diff --git a/src/operator/tensor/la_op-inl.h b/src/operator/tensor/la_op-inl.h
index c795c2a3f375..de27187bca9a 100644
--- a/src/operator/tensor/la_op-inl.h
+++ b/src/operator/tensor/la_op-inl.h
@@ -510,25 +510,6 @@ struct det {
   }
 };
 
-// logdet = log(det(A))
-struct logdet {
-  template<typename xpu, typename DType>
-  static void op(const Tensor<xpu, 3, DType>& A, const Tensor<xpu, 1, DType>& logdet,
-                 const Tensor<xpu, 3, DType>& LU, const Tensor<xpu, 2, int>& pivot,
-                 const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
-    Stream<xpu> *s = ctx.get_stream<xpu>();
-    Tensor<xpu, 1, DType> sign = ctx.requested[0]
-      .get_space_typed<xpu, 1, DType>(logdet.shape_, s);
-    Copy(LU, A, s);
-    linalg_batch_getrf(LU, pivot, false, s);
-    using namespace mxnet_op;
-    using namespace mshadow::expr;
-    Kernel<SignedLogDet, xpu>::Launch(s, pivot.size(0), pivot.size(1), pivot.dptr_,
-                                      LU.dptr_, sign.dptr_, logdet.dptr_);
-    const_cast<Tensor<xpu, 1, DType>&>(logdet) = F<mshadow_op::log>(sign) + logdet;
-  }
-};
-
 // sign = sign(det(A))
 // logabsdet = log(abs(det(A)))
 struct slogdet {
@@ -941,33 +922,6 @@ struct det_backward {
   }
 };
 
-// Backward of logdet(A) is derived from Jacobi's formula.
-// The closed form solution is pretty easy when A is invertible.
-// For non-invertible A, grad is not backwarded now.
-// TODO(arcadiaphy) add implementation for non-invertible case
-struct logdet_backward {
-  template<typename xpu, typename DType>
-  static void op(const Tensor<xpu, 1, DType>& dlogdet,
-                 const Tensor<xpu, 1, DType>& logdet,
-                 const Tensor<xpu, 3, DType>& LU,
-                 const Tensor<xpu, 2, int>& pivot,
-                 const Tensor<xpu, 3, DType>& dA,
-                 const OpContext& ctx, const nnvm::NodeAttrs& attrs) {
-    using namespace mshadow;
-    using namespace mshadow::expr;
-    using namespace mxnet_op;
-    // compute inverse(A) and stores it to LU
-    linalg_batch_det_backward_helper(LU, pivot, logdet, dA, DType(-INFINITY), ctx);
-    const_cast<Tensor<xpu, 3, DType>&>(dA) = broadcast_to(reshape(dlogdet, \
-      Shape3(logdet.size(0), 1, 1)), mxnet::TShape(LU.shape_)) * \
-      transpose(LU, Shape3(0, 2, 1));
-    Stream<xpu> *s = ctx.get_stream<xpu>();
-    // stop grad for zero det temporarily
-    Kernel<StopZeroDetGrad, xpu>::Launch(s, dA.shape_.Size(), dA.size(1) * dA.size(2), \
-                                         dA.dptr_, logdet.dptr_, DType(-INFINITY));
-  }
-};
-
 // Backward of slogdet(A) is derived from Jacobi's formula.
 // The closed form solution is pretty easy when A is invertible.
 // For non-invertible A, grad is not backwarded now.
diff --git a/src/operator/tensor/la_op.cc b/src/operator/tensor/la_op.cc
index 2c322846c42e..dade5692800d 100644
--- a/src/operator/tensor/la_op.cc
+++ b/src/operator/tensor/la_op.cc
@@ -986,54 +986,6 @@ NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<nnvm::TIsBackward>("TIsBackward", true)
 .set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, det_backward>);
 
-NNVM_REGISTER_OP(_linalg_logdet)
-.add_alias("linalg_logdet")
-.describe(R"code(Compute the log determinant of a matrix.
-Input is a tensor *A* of dimension *n >= 2*.
-
-If *n=2*, *A* is a square matrix. We compute:
-
-  *out* = *log(det(A))*
-
-If *n>2*, *logdet* is performed separately on the trailing two dimensions
-for all inputs (batch mode).
-
-.. note:: The operator supports float32 and float64 data types only.
-
-Examples::
-
-   Single matrix inversion
-   A = [[2., 3.], [1., 4.]]
-   logdet(A) = [1.609438]
-
-   Batch matrix inversion
-   A = [[[2., 3.], [1., 4.]],
-        [[1., 2.], [2., 4.]],
-        [[1., 2.], [4., 3.]]]
-   logdet(A) = [1.609438, -inf, nan]
-)code" ADD_FILELINE)
-.set_num_inputs(1)
-.set_num_outputs(3)
-.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs)
-  { return std::vector<std::string>{"A"}; })
-.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs", [](const NodeAttrs& attrs) {
-  return 1; })
-.set_attr<mxnet::FInferShape>("FInferShape", DetShape<1>)
-.set_attr<nnvm::FInferType>("FInferType", DetType<1>)
-.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
-  { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
-.set_attr<FCompute>("FCompute<cpu>", LaOpDetForward<cpu, 1, logdet>)
-.set_attr<nnvm::FGradient>("FGradient", ReduceDetGrad<1>{"_backward_linalg_logdet"})
-.add_argument("A", "NDArray-or-Symbol", "Tensor of square matrix");
-
-NNVM_REGISTER_OP(_backward_linalg_logdet)
-.set_num_inputs(4)
-.set_num_outputs(1)
-.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs)
-  { return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; })
-.set_attr<nnvm::TIsBackward>("TIsBackward", true)
-.set_attr<FCompute>("FCompute<cpu>", LaOpDetBackward<cpu, 1, logdet_backward>);
-
 NNVM_REGISTER_OP(_linalg_slogdet)
 .add_alias("linalg_slogdet")
 .describe(R"code(Compute the sign and log of the determinant of a matrix.
diff --git a/src/operator/tensor/la_op.cu b/src/operator/tensor/la_op.cu
index 74fc97b75d6b..68c33180e3d5 100644
--- a/src/operator/tensor/la_op.cu
+++ b/src/operator/tensor/la_op.cu
@@ -105,12 +105,6 @@ NNVM_REGISTER_OP(_linalg_det)
 NNVM_REGISTER_OP(_backward_linalg_det)
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, det_backward>);
 
-NNVM_REGISTER_OP(_linalg_logdet)
-.set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 1, logdet>);
-
-NNVM_REGISTER_OP(_backward_linalg_logdet)
-.set_attr<FCompute>("FCompute<gpu>", LaOpDetBackward<gpu, 1, logdet_backward>);
-
 NNVM_REGISTER_OP(_linalg_slogdet)
 .set_attr<FCompute>("FCompute<gpu>", LaOpDetForward<gpu, 2, slogdet>);
 
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 764f0e59a6d7..3c59524b4fee 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -6536,11 +6536,6 @@ def test_laop_6():
     test_det = mx.sym.linalg.det(data)
     check_fw(test_det, [a], [r])
     check_grad(test_det, [a])
-    # logdet
-    r = np.log(np.linalg.det(a))
-    test_logdet = mx.sym.linalg.logdet(data)
-    check_fw(test_logdet, [a], [r])
-    check_grad(test_logdet, [a])
     # test slogdet
     r1 = np.array([1., 1., 1.])
     r2 = np.log(np.abs(np.linalg.det(a)))