Program: CWE-125
Project: CVE-2021-37664
Buggy source:
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

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 <limits>
#include <vector>

#include "third_party/eigen3/Eigen/Core"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/kernels/boosted_trees/boosted_trees.pb.h"
#include "tensorflow/core/kernels/boosted_trees/tree_helper.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

using Matrix =
    Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
using ConstMatrixMap = Eigen::Map<const Matrix>;
using MatrixMap = Eigen::Map<Matrix>;

using ConstVectorMap = Eigen::Map<const Eigen::VectorXf>;
using VectorMap = Eigen::Map<Eigen::VectorXf>;

constexpr char kInequalitySplit[] = "inequality";
constexpr char kEqualitySplit[] = "equality";

// V1 Op. Deprecated. BoostedTreesCalculateBestFeatureSplitOpV2 is V2.
class BoostedTreesCalculateBestGainsPerFeatureOp : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestGainsPerFeatureOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    OP_REQUIRES(
        context, node_id_range_t->dims() == 1,
        errors::InvalidArgument("node_id_range must be a rank 1 tensor, but "
                                "given node_id_range has dims of ",
                                node_id_range_t->dims()));
    OP_REQUIRES(context, node_id_range_t->dim_size(0) == 2,
                errors::InvalidArgument(
                    "node_id_range must be a rank 1 tensor with shape=[2], but "
                    "given node_id_range has shape ",
                    node_id_range_t->dim_size(0), " on its first dim"));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive
    // stats_summary_list
    OpInputList stats_summary_list;
    OP_REQUIRES_OK(context, context->input_list("stats_summary_list",
                                                &stats_summary_list));
    const int64_t num_buckets = stats_summary_list[0].dim_size(1);
    // Check for single logit: 1 gradient + 1 hessian value.
    DCHECK_EQ(stats_summary_list[0].dim_size(2), 2);
    std::vector<TTypes<float, 3>::ConstTensor> stats_summary;
    stats_summary.reserve(stats_summary_list.size());
    for (const auto& tensor : stats_summary_list) {
      stats_summary.emplace_back(tensor.tensor<float, 3>());
    }
    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();
    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    // Allocate output lists of tensors:
    OpOutputList output_node_ids_list;
    OP_REQUIRES_OK(
        context, context->output_list("node_ids_list", &output_node_ids_list));
    OpOutputList output_gains_list;
    OP_REQUIRES_OK(context,
                   context->output_list("gains_list", &output_gains_list));
    OpOutputList output_thresholds_list;
    OP_REQUIRES_OK(context, context->output_list("thresholds_list",
                                                 &output_thresholds_list));
    OpOutputList output_left_node_contribs_list;
    OP_REQUIRES_OK(context,
                   context->output_list("left_node_contribs_list",
                                        &output_left_node_contribs_list));
    OpOutputList output_right_node_contribs_list;
    OP_REQUIRES_OK(context,
                   context->output_list("right_node_contribs_list",
                                        &output_right_node_contribs_list));

    // Use identity later to convert float to Eigen::Matrix type for input to
    // CalculateWeightsAndGains. This op only supports single dimension logits.
    Eigen::MatrixXf identity;
    identity.setIdentity(1, 1);
    // Get the best split info per node for each feature.
    for (int feature_idx = 0; feature_idx < num_features_; ++feature_idx) {
      std::vector<float> cum_grad;
      std::vector<float> cum_hess;
      cum_grad.reserve(num_buckets);
      cum_hess.reserve(num_buckets);

      std::vector<int32> output_node_ids;
      std::vector<float> output_gains;
      std::vector<int32> output_thresholds;
      std::vector<float> output_left_node_contribs;
      std::vector<float> output_right_node_contribs;
      for (int node_id = node_id_first; node_id < node_id_last; ++node_id) {
        // Calculate gains.
        cum_grad.clear();
        cum_hess.clear();
        float total_grad = 0.0;
        float total_hess = 0.0;
        for (int bucket = 0; bucket < num_buckets; ++bucket) {
          // TODO(nponomareva): Consider multi-dimensional gradients/hessians.
          total_grad += stats_summary[feature_idx](node_id, bucket, 0);
          total_hess += stats_summary[feature_idx](node_id, bucket, 1);
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
        // Check if node has enough of average hessian.
        if (total_hess < min_node_weight) {
          // Do not split the node because not enough avg hessian.
          continue;
        }
        float best_gain = std::numeric_limits<float>::lowest();
        float best_bucket = 0;
        float best_contrib_for_left = 0.0;
        float best_contrib_for_right = 0.0;
        // Parent gain.
        float parent_gain;
        Eigen::VectorXf unused(1);
        CalculateWeightsAndGains(total_grad * identity, total_hess * identity,
                                 l1, l2, &unused, &parent_gain);

        for (int bucket = 0; bucket < num_buckets; ++bucket) {
          const float cum_grad_bucket = cum_grad[bucket];
          const float cum_hess_bucket = cum_hess[bucket];
          // Left child.
          Eigen::VectorXf contrib_for_left(1);
          float gain_for_left;
          CalculateWeightsAndGains(cum_grad_bucket * identity,
                                   cum_hess_bucket * identity, l1, l2,
                                   &contrib_for_left, &gain_for_left);
          // Right child.
          // use contrib_for_right.
          Eigen::VectorXf contrib_for_right(1);
          float gain_for_right;
          CalculateWeightsAndGains((total_grad - cum_grad_bucket) * identity,
                                   (total_hess - cum_hess_bucket) * identity,
                                   l1, l2, &contrib_for_right, &gain_for_right);

          if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
            best_gain = gain_for_left + gain_for_right;
            best_bucket = bucket;
            best_contrib_for_left = contrib_for_left[0];
            best_contrib_for_right = contrib_for_right[0];
          }
        }  // for bucket
        output_node_ids.push_back(node_id);
        // Remove the parent gain for the parent node.
        output_gains.push_back(best_gain - parent_gain);
        output_thresholds.push_back(best_bucket);
        output_left_node_contribs.push_back(best_contrib_for_left);
        output_right_node_contribs.push_back(best_contrib_for_right);
      }  // for node_id
      const int num_nodes = output_node_ids.size();
      // output_node_ids
      Tensor* output_node_ids_t;
      OP_REQUIRES_OK(context,
                     output_node_ids_list.allocate(feature_idx, {num_nodes},
                                                   &output_node_ids_t));
      auto output_node_ids_vec = output_node_ids_t->vec<int32>();
      // output_gains
      Tensor* output_gains_t;
      OP_REQUIRES_OK(context, output_gains_list.allocate(
                                  feature_idx, {num_nodes}, &output_gains_t));
      auto output_gains_vec = output_gains_t->vec<float>();
      // output_thresholds
      Tensor* output_thresholds_t;
      OP_REQUIRES_OK(context,
                     output_thresholds_list.allocate(feature_idx, {num_nodes},
                                                     &output_thresholds_t));
      auto output_thresholds_vec = output_thresholds_t->vec<int32>();
      // output_left_node_contribs
      Tensor* output_left_node_contribs_t;
      OP_REQUIRES_OK(context, output_left_node_contribs_list.allocate(
                                  feature_idx, {num_nodes, 1},
                                  &output_left_node_contribs_t));
      auto output_left_node_contribs_matrix =
          output_left_node_contribs_t->matrix<float>();
      // output_right_node_contribs
      Tensor* output_right_node_contribs_t;
      OP_REQUIRES_OK(context, output_right_node_contribs_list.allocate(
                                  feature_idx, {num_nodes, 1},
                                  &output_right_node_contribs_t));
      auto output_right_node_contribs_matrix =
          output_right_node_contribs_t->matrix<float>();
      // Sets output tensors from vectors.
      for (int i = 0; i < num_nodes; ++i) {
        output_node_ids_vec(i) = output_node_ids[i];
        // Adjust the gains to penalize by tree complexity.
        output_gains_vec(i) = output_gains[i] - tree_complexity;
        output_thresholds_vec(i) = output_thresholds[i];
        output_left_node_contribs_matrix(i, 0) = output_left_node_contribs[i];
        // This op only supports 1-dimensional logits.
        output_right_node_contribs_matrix(i, 0) = output_right_node_contribs[i];
      }
    }  // for f
  }

 private:
  int max_splits_;
  int num_features_;
};

// V1 op that only supports single dimensional logit.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestGainsPerFeature").Device(DEVICE_CPU),
    BoostedTreesCalculateBestGainsPerFeatureOp);

// Deprecated op. Use BoostedTreesCalculateBestFeatureSplitOpV2.
class BoostedTreesCalculateBestFeatureSplitOp : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestFeatureSplitOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    OP_REQUIRES_OK(context, context->GetAttr("split_type", &split_type_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive

    const Tensor* stats_summary_t;
    OP_REQUIRES_OK(context, context->input("stats_summary", &stats_summary_t));
    TTypes<float, 4>::ConstTensor stats_summary =
        stats_summary_t->tensor<float, 4>();
    const int32_t feature_dims = stats_summary_t->dim_size(1);
    // The last bucket is for default/missing value.
    const int32_t num_buckets = stats_summary_t->dim_size(2) - 1;
    const int32_t logits_dim = logits_dim_;
    const int32_t hessian_dim = stats_summary_t->dim_size(3) - logits_dim;
    DCHECK_GT(hessian_dim, 0);
    DCHECK_LE(hessian_dim, logits_dim * logits_dim);

    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    DCHECK_GE(l1, 0);
    if (logits_dim_ > 1) {
      // Multi-class L1 regularization not supported yet.
      DCHECK_EQ(l1, 0);
    }

    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    DCHECK_GE(l2, 0);

    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();

    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<Eigen::VectorXf> output_left_node_contribs;
    std::vector<Eigen::VectorXf> output_right_node_contribs;
    std::vector<string> output_split_types;

    // TODO(tanzheny) parallelize the computation.
    // Iterate each node and find the best gain per node.
    for (int32_t node_id = node_id_first; node_id < node_id_last; ++node_id) {
      float best_gain = std::numeric_limits<float>::lowest();
      int32_t best_bucket = 0;
      int32_t best_f_dim = 0;
      string best_split_type;
      Eigen::VectorXf best_contrib_for_left(logits_dim);
      Eigen::VectorXf best_contrib_for_right(logits_dim);
      float parent_gain;

      // Including default bucket.
      ConstMatrixMap stats_mat(&stats_summary(node_id, 0, 0, 0),
                               num_buckets + 1, logits_dim + hessian_dim);
      const Eigen::VectorXf total_grad =
          stats_mat.leftCols(logits_dim).colwise().sum();
      const Eigen::VectorXf total_hess =
          stats_mat.rightCols(hessian_dim).colwise().sum();
      if (total_hess.norm() < min_node_weight) {
        continue;
      }
      Eigen::VectorXf parent_weight(logits_dim);
      CalculateWeightsAndGains(total_grad, total_hess, l1, l2, &parent_weight,
                               &parent_gain);

      if (split_type_ == "inequality") {
        CalculateBestInequalitySplit(
            stats_summary, node_id, feature_dims, logits_dim, hessian_dim,
            num_buckets, min_node_weight, l1, l2, &best_gain, &best_bucket,
            &best_f_dim, &best_split_type, &best_contrib_for_left,
            &best_contrib_for_right);
      } else {
        CalculateBestEqualitySplit(
            stats_summary, total_grad, total_hess, node_id, feature_dims,
            logits_dim, hessian_dim, num_buckets, l1, l2, &best_gain,
            &best_bucket, &best_f_dim, &best_split_type, &best_contrib_for_left,
            &best_contrib_for_right);
      }

      if (best_gain == std::numeric_limits<float>::lowest()) {
        // Do not add the node if not split if found.
        continue;
      }
      output_node_ids.push_back(node_id);
      // Remove the parent gain for the parent node.
      output_gains.push_back(best_gain - parent_gain);
      output_feature_dimensions.push_back(best_f_dim);
      // default direction is fixed for dense splits.
      // TODO(tanzheny) account for default values.
      output_split_types.push_back(best_split_type);
      output_thresholds.push_back(best_bucket);
      output_left_node_contribs.push_back(best_contrib_for_left);
      output_right_node_contribs.push_back(best_contrib_for_right);
    }  // for node id
    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "left_node_contribs", {num_nodes, logits_dim},
                                &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "right_node_contribs", {num_nodes, logits_dim},
                                &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      for (int j = 0; j < logits_dim; ++j) {
        output_left_node_contribs_matrix(i, j) =
            output_left_node_contribs[i][j];
        output_right_node_contribs_matrix(i, j) =
            output_right_node_contribs[i][j];
      }
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 private:
  // TODO(crawles): Simplify inequality path just like equality b/138329196
  // Currently this is not simplify-able due to numerical instability in math
  // i.e. gain = -g.transpose() * hessian_and_reg.colPivHouseholderQr().solve(g)
  // It caused gain to be Inf when g is approaching 0 but not exactly 0 while
  // there is no regularization.
  // Calculate the best inequality split per node.
  void CalculateBestInequalitySplit(
      TTypes<float, 4>::ConstTensor stats_summary, const int32_t node_id,
      const int32_t feature_dims, const int32_t logits_dim,
      const int32_t hessian_dim, const int32_t num_buckets,
      const float min_node_weight, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    std::vector<Eigen::VectorXf> cum_grad;
    std::vector<Eigen::VectorXf> cum_hess;
    // get all cumulative gradients including default bucket.
    cum_grad.reserve(num_buckets);
    cum_hess.reserve(num_buckets);

    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      ConstVectorMap default_stats_vec(
          &stats_summary(node_id, f_dim, num_buckets, 0),
          logits_dim + hessian_dim);
      Eigen::VectorXf missing_bucket_grad = default_stats_vec.head(logits_dim);
      Eigen::VectorXf missing_bucket_hess = default_stats_vec.tail(hessian_dim);
      cum_grad.clear();
      cum_hess.clear();
      Eigen::VectorXf total_grad = Eigen::VectorXf::Zero(logits_dim);
      Eigen::VectorXf total_hess = Eigen::VectorXf::Zero(hessian_dim);
      // sum all the gradients including default bucket.
      for (int bucket = 0; bucket <= num_buckets; ++bucket) {
        for (int i = 0; i < logits_dim; ++i) {
          total_grad[i] += stats_summary(node_id, f_dim, bucket, i);
        }
        for (int i = 0; i < hessian_dim; ++i) {
          // Full hessian.
          total_hess[i] +=
              stats_summary(node_id, f_dim, bucket, logits_dim + i);
        }
        if (bucket < num_buckets) {
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
      }
      const string kInequalityDefaultLeft =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
      const string kInequalityDefaultRight =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);

      // Iterate from left to right, excluding default bucket.
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        // default value goes to left node.
        const Eigen::VectorXf total_left_grad =
            cum_grad[bucket] + missing_bucket_grad;
        const Eigen::VectorXf total_left_hess =
            cum_hess[bucket] + missing_bucket_hess;
        MaybeUpdateBestSplit(
            total_left_grad, total_grad - total_left_grad, total_left_hess,
            total_hess - total_left_hess, logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultLeft, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
        // default value goes to right node.
        MaybeUpdateBestSplit(
            cum_grad[bucket], total_grad - cum_grad[bucket], cum_hess[bucket],
            total_hess - cum_hess[bucket], logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultRight, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
      }  // for bucket
    }
  }

  // Calculate the best equality split per node.
  void CalculateBestEqualitySplit(
      TTypes<float, 4>::ConstTensor stats_summary,
      const Eigen::VectorXf& total_grad, const Eigen::VectorXf& total_hess,
      const int32_t node_id, const int32_t feature_dims,
      const int32_t logits_dim, const int32_t hessian_dim,
      const int32_t num_buckets, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    const string kEqualityDefaultRight =
        boosted_trees::SplitTypeWithDefault_Name(
            boosted_trees::EQUALITY_DEFAULT_RIGHT);
    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        ConstVectorMap stats_vec(&stats_summary(node_id, f_dim, bucket, 0),
                                 logits_dim + hessian_dim);
        Eigen::VectorXf curr_grad = stats_vec.head(logits_dim);
        Eigen::VectorXf curr_hess = stats_vec.tail(hessian_dim);
        MaybeUpdateBestSplit(curr_grad, total_grad - curr_grad, curr_hess,
                             total_hess - curr_hess, logits_dim, bucket, f_dim,
                             l1, l2, kEqualityDefaultRight, best_gain,
                             best_bucket, best_f_dim, best_split_type,
                             best_contrib_for_left, best_contrib_for_right);
      }
    }
  }

  void MaybeUpdateBestSplit(const Eigen::VectorXf& grad_for_left,
                            const Eigen::VectorXf& grad_for_right,
                            const Eigen::VectorXf& hess_for_left,
                            const Eigen::VectorXf& hess_for_right,
                            const int32_t logits_dim, const int32_t bucket,
                            const int32_t f_dim, const float l1, const float l2,
                            const string split_type, float* best_gain,
                            int32* best_bucket, int32* best_f_dim,
                            string* best_split_type,
                            Eigen::VectorXf* best_contrib_for_left,
                            Eigen::VectorXf* best_contrib_for_right) {
    // Left child.
    Eigen::VectorXf contrib_for_left(logits_dim);
    float gain_for_left;
    CalculateWeightsAndGains(grad_for_left, hess_for_left, l1, l2,
                             &contrib_for_left, &gain_for_left);
    Eigen::VectorXf contrib_for_right(logits_dim);
    float gain_for_right;
    CalculateWeightsAndGains(grad_for_right, hess_for_right, l1, l2,
                             &contrib_for_right, &gain_for_right);
    if (GainIsLarger(gain_for_left + gain_for_right, *best_gain)) {
      *best_gain = gain_for_left + gain_for_right;
      *best_bucket = bucket;
      *best_f_dim = f_dim;
      *best_contrib_for_left = contrib_for_left;
      *best_contrib_for_right = contrib_for_right;
      *best_split_type = split_type;
    }
  }

  int logits_dim_;
  string split_type_;
};

// Deprecated op. Use BoostedTreesCalculateBestFeatureSplitOpV2.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestFeatureSplit").Device(DEVICE_CPU),
    BoostedTreesCalculateBestFeatureSplitOp);

// V2 Op.
class BoostedTreesCalculateBestFeatureSplitV2 : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestFeatureSplitV2(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    OP_REQUIRES(
        context, node_id_range_t->dims() == 1,
        errors::InvalidArgument("node_id_range must be a rank 1 tensor, but "
                                "given node_id_range has dims of ",
                                node_id_range_t->dims()));
    OP_REQUIRES(context, node_id_range_t->dim_size(0) == 2,
                errors::InvalidArgument(
                    "node_id_range must be a rank 1 tensor with shape=[2], but "
                    "given node_id_range has shape ",
                    node_id_range_t->dim_size(0), " on its first dim"));
    const int32_t node_id_first = node_id_range(0);  // Inclusive.
    const int32_t node_id_last = node_id_range(1);   // Exclusive.

    // Get stats_summaries_list.
    OpInputList stats_summaries_list;
    OP_REQUIRES_OK(context, context->input_list("stats_summaries_list",
                                                &stats_summaries_list));

    // Infer dimensions of a stats_summary.
    DCHECK_GT(stats_summaries_list.size(), 0);
    const int32_t feature_dims = stats_summaries_list[0].dim_size(1);
    // The last bucket is for default/missing value.
    const int32_t num_buckets = stats_summaries_list[0].dim_size(2) - 1;
    const int32_t logits_dim = logits_dim_;
    const int32_t hessian_dim =
        stats_summaries_list[0].dim_size(3) - logits_dim;
    DCHECK_GT(hessian_dim, 0);
    DCHECK_LE(hessian_dim, logits_dim * logits_dim);

    // Vector of stats_summaries; each element is stats for feature of shape
    // [max_splits, feature_dim, num_buckets, logits_dim + hessian_dim].
    std::vector<TTypes<float, 4>::ConstTensor> stats_summaries;
    DCHECK_EQ(stats_summaries_list.size(), num_features_);
    stats_summaries.reserve(num_features_);
    for (const auto& tensor : stats_summaries_list) {
      stats_summaries.emplace_back(tensor.tensor<float, 4>());
    }

    // Split types.
    const Tensor* split_types_t;
    OP_REQUIRES_OK(context, context->input("split_types", &split_types_t));
    const auto split_types = split_types_t->vec<tstring>();
    DCHECK_EQ(split_types.size(), num_features_);
    // Validate.
    for (int i = 0; i < num_features_; ++i) {
      if (!(split_types(i) == kInequalitySplit ||
            split_types(i) == kEqualitySplit)) {
        OP_REQUIRES_OK(
            context,
            errors::Aborted(
                "Operation received an exception: Incorrect split type"));
      }
    }
    // Feature ids.
    const Tensor* candidate_feature_ids_t;
    OP_REQUIRES_OK(context, context->input("candidate_feature_ids",
                                           &candidate_feature_ids_t));
    const auto candidate_feature_ids = candidate_feature_ids_t->vec<int32>();
    DCHECK_EQ(candidate_feature_ids.size(), num_features_);

    // L1, L2, tree_complexity, min_node_weight.
    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    DCHECK_GE(l1, 0);
    if (logits_dim_ > 1) {
      // Multi-class L1 regularization not supported yet.
      DCHECK_EQ(l1, 0);
    }
    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    DCHECK_GE(l2, 0);
    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();
    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_ids;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<Eigen::VectorXf> output_left_node_contribs;
    std::vector<Eigen::VectorXf> output_right_node_contribs;
    std::vector<string> output_split_types;

    // TODO(tanzheny) parallelize the computation.
    // Iterate each node and find the best gain per node.
    float parent_gain;
    for (int32_t node_id = node_id_first; node_id < node_id_last; ++node_id) {
      float best_gain = std::numeric_limits<float>::lowest();
      int32_t best_bucket;
      int32_t best_f_id;
      int32_t best_f_dim;
      string best_split_type;
      Eigen::VectorXf best_contrib_for_left(logits_dim);
      Eigen::VectorXf best_contrib_for_right(logits_dim);

      // Sum of gradient and hessian. Compute parent gain using first feature.
      ConstMatrixMap stats_mat(&stats_summaries[0](node_id, 0, 0, 0),
                               num_buckets + 1,  // Including default bucket.
                               logits_dim + hessian_dim);
      const Eigen::VectorXf total_grad =
          stats_mat.leftCols(logits_dim).colwise().sum();
      const Eigen::VectorXf total_hess =
          stats_mat.rightCols(hessian_dim).colwise().sum();
      if (total_hess.norm() < min_node_weight) {
        continue;
      }
      Eigen::VectorXf unused(logits_dim);
      CalculateWeightsAndGains(total_grad, total_hess, l1, l2, &unused,
                               &parent_gain);
      for (int f_idx = 0; f_idx < num_features_; ++f_idx) {
        const string split_type = split_types(f_idx);
        TTypes<float, 4>::ConstTensor stats_summary = stats_summaries[f_idx];
        float f_best_gain = std::numeric_limits<float>::lowest();
        int32_t f_best_bucket;
        int32_t f_best_f_dim;
        string f_best_split_type;
        Eigen::VectorXf f_best_contrib_for_left(logits_dim);
        Eigen::VectorXf f_best_contrib_for_right(logits_dim);

        if (split_type == kInequalitySplit) {
          CalculateBestInequalitySplit(
              stats_summary, node_id, feature_dims, logits_dim, hessian_dim,
              num_buckets, min_node_weight, l1, l2, &f_best_gain,
              &f_best_bucket, &f_best_f_dim, &f_best_split_type,
              &f_best_contrib_for_left, &f_best_contrib_for_right);
        } else {
          CalculateBestEqualitySplit(
              stats_summary, total_grad, total_hess, node_id, feature_dims,
              logits_dim, hessian_dim, num_buckets, l1, l2, &f_best_gain,
              &f_best_bucket, &f_best_f_dim, &f_best_split_type,
              &f_best_contrib_for_left, &f_best_contrib_for_right);
        }
        if (f_best_gain > best_gain) {
          best_gain = f_best_gain;
          best_f_id = candidate_feature_ids(f_idx);
          best_f_dim = f_best_f_dim;
          best_split_type = f_best_split_type;
          best_bucket = f_best_bucket;
          best_contrib_for_left = f_best_contrib_for_left;
          best_contrib_for_right = f_best_contrib_for_right;
        }
      }  // For feature id.
      if (best_gain == std::numeric_limits<float>::lowest()) {
        // Do not add the node if no split is found.
        continue;
      }
      output_node_ids.push_back(node_id);
      // Remove the parent gain for the parent node.
      output_gains.push_back(best_gain - parent_gain);
      output_feature_ids.push_back(best_f_id);
      output_feature_dimensions.push_back(best_f_dim);
      // Default direction is fixed for dense splits.
      // TODO(tanzheny) account for default values.
      output_split_types.push_back(best_split_type);
      output_thresholds.push_back(best_bucket);
      output_left_node_contribs.push_back(best_contrib_for_left);
      output_right_node_contribs.push_back(best_contrib_for_right);
    }  // for node id.
    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_ids
    Tensor* output_features_ids_t;
    OP_REQUIRES_OK(context, context->allocate_output("feature_ids", {num_nodes},
                                                     &output_features_ids_t));
    auto output_features_vec = output_features_ids_t->vec<int32>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "left_node_contribs", {num_nodes, logits_dim},
                                &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "right_node_contribs", {num_nodes, logits_dim},
                                &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      output_features_vec(i) = output_feature_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      for (int j = 0; j < logits_dim; ++j) {
        output_left_node_contribs_matrix(i, j) =
            output_left_node_contribs[i][j];
        output_right_node_contribs_matrix(i, j) =
            output_right_node_contribs[i][j];
      }
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 private:
  // TODO(crawles): Simplify inequality path just like equality b/138329196
  // Currently this is not simplify-able due to numerical instability in math
  // i.e. gain = -g.transpose() * hessian_and_reg.colPivHouseholderQr().solve(g)
  // It caused gain to be Inf when g is approaching 0 but not exactly 0 while
  // there is no regularization.
  // Calculate the best inequality split per node.
  void CalculateBestInequalitySplit(
      TTypes<float, 4>::ConstTensor stats_summary, const int32_t node_id,
      const int32_t feature_dims, const int32_t logits_dim,
      const int32_t hessian_dim, const int32_t num_buckets,
      const float min_node_weight, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    std::vector<Eigen::VectorXf> cum_grad;
    std::vector<Eigen::VectorXf> cum_hess;
    // get all cumulative gradients including default bucket.
    cum_grad.reserve(num_buckets);
    cum_hess.reserve(num_buckets);

    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      ConstVectorMap default_stats_vec(
          &stats_summary(node_id, f_dim, num_buckets, 0),
          logits_dim + hessian_dim);
      Eigen::VectorXf missing_bucket_grad = default_stats_vec.head(logits_dim);
      Eigen::VectorXf missing_bucket_hess = default_stats_vec.tail(hessian_dim);
      cum_grad.clear();
      cum_hess.clear();
      Eigen::VectorXf total_grad = Eigen::VectorXf::Zero(logits_dim);
      Eigen::VectorXf total_hess = Eigen::VectorXf::Zero(hessian_dim);
      // sum all the gradients including default bucket.
      for (int bucket = 0; bucket <= num_buckets; ++bucket) {
        for (int i = 0; i < logits_dim; ++i) {
          total_grad[i] += stats_summary(node_id, f_dim, bucket, i);
        }
        for (int i = 0; i < hessian_dim; ++i) {
          // Full hessian.
          total_hess[i] +=
              stats_summary(node_id, f_dim, bucket, logits_dim + i);
        }
        if (bucket < num_buckets) {
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
      }
      const string kInequalityDefaultLeft =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
      const string kInequalityDefaultRight =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);

      // Iterate from left to right, excluding default bucket.
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        // default value goes to left node.
        const Eigen::VectorXf total_left_grad =
            cum_grad[bucket] + missing_bucket_grad;
        const Eigen::VectorXf total_left_hess =
            cum_hess[bucket] + missing_bucket_hess;
        MaybeUpdateBestSplit(
            total_left_grad, total_grad - total_left_grad, total_left_hess,
            total_hess - total_left_hess, logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultLeft, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
        // default value goes to right node.
        MaybeUpdateBestSplit(
            cum_grad[bucket], total_grad - cum_grad[bucket], cum_hess[bucket],
            total_hess - cum_hess[bucket], logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultRight, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
      }  // for bucket
    }
  }

  // Calculate the best equality split per node.
  void CalculateBestEqualitySplit(
      TTypes<float, 4>::ConstTensor stats_summary,
      const Eigen::VectorXf& total_grad, const Eigen::VectorXf& total_hess,
      const int32_t node_id, const int32_t feature_dims,
      const int32_t logits_dim, const int32_t hessian_dim,
      const int32_t num_buckets, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    const string kEqualityDefaultRight =
        boosted_trees::SplitTypeWithDefault_Name(
            boosted_trees::EQUALITY_DEFAULT_RIGHT);
    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        ConstVectorMap stats_vec(&stats_summary(node_id, f_dim, bucket, 0),
                                 logits_dim + hessian_dim);
        Eigen::VectorXf curr_grad = stats_vec.head(logits_dim);
        Eigen::VectorXf curr_hess = stats_vec.tail(hessian_dim);
        MaybeUpdateBestSplit(curr_grad, total_grad - curr_grad, curr_hess,
                             total_hess - curr_hess, logits_dim, bucket, f_dim,
                             l1, l2, kEqualityDefaultRight, best_gain,
                             best_bucket, best_f_dim, best_split_type,
                             best_contrib_for_left, best_contrib_for_right);
      }
    }
  }

  void MaybeUpdateBestSplit(const Eigen::VectorXf& grad_for_left,
                            const Eigen::VectorXf& grad_for_right,
                            const Eigen::VectorXf& hess_for_left,
                            const Eigen::VectorXf& hess_for_right,
                            const int32_t logits_dim, const int32_t bucket,
                            const int32_t f_dim, const float l1, const float l2,
                            const string split_type, float* best_gain,
                            int32* best_bucket, int32* best_f_dim,
                            string* best_split_type,
                            Eigen::VectorXf* best_contrib_for_left,
                            Eigen::VectorXf* best_contrib_for_right) {
    // Left child.
    Eigen::VectorXf contrib_for_left(logits_dim);
    float gain_for_left;
    CalculateWeightsAndGains(grad_for_left, hess_for_left, l1, l2,
                             &contrib_for_left, &gain_for_left);
    Eigen::VectorXf contrib_for_right(logits_dim);
    float gain_for_right;
    CalculateWeightsAndGains(grad_for_right, hess_for_right, l1, l2,
                             &contrib_for_right, &gain_for_right);
    if (GainIsLarger(gain_for_left + gain_for_right, *best_gain)) {
      *best_gain = gain_for_left + gain_for_right;
      *best_bucket = bucket;
      *best_f_dim = f_dim;
      *best_contrib_for_left = contrib_for_left;
      *best_contrib_for_right = contrib_for_right;
      *best_split_type = split_type;
    }
  }
  int num_features_;
  int logits_dim_;
};

// v2 op that supports multi-class.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestFeatureSplitV2").Device(DEVICE_CPU),
    BoostedTreesCalculateBestFeatureSplitV2);

// Map from bucket id to vector of statistics.
typedef std::map<int32, std::vector<float>> BucketMap;
typedef BucketMap::iterator BucketMapIterator;
// Map from feature dimension to BucketMap.
typedef std::map<int32, BucketMap> FeatureMap;
typedef FeatureMap::iterator FeatureMapIterator;

class BoostedTreesSparseCalculateBestFeatureSplitOp : public OpKernel {
 public:
  explicit BoostedTreesSparseCalculateBestFeatureSplitOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    // TODO(crawles): Using logits_dim_ for multi-class split.
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    // TODO(tanzheny): Using this for equality split.
    OP_REQUIRES_OK(context, context->GetAttr("split_type", &split_type_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive

    const Tensor* stats_summary_indices_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_indices",
                                           &stats_summary_indices_t));
    const auto stats_summary_indices = stats_summary_indices_t->matrix<int32>();
    const int32_t num_sparse_entries = stats_summary_indices_t->dim_size(0);

    const Tensor* stats_summary_values_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_values",
                                           &stats_summary_values_t));
    const auto stats_summary_values = stats_summary_values_t->vec<float>();

    const Tensor* stats_summary_shape_t;
    OP_REQUIRES_OK(
        context, context->input("stats_summary_shape", &stats_summary_shape_t));
    const auto stats_summary_shape = stats_summary_shape_t->vec<int32>();
    const int32_t num_buckets = stats_summary_shape(2) - 1;
    const int32_t stats_dims = stats_summary_shape(3);

    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();

    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();

    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();

    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<float> output_left_node_contribs;
    std::vector<float> output_right_node_contribs;
    std::vector<string> output_split_types;

    FeatureMap f_map;

    int32_t previous_node_id = -1;
    for (int idx = 0; idx < num_sparse_entries; ++idx) {
      int32_t node_id = stats_summary_indices(idx, 0);
      if (node_id != previous_node_id) {
        process_node(f_map, &output_node_ids, &output_gains,
                     &output_feature_dimensions, &output_thresholds,
                     &output_left_node_contribs, &output_right_node_contribs,
                     &output_split_types, previous_node_id, min_node_weight, l1,
                     l2, num_buckets);
        f_map.clear();
      }
      previous_node_id = node_id;
      DCHECK_LE(node_id_first, node_id);
      DCHECK_LT(node_id, node_id_last);
      const int32_t feature_dim = stats_summary_indices(idx, 1);
      const int32_t bucket_id = stats_summary_indices(idx, 2);
      const int32_t stat_dim = stats_summary_indices(idx, 3);
      std::pair<FeatureMapIterator, bool> const& f_insert_result = f_map.insert(
          FeatureMapIterator::value_type(feature_dim, BucketMap()));
      auto& b_map = f_insert_result.first->second;
      std::pair<BucketMapIterator, bool> const& b_insert_result =
          b_map.insert(BucketMapIterator::value_type(
              bucket_id, std::vector<float>(stats_dims)));
      auto& stats = b_insert_result.first->second;
      stats[stat_dim] = stats_summary_values(idx);
    }  // for node_id
    // process the last node id
    process_node(f_map, &output_node_ids, &output_gains,
                 &output_feature_dimensions, &output_thresholds,
                 &output_left_node_contribs, &output_right_node_contribs,
                 &output_split_types, previous_node_id, min_node_weight, l1, l2,
                 num_buckets);

    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("left_node_contribs", {num_nodes, 1},
                                          &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("right_node_contribs", {num_nodes, 1},
                                          &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      // TODO(crawles): change this for multi-class.
      output_left_node_contribs_matrix(i, 0) = output_left_node_contribs[i];
      output_right_node_contribs_matrix(i, 0) = output_right_node_contribs[i];
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 protected:
  void process_node(const FeatureMap& f_map,
                    std::vector<int32>* output_node_ids,
                    std::vector<float>* output_gains,
                    std::vector<int32>* output_feature_dimensions,
                    std::vector<int32>* output_thresholds,
                    std::vector<float>* output_left_node_contribs,
                    std::vector<float>* output_right_node_contribs,
                    std::vector<string>* output_split_types,
                    const int32_t node_id, const float min_node_weight,
                    const float l1, const float l2, const int32_t num_buckets) {
    float parent_gain;
    Eigen::VectorXf unused(logits_dim_);
    Eigen::MatrixXf identity;
    identity.setIdentity(1, 1);

    // start processing for previous node id.
    float best_gain = std::numeric_limits<float>::lowest();
    float best_bucket = 0;
    float best_f_dim = 0;
    string best_split_type = boosted_trees::SplitTypeWithDefault_Name(
        boosted_trees::INEQUALITY_DEFAULT_LEFT);
    float best_contrib_for_left = 0.0;
    float best_contrib_for_right = 0.0;
    // the sum of gradients including default bucket.
    float total_grad = 0;
    // the sum of hessians including default bucket.
    float total_hess = 0;

    for (auto f_iter = f_map.begin(); f_iter != f_map.end(); ++f_iter) {
      const int32_t feature_dim = f_iter->first;
      const auto buckets_to_stats_map = f_iter->second;

      // The very last bucket contains stats for missing values.
      // TODO(crawles): use vector for multi-class.
      const float default_grad =
          (buckets_to_stats_map.find(num_buckets) == buckets_to_stats_map.end()
               ? 0
               : buckets_to_stats_map.at(num_buckets)[0]);
      const float default_hess =
          (buckets_to_stats_map.find(num_buckets) == buckets_to_stats_map.end()
               ? 0
               : buckets_to_stats_map.at(num_buckets)[1]);

      if (f_iter == f_map.begin()) {
        // first get the sum of grads, including default bucket.
        for (auto b_iter = buckets_to_stats_map.begin();
             b_iter != buckets_to_stats_map.end(); ++b_iter) {
          total_grad += b_iter->second[0];
          total_hess += b_iter->second[1];
        }
        if (total_hess < min_node_weight) {
          // Do not split the node because not enough avg hessian.
          break;
        }
        CalculateWeightsAndGains(total_grad * identity, total_hess * identity,
                                 l1, l2, &unused, &parent_gain);
      }

      float total_left_grad = 0;
      float total_left_hess = 0;
      for (auto b_iter = buckets_to_stats_map.begin();
           b_iter != buckets_to_stats_map.end(); ++b_iter) {
        const int32_t bucket_id = b_iter->first;
        // total_left_stats should exclude stats from default bucket.
        if (bucket_id == num_buckets) {
          break;
        }
        // TODO(crawles): vector for multi-class.
        total_left_grad += b_iter->second[0];
        total_left_hess += b_iter->second[1];
        // From left to right, default right.
        // Left child.
        Eigen::VectorXf contrib_for_left(1);
        float gain_for_left;
        CalculateWeightsAndGains(total_left_grad * identity,
                                 total_left_hess * identity, l1, l2,
                                 &contrib_for_left, &gain_for_left);
        // Right child.
        Eigen::VectorXf contrib_for_right(1);
        float gain_for_right;
        CalculateWeightsAndGains((total_grad - total_left_grad) * identity,
                                 (total_hess - total_left_hess) * identity, l1,
                                 l2, &contrib_for_right, &gain_for_right);
        if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
          best_gain = gain_for_left + gain_for_right;
          best_bucket = bucket_id;
          best_f_dim = feature_dim;
          best_split_type = boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);
          best_contrib_for_left = contrib_for_left[0];
          best_contrib_for_right = contrib_for_right[0];
        }

        // From right to left, default left.
        CalculateWeightsAndGains((total_left_grad + default_grad) * identity,
                                 (total_left_hess + default_hess) * identity,
                                 l1, l2, &contrib_for_left, &gain_for_left);
        CalculateWeightsAndGains(
            (total_grad - default_grad - total_left_grad) * identity,
            (total_hess - default_hess - total_left_hess) * identity, l1, l2,
            &contrib_for_right, &gain_for_right);
        if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
          best_gain = gain_for_left + gain_for_right;
          best_bucket = bucket_id;
          best_f_dim = feature_dim;
          best_split_type = boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
          best_contrib_for_left = contrib_for_left[0];
          best_contrib_for_right = contrib_for_right[0];
        }
      }  // for bucket_id
    }    // for feature_dim
    if (best_gain != std::numeric_limits<float>::lowest()) {
      output_node_ids->push_back(node_id);
      // Remove the parent gain.
      output_gains->push_back(best_gain - parent_gain);
      output_feature_dimensions->push_back(best_f_dim);
      output_split_types->push_back(best_split_type);
      output_thresholds->push_back(best_bucket);
      output_left_node_contribs->push_back(best_contrib_for_left);
      output_right_node_contribs->push_back(best_contrib_for_right);
    }
  }

 private:
  int logits_dim_;
  string split_type_;
};

REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesSparseCalculateBestFeatureSplit").Device(DEVICE_CPU),
    BoostedTreesSparseCalculateBestFeatureSplitOp);

class BoostedTreesMakeStatsSummaryOp : public OpKernel {
 public:
  explicit BoostedTreesMakeStatsSummaryOp(OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();
    // gradients
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();
    // hessians
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();
    // bucketized_features
    OpInputList bucketized_features_list;
    OP_REQUIRES_OK(context, context->input_list("bucketized_features_list",
                                                &bucketized_features_list));
    // Infer batch size.
    const int64_t batch_size = node_ids_t->dim_size(0);

    // Allocate temporary stats tensor (Rank 4).
    Tensor temp_stats_double_t;
    OP_REQUIRES_OK(context, context->allocate_temp(
                                DT_DOUBLE,
                                {num_features_, max_splits_, num_buckets_, 2},
                                &temp_stats_double_t));
    auto temp_stats_double = temp_stats_double_t.tensor<double, 4>();
    temp_stats_double.setZero();

    // Partition by node, and then bucketize.
    for (int feature_idx = 0; feature_idx < num_features_; ++feature_idx) {
      const auto& features = bucketized_features_list[feature_idx].vec<int32>();
      for (int i = 0; i < batch_size; ++i) {
        const int32_t node = node_ids(i);
        const int32_t bucket = features(i);
        temp_stats_double(feature_idx, node, bucket, 0) += gradients(i, 0);
        temp_stats_double(feature_idx, node, bucket, 1) += hessians(i, 0);
      }
    }

    // Copy temp tensor over to output tensor.
    Tensor* output_stats_summary_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "stats_summary", temp_stats_double_t.shape(),
                                &output_stats_summary_t));
    output_stats_summary_t->tensor<float, 4>() =
        temp_stats_double.template cast<float>();
  }

 private:
  int max_splits_;
  int num_buckets_;
  int num_features_;
};

REGISTER_KERNEL_BUILDER(Name("BoostedTreesMakeStatsSummary").Device(DEVICE_CPU),
                        BoostedTreesMakeStatsSummaryOp);

// TODO(tanzheny): Add an option of default value into the API interface.
class BoostedTreesAggregateStatsOp : public OpKernel {
 public:
  explicit BoostedTreesAggregateStatsOp(OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids.
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();

    // gradients.
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();

    // hessians.
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();

    // feature.
    const Tensor* feature_t;
    OP_REQUIRES_OK(context, context->input("feature", &feature_t));
    const auto feature = feature_t->matrix<int32>();

    // Infer batch size, feature dimension and stats dimension.
    const int64_t batch_size = node_ids_t->dim_size(0);
    const int64_t logits_dims = gradients_t->dim_size(1);
    const int64_t hessians_dims = hessians_t->dim_size(1);
    const int64_t stats_dims = logits_dims + hessians_dims;
    const int64_t feature_dims = feature_t->dim_size(1);

    // Allocate temporary stats tensor (Rank 4), upcasting to double.
    // A default bucket is added to the end for missing/default values.
    Tensor temp_stats_double_t;
    OP_REQUIRES_OK(
        context, context->allocate_temp(
                     DT_DOUBLE,
                     {max_splits_, feature_dims, num_buckets_ + 1, stats_dims},
                     &temp_stats_double_t));
    auto temp_stats_double = temp_stats_double_t.tensor<double, 4>();
    temp_stats_double.setZero();

    for (int i = 0; i < batch_size; ++i) {
      const int32_t node = node_ids(i);
      for (int feature_dim = 0; feature_dim < feature_dims; ++feature_dim) {
        const int32_t feature_value = feature(i, feature_dim);
        const int32_t bucket =
            (feature_value == -1) ? num_buckets_ : feature_value;
        for (int stat_dim = 0; stat_dim < logits_dims; ++stat_dim) {
          temp_stats_double(node, feature_dim, bucket, stat_dim) +=
              gradients(i, stat_dim);
        }
        for (int stat_dim = logits_dims; stat_dim < stats_dims; ++stat_dim) {
          temp_stats_double(node, feature_dim, bucket, stat_dim) +=
              hessians(i, stat_dim - logits_dims);
        }
      }
    }

    // Copy temp tensor over to output tensor, downcasting to float.
    Tensor* output_stats_summary_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "stats_summary", temp_stats_double_t.shape(),
                                &output_stats_summary_t));
    output_stats_summary_t->tensor<float, 4>() =
        temp_stats_double.template cast<float>();
  }

 private:
  int max_splits_;
  int num_buckets_;
};

REGISTER_KERNEL_BUILDER(Name("BoostedTreesAggregateStats").Device(DEVICE_CPU),
                        BoostedTreesAggregateStatsOp);

// Key based on node id, feature dimension and bucket id.
struct StatsPartitionKey {
  StatsPartitionKey(const int32_t node_id, const int32_t feature_dim,
                    const int32_t bucket_id)
      : node_id(node_id), feature_dim(feature_dim), bucket_id(bucket_id) {}

  bool operator==(const StatsPartitionKey& other) const {
    return (node_id == other.node_id) && (feature_dim == other.feature_dim) &&
           (bucket_id == other.bucket_id);
  }

  // Compare for StatsPartitionKey.
  struct Less {
    bool operator()(const StatsPartitionKey& a,
                    const StatsPartitionKey& b) const {
      if (a.node_id < b.node_id) {
        return true;
      }
      if ((a.node_id == b.node_id) && (a.feature_dim < b.feature_dim)) {
        return true;
      }
      if ((a.node_id == b.node_id) && (a.feature_dim == b.feature_dim) &&
          (a.bucket_id < b.bucket_id)) {
        return true;
      }
      return false;
    }
  };

  // Tree node id.
  int32 node_id;
  // Dimension within feature column.
  int32 feature_dim;
  // bucketized feature value .
  int32 bucket_id;
};

typedef std::map<StatsPartitionKey, std::vector<float>, StatsPartitionKey::Less>
    StatsPartitionMap;
typedef StatsPartitionMap::iterator StatsPartitionIterator;

// Key based on instance and feature dimension.
struct InstanceFeatureDimKey {
  InstanceFeatureDimKey() : instance(-1), feature_dim(-1) {}

  InstanceFeatureDimKey(const int32_t instance, const int32_t feature_dim)
      : instance(instance), feature_dim(feature_dim) {}

  bool operator==(const InstanceFeatureDimKey& other) const {
    return (instance == other.instance) && (feature_dim == other.feature_dim);
  }

  // Compare for InstanceFeatureDimKey.
  struct Less {
    bool operator()(const InstanceFeatureDimKey& a,
                    const InstanceFeatureDimKey& b) const {
      if (a.instance < b.instance) {
        return true;
      }
      if ((a.instance == b.instance) && (a.feature_dim < b.feature_dim)) {
        return true;
      }
      return false;
    }
  };

  // Instance id within a batch.
  int32 instance;
  // Dimension within feature column.
  int32 feature_dim;
};

// Add statistics to StatsPartitionMap for (instance, feature dim, bucket id).
static void AddInstanceStatsToMap(
    const int32_t instance, const int32_t feature_dim, const int32_t bucket_id,
    const int32_t logits_dims, const int32_t stats_dims,
    StatsPartitionMap* stats_map, const TTypes<float>::ConstMatrix& gradients,
    const TTypes<float>::ConstMatrix& hessians,
    const TTypes<int32>::ConstVec& node_ids) {
  const int32_t node_id = node_ids(instance);
  const auto key = StatsPartitionKey(node_id, feature_dim, bucket_id);
  std::pair<StatsPartitionIterator, bool> const& insert_result =
      stats_map->insert(StatsPartitionIterator::value_type(
          key, std::vector<float>(stats_dims, 0.0f)));
  auto& stats = insert_result.first->second;
  for (int stat_dim = 0; stat_dim < logits_dims; ++stat_dim) {
    stats[stat_dim] += gradients(instance, stat_dim);
  }
  for (int stat_dim = logits_dims; stat_dim < stats_dims; ++stat_dim) {
    stats[stat_dim] += hessians(instance, stat_dim - logits_dims);
  }
}

// Add statistics to StatsPartitionMap for bucket_id ranging from
// (start_instance, start_feature_dim) to (end_instance, end_feature_dim),
// inclusive on start and end instances, exclusive on end feature dim.
static void AddRangeStats(const int start_instance, const int end_instance,
                          const int start_feature_dim,
                          const int end_feature_dim,
                          StatsPartitionMap* stats_map,
                          const TTypes<float>::ConstMatrix& gradients,
                          const TTypes<float>::ConstMatrix& hessians,
                          const TTypes<int32>::ConstVec& node_ids,
                          const int32_t feature_dims, const int32_t bucket_id,
                          const int32_t logits_dims, const int32_t stats_dims) {
  DCHECK_LE(start_instance, end_instance);
  if (start_instance == end_instance) {
    DCHECK_LT(start_feature_dim, end_feature_dim);
  }
  for (int32_t instance = start_instance; instance <= end_instance;
       ++instance) {
    const int32_t start_f_dim =
        (instance == start_instance) ? start_feature_dim + 1 : 0;
    const int32_t end_f_dim =
        (instance == end_instance) ? end_feature_dim : feature_dims;
    for (int32_t f_dim = start_f_dim; f_dim < end_f_dim; ++f_dim) {
      AddInstanceStatsToMap(instance, f_dim, bucket_id, logits_dims, stats_dims,
                            stats_map, gradients, hessians, node_ids);
    }
  }
}

class BoostedTreesSparseAggregateStatsOp : public OpKernel {
 public:
  explicit BoostedTreesSparseAggregateStatsOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids.
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();

    // gradients.
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();

    // hessians.
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();

    // feature indices.
    const Tensor* feature_indices_t;
    OP_REQUIRES_OK(context,
                   context->input("feature_indices", &feature_indices_t));
    const auto feature_indices = feature_indices_t->matrix<int32>();

    // feature values.
    const Tensor* feature_values_t;
    OP_REQUIRES_OK(context,
                   context->input("feature_values", &feature_values_t));
    const auto feature_values = feature_values_t->vec<int32>();

    // feature shape.
    const Tensor* feature_shape_t;
    OP_REQUIRES_OK(context, context->input("feature_shape", &feature_shape_t));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(feature_shape_t->shape()),
                errors::InvalidArgument(
                    "Input shapes should be a vector but received shapes ",
                    feature_shape_t->shape().DebugString()));
    const auto feature_shape = feature_shape_t->vec<int32>();

    const int64_t batch_size = gradients_t->dim_size(0);
    const int64_t logits_dims = gradients_t->dim_size(1);
    const int64_t hessians_dims = hessians_t->dim_size(1);
    const int64_t stats_dims = logits_dims + hessians_dims;
    const int64_t num_sparse_entries = feature_indices_t->dim_size(0);
    const int32_t feature_dims = feature_shape(1);
    DCHECK_LE(num_sparse_entries, batch_size * feature_dims);

    // Aggregate statistics info to map.
    StatsPartitionMap stats_map;

    int prev_instance = 0;
    int prev_f_dim = -1;

    for (int i = 0; i < num_sparse_entries; ++i) {
      // the instance number within a batch
      const int32_t instance = feature_indices(i, 0);
      DCHECK_LE(instance, batch_size);
      DCHECK_GE(instance, prev_instance);
      // the node id within a tree.
      const int32_t node_id = node_ids(instance);
      DCHECK_LE(node_id, max_splits_);
      // the feature dimension.
      const int32_t f_dim = feature_indices(i, 1);
      DCHECK_LE(f_dim, feature_dims);
      // the bucket id of the value.
      const int32_t bucket_id = feature_values(i);
      DCHECK_LE(bucket_id, num_buckets_);

      // Add statistics for the missing entries into default bucket.
      // The last bucket is default bucket.
      const int missing_entry_bucket = num_buckets_;
      AddRangeStats(prev_instance, instance, prev_f_dim, f_dim, &stats_map,
                    gradients, hessians, node_ids, feature_dims,
                    missing_entry_bucket, logits_dims, stats_dims);
      prev_instance = instance;
      prev_f_dim = f_dim;
      // Add statistics for the non-missing entry into
      // (cur_instance, cur_f_dim, bucket_id).
      AddInstanceStatsToMap(instance, f_dim, bucket_id, logits_dims, stats_dims,
                            &stats_map, gradients, hessians, node_ids);
    }
    AddRangeStats(prev_instance, batch_size - 1, prev_f_dim, feature_dims,
                  &stats_map, gradients, hessians, node_ids, feature_dims,
                  num_buckets_, logits_dims, stats_dims);

    // Serialize statistics info map to tensor output.
    const int64_t num_slots = stats_map.size() * stats_dims;
    Tensor* summary_indices_t = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output("stats_summary_indices",
                                            TensorShape({num_slots, 4}),
                                            &summary_indices_t));
    auto summary_indices = summary_indices_t->matrix<int32>();
    Tensor* summary_values_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("stats_summary_values",
                                                     TensorShape({num_slots}),
                                                     &summary_values_t));
    auto summary_values = summary_values_t->vec<float>();
    int entry_index = 0;
    for (auto& iter : stats_map) {
      for (int stat_dim = 0; stat_dim < stats_dims; ++stat_dim) {
        summary_indices(entry_index, 0) = iter.first.node_id;
        summary_indices(entry_index, 1) = iter.first.feature_dim;
        summary_indices(entry_index, 2) = iter.first.bucket_id;
        summary_indices(entry_index, 3) = stat_dim;
        summary_values(entry_index) = iter.second[stat_dim];
        ++entry_index;
      }
    }

    Tensor* summary_shape_t = nullptr;
    OP_REQUIRES_OK(
        context, context->allocate_output("stats_summary_shape",
                                          TensorShape({4}), &summary_shape_t));
    auto summary_shape = summary_shape_t->vec<int32>();
    summary_shape(0) = max_splits_;
    summary_shape(1) = feature_dims;
    summary_shape(2) = num_buckets_ + 1;
    summary_shape(3) = stats_dims;
  }

 private:
  int max_splits_;
  int num_buckets_;
};

REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesSparseAggregateStats").Device(DEVICE_CPU),
    BoostedTreesSparseAggregateStatsOp);

}  // namespace tensorflow


Fixed version (ground truth):
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

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 <limits>
#include <vector>

#include "third_party/eigen3/Eigen/Core"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/kernels/boosted_trees/boosted_trees.pb.h"
#include "tensorflow/core/kernels/boosted_trees/tree_helper.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

using Matrix =
    Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
using ConstMatrixMap = Eigen::Map<const Matrix>;
using MatrixMap = Eigen::Map<Matrix>;

using ConstVectorMap = Eigen::Map<const Eigen::VectorXf>;
using VectorMap = Eigen::Map<Eigen::VectorXf>;

constexpr char kInequalitySplit[] = "inequality";
constexpr char kEqualitySplit[] = "equality";

// V1 Op. Deprecated. BoostedTreesCalculateBestFeatureSplitOpV2 is V2.
class BoostedTreesCalculateBestGainsPerFeatureOp : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestGainsPerFeatureOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    OP_REQUIRES(
        context, node_id_range_t->dims() == 1,
        errors::InvalidArgument("node_id_range must be a rank 1 tensor, but "
                                "given node_id_range has dims of ",
                                node_id_range_t->dims()));
    OP_REQUIRES(context, node_id_range_t->dim_size(0) == 2,
                errors::InvalidArgument(
                    "node_id_range must be a rank 1 tensor with shape=[2], but "
                    "given node_id_range has shape ",
                    node_id_range_t->dim_size(0), " on its first dim"));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive
    // stats_summary_list
    OpInputList stats_summary_list;
    OP_REQUIRES_OK(context, context->input_list("stats_summary_list",
                                                &stats_summary_list));
    const int64_t num_buckets = stats_summary_list[0].dim_size(1);
    // Check for single logit: 1 gradient + 1 hessian value.
    DCHECK_EQ(stats_summary_list[0].dim_size(2), 2);
    std::vector<TTypes<float, 3>::ConstTensor> stats_summary;
    stats_summary.reserve(stats_summary_list.size());
    for (const auto& tensor : stats_summary_list) {
      stats_summary.emplace_back(tensor.tensor<float, 3>());
    }
    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();
    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    // Allocate output lists of tensors:
    OpOutputList output_node_ids_list;
    OP_REQUIRES_OK(
        context, context->output_list("node_ids_list", &output_node_ids_list));
    OpOutputList output_gains_list;
    OP_REQUIRES_OK(context,
                   context->output_list("gains_list", &output_gains_list));
    OpOutputList output_thresholds_list;
    OP_REQUIRES_OK(context, context->output_list("thresholds_list",
                                                 &output_thresholds_list));
    OpOutputList output_left_node_contribs_list;
    OP_REQUIRES_OK(context,
                   context->output_list("left_node_contribs_list",
                                        &output_left_node_contribs_list));
    OpOutputList output_right_node_contribs_list;
    OP_REQUIRES_OK(context,
                   context->output_list("right_node_contribs_list",
                                        &output_right_node_contribs_list));

    // Use identity later to convert float to Eigen::Matrix type for input to
    // CalculateWeightsAndGains. This op only supports single dimension logits.
    Eigen::MatrixXf identity;
    identity.setIdentity(1, 1);
    // Get the best split info per node for each feature.
    for (int feature_idx = 0; feature_idx < num_features_; ++feature_idx) {
      std::vector<float> cum_grad;
      std::vector<float> cum_hess;
      cum_grad.reserve(num_buckets);
      cum_hess.reserve(num_buckets);

      std::vector<int32> output_node_ids;
      std::vector<float> output_gains;
      std::vector<int32> output_thresholds;
      std::vector<float> output_left_node_contribs;
      std::vector<float> output_right_node_contribs;
      for (int node_id = node_id_first; node_id < node_id_last; ++node_id) {
        // Calculate gains.
        cum_grad.clear();
        cum_hess.clear();
        float total_grad = 0.0;
        float total_hess = 0.0;
        for (int bucket = 0; bucket < num_buckets; ++bucket) {
          // TODO(nponomareva): Consider multi-dimensional gradients/hessians.
          total_grad += stats_summary[feature_idx](node_id, bucket, 0);
          total_hess += stats_summary[feature_idx](node_id, bucket, 1);
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
        // Check if node has enough of average hessian.
        if (total_hess < min_node_weight) {
          // Do not split the node because not enough avg hessian.
          continue;
        }
        float best_gain = std::numeric_limits<float>::lowest();
        float best_bucket = 0;
        float best_contrib_for_left = 0.0;
        float best_contrib_for_right = 0.0;
        // Parent gain.
        float parent_gain;
        Eigen::VectorXf unused(1);
        CalculateWeightsAndGains(total_grad * identity, total_hess * identity,
                                 l1, l2, &unused, &parent_gain);

        for (int bucket = 0; bucket < num_buckets; ++bucket) {
          const float cum_grad_bucket = cum_grad[bucket];
          const float cum_hess_bucket = cum_hess[bucket];
          // Left child.
          Eigen::VectorXf contrib_for_left(1);
          float gain_for_left;
          CalculateWeightsAndGains(cum_grad_bucket * identity,
                                   cum_hess_bucket * identity, l1, l2,
                                   &contrib_for_left, &gain_for_left);
          // Right child.
          // use contrib_for_right.
          Eigen::VectorXf contrib_for_right(1);
          float gain_for_right;
          CalculateWeightsAndGains((total_grad - cum_grad_bucket) * identity,
                                   (total_hess - cum_hess_bucket) * identity,
                                   l1, l2, &contrib_for_right, &gain_for_right);

          if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
            best_gain = gain_for_left + gain_for_right;
            best_bucket = bucket;
            best_contrib_for_left = contrib_for_left[0];
            best_contrib_for_right = contrib_for_right[0];
          }
        }  // for bucket
        output_node_ids.push_back(node_id);
        // Remove the parent gain for the parent node.
        output_gains.push_back(best_gain - parent_gain);
        output_thresholds.push_back(best_bucket);
        output_left_node_contribs.push_back(best_contrib_for_left);
        output_right_node_contribs.push_back(best_contrib_for_right);
      }  // for node_id
      const int num_nodes = output_node_ids.size();
      // output_node_ids
      Tensor* output_node_ids_t;
      OP_REQUIRES_OK(context,
                     output_node_ids_list.allocate(feature_idx, {num_nodes},
                                                   &output_node_ids_t));
      auto output_node_ids_vec = output_node_ids_t->vec<int32>();
      // output_gains
      Tensor* output_gains_t;
      OP_REQUIRES_OK(context, output_gains_list.allocate(
                                  feature_idx, {num_nodes}, &output_gains_t));
      auto output_gains_vec = output_gains_t->vec<float>();
      // output_thresholds
      Tensor* output_thresholds_t;
      OP_REQUIRES_OK(context,
                     output_thresholds_list.allocate(feature_idx, {num_nodes},
                                                     &output_thresholds_t));
      auto output_thresholds_vec = output_thresholds_t->vec<int32>();
      // output_left_node_contribs
      Tensor* output_left_node_contribs_t;
      OP_REQUIRES_OK(context, output_left_node_contribs_list.allocate(
                                  feature_idx, {num_nodes, 1},
                                  &output_left_node_contribs_t));
      auto output_left_node_contribs_matrix =
          output_left_node_contribs_t->matrix<float>();
      // output_right_node_contribs
      Tensor* output_right_node_contribs_t;
      OP_REQUIRES_OK(context, output_right_node_contribs_list.allocate(
                                  feature_idx, {num_nodes, 1},
                                  &output_right_node_contribs_t));
      auto output_right_node_contribs_matrix =
          output_right_node_contribs_t->matrix<float>();
      // Sets output tensors from vectors.
      for (int i = 0; i < num_nodes; ++i) {
        output_node_ids_vec(i) = output_node_ids[i];
        // Adjust the gains to penalize by tree complexity.
        output_gains_vec(i) = output_gains[i] - tree_complexity;
        output_thresholds_vec(i) = output_thresholds[i];
        output_left_node_contribs_matrix(i, 0) = output_left_node_contribs[i];
        // This op only supports 1-dimensional logits.
        output_right_node_contribs_matrix(i, 0) = output_right_node_contribs[i];
      }
    }  // for f
  }

 private:
  int max_splits_;
  int num_features_;
};

// V1 op that only supports single dimensional logit.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestGainsPerFeature").Device(DEVICE_CPU),
    BoostedTreesCalculateBestGainsPerFeatureOp);

// Deprecated op. Use BoostedTreesCalculateBestFeatureSplitOpV2.
class BoostedTreesCalculateBestFeatureSplitOp : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestFeatureSplitOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    OP_REQUIRES_OK(context, context->GetAttr("split_type", &split_type_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive

    const Tensor* stats_summary_t;
    OP_REQUIRES_OK(context, context->input("stats_summary", &stats_summary_t));
    TTypes<float, 4>::ConstTensor stats_summary =
        stats_summary_t->tensor<float, 4>();
    const int32_t feature_dims = stats_summary_t->dim_size(1);
    // The last bucket is for default/missing value.
    const int32_t num_buckets = stats_summary_t->dim_size(2) - 1;
    const int32_t logits_dim = logits_dim_;
    const int32_t hessian_dim = stats_summary_t->dim_size(3) - logits_dim;
    DCHECK_GT(hessian_dim, 0);
    DCHECK_LE(hessian_dim, logits_dim * logits_dim);

    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    DCHECK_GE(l1, 0);
    if (logits_dim_ > 1) {
      // Multi-class L1 regularization not supported yet.
      DCHECK_EQ(l1, 0);
    }

    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    DCHECK_GE(l2, 0);

    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();

    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<Eigen::VectorXf> output_left_node_contribs;
    std::vector<Eigen::VectorXf> output_right_node_contribs;
    std::vector<string> output_split_types;

    // TODO(tanzheny) parallelize the computation.
    // Iterate each node and find the best gain per node.
    for (int32_t node_id = node_id_first; node_id < node_id_last; ++node_id) {
      float best_gain = std::numeric_limits<float>::lowest();
      int32_t best_bucket = 0;
      int32_t best_f_dim = 0;
      string best_split_type;
      Eigen::VectorXf best_contrib_for_left(logits_dim);
      Eigen::VectorXf best_contrib_for_right(logits_dim);
      float parent_gain;

      // Including default bucket.
      ConstMatrixMap stats_mat(&stats_summary(node_id, 0, 0, 0),
                               num_buckets + 1, logits_dim + hessian_dim);
      const Eigen::VectorXf total_grad =
          stats_mat.leftCols(logits_dim).colwise().sum();
      const Eigen::VectorXf total_hess =
          stats_mat.rightCols(hessian_dim).colwise().sum();
      if (total_hess.norm() < min_node_weight) {
        continue;
      }
      Eigen::VectorXf parent_weight(logits_dim);
      CalculateWeightsAndGains(total_grad, total_hess, l1, l2, &parent_weight,
                               &parent_gain);

      if (split_type_ == "inequality") {
        CalculateBestInequalitySplit(
            stats_summary, node_id, feature_dims, logits_dim, hessian_dim,
            num_buckets, min_node_weight, l1, l2, &best_gain, &best_bucket,
            &best_f_dim, &best_split_type, &best_contrib_for_left,
            &best_contrib_for_right);
      } else {
        CalculateBestEqualitySplit(
            stats_summary, total_grad, total_hess, node_id, feature_dims,
            logits_dim, hessian_dim, num_buckets, l1, l2, &best_gain,
            &best_bucket, &best_f_dim, &best_split_type, &best_contrib_for_left,
            &best_contrib_for_right);
      }

      if (best_gain == std::numeric_limits<float>::lowest()) {
        // Do not add the node if not split if found.
        continue;
      }
      output_node_ids.push_back(node_id);
      // Remove the parent gain for the parent node.
      output_gains.push_back(best_gain - parent_gain);
      output_feature_dimensions.push_back(best_f_dim);
      // default direction is fixed for dense splits.
      // TODO(tanzheny) account for default values.
      output_split_types.push_back(best_split_type);
      output_thresholds.push_back(best_bucket);
      output_left_node_contribs.push_back(best_contrib_for_left);
      output_right_node_contribs.push_back(best_contrib_for_right);
    }  // for node id
    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "left_node_contribs", {num_nodes, logits_dim},
                                &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "right_node_contribs", {num_nodes, logits_dim},
                                &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      for (int j = 0; j < logits_dim; ++j) {
        output_left_node_contribs_matrix(i, j) =
            output_left_node_contribs[i][j];
        output_right_node_contribs_matrix(i, j) =
            output_right_node_contribs[i][j];
      }
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 private:
  // TODO(crawles): Simplify inequality path just like equality b/138329196
  // Currently this is not simplify-able due to numerical instability in math
  // i.e. gain = -g.transpose() * hessian_and_reg.colPivHouseholderQr().solve(g)
  // It caused gain to be Inf when g is approaching 0 but not exactly 0 while
  // there is no regularization.
  // Calculate the best inequality split per node.
  void CalculateBestInequalitySplit(
      TTypes<float, 4>::ConstTensor stats_summary, const int32_t node_id,
      const int32_t feature_dims, const int32_t logits_dim,
      const int32_t hessian_dim, const int32_t num_buckets,
      const float min_node_weight, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    std::vector<Eigen::VectorXf> cum_grad;
    std::vector<Eigen::VectorXf> cum_hess;
    // get all cumulative gradients including default bucket.
    cum_grad.reserve(num_buckets);
    cum_hess.reserve(num_buckets);

    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      ConstVectorMap default_stats_vec(
          &stats_summary(node_id, f_dim, num_buckets, 0),
          logits_dim + hessian_dim);
      Eigen::VectorXf missing_bucket_grad = default_stats_vec.head(logits_dim);
      Eigen::VectorXf missing_bucket_hess = default_stats_vec.tail(hessian_dim);
      cum_grad.clear();
      cum_hess.clear();
      Eigen::VectorXf total_grad = Eigen::VectorXf::Zero(logits_dim);
      Eigen::VectorXf total_hess = Eigen::VectorXf::Zero(hessian_dim);
      // sum all the gradients including default bucket.
      for (int bucket = 0; bucket <= num_buckets; ++bucket) {
        for (int i = 0; i < logits_dim; ++i) {
          total_grad[i] += stats_summary(node_id, f_dim, bucket, i);
        }
        for (int i = 0; i < hessian_dim; ++i) {
          // Full hessian.
          total_hess[i] +=
              stats_summary(node_id, f_dim, bucket, logits_dim + i);
        }
        if (bucket < num_buckets) {
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
      }
      const string kInequalityDefaultLeft =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
      const string kInequalityDefaultRight =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);

      // Iterate from left to right, excluding default bucket.
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        // default value goes to left node.
        const Eigen::VectorXf total_left_grad =
            cum_grad[bucket] + missing_bucket_grad;
        const Eigen::VectorXf total_left_hess =
            cum_hess[bucket] + missing_bucket_hess;
        MaybeUpdateBestSplit(
            total_left_grad, total_grad - total_left_grad, total_left_hess,
            total_hess - total_left_hess, logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultLeft, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
        // default value goes to right node.
        MaybeUpdateBestSplit(
            cum_grad[bucket], total_grad - cum_grad[bucket], cum_hess[bucket],
            total_hess - cum_hess[bucket], logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultRight, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
      }  // for bucket
    }
  }

  // Calculate the best equality split per node.
  void CalculateBestEqualitySplit(
      TTypes<float, 4>::ConstTensor stats_summary,
      const Eigen::VectorXf& total_grad, const Eigen::VectorXf& total_hess,
      const int32_t node_id, const int32_t feature_dims,
      const int32_t logits_dim, const int32_t hessian_dim,
      const int32_t num_buckets, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    const string kEqualityDefaultRight =
        boosted_trees::SplitTypeWithDefault_Name(
            boosted_trees::EQUALITY_DEFAULT_RIGHT);
    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        ConstVectorMap stats_vec(&stats_summary(node_id, f_dim, bucket, 0),
                                 logits_dim + hessian_dim);
        Eigen::VectorXf curr_grad = stats_vec.head(logits_dim);
        Eigen::VectorXf curr_hess = stats_vec.tail(hessian_dim);
        MaybeUpdateBestSplit(curr_grad, total_grad - curr_grad, curr_hess,
                             total_hess - curr_hess, logits_dim, bucket, f_dim,
                             l1, l2, kEqualityDefaultRight, best_gain,
                             best_bucket, best_f_dim, best_split_type,
                             best_contrib_for_left, best_contrib_for_right);
      }
    }
  }

  void MaybeUpdateBestSplit(const Eigen::VectorXf& grad_for_left,
                            const Eigen::VectorXf& grad_for_right,
                            const Eigen::VectorXf& hess_for_left,
                            const Eigen::VectorXf& hess_for_right,
                            const int32_t logits_dim, const int32_t bucket,
                            const int32_t f_dim, const float l1, const float l2,
                            const string split_type, float* best_gain,
                            int32* best_bucket, int32* best_f_dim,
                            string* best_split_type,
                            Eigen::VectorXf* best_contrib_for_left,
                            Eigen::VectorXf* best_contrib_for_right) {
    // Left child.
    Eigen::VectorXf contrib_for_left(logits_dim);
    float gain_for_left;
    CalculateWeightsAndGains(grad_for_left, hess_for_left, l1, l2,
                             &contrib_for_left, &gain_for_left);
    Eigen::VectorXf contrib_for_right(logits_dim);
    float gain_for_right;
    CalculateWeightsAndGains(grad_for_right, hess_for_right, l1, l2,
                             &contrib_for_right, &gain_for_right);
    if (GainIsLarger(gain_for_left + gain_for_right, *best_gain)) {
      *best_gain = gain_for_left + gain_for_right;
      *best_bucket = bucket;
      *best_f_dim = f_dim;
      *best_contrib_for_left = contrib_for_left;
      *best_contrib_for_right = contrib_for_right;
      *best_split_type = split_type;
    }
  }

  int logits_dim_;
  string split_type_;
};

// Deprecated op. Use BoostedTreesCalculateBestFeatureSplitOpV2.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestFeatureSplit").Device(DEVICE_CPU),
    BoostedTreesCalculateBestFeatureSplitOp);

// V2 Op.
class BoostedTreesCalculateBestFeatureSplitV2 : public OpKernel {
 public:
  explicit BoostedTreesCalculateBestFeatureSplitV2(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    OP_REQUIRES(
        context, node_id_range_t->dims() == 1,
        errors::InvalidArgument("node_id_range must be a rank 1 tensor, but "
                                "given node_id_range has dims of ",
                                node_id_range_t->dims()));
    OP_REQUIRES(context, node_id_range_t->dim_size(0) == 2,
                errors::InvalidArgument(
                    "node_id_range must be a rank 1 tensor with shape=[2], but "
                    "given node_id_range has shape ",
                    node_id_range_t->dim_size(0), " on its first dim"));
    const int32_t node_id_first = node_id_range(0);  // Inclusive.
    const int32_t node_id_last = node_id_range(1);   // Exclusive.

    // Get stats_summaries_list.
    OpInputList stats_summaries_list;
    OP_REQUIRES_OK(context, context->input_list("stats_summaries_list",
                                                &stats_summaries_list));

    // Infer dimensions of a stats_summary.
    DCHECK_GT(stats_summaries_list.size(), 0);
    const int32_t feature_dims = stats_summaries_list[0].dim_size(1);
    // The last bucket is for default/missing value.
    const int32_t num_buckets = stats_summaries_list[0].dim_size(2) - 1;
    const int32_t logits_dim = logits_dim_;
    const int32_t hessian_dim =
        stats_summaries_list[0].dim_size(3) - logits_dim;
    DCHECK_GT(hessian_dim, 0);
    DCHECK_LE(hessian_dim, logits_dim * logits_dim);

    // Vector of stats_summaries; each element is stats for feature of shape
    // [max_splits, feature_dim, num_buckets, logits_dim + hessian_dim].
    std::vector<TTypes<float, 4>::ConstTensor> stats_summaries;
    DCHECK_EQ(stats_summaries_list.size(), num_features_);
    stats_summaries.reserve(num_features_);
    for (const auto& tensor : stats_summaries_list) {
      stats_summaries.emplace_back(tensor.tensor<float, 4>());
    }

    // Split types.
    const Tensor* split_types_t;
    OP_REQUIRES_OK(context, context->input("split_types", &split_types_t));
    const auto split_types = split_types_t->vec<tstring>();
    DCHECK_EQ(split_types.size(), num_features_);
    // Validate.
    for (int i = 0; i < num_features_; ++i) {
      if (!(split_types(i) == kInequalitySplit ||
            split_types(i) == kEqualitySplit)) {
        OP_REQUIRES_OK(
            context,
            errors::Aborted(
                "Operation received an exception: Incorrect split type"));
      }
    }
    // Feature ids.
    const Tensor* candidate_feature_ids_t;
    OP_REQUIRES_OK(context, context->input("candidate_feature_ids",
                                           &candidate_feature_ids_t));
    const auto candidate_feature_ids = candidate_feature_ids_t->vec<int32>();
    DCHECK_EQ(candidate_feature_ids.size(), num_features_);

    // L1, L2, tree_complexity, min_node_weight.
    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();
    DCHECK_GE(l1, 0);
    if (logits_dim_ > 1) {
      // Multi-class L1 regularization not supported yet.
      DCHECK_EQ(l1, 0);
    }
    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();
    DCHECK_GE(l2, 0);
    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();
    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_ids;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<Eigen::VectorXf> output_left_node_contribs;
    std::vector<Eigen::VectorXf> output_right_node_contribs;
    std::vector<string> output_split_types;

    // TODO(tanzheny) parallelize the computation.
    // Iterate each node and find the best gain per node.
    float parent_gain;
    for (int32_t node_id = node_id_first; node_id < node_id_last; ++node_id) {
      float best_gain = std::numeric_limits<float>::lowest();
      int32_t best_bucket;
      int32_t best_f_id;
      int32_t best_f_dim;
      string best_split_type;
      Eigen::VectorXf best_contrib_for_left(logits_dim);
      Eigen::VectorXf best_contrib_for_right(logits_dim);

      // Sum of gradient and hessian. Compute parent gain using first feature.
      ConstMatrixMap stats_mat(&stats_summaries[0](node_id, 0, 0, 0),
                               num_buckets + 1,  // Including default bucket.
                               logits_dim + hessian_dim);
      const Eigen::VectorXf total_grad =
          stats_mat.leftCols(logits_dim).colwise().sum();
      const Eigen::VectorXf total_hess =
          stats_mat.rightCols(hessian_dim).colwise().sum();
      if (total_hess.norm() < min_node_weight) {
        continue;
      }
      Eigen::VectorXf unused(logits_dim);
      CalculateWeightsAndGains(total_grad, total_hess, l1, l2, &unused,
                               &parent_gain);
      for (int f_idx = 0; f_idx < num_features_; ++f_idx) {
        const string split_type = split_types(f_idx);
        TTypes<float, 4>::ConstTensor stats_summary = stats_summaries[f_idx];
        float f_best_gain = std::numeric_limits<float>::lowest();
        int32_t f_best_bucket;
        int32_t f_best_f_dim;
        string f_best_split_type;
        Eigen::VectorXf f_best_contrib_for_left(logits_dim);
        Eigen::VectorXf f_best_contrib_for_right(logits_dim);

        if (split_type == kInequalitySplit) {
          CalculateBestInequalitySplit(
              stats_summary, node_id, feature_dims, logits_dim, hessian_dim,
              num_buckets, min_node_weight, l1, l2, &f_best_gain,
              &f_best_bucket, &f_best_f_dim, &f_best_split_type,
              &f_best_contrib_for_left, &f_best_contrib_for_right);
        } else {
          CalculateBestEqualitySplit(
              stats_summary, total_grad, total_hess, node_id, feature_dims,
              logits_dim, hessian_dim, num_buckets, l1, l2, &f_best_gain,
              &f_best_bucket, &f_best_f_dim, &f_best_split_type,
              &f_best_contrib_for_left, &f_best_contrib_for_right);
        }
        if (f_best_gain > best_gain) {
          best_gain = f_best_gain;
          best_f_id = candidate_feature_ids(f_idx);
          best_f_dim = f_best_f_dim;
          best_split_type = f_best_split_type;
          best_bucket = f_best_bucket;
          best_contrib_for_left = f_best_contrib_for_left;
          best_contrib_for_right = f_best_contrib_for_right;
        }
      }  // For feature id.
      if (best_gain == std::numeric_limits<float>::lowest()) {
        // Do not add the node if no split is found.
        continue;
      }
      output_node_ids.push_back(node_id);
      // Remove the parent gain for the parent node.
      output_gains.push_back(best_gain - parent_gain);
      output_feature_ids.push_back(best_f_id);
      output_feature_dimensions.push_back(best_f_dim);
      // Default direction is fixed for dense splits.
      // TODO(tanzheny) account for default values.
      output_split_types.push_back(best_split_type);
      output_thresholds.push_back(best_bucket);
      output_left_node_contribs.push_back(best_contrib_for_left);
      output_right_node_contribs.push_back(best_contrib_for_right);
    }  // for node id.
    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_ids
    Tensor* output_features_ids_t;
    OP_REQUIRES_OK(context, context->allocate_output("feature_ids", {num_nodes},
                                                     &output_features_ids_t));
    auto output_features_vec = output_features_ids_t->vec<int32>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "left_node_contribs", {num_nodes, logits_dim},
                                &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "right_node_contribs", {num_nodes, logits_dim},
                                &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      output_features_vec(i) = output_feature_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      for (int j = 0; j < logits_dim; ++j) {
        output_left_node_contribs_matrix(i, j) =
            output_left_node_contribs[i][j];
        output_right_node_contribs_matrix(i, j) =
            output_right_node_contribs[i][j];
      }
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 private:
  // TODO(crawles): Simplify inequality path just like equality b/138329196
  // Currently this is not simplify-able due to numerical instability in math
  // i.e. gain = -g.transpose() * hessian_and_reg.colPivHouseholderQr().solve(g)
  // It caused gain to be Inf when g is approaching 0 but not exactly 0 while
  // there is no regularization.
  // Calculate the best inequality split per node.
  void CalculateBestInequalitySplit(
      TTypes<float, 4>::ConstTensor stats_summary, const int32_t node_id,
      const int32_t feature_dims, const int32_t logits_dim,
      const int32_t hessian_dim, const int32_t num_buckets,
      const float min_node_weight, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    std::vector<Eigen::VectorXf> cum_grad;
    std::vector<Eigen::VectorXf> cum_hess;
    // get all cumulative gradients including default bucket.
    cum_grad.reserve(num_buckets);
    cum_hess.reserve(num_buckets);

    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      ConstVectorMap default_stats_vec(
          &stats_summary(node_id, f_dim, num_buckets, 0),
          logits_dim + hessian_dim);
      Eigen::VectorXf missing_bucket_grad = default_stats_vec.head(logits_dim);
      Eigen::VectorXf missing_bucket_hess = default_stats_vec.tail(hessian_dim);
      cum_grad.clear();
      cum_hess.clear();
      Eigen::VectorXf total_grad = Eigen::VectorXf::Zero(logits_dim);
      Eigen::VectorXf total_hess = Eigen::VectorXf::Zero(hessian_dim);
      // sum all the gradients including default bucket.
      for (int bucket = 0; bucket <= num_buckets; ++bucket) {
        for (int i = 0; i < logits_dim; ++i) {
          total_grad[i] += stats_summary(node_id, f_dim, bucket, i);
        }
        for (int i = 0; i < hessian_dim; ++i) {
          // Full hessian.
          total_hess[i] +=
              stats_summary(node_id, f_dim, bucket, logits_dim + i);
        }
        if (bucket < num_buckets) {
          cum_grad.push_back(total_grad);
          cum_hess.push_back(total_hess);
        }
      }
      const string kInequalityDefaultLeft =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
      const string kInequalityDefaultRight =
          boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);

      // Iterate from left to right, excluding default bucket.
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        // default value goes to left node.
        const Eigen::VectorXf total_left_grad =
            cum_grad[bucket] + missing_bucket_grad;
        const Eigen::VectorXf total_left_hess =
            cum_hess[bucket] + missing_bucket_hess;
        MaybeUpdateBestSplit(
            total_left_grad, total_grad - total_left_grad, total_left_hess,
            total_hess - total_left_hess, logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultLeft, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
        // default value goes to right node.
        MaybeUpdateBestSplit(
            cum_grad[bucket], total_grad - cum_grad[bucket], cum_hess[bucket],
            total_hess - cum_hess[bucket], logits_dim, bucket, f_dim, l1, l2,
            kInequalityDefaultRight, best_gain, best_bucket, best_f_dim,
            best_split_type, best_contrib_for_left, best_contrib_for_right);
      }  // for bucket
    }
  }

  // Calculate the best equality split per node.
  void CalculateBestEqualitySplit(
      TTypes<float, 4>::ConstTensor stats_summary,
      const Eigen::VectorXf& total_grad, const Eigen::VectorXf& total_hess,
      const int32_t node_id, const int32_t feature_dims,
      const int32_t logits_dim, const int32_t hessian_dim,
      const int32_t num_buckets, const float l1, const float l2,
      float* best_gain, int32* best_bucket, int32* best_f_dim,
      string* best_split_type, Eigen::VectorXf* best_contrib_for_left,
      Eigen::VectorXf* best_contrib_for_right) {
    const string kEqualityDefaultRight =
        boosted_trees::SplitTypeWithDefault_Name(
            boosted_trees::EQUALITY_DEFAULT_RIGHT);
    for (int f_dim = 0; f_dim < feature_dims; ++f_dim) {
      for (int bucket = 0; bucket < num_buckets; ++bucket) {
        ConstVectorMap stats_vec(&stats_summary(node_id, f_dim, bucket, 0),
                                 logits_dim + hessian_dim);
        Eigen::VectorXf curr_grad = stats_vec.head(logits_dim);
        Eigen::VectorXf curr_hess = stats_vec.tail(hessian_dim);
        MaybeUpdateBestSplit(curr_grad, total_grad - curr_grad, curr_hess,
                             total_hess - curr_hess, logits_dim, bucket, f_dim,
                             l1, l2, kEqualityDefaultRight, best_gain,
                             best_bucket, best_f_dim, best_split_type,
                             best_contrib_for_left, best_contrib_for_right);
      }
    }
  }

  void MaybeUpdateBestSplit(const Eigen::VectorXf& grad_for_left,
                            const Eigen::VectorXf& grad_for_right,
                            const Eigen::VectorXf& hess_for_left,
                            const Eigen::VectorXf& hess_for_right,
                            const int32_t logits_dim, const int32_t bucket,
                            const int32_t f_dim, const float l1, const float l2,
                            const string split_type, float* best_gain,
                            int32* best_bucket, int32* best_f_dim,
                            string* best_split_type,
                            Eigen::VectorXf* best_contrib_for_left,
                            Eigen::VectorXf* best_contrib_for_right) {
    // Left child.
    Eigen::VectorXf contrib_for_left(logits_dim);
    float gain_for_left;
    CalculateWeightsAndGains(grad_for_left, hess_for_left, l1, l2,
                             &contrib_for_left, &gain_for_left);
    Eigen::VectorXf contrib_for_right(logits_dim);
    float gain_for_right;
    CalculateWeightsAndGains(grad_for_right, hess_for_right, l1, l2,
                             &contrib_for_right, &gain_for_right);
    if (GainIsLarger(gain_for_left + gain_for_right, *best_gain)) {
      *best_gain = gain_for_left + gain_for_right;
      *best_bucket = bucket;
      *best_f_dim = f_dim;
      *best_contrib_for_left = contrib_for_left;
      *best_contrib_for_right = contrib_for_right;
      *best_split_type = split_type;
    }
  }
  int num_features_;
  int logits_dim_;
};

// v2 op that supports multi-class.
REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesCalculateBestFeatureSplitV2").Device(DEVICE_CPU),
    BoostedTreesCalculateBestFeatureSplitV2);

// Map from bucket id to vector of statistics.
typedef std::map<int32, std::vector<float>> BucketMap;
typedef BucketMap::iterator BucketMapIterator;
// Map from feature dimension to BucketMap.
typedef std::map<int32, BucketMap> FeatureMap;
typedef FeatureMap::iterator FeatureMapIterator;

class BoostedTreesSparseCalculateBestFeatureSplitOp : public OpKernel {
 public:
  explicit BoostedTreesSparseCalculateBestFeatureSplitOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    // TODO(crawles): Using logits_dim_ for multi-class split.
    OP_REQUIRES_OK(context, context->GetAttr("logits_dimension", &logits_dim_));
    // TODO(tanzheny): Using this for equality split.
    OP_REQUIRES_OK(context, context->GetAttr("split_type", &split_type_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive

    const Tensor* stats_summary_indices_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_indices",
                                           &stats_summary_indices_t));
    const auto stats_summary_indices = stats_summary_indices_t->matrix<int32>();
    const int32_t num_sparse_entries = stats_summary_indices_t->dim_size(0);

    const Tensor* stats_summary_values_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_values",
                                           &stats_summary_values_t));
    const auto stats_summary_values = stats_summary_values_t->vec<float>();

    const Tensor* stats_summary_shape_t;
    OP_REQUIRES_OK(
        context, context->input("stats_summary_shape", &stats_summary_shape_t));
    const auto stats_summary_shape = stats_summary_shape_t->vec<int32>();
    const int32_t num_buckets = stats_summary_shape(2) - 1;
    const int32_t stats_dims = stats_summary_shape(3);

    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();

    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();

    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();

    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<float> output_left_node_contribs;
    std::vector<float> output_right_node_contribs;
    std::vector<string> output_split_types;

    FeatureMap f_map;

    int32_t previous_node_id = -1;
    for (int idx = 0; idx < num_sparse_entries; ++idx) {
      int32_t node_id = stats_summary_indices(idx, 0);
      if (node_id != previous_node_id) {
        process_node(f_map, &output_node_ids, &output_gains,
                     &output_feature_dimensions, &output_thresholds,
                     &output_left_node_contribs, &output_right_node_contribs,
                     &output_split_types, previous_node_id, min_node_weight, l1,
                     l2, num_buckets);
        f_map.clear();
      }
      previous_node_id = node_id;
      DCHECK_LE(node_id_first, node_id);
      DCHECK_LT(node_id, node_id_last);
      const int32_t feature_dim = stats_summary_indices(idx, 1);
      const int32_t bucket_id = stats_summary_indices(idx, 2);
      const int32_t stat_dim = stats_summary_indices(idx, 3);
      OP_REQUIRES(context, stat_dim < stats_dims,
                  errors::InvalidArgument(
                      "Stat dim, the sum of logits dim and hessian dim in "
                      "stats_summary_indices, cannot be greater than stats "
                      "dims, the last value in stats_summary_shape, which was ",
                      stats_dims, ". At index (", idx,
                      ", 4), stats_summary_indices contains value ", stat_dim));
      std::pair<FeatureMapIterator, bool> const& f_insert_result = f_map.insert(
          FeatureMapIterator::value_type(feature_dim, BucketMap()));
      auto& b_map = f_insert_result.first->second;
      std::pair<BucketMapIterator, bool> const& b_insert_result =
          b_map.insert(BucketMapIterator::value_type(
              bucket_id, std::vector<float>(stats_dims)));
      auto& stats = b_insert_result.first->second;
      stats[stat_dim] = stats_summary_values(idx);
    }  // for node_id
    // process the last node id
    process_node(f_map, &output_node_ids, &output_gains,
                 &output_feature_dimensions, &output_thresholds,
                 &output_left_node_contribs, &output_right_node_contribs,
                 &output_split_types, previous_node_id, min_node_weight, l1, l2,
                 num_buckets);

    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("left_node_contribs", {num_nodes, 1},
                                          &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("right_node_contribs", {num_nodes, 1},
                                          &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      // TODO(crawles): change this for multi-class.
      output_left_node_contribs_matrix(i, 0) = output_left_node_contribs[i];
      output_right_node_contribs_matrix(i, 0) = output_right_node_contribs[i];
      output_split_types_vec(i) = output_split_types[i];
    }
  }

 protected:
  void process_node(const FeatureMap& f_map,
                    std::vector<int32>* output_node_ids,
                    std::vector<float>* output_gains,
                    std::vector<int32>* output_feature_dimensions,
                    std::vector<int32>* output_thresholds,
                    std::vector<float>* output_left_node_contribs,
                    std::vector<float>* output_right_node_contribs,
                    std::vector<string>* output_split_types,
                    const int32_t node_id, const float min_node_weight,
                    const float l1, const float l2, const int32_t num_buckets) {
    float parent_gain;
    Eigen::VectorXf unused(logits_dim_);
    Eigen::MatrixXf identity;
    identity.setIdentity(1, 1);

    // start processing for previous node id.
    float best_gain = std::numeric_limits<float>::lowest();
    float best_bucket = 0;
    float best_f_dim = 0;
    string best_split_type = boosted_trees::SplitTypeWithDefault_Name(
        boosted_trees::INEQUALITY_DEFAULT_LEFT);
    float best_contrib_for_left = 0.0;
    float best_contrib_for_right = 0.0;
    // the sum of gradients including default bucket.
    float total_grad = 0;
    // the sum of hessians including default bucket.
    float total_hess = 0;

    for (auto f_iter = f_map.begin(); f_iter != f_map.end(); ++f_iter) {
      const int32_t feature_dim = f_iter->first;
      const auto buckets_to_stats_map = f_iter->second;

      // The very last bucket contains stats for missing values.
      // TODO(crawles): use vector for multi-class.
      const float default_grad =
          (buckets_to_stats_map.find(num_buckets) == buckets_to_stats_map.end()
               ? 0
               : buckets_to_stats_map.at(num_buckets)[0]);
      const float default_hess =
          (buckets_to_stats_map.find(num_buckets) == buckets_to_stats_map.end()
               ? 0
               : buckets_to_stats_map.at(num_buckets)[1]);

      if (f_iter == f_map.begin()) {
        // first get the sum of grads, including default bucket.
        for (auto b_iter = buckets_to_stats_map.begin();
             b_iter != buckets_to_stats_map.end(); ++b_iter) {
          total_grad += b_iter->second[0];
          total_hess += b_iter->second[1];
        }
        if (total_hess < min_node_weight) {
          // Do not split the node because not enough avg hessian.
          break;
        }
        CalculateWeightsAndGains(total_grad * identity, total_hess * identity,
                                 l1, l2, &unused, &parent_gain);
      }

      float total_left_grad = 0;
      float total_left_hess = 0;
      for (auto b_iter = buckets_to_stats_map.begin();
           b_iter != buckets_to_stats_map.end(); ++b_iter) {
        const int32_t bucket_id = b_iter->first;
        // total_left_stats should exclude stats from default bucket.
        if (bucket_id == num_buckets) {
          break;
        }
        // TODO(crawles): vector for multi-class.
        total_left_grad += b_iter->second[0];
        total_left_hess += b_iter->second[1];
        // From left to right, default right.
        // Left child.
        Eigen::VectorXf contrib_for_left(1);
        float gain_for_left;
        CalculateWeightsAndGains(total_left_grad * identity,
                                 total_left_hess * identity, l1, l2,
                                 &contrib_for_left, &gain_for_left);
        // Right child.
        Eigen::VectorXf contrib_for_right(1);
        float gain_for_right;
        CalculateWeightsAndGains((total_grad - total_left_grad) * identity,
                                 (total_hess - total_left_hess) * identity, l1,
                                 l2, &contrib_for_right, &gain_for_right);
        if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
          best_gain = gain_for_left + gain_for_right;
          best_bucket = bucket_id;
          best_f_dim = feature_dim;
          best_split_type = boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_RIGHT);
          best_contrib_for_left = contrib_for_left[0];
          best_contrib_for_right = contrib_for_right[0];
        }

        // From right to left, default left.
        CalculateWeightsAndGains((total_left_grad + default_grad) * identity,
                                 (total_left_hess + default_hess) * identity,
                                 l1, l2, &contrib_for_left, &gain_for_left);
        CalculateWeightsAndGains(
            (total_grad - default_grad - total_left_grad) * identity,
            (total_hess - default_hess - total_left_hess) * identity, l1, l2,
            &contrib_for_right, &gain_for_right);
        if (GainIsLarger(gain_for_left + gain_for_right, best_gain)) {
          best_gain = gain_for_left + gain_for_right;
          best_bucket = bucket_id;
          best_f_dim = feature_dim;
          best_split_type = boosted_trees::SplitTypeWithDefault_Name(
              boosted_trees::INEQUALITY_DEFAULT_LEFT);
          best_contrib_for_left = contrib_for_left[0];
          best_contrib_for_right = contrib_for_right[0];
        }
      }  // for bucket_id
    }    // for feature_dim
    if (best_gain != std::numeric_limits<float>::lowest()) {
      output_node_ids->push_back(node_id);
      // Remove the parent gain.
      output_gains->push_back(best_gain - parent_gain);
      output_feature_dimensions->push_back(best_f_dim);
      output_split_types->push_back(best_split_type);
      output_thresholds->push_back(best_bucket);
      output_left_node_contribs->push_back(best_contrib_for_left);
      output_right_node_contribs->push_back(best_contrib_for_right);
    }
  }

 private:
  int logits_dim_;
  string split_type_;
};

REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesSparseCalculateBestFeatureSplit").Device(DEVICE_CPU),
    BoostedTreesSparseCalculateBestFeatureSplitOp);

class BoostedTreesMakeStatsSummaryOp : public OpKernel {
 public:
  explicit BoostedTreesMakeStatsSummaryOp(OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
    OP_REQUIRES_OK(context, context->GetAttr("num_features", &num_features_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();
    // gradients
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();
    // hessians
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();
    // bucketized_features
    OpInputList bucketized_features_list;
    OP_REQUIRES_OK(context, context->input_list("bucketized_features_list",
                                                &bucketized_features_list));
    // Infer batch size.
    const int64_t batch_size = node_ids_t->dim_size(0);

    // Allocate temporary stats tensor (Rank 4).
    Tensor temp_stats_double_t;
    OP_REQUIRES_OK(context, context->allocate_temp(
                                DT_DOUBLE,
                                {num_features_, max_splits_, num_buckets_, 2},
                                &temp_stats_double_t));
    auto temp_stats_double = temp_stats_double_t.tensor<double, 4>();
    temp_stats_double.setZero();

    // Partition by node, and then bucketize.
    for (int feature_idx = 0; feature_idx < num_features_; ++feature_idx) {
      const auto& features = bucketized_features_list[feature_idx].vec<int32>();
      for (int i = 0; i < batch_size; ++i) {
        const int32_t node = node_ids(i);
        const int32_t bucket = features(i);
        temp_stats_double(feature_idx, node, bucket, 0) += gradients(i, 0);
        temp_stats_double(feature_idx, node, bucket, 1) += hessians(i, 0);
      }
    }

    // Copy temp tensor over to output tensor.
    Tensor* output_stats_summary_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "stats_summary", temp_stats_double_t.shape(),
                                &output_stats_summary_t));
    output_stats_summary_t->tensor<float, 4>() =
        temp_stats_double.template cast<float>();
  }

 private:
  int max_splits_;
  int num_buckets_;
  int num_features_;
};

REGISTER_KERNEL_BUILDER(Name("BoostedTreesMakeStatsSummary").Device(DEVICE_CPU),
                        BoostedTreesMakeStatsSummaryOp);

// TODO(tanzheny): Add an option of default value into the API interface.
class BoostedTreesAggregateStatsOp : public OpKernel {
 public:
  explicit BoostedTreesAggregateStatsOp(OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids.
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();

    // gradients.
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();

    // hessians.
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();

    // feature.
    const Tensor* feature_t;
    OP_REQUIRES_OK(context, context->input("feature", &feature_t));
    const auto feature = feature_t->matrix<int32>();

    // Infer batch size, feature dimension and stats dimension.
    const int64_t batch_size = node_ids_t->dim_size(0);
    const int64_t logits_dims = gradients_t->dim_size(1);
    const int64_t hessians_dims = hessians_t->dim_size(1);
    const int64_t stats_dims = logits_dims + hessians_dims;
    const int64_t feature_dims = feature_t->dim_size(1);

    // Allocate temporary stats tensor (Rank 4), upcasting to double.
    // A default bucket is added to the end for missing/default values.
    Tensor temp_stats_double_t;
    OP_REQUIRES_OK(
        context, context->allocate_temp(
                     DT_DOUBLE,
                     {max_splits_, feature_dims, num_buckets_ + 1, stats_dims},
                     &temp_stats_double_t));
    auto temp_stats_double = temp_stats_double_t.tensor<double, 4>();
    temp_stats_double.setZero();

    for (int i = 0; i < batch_size; ++i) {
      const int32_t node = node_ids(i);
      for (int feature_dim = 0; feature_dim < feature_dims; ++feature_dim) {
        const int32_t feature_value = feature(i, feature_dim);
        const int32_t bucket =
            (feature_value == -1) ? num_buckets_ : feature_value;
        for (int stat_dim = 0; stat_dim < logits_dims; ++stat_dim) {
          temp_stats_double(node, feature_dim, bucket, stat_dim) +=
              gradients(i, stat_dim);
        }
        for (int stat_dim = logits_dims; stat_dim < stats_dims; ++stat_dim) {
          temp_stats_double(node, feature_dim, bucket, stat_dim) +=
              hessians(i, stat_dim - logits_dims);
        }
      }
    }

    // Copy temp tensor over to output tensor, downcasting to float.
    Tensor* output_stats_summary_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "stats_summary", temp_stats_double_t.shape(),
                                &output_stats_summary_t));
    output_stats_summary_t->tensor<float, 4>() =
        temp_stats_double.template cast<float>();
  }

 private:
  int max_splits_;
  int num_buckets_;
};

REGISTER_KERNEL_BUILDER(Name("BoostedTreesAggregateStats").Device(DEVICE_CPU),
                        BoostedTreesAggregateStatsOp);

// Key based on node id, feature dimension and bucket id.
struct StatsPartitionKey {
  StatsPartitionKey(const int32_t node_id, const int32_t feature_dim,
                    const int32_t bucket_id)
      : node_id(node_id), feature_dim(feature_dim), bucket_id(bucket_id) {}

  bool operator==(const StatsPartitionKey& other) const {
    return (node_id == other.node_id) && (feature_dim == other.feature_dim) &&
           (bucket_id == other.bucket_id);
  }

  // Compare for StatsPartitionKey.
  struct Less {
    bool operator()(const StatsPartitionKey& a,
                    const StatsPartitionKey& b) const {
      if (a.node_id < b.node_id) {
        return true;
      }
      if ((a.node_id == b.node_id) && (a.feature_dim < b.feature_dim)) {
        return true;
      }
      if ((a.node_id == b.node_id) && (a.feature_dim == b.feature_dim) &&
          (a.bucket_id < b.bucket_id)) {
        return true;
      }
      return false;
    }
  };

  // Tree node id.
  int32 node_id;
  // Dimension within feature column.
  int32 feature_dim;
  // bucketized feature value .
  int32 bucket_id;
};

typedef std::map<StatsPartitionKey, std::vector<float>, StatsPartitionKey::Less>
    StatsPartitionMap;
typedef StatsPartitionMap::iterator StatsPartitionIterator;

// Key based on instance and feature dimension.
struct InstanceFeatureDimKey {
  InstanceFeatureDimKey() : instance(-1), feature_dim(-1) {}

  InstanceFeatureDimKey(const int32_t instance, const int32_t feature_dim)
      : instance(instance), feature_dim(feature_dim) {}

  bool operator==(const InstanceFeatureDimKey& other) const {
    return (instance == other.instance) && (feature_dim == other.feature_dim);
  }

  // Compare for InstanceFeatureDimKey.
  struct Less {
    bool operator()(const InstanceFeatureDimKey& a,
                    const InstanceFeatureDimKey& b) const {
      if (a.instance < b.instance) {
        return true;
      }
      if ((a.instance == b.instance) && (a.feature_dim < b.feature_dim)) {
        return true;
      }
      return false;
    }
  };

  // Instance id within a batch.
  int32 instance;
  // Dimension within feature column.
  int32 feature_dim;
};

// Add statistics to StatsPartitionMap for (instance, feature dim, bucket id).
static void AddInstanceStatsToMap(
    const int32_t instance, const int32_t feature_dim, const int32_t bucket_id,
    const int32_t logits_dims, const int32_t stats_dims,
    StatsPartitionMap* stats_map, const TTypes<float>::ConstMatrix& gradients,
    const TTypes<float>::ConstMatrix& hessians,
    const TTypes<int32>::ConstVec& node_ids) {
  const int32_t node_id = node_ids(instance);
  const auto key = StatsPartitionKey(node_id, feature_dim, bucket_id);
  std::pair<StatsPartitionIterator, bool> const& insert_result =
      stats_map->insert(StatsPartitionIterator::value_type(
          key, std::vector<float>(stats_dims, 0.0f)));
  auto& stats = insert_result.first->second;
  for (int stat_dim = 0; stat_dim < logits_dims; ++stat_dim) {
    stats[stat_dim] += gradients(instance, stat_dim);
  }
  for (int stat_dim = logits_dims; stat_dim < stats_dims; ++stat_dim) {
    stats[stat_dim] += hessians(instance, stat_dim - logits_dims);
  }
}

// Add statistics to StatsPartitionMap for bucket_id ranging from
// (start_instance, start_feature_dim) to (end_instance, end_feature_dim),
// inclusive on start and end instances, exclusive on end feature dim.
static void AddRangeStats(const int start_instance, const int end_instance,
                          const int start_feature_dim,
                          const int end_feature_dim,
                          StatsPartitionMap* stats_map,
                          const TTypes<float>::ConstMatrix& gradients,
                          const TTypes<float>::ConstMatrix& hessians,
                          const TTypes<int32>::ConstVec& node_ids,
                          const int32_t feature_dims, const int32_t bucket_id,
                          const int32_t logits_dims, const int32_t stats_dims) {
  DCHECK_LE(start_instance, end_instance);
  if (start_instance == end_instance) {
    DCHECK_LT(start_feature_dim, end_feature_dim);
  }
  for (int32_t instance = start_instance; instance <= end_instance;
       ++instance) {
    const int32_t start_f_dim =
        (instance == start_instance) ? start_feature_dim + 1 : 0;
    const int32_t end_f_dim =
        (instance == end_instance) ? end_feature_dim : feature_dims;
    for (int32_t f_dim = start_f_dim; f_dim < end_f_dim; ++f_dim) {
      AddInstanceStatsToMap(instance, f_dim, bucket_id, logits_dims, stats_dims,
                            stats_map, gradients, hessians, node_ids);
    }
  }
}

class BoostedTreesSparseAggregateStatsOp : public OpKernel {
 public:
  explicit BoostedTreesSparseAggregateStatsOp(
      OpKernelConstruction* const context)
      : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("max_splits", &max_splits_));
    OP_REQUIRES_OK(context, context->GetAttr("num_buckets", &num_buckets_));
  }

  void Compute(OpKernelContext* const context) override {
    // node_ids.
    const Tensor* node_ids_t;
    OP_REQUIRES_OK(context, context->input("node_ids", &node_ids_t));
    const auto node_ids = node_ids_t->vec<int32>();

    // gradients.
    const Tensor* gradients_t;
    OP_REQUIRES_OK(context, context->input("gradients", &gradients_t));
    const auto gradients = gradients_t->matrix<float>();

    // hessians.
    const Tensor* hessians_t;
    OP_REQUIRES_OK(context, context->input("hessians", &hessians_t));
    const auto hessians = hessians_t->matrix<float>();

    // feature indices.
    const Tensor* feature_indices_t;
    OP_REQUIRES_OK(context,
                   context->input("feature_indices", &feature_indices_t));
    const auto feature_indices = feature_indices_t->matrix<int32>();

    // feature values.
    const Tensor* feature_values_t;
    OP_REQUIRES_OK(context,
                   context->input("feature_values", &feature_values_t));
    const auto feature_values = feature_values_t->vec<int32>();

    // feature shape.
    const Tensor* feature_shape_t;
    OP_REQUIRES_OK(context, context->input("feature_shape", &feature_shape_t));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(feature_shape_t->shape()),
                errors::InvalidArgument(
                    "Input shapes should be a vector but received shapes ",
                    feature_shape_t->shape().DebugString()));
    const auto feature_shape = feature_shape_t->vec<int32>();

    const int64_t batch_size = gradients_t->dim_size(0);
    const int64_t logits_dims = gradients_t->dim_size(1);
    const int64_t hessians_dims = hessians_t->dim_size(1);
    const int64_t stats_dims = logits_dims + hessians_dims;
    const int64_t num_sparse_entries = feature_indices_t->dim_size(0);
    const int32_t feature_dims = feature_shape(1);
    DCHECK_LE(num_sparse_entries, batch_size * feature_dims);

    // Aggregate statistics info to map.
    StatsPartitionMap stats_map;

    int prev_instance = 0;
    int prev_f_dim = -1;

    for (int i = 0; i < num_sparse_entries; ++i) {
      // the instance number within a batch
      const int32_t instance = feature_indices(i, 0);
      DCHECK_LE(instance, batch_size);
      DCHECK_GE(instance, prev_instance);
      // the node id within a tree.
      const int32_t node_id = node_ids(instance);
      DCHECK_LE(node_id, max_splits_);
      // the feature dimension.
      const int32_t f_dim = feature_indices(i, 1);
      DCHECK_LE(f_dim, feature_dims);
      // the bucket id of the value.
      const int32_t bucket_id = feature_values(i);
      DCHECK_LE(bucket_id, num_buckets_);

      // Add statistics for the missing entries into default bucket.
      // The last bucket is default bucket.
      const int missing_entry_bucket = num_buckets_;
      AddRangeStats(prev_instance, instance, prev_f_dim, f_dim, &stats_map,
                    gradients, hessians, node_ids, feature_dims,
                    missing_entry_bucket, logits_dims, stats_dims);
      prev_instance = instance;
      prev_f_dim = f_dim;
      // Add statistics for the non-missing entry into
      // (cur_instance, cur_f_dim, bucket_id).
      AddInstanceStatsToMap(instance, f_dim, bucket_id, logits_dims, stats_dims,
                            &stats_map, gradients, hessians, node_ids);
    }
    AddRangeStats(prev_instance, batch_size - 1, prev_f_dim, feature_dims,
                  &stats_map, gradients, hessians, node_ids, feature_dims,
                  num_buckets_, logits_dims, stats_dims);

    // Serialize statistics info map to tensor output.
    const int64_t num_slots = stats_map.size() * stats_dims;
    Tensor* summary_indices_t = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output("stats_summary_indices",
                                            TensorShape({num_slots, 4}),
                                            &summary_indices_t));
    auto summary_indices = summary_indices_t->matrix<int32>();
    Tensor* summary_values_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("stats_summary_values",
                                                     TensorShape({num_slots}),
                                                     &summary_values_t));
    auto summary_values = summary_values_t->vec<float>();
    int entry_index = 0;
    for (auto& iter : stats_map) {
      for (int stat_dim = 0; stat_dim < stats_dims; ++stat_dim) {
        summary_indices(entry_index, 0) = iter.first.node_id;
        summary_indices(entry_index, 1) = iter.first.feature_dim;
        summary_indices(entry_index, 2) = iter.first.bucket_id;
        summary_indices(entry_index, 3) = stat_dim;
        summary_values(entry_index) = iter.second[stat_dim];
        ++entry_index;
      }
    }

    Tensor* summary_shape_t = nullptr;
    OP_REQUIRES_OK(
        context, context->allocate_output("stats_summary_shape",
                                          TensorShape({4}), &summary_shape_t));
    auto summary_shape = summary_shape_t->vec<int32>();
    summary_shape(0) = max_splits_;
    summary_shape(1) = feature_dims;
    summary_shape(2) = num_buckets_ + 1;
    summary_shape(3) = stats_dims;
  }

 private:
  int max_splits_;
  int num_buckets_;
};

REGISTER_KERNEL_BUILDER(
    Name("BoostedTreesSparseAggregateStats").Device(DEVICE_CPU),
    BoostedTreesSparseAggregateStatsOp);

}  // namespace tensorflow