diff --git a/tensorflow/core/kernels/scan_ops.h b/tensorflow/core/kernels/scan_ops.h
index 1fd98f6656d..8afcac86c3f 100644
--- a/tensorflow/core/kernels/scan_ops.h
+++ b/tensorflow/core/kernels/scan_ops.h
@@ -24,6 +24,7 @@ namespace functor {
 
 typedef Eigen::Index Index;
 
+// TODO(b/154339590): Needs to be vectorized.
 template <typename Device, typename Reducer, typename T>
 struct Scan {
   void operator()(const Device& d, typename TTypes<T, 3>::ConstTensor in,
@@ -44,18 +45,33 @@ template <typename T>
 struct LogSumExp {
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& a,
                                                      const T& b) const {
-    Eigen::internal::scalar_sum_op<T> sum_op;
-    Eigen::internal::scalar_exp_op<T> exp_op;
-    Eigen::internal::scalar_log_op<T> log_op;
-    Eigen::internal::scalar_max_op<T> max_op;
-    Eigen::internal::scalar_min_op<T> min_op;
-    Eigen::internal::scalar_log1p_op<T> log1p_op;
-    Eigen::internal::scalar_difference_op<T> diff_op;
+    auto mi = Eigen::internal::scalar_min_op<T>()(a, b);
+    auto ma = Eigen::internal::scalar_max_op<T>()(a, b);
 
-    auto mi = min_op(a, b);
-    auto ma = max_op(a, b);
+    auto sub = Eigen::internal::scalar_difference_op<T>();
+    auto add = Eigen::internal::scalar_sum_op<T>();
+    auto exp = Eigen::internal::scalar_exp_op<T>();
+    auto log1p = Eigen::internal::scalar_log1p_op<T>();
+    auto cmp_lt =
+        Eigen::internal::scalar_cmp_op<T, T, Eigen::internal::cmp_LT>();
 
-    return sum_op(log1p_op(exp_op(diff_op(mi, ma))), ma);
+    auto logsumexp = add(log1p(exp(sub(mi, ma))), ma);
+    return cmp_lt(ma, Eigen::NumTraits<T>::lowest()) ? ma : logsumexp;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const T& a,
+                                                   const T& b) const {
+    auto mi = Eigen::internal::pmin(a, b);
+    auto ma = Eigen::internal::pmax(a, b);
+    using Eigen::internal::padd;
+    using Eigen::internal::pcmp_lt;
+    using Eigen::internal::pexp;
+    using Eigen::internal::plog1p;
+    using Eigen::internal::pset1;
+    using Eigen::internal::psub;
+
+    auto logsumexp = padd(plog1p(pexp(psub(mi, ma))), ma);
+    return pselect(pcmp_lt(ma, pset1(Eigen::NumTraits<T>::lowest())), ma,
+                   logsumexp);
   }
 };
 
@@ -66,13 +82,58 @@ struct LogSumExpReducer {
     *accum = logsumexp(*accum, t);
   }
 
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p,
+                                                          Packet* accum) const {
+    LogSumExp<T> logsumexp;
+    *accum = logsumexp.packetOp(*accum, p);
+  }
+
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const {
-    return Eigen::NumTraits<T>::lowest();
+    return -Eigen::NumTraits<T>::infinity();
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const {
+    return Eigen::internal::pset1(initialize());
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const {
     return accum;
   }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
+  finalizePacket(const Packet& vaccum) const {
+    return vaccum;
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T
+  finalizeBoth(const T saccum, const Packet& vaccum) const {
+    auto max_reducer = Eigen::internal::MaxReducer<T>();
+    auto sum_reducer = Eigen::internal::SumReducer<T>();
+    auto exp = Eigen::internal::scalar_exp_op<T>();
+    auto cmp_lt =
+        Eigen::internal::scalar_cmp_op<T, T, Eigen::internal::cmp_LT>();
+    auto log = Eigen::internal::scalar_log_op<T>();
+    auto add = Eigen::internal::scalar_sum_op<T>();
+
+    using Eigen::internal::pexp;
+    using Eigen::internal::psub;
+
+    // `ma = max(x1, ..., xn)`
+    // If the max of all of the `xi` is `-infinity` then the result is
+    // -infinity. If the max is larger than `-infinity` then it's safe to use
+    // for normalization even if the other elements are `-infinity`.
+    //
+    // `logsumexp(x1, ..., xn) = ma + log (exp(x1 - ma) + ... + exp(xn - ma))`
+    auto ma = max_reducer.finalizeBoth(saccum, vaccum);
+    auto logsumexp = add(log(sum_reducer.finalizeBoth(
+                             exp(saccum - ma), pexp(psub(vaccum, pset1(ma))))),
+                         ma);
+    return cmp_lt(ma, Eigen::NumTraits<T>::lowest()) ? initialize() : logsumexp;
+  }
 };
 
 }  // namespace functor
diff --git a/tensorflow/python/kernel_tests/cumulative_logsumexp_test.py b/tensorflow/python/kernel_tests/cumulative_logsumexp_test.py
index aae624f6605..2b0309f26c4 100644
--- a/tensorflow/python/kernel_tests/cumulative_logsumexp_test.py
+++ b/tensorflow/python/kernel_tests/cumulative_logsumexp_test.py
@@ -55,6 +55,11 @@ class CumulativeLogsumexpTest(test.TestCase):
               reverse=reverse, exclusive=exclusive,
               axis=axis)
 
+  def testMinusInfinity(self):
+    x = np.log([0., 0., 1., 1., 1., 1., 0., 0.])
+    self._testLogSumExpAllArgs(x, use_gpu=False)
+    self._testLogSumExpAllArgs(x, use_gpu=True)
+
   def test1D(self):
     x = np.arange(10) / 10.0 - 0.5
     self._testLogSumExpAllArgs(x, use_gpu=False)