diff --git a/dmlc-core b/dmlc-core
index c39001019..9fd3b4846 160000
--- a/dmlc-core
+++ b/dmlc-core
@@ -1 +1 @@
-Subproject commit c39001019e443c7a061789bd1180f58ce85fc3e6
+Subproject commit 9fd3b48462a7a651e12a197679f71e043dcb25a2
diff --git a/doc/parameter.md b/doc/parameter.md
index 0c1c989d3..a549c25c9 100644
--- a/doc/parameter.md
+++ b/doc/parameter.md
@@ -138,6 +138,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
   - "ndcg@n","map@n": n can be assigned as an integer to cut off the top positions in the lists for evaluation.
   - "ndcg-","map-","ndcg@n-","map@n-": In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding "-" in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions.
 training repeatively
+  - "gamma-deviance": [residual deviance for gamma regression]
 * seed [ default=0 ]
  - random number seed.
 
diff --git a/src/metric/elementwise_metric.cc b/src/metric/elementwise_metric.cc
index db2869efb..6e087ba26 100644
--- a/src/metric/elementwise_metric.cc
+++ b/src/metric/elementwise_metric.cc
@@ -135,6 +135,34 @@ struct EvalPoissionNegLogLik : public EvalEWiseBase<EvalPoissionNegLogLik> {
   }
 };
 
+struct EvalGammaDeviance : public EvalEWiseBase<EvalGammaDeviance> {
+  const char *Name() const override {
+    return "gamma-deviance";
+  }
+  inline float EvalRow(float label, float pred) const {
+    float epsilon = 1.0e-9;
+    float tmp = label / (pred + epsilon);
+    return tmp - std::log(tmp) - 1;
+  }
+  inline static float GetFinal(float esum, float wsum) {
+    return 2 * esum;
+  }
+};
+
+struct EvalGammaNLogLik: public EvalEWiseBase<EvalGammaNLogLik> {
+  const char *Name() const override {
+    return "gamma-nloglik";
+  }
+  inline float EvalRow(float y, float py) const {
+    float psi = 1.0;
+    float theta = -1. / py;
+    float a = psi;
+    float b = -std::log(-theta);
+    float c = 1. / psi * std::log(y/psi) - std::log(y) - common::LogGamma(1. / psi);
+    return -((y * theta - b) / a + c);
+  }
+};
+
 XGBOOST_REGISTER_METRIC(RMSE, "rmse")
 .describe("Rooted mean square error.")
 .set_body([](const char* param) { return new EvalRMSE(); });
@@ -155,87 +183,13 @@ XGBOOST_REGISTER_METRIC(PossionNegLoglik, "poisson-nloglik")
 .describe("Negative loglikelihood for poisson regression.")
 .set_body([](const char* param) { return new EvalPoissionNegLogLik(); });
 
-/*!
- * \brief base class of element-wise evaluation
- *      with additonal dispersion parameter
- * \tparam Derived the name of subclass
- */
-template<typename Derived>
-struct EvalEWiseBase2 : public Metric {
-  float Eval(const std::vector<float>& preds,
-             const MetaInfo& info,
-             bool distributed) const override {
-    CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
-    CHECK_EQ(preds.size(), info.labels.size())
-        << "label and prediction size not match, "
-        << "hint: use merror or mlogloss for multi-class classification";
-    const omp_ulong ndata = static_cast<omp_ulong>(info.labels.size());
+XGBOOST_REGISTER_METRIC(GammaDeviance, "gamma-deviance")
+.describe("Residual deviance for gamma regression.")
+.set_body([](const char* param) { return new EvalGammaDeviance(); });
 
-    // Computer dispersion
-    double sum = 0.0, wsum = 0.0;
-    #pragma omp parallel for schedule(static)
-    for (omp_ulong i = 0; i < ndata; ++i) {
-      const float wt = info.GetWeight(i);
-      sum += static_cast<const Derived*>(this)->EvalDispersion(info.labels[i], preds[i]) * wt;
-      wsum += wt;
-    }
-    double dat[2]; dat[0] = sum, dat[1] = wsum;
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    double dispersion = dat[0] / (dat[1] - info.num_col);
-
-    // Computer metric
-    sum = 0.0, wsum = 0.0;
-    #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
-    for (omp_ulong i = 0; i < ndata; ++i) {
-      const float wt = info.GetWeight(i);
-      sum += static_cast<const Derived*>(this)->EvalRow(info.labels[i], preds[i], dispersion) * wt;
-      wsum += wt;
-    }
-    dat[0] = sum, dat[1] = wsum;
-    if (distributed) {
-      rabit::Allreduce<rabit::op::Sum>(dat, 2);
-    }
-    return Derived::GetFinal(dat[0], dat[1]);
-  }
-  /*!
-   * \brief to be implemented by subclass,
-   *   get evaluation result from one row
-   * \param label label of current instance
-   * \param pred prediction value of current instance
-   */
-  inline float EvalRow(float label, float pred, float dispersion) const;
-  /*!
-   * \brief to be overridden by subclass, final transformation
-   * \param esum the sum statistics returned by EvalRow
-   * \param wsum sum of weight
-   */
-  inline static float GetFinal(float esum, float wsum) {
-    return esum / wsum;
-  }
-  inline float EvalDispersion(float label, float pred) const;
-};
-
-struct EvalGammaNegLogLik : public EvalEWiseBase2<EvalGammaNegLogLik> {
-  const char *Name() const override {
-    return "gamma-nloglik";
-  }
-  inline float EvalRow(float y, float py, float psi) const {
-    double theta = -1. / py;
-    double a = psi;
-    double b = -std::log(-theta);
-    double c = 1. / psi * std::log(y/psi) - std::log(y) - common::LogGamma(1. / psi);
-    return -((y * theta - b) / a + c);
-  }
-  inline float EvalDispersion(float y, float py) const {
-    return ((y - py) * (y - py)) / (py * py);
-  }
-};
-
-XGBOOST_REGISTER_METRIC(GammaNegLoglik, "gamma-nloglik")
-.describe("Negative loglikelihood for gamma regression.")
-.set_body([](const char* param) { return new EvalGammaNegLogLik(); });
+XGBOOST_REGISTER_METRIC(GammaNLogLik, "gamma-nloglik")
+.describe("Negative log-likelihood for gamma regression.")
+.set_body([](const char* param) { return new EvalGammaNLogLik(); });
 
 }  // namespace metric
 }  // namespace xgboost