Paddle/paddle/framework/backward.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License. */

#include "paddle/framework/backward.h"
#include <list>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"

namespace paddle {
namespace framework {

static bool AllInSet(const std::vector<std::string>& names,
                     const std::string& suffix,
                     const std::unordered_set<std::string>& set) {
  for (auto& name : names) {
    if (set.find(name + suffix) == set.end()) {
      return false;
    }
  }
  return true;
}

static std::vector<size_t> InSetIdx(
    const std::vector<std::string>& names, const std::string& suffix,
    const std::unordered_set<std::string>& set) {
  std::vector<size_t> ret_val;
  ret_val.reserve(names.size());
  for (size_t i = 0; i < names.size(); ++i) {
    if (set.find(names[i] + suffix) != set.end()) {
      ret_val.push_back(i);
    }
  }
  return ret_val;
}

static std::shared_ptr<OperatorBase> EmptyOp() {
  auto net_op = std::make_shared<NetOp>();
  net_op->type_ = "@EMPTY_OP@";
  net_op->CompleteAddOp();
  return net_op;
}

/**
 * @brief Backward an operator, implementation
 * @param forwardOp the forward operator
 * @param no_grad_names variable names not calculate for gradient. Like X@GRAD
 * is not needed.
 * @param uniq_id a unique index used inside BackwardImpl, it will be shared
 * through recursive invoke.
 * @return The backward operator. For simple situation, it is a simple operator.
 * For complex situation, it is a NetOp.
 *
 * See Backward.h for details
 */
static std::shared_ptr<OperatorBase> BackwardImpl(
    const OperatorBase& forwardOp,
    std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
  /**
   *  If all input gradients of forwarding operator do not need to calculate,
   *  just return an EmptyOp. Not return null ptr because EmptyOp does not take
   *  too much time for calculation, but it is useful for simplifying logic.
   */
  if (AllInSet(forwardOp.inputs_, OperatorBase::GRAD_VAR_SUFFIX(),
               no_grad_names)) {
    return EmptyOp();
  }

  /**
   * All output gradients of forwarding operator do not need to calculate. Then
   * all input gradients cannot be computed at all, and we put them into
   * `no_grad_names` set. Return an EmptyOp.
   */
  if (AllInSet(forwardOp.outputs_, OperatorBase::GRAD_VAR_SUFFIX(),
               no_grad_names)) {
    for (auto& name : forwardOp.inputs_) {
      /// Mark all input is not need
      no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
    }
    return EmptyOp();
  }

  //! Returned gradient network
  auto net = std::make_shared<NetOp>();

  if (forwardOp.IsNetOp()) {
    /// Because forwardOp is a net op, it can static_cast.
    auto& forwardNet = static_cast<const NetOp&>(forwardOp);

    //! Map from output gradient variable name to operator's indices in backward
    //! net. That operator generates that variable.
    std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;

    size_t local_op_id = 0;
    /// reversely travel forwardNet
    for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
         ++it, ++local_op_id) {
      auto fwd = *it;
      auto bwd = BackwardImpl(*fwd, no_grad_names, uniq_id);
      net->AddOp(bwd);
      for (auto& out : bwd->outputs_) {
        dup_output_ops[out].emplace_back(local_op_id);
      }
    }
    /// Get unique ID for this method.
    auto uid = uniq_id++;
    // TODO(dzh): more comment
    using Pos = std::pair<size_t, std::shared_ptr<OperatorBase>>;
    std::list<Pos> insert_position;
    for (auto& dup_output_op : dup_output_ops) {
      const std::string& name = dup_output_op.first;
      auto& dup_op = dup_output_op.second;
      if (dup_op.size() == 1) continue;
      std::vector<std::string> dup_outputs;

      for (size_t i = 0; i < dup_op.size(); ++i) {
        auto op_offset = dup_op[i];
        dup_outputs.push_back(name + "@RENAME@" + std::to_string(uid) + "@" +
                              std::to_string(i));
        net->ops_[op_offset]->Rename(name, dup_outputs.back());
      }
      insert_position.push_back(
          {dup_op.back(),
           OpRegistry::CreateOp(
               "add", {dup_outputs}, {name},
               {{"input_format",
                 std::vector<int>{0, (int)dup_outputs.size()}}})});
    }

    insert_position.sort(
        [](const Pos& l, const Pos& r) { return l.first > r.first; });

    for (auto& pos : insert_position) {
      net->InsertOp(pos.first + 1, pos.second);
    }

  } else {
    //! TODO(fjy)
    std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
    for (std::string& grad_input : grad_op->inputs_) {
      if (no_grad_names.count(grad_input)) {
        std::string prefix = grad_input.substr(
            0, grad_input.size() - OperatorBase::GRAD_VAR_SUFFIX().size());
        grad_input = prefix + OperatorBase::ZERO_VAR_SUFFIX();
        net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {prefix},
                                        {grad_input}, {}));
      }
    }

    for (std::string& grad_output : grad_op->outputs_) {
      if (no_grad_names.count(grad_output)) {
        grad_output = OperatorBase::EMPTY_VAR_NAME();
      }
    }

    if (net->ops_.empty()) {  // Current no aux op is added to network
      return grad_op;
    }
    net->AddOp(grad_op);
  }
  net->type_ = "@GENERATED_BACKWARD@";
  net->CompleteAddOp();
  return net;
}

//! See header for comments
extern std::shared_ptr<OperatorBase> Backward(
    const OperatorBase& forwardOp,
    const std::unordered_set<std::string>& no_grad_vars) {
  std::unordered_set<std::string> no_grad_names;
  no_grad_names.reserve(no_grad_vars.size());

  for (auto& name : no_grad_vars) {
    no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
  }
  size_t uid = 0;
  return BackwardImpl(forwardOp, no_grad_names, uid);
}
}  // namespace framework
}  // namespace paddle