
    for (const auto& replica : bucket.replicas) {
      // TODO(@pietern): Ensure proper synchronization with the CUDA events
      // that recorded copies into this contents tensor. If these copies are
      // executed on non-default streams, the current stream for the device
      // that holds the contents tensor must wait on these events.
      //
      // As long as autograd uses the default stream for every device,
      // these operations are implicitly sequenced, and we don't need to
      // do any extra synchronization here.
      //
      tensors.push_back(replica.contents);
    }
    bucket.work = process_group_->allreduce(tensors);
  }
}

void Reducer::initialize_buckets(
    std::vector<std::vector<size_t>> bucket_indices) {
  std::lock_guard<std::mutex> lock(mutex_);

  // This shouldn't be called if we're expecting autograd hooks to fire.
  TORCH_CHECK(
      !expect_autograd_hooks_,
      "`initialize_buckets` must NOT be called during autograd execution.");

  // Clear current bucket assignment.
  buckets_.clear();
  variable_locators_.clear();

  // Ensure we have a bucket index for every variable.
  variable_locators_.resize(replicas_[0].size());

  // Iterate over buckets.
  const auto bucket_count = bucket_indices.size();
  const auto replica_count = replicas_.size();
  buckets_.reserve(bucket_count);
  for (size_t bucket_index = 0; bucket_index < bucket_count; bucket_index++) {
    Bucket bucket;

    // TODO(@pietern): Validate indices.
    // Must be non-empty, unique, and unique across buckets.
    TORCH_CHECK(
        bucket_indices[bucket_index].size() > 0, "Empty bucket specified.");

    // Variables that expect sparse gradients must have their own bucket.
    if (bucket_indices[bucket_index].size() == 1) {
      const auto variable_index = bucket_indices[bucket_index].front();
      bucket.expect_sparse_gradient =
          expect_sparse_gradients_[0][variable_index];
    } else {
      for (const auto variable_index : bucket_indices[bucket_index]) {
        TORCH_CHECK(
            !expect_sparse_gradients_[0][variable_index],
            "Buckets with more than one variable cannot include variables ",
            "that expect a sparse gradient.");
      }
    }

    // Iterate over model replicas.
    for (size_t replica_index = 0; replica_index < replica_count;
         replica_index++) {
      BucketReplica replica;

      if (bucket.expect_sparse_gradient) {
        const auto variable_index = bucket_indices[bucket_index].front();
        const auto& variable = replicas_[replica_index][variable_index];
        TORCH_INTERNAL_ASSERT(bucket_indices[bucket_index].size() == 1);
        replica.variables = {variable};
      } else {
        at::TensorOptions options;
        size_t offset = 0;

        // Iterate over bucket variables.
        for (const auto variable_index : bucket_indices[bucket_index]) {
          TORCH_CHECK(
              variable_index < replicas_[replica_index].size(),
              "Out of range variable index specified.");
          const auto& variable = replicas_[replica_index][variable_index];
          if (!options.has_device()) {
            options = options.device(variable.device());
          } else {
            TORCH_CHECK(
                variable.device() == options.device(),
                "All parameters in a bucket must be ",
                "placed on the same device.");
          }
          if (!options.has_dtype()) {
            options = options.dtype(variable.dtype());
          } else {
            TORCH_CHECK(
                variable.dtype() == options.dtype(),
                "All parameters in a bucket must have the same dtype.");
          }
          const auto length = variable.numel();
          replica.variables.push_back(variable);
          replica.offsets.push_back(offset);
          replica.lengths.push_back(length);
          offset += length;
        }

        // Allocate bucket contents tensor.
        replica.contents = at::empty({static_cast<long>(offset)}, options);
      }

      // Add bucket replica to enclosing bucket.
      bucket.replicas.push_back(std::move(replica));
    }

    // Map participating variables to this bucket.
    // This is identical across replicas so we only need to do this once.
    size_t intra_bucket_index = 0;
    for (const auto variable_index : bucket_indices[bucket_index]) {
      TORCH_CHECK(
          variable_index < variable_locators_.size(),
          "Out of range variable index specified.");
      variable_locators_[variable_index] = VariableLocator{
          .bucket_index = bucket_index,
          .intra_bucket_index = intra_bucket_index++,
      };
    }
    bucket.variable_indices = std::move(bucket_indices[bucket_index]);

    buckets_.push_back(std::move(bucket));
  }
}

// Traverse the autograd graph starting at the specified output.
// All parameters for which we have a pointer to their gradient accumulation
// functions, but don't show up in the autograd graph will be marked ready for
// for reduction as soon as the first autograd hook is called. This is not
// done immediately because the model output may be ignored, and we only
// want to start performing reductions on `torch.autograd.backward()`.
void Reducer::prepare_for_backward(
    const std::vector<torch::autograd::Variable>& outputs) {
  std::lock_guard<std::mutex> lock(mutex_);
  std::unordered_set<torch::autograd::Node*> seen;
  std::vector<torch::autograd::Node*> queue;

  // Check that any prior reduction has finished.
  // The variable `require_finalize_` is true until all gradients
  // have been computed and reduction of all buckets has been kicked off.
  if (require_finalize_) {
    TORCH_CHECK(
        false,
        "Expected to have finished reduction in the prior iteration before ",
        "starting a new one. ",
        "",
        "This error indicates that your module has parameters that were ",
        "not used in producing loss. ",
        "",
        "You can enable unused parameter detection by (1) passing the keyword "
        "argument `find_unused_parameters=True` to ",
        "`torch.nn.parallel.DistributedDataParallel`; (2) making sure all ",
        "`forward` function outputs participate in calculating loss. "
        "",
        "If you already have done the above two steps, then the distributed ",
        "data parallel module wasn't able to locate the output tensors in the ",
        "return value of your module's `forward` function. ",
        "Please include the loss function and the structure of the return ",
        "value of `forward` of your module when reporting this issue (e.g. ",
        "list, dict, iterable).");
  }

  // Reset accounting.
  expect_autograd_hooks_ = true;
  next_bucket_ = 0;
  backward_stats_base_ = current_time_in_nanos();
  for (auto& bucket : buckets_) {
    for (auto& replica : bucket.replicas) {
      replica.pending = replica.variables.size();
    }
    bucket.pending = bucket.replicas.size();
  }

  // Reset unused parameter accounting.
  has_marked_unused_parameters_ = false;
  unused_parameters_.clear();

  // If no outputs are specified, we assume that autograd hooks for ALL
  // variables will be called, and we don't have to search the autograd graph
  // for presence of these hooks.
  if (outputs.empty()) {
    return;
  }

  // Seed queue with the grad functions of all outputs.
  for (const auto& output : outputs) {
    const auto& grad_fn = output.grad_fn();
    if (grad_fn) {
      queue.push_back(grad_fn.get());
    }
  }

  // Traverse the autograd graph starting at the specified output.
  while (!queue.empty()) {
    auto fn = queue.back();
    queue.pop_back();
    for (const auto& edge : fn->next_edges()) {
      if (auto next_ptr = edge.function.get()) {
        const bool was_inserted = seen.insert(next_ptr).second;
        if (was_inserted) {
          queue.push_back(next_ptr);
        }
      }
    }
  }

  // Find accumulator functions that don't show up in this graph.
  for (const auto& it : func_) {
    // If the accumulator function is present in the graph, we know
    // a gradient will be computed for the corresponding parameter.
    if (seen.count(it.first) > 0) {
      continue;
    }

    unused_parameters_.push_back(it.second);
  }
}

// A bucket with one or more dense tensors needs to be unflattened.
void Reducer::finalize_bucket_dense(Bucket& bucket) {
  for (size_t replica_index = 0; replica_index < bucket.replicas.size();
       replica_index++) {
    auto& replica = bucket.replicas[replica_index];
    for (size_t intra_bucket_index = 0;
         intra_bucket_index < replica.variables.size();
         intra_bucket_index++) {
      auto& variable = replica.variables[intra_bucket_index];
      const auto offset = replica.offsets[intra_bucket_index];
      const auto length = replica.lengths[intra_bucket_index];

      // Determine if this param has been used globally or not.
      //
      // If the variable was used locally, it is also used globally and then
      // we don't need to wait for the reduction. Otherwise we lazily wait for
      // the reduction to complete, only when we see a variable that was unused
      // locally. Then we end up delaying the synchronization point that
      // local_used_work_->wait() implies. If we don't have any unused
      // parameters at all, we can skip waiting for the work to complete
      // altogether, and cause negligible performance overhead for models where
      // all parameters are used. Such lazily waiting means minimizing
      // performance impact for the big majority of models where all parameters
      // are always used. Then we only pay the overhead cost if there is indeed
      // a parameter that is locally unused, because we need to check if it's
      // also globally unused.
      size_t variable_index = bucket.variable_indices[intra_bucket_index];
      // Note: global_unused might not be global yet. As we lazily wait for the
      // reduction to complete, it becomes really global only if we get to the
      // point as below where we wait for the reduction work, make D2H copy,
      // and update global_unused with the real global consensus, i.e.
      // local_used_maps_reduced_ is true.
      bool global_unused =
          local_used_maps_[replica_index][variable_index].item<int>() == 0;
      if (global_unused && !local_used_maps_reduced_) {
        // Wait for local_used_maps reduction to complete.
        local_used_work_->wait();
        // D2H from local_used_maps_dev_ to local_used_maps_
        for (size_t i = 0; i < local_used_maps_.size(); i++) {
          local_used_maps_[i].copy_(local_used_maps_dev_[i]);
        }
        global_unused =
            local_used_maps_[replica_index][variable_index].item<int>() == 0;
        local_used_maps_reduced_ = true;
      }

      auto bucket_view =
          replica.contents.narrow(0, offset, length).view(variable.sizes());
      auto& grad = variable.grad();

      // If a parameter is globally unused, we keep its grad untouched.
      if (!global_unused) {
        if (!grad.defined()) {
          grad = at::empty(bucket_view.sizes(), bucket_view.options());
        }
        grad.copy_(bucket_view);
      }
    }
  }
}

// A bucket with a single sparse tensor doesn't need to be unflattened,
// but merely assigned to the corresponding variable its grad.
void Reducer::finalize_bucket_sparse(Bucket& bucket) {
  const auto result = bucket.work->result();
  TORCH_INTERNAL_ASSERT(bucket.replicas.size() == result.size());
  for (size_t i = 0; i < bucket.replicas.size(); i++) {
    auto& replica = bucket.replicas[i];
    TORCH_INTERNAL_ASSERT(replica.variables.size() == 1);
    auto& variable = replica.variables.front();
    variable.grad() = result[i];
  }
}

void Reducer::finalize_backward() {
  // No longer expect autograd hooks to fire after this function returns.
  TORCH_INTERNAL_ASSERT(expect_autograd_hooks_);
  expect_autograd_hooks_ = false;

  // No longer require call to finalize after this function returns.
  TORCH_INTERNAL_ASSERT(require_finalize_);
  require_finalize_ = false;

  // Check that all buckets were completed and had their work kicked off.
  TORCH_INTERNAL_ASSERT(next_bucket_ == buckets_.size());

  // Wait for asynchronous reduction to complete and unflatten contents.
  for (auto& bucket : buckets_) {
    TORCH_INTERNAL_ASSERT(bucket.work);
    bucket.work->wait();
    if (bucket.expect_sparse_gradient) {
      finalize_bucket_sparse(bucket);
    } else {
      finalize_bucket_dense(bucket);
    }
  }

  // Reset unused parameter accounting.
  for (auto& local_used : local_used_maps_) {
    local_used.fill_(0);
  }
  // Due to the lazy wait, it is possible that reduction of the current
  // iteration is still going when the one for next iteration gets kicked off.
  // For such case, we want to wait explicitly to make sure the reduction does
  // complete before kicking off next one. Otherwise the prevous one may
  // interfere, write to the device-side memory and clobber the content of
  // local_unused_maps_dev_.
  if (!local_used_maps_reduced_) {
    local_used_work_->wait();
  }
  local_used_maps_reduced_ = false;
}

namespace {

// Tensors may be coalesced into buckets. Buckets must contain tensors of
// the same type, on the same device, so a bucket can identified by a
// composite key of a tensor's type identifier and its device.
struct BucketKey {
  BucketKey(c10::ScalarType type, c10::Device device)
      : type(std::move(type)), device(std::move(device)) {}

  const c10::ScalarType type;
  const c10::Device device;

  // See torch/csrc/utils/hash.h for dispatch code.
  static size_t hash(const BucketKey& key) {
    return torch::get_hash(key.type, key.device);
  }
};

inline bool operator==(const BucketKey& lhs, const BucketKey& rhs) {
  return lhs.type == rhs.type && lhs.device == rhs.device;
}

} // namespace

// This is equivalent to take_tensors but returns indices into the
// tensor list argument for bucket assignment. Also, it is aware
// of device placement and will not allow buckets to span devices.
std::vector<std::vector<size_t>> compute_bucket_assignment_by_size(
    const std::vector<at::Tensor>& tensors,
    const std::vector<size_t>& bucket_size_limits,
    const std::vector<bool>& expect_sparse_gradient) {
  // Either expect_sparse_gradient is not specified or it has as many elements
  // as the vector with tensors.
  TORCH_INTERNAL_ASSERT(
      expect_sparse_gradient.empty() ||
      (tensors.size() == expect_sparse_gradient.size()));
  TORCH_INTERNAL_ASSERT(tensors.size() > 0);

  std::vector<std::vector<size_t>> result;
  result.reserve(tensors.size());

  // Keep iterator into the size_limit vector by tensor type and device.
  // This is done so that we can use the consecutive bucket limits per type.
  std::unordered_map<
      BucketKey,
      std::vector<size_t>::const_iterator,
      torch::hash<BucketKey>>
      bucket_size_limit_iterators;

  // Local accumulator type for a single bucket.
  struct BucketAccumulator {
    std::vector<size_t> indices;
    size_t size = 0;
  };

  // Keep vector of indices and size accumulator by tensor type and device.
  std::unordered_map<BucketKey, BucketAccumulator, torch::hash<BucketKey>>
      buckets;

  for (size_t i = 0; i < tensors.size(); i++) {
    const auto& tensor = tensors[i];
    TORCH_CHECK(!tensor.is_sparse(), "No support for sparse tensors.");

    // If we expect a sparse gradient to be produced for this tensor, it cannot
    // be grouped together with other gradients and gets its own bucket.
    if (!expect_sparse_gradient.empty() && expect_sparse_gradient[i]) {
      result.push_back({i});
      continue;
    }

    auto key = BucketKey(tensor.scalar_type(), tensor.device());
    auto& bucket = buckets[key];
    bucket.indices.push_back(i);
    bucket.size += tensor.numel() * tensor.element_size();

    // Initialize bucket size limit iterator if necessary.
    if (bucket_size_limit_iterators.count(key) == 0) {
      bucket_size_limit_iterators[key] = bucket_size_limits.begin();
    }

    auto& bucket_size_limit_iterator = bucket_size_limit_iterators[key];
    const auto bucket_size_limit = *bucket_size_limit_iterator;
    if (bucket.size >= bucket_size_limit) {
      result.emplace_back(std::move(bucket.indices));
      bucket = BucketAccumulator();

      // Advance to the next bucket size limit for this type/device.
      auto next = bucket_size_limit_iterator + 1;
      if (next != bucket_size_limits.end()) {
        bucket_size_limit_iterator = next;
      }
    }
  }

  // Add remaining buckets.
  for (auto& it : buckets) {
    auto& bucket = it.second;
    if (!bucket.indices.empty()) {
      result.emplace_back(std::move(bucket.indices));
    }
  }

  // Sort resulting buckets by the minimum tensor index they include.
  // We assume that the order of the tensors is the order in which they are
  // used (or the reverse order in which their gradients are produced).
  // This sorting step ensures that the buckets are ready in consecutive order.
  std::sort(
      result.begin(),
      result.end(),
      [](const std::vector<size_t>& a, const std::vector<size_t>& b) {
        const auto amin = std::min_element(a.begin(), a.end());
        const auto bmin = std::min_element(b.begin(), b.end());
        return *amin < *bmin;
      });

  return result;
}

} // namespace c10d
