diff --git a/src/operator/random/sample_op.cc b/src/operator/random/sample_op.cc
index 8d87d2b99d14..363163cbc697 100644
--- a/src/operator/random/sample_op.cc
+++ b/src/operator/random/sample_op.cc
@@ -61,7 +61,8 @@ Example::
                                                  [ 0.54488319,  0.84725171]]
 
 )code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", SampleUniform_<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", SampleUniform_<cpu>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", SampleUniformEx_<cpu>);
 
 // Add "normal" alias for backward compatibility
 MXNET_OPERATOR_REGISTER_SAMPLE(random_normal, SampleNormalParam)
@@ -78,7 +79,8 @@ Example::
    random_normal(loc=0, scale=1, shape=(2,2)) = [[ 1.89171135, -1.16881478],
                                                  [-1.23474145,  1.55807114]]
 )code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", SampleNormal_<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", SampleNormal_<cpu>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", SampleNormalEx_<cpu>);
 
 MXNET_OPERATOR_REGISTER_SAMPLE(random_gamma, SampleGammaParam)
 .add_alias("_sample_gamma")
@@ -91,7 +93,8 @@ Example::
    random_gamma(alpha=9, beta=0.5, shape=(2,2)) = [[ 7.10486984,  3.37695289],
                                                    [ 3.91697288,  3.65933681]]
 )code" ADD_FILELINE)
-.set_attr<FCompute>("FCompute<cpu>", SampleGamma_<cpu>);
+.set_attr<FCompute>("FCompute<cpu>", SampleGamma_<cpu>)
+.set_attr<FComputeEx>("FComputeEx<cpu>", SampleGammaEx_<cpu>);
 
 MXNET_OPERATOR_REGISTER_SAMPLE(random_exponential, SampleExponentialParam)
 .add_alias("_sample_exponential")
diff --git a/src/operator/random/sample_op.h b/src/operator/random/sample_op.h
index a1a6a2345b1b..0cd3f6bc2efb 100644
--- a/src/operator/random/sample_op.h
+++ b/src/operator/random/sample_op.h
@@ -232,29 +232,75 @@ struct SampleGenNegBinomialParam : public dmlc::Parameter<SampleGenNegBinomialPa
   }
 };
 
+using FSampleCompute = std::function<void (const nnvm::NodeAttrs& attrs,
+                                           const OpContext& ctx,
+                                           const OpReqType& req,
+                                           TBlob* outputs)>;
+
 template<typename xpu>
-void SampleUniform_(const nnvm::NodeAttrs& attrs,
-                    const OpContext& ctx,
-                    const std::vector<TBlob>& inputs,
-                    const std::vector<OpReqType>& req,
-                    const std::vector<TBlob>& outputs) {
+void SampleComputeEx_(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<NDArray>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<NDArray>& outputs,
+                      FSampleCompute fcomp) {
+  NDArray output = outputs[0];
+  mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
+  if (output.storage_type() == kRowSparseStorage) {
+    // indices
+    nnvm::dim_t nnr = output.shape()[0];
+    output.CheckAndAlloc({mshadow::Shape1(nnr)});
+    PopulateFullIdxRspImpl(s, &output);
+    // data
+    TBlob out_blob = output.data();
+    fcomp(attrs, ctx, req[0], &out_blob);
+  } else {
+    LOG(FATAL) << "Unexpected storage type for SampleComputeEx_: "
+               << output.storage_type();
+  }
+}
+
+template<typename xpu>
+void SampleUniformDnsImpl(const nnvm::NodeAttrs& attrs,
+                          const OpContext& ctx,
+                          const OpReqType& req,
+                          TBlob* output) {
   using namespace mxnet::op;
   using namespace mshadow::expr;
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
   const SampleUniformParam& param = nnvm::get<SampleUniformParam>(attrs.parsed);
-  MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+  MSHADOW_REAL_TYPE_SWITCH(output->type_flag_, DType, {
     mshadow::Random<xpu, DType> *prnd = ctx.requested[0].get_random<xpu, DType>(s);
-    mshadow::Tensor<xpu, 2, DType> out = outputs[0].FlatTo2D<xpu, DType>(s);
+    mshadow::Tensor<xpu, 2, DType> out = output->FlatTo2D<xpu, DType>(s);
     prnd->SampleUniform(&out, param.low, param.high);
   });
 }
 
 template<typename xpu>
-void SampleNormal_(const nnvm::NodeAttrs& attrs,
-                   const OpContext& ctx,
-                   const std::vector<TBlob>& inputs,
-                   const std::vector<OpReqType>& req,
-                   const std::vector<TBlob>& outputs) {
+void SampleUniform_(const nnvm::NodeAttrs& attrs,
+                    const OpContext& ctx,
+                    const std::vector<TBlob>& inputs,
+                    const std::vector<OpReqType>& req,
+                    const std::vector<TBlob>& outputs) {
+  TBlob out = outputs[0];
+  SampleUniformDnsImpl<xpu>(attrs, ctx, req[0], &out);
+}
+
+
+template<typename xpu>
+void SampleUniformEx_(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<NDArray>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<NDArray>& outputs) {
+  SampleComputeEx_<xpu>(attrs, ctx, inputs, req, outputs, SampleUniformDnsImpl<xpu>);
+}
+
+template<typename xpu>
+void SampleNormalDnsImpl(const nnvm::NodeAttrs& attrs,
+                         const OpContext& ctx,
+                         const OpReqType& req,
+                         TBlob* outputs) {
   using namespace mxnet::op;
   using namespace mshadow::expr;
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
@@ -268,11 +314,29 @@ void SampleNormal_(const nnvm::NodeAttrs& attrs,
 }
 
 template<typename xpu>
-void SampleGamma_(const nnvm::NodeAttrs& attrs,
+void SampleNormal_(const nnvm::NodeAttrs& attrs,
                    const OpContext& ctx,
                    const std::vector<TBlob>& inputs,
                    const std::vector<OpReqType>& req,
                    const std::vector<TBlob>& outputs) {
+  TBlob out = outputs[0];
+  SampleNormalDnsImpl<xpu>(attrs, ctx, req[0], &out);
+}
+
+template<typename xpu>
+void SampleNormalEx_(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<NDArray>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<NDArray>& outputs) {
+  SampleComputeEx_<xpu>(attrs, ctx, inputs, req, outputs, SampleNormalDnsImpl<xpu>);
+}
+
+template<typename xpu>
+void SampleGammaDnsImpl(const nnvm::NodeAttrs& attrs,
+                        const OpContext& ctx,
+                        const OpReqType& req,
+                        TBlob* outputs) {
   using namespace mxnet::op;
   using namespace mshadow::expr;
   mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
@@ -286,6 +350,25 @@ void SampleGamma_(const nnvm::NodeAttrs& attrs,
   });
 }
 
+template<typename xpu>
+void SampleGamma_(const nnvm::NodeAttrs& attrs,
+                  const OpContext& ctx,
+                  const std::vector<TBlob>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<TBlob>& outputs) {
+  TBlob out = outputs[0];
+  SampleGammaDnsImpl<xpu>(attrs, ctx, req[0], &out);
+}
+
+template<typename xpu>
+void SampleGammaEx_(const nnvm::NodeAttrs& attrs,
+                   const OpContext& ctx,
+                   const std::vector<NDArray>& inputs,
+                   const std::vector<OpReqType>& req,
+                   const std::vector<NDArray>& outputs) {
+  SampleComputeEx_<xpu>(attrs, ctx, inputs, req, outputs, SampleGammaDnsImpl<xpu>);
+}
+
 template<typename xpu>
 void SampleExponential_(const nnvm::NodeAttrs& attrs,
                    const OpContext& ctx,
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index 1b244251fca1..1ac933ddaef5 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -167,6 +167,26 @@ inline void FillDnsZerosRspImpl(mshadow::Stream<xpu> *s, NDArray *dst) {
   });
 }
 
+struct PopulateFullIdxRspKernel {
+  template<typename IType>
+  MSHADOW_XINLINE static void Map(int i, IType* out) {
+    KERNEL_ASSIGN(out[i], kWriteTo, i);
+  }
+};
+
+// Fill full indices NDArray with zeros by updating the aux shape.
+template<typename xpu>
+void PopulateFullIdxRspImpl(mshadow::Stream<xpu> *s, NDArray *dst) {
+  using namespace rowsparse;
+  CHECK_EQ(dst->storage_type(), kRowSparseStorage);
+  nnvm::dim_t nnr = dst->shape()[0];
+  dst->CheckAndAllocAuxData(kIdx, mshadow::Shape1(nnr));
+  MSHADOW_IDX_TYPE_SWITCH(dst->aux_type(kIdx), IType, {
+    IType* idx = dst->aux_data(kIdx).dptr<IType>();
+    mxnet_op::Kernel<PopulateFullIdxRspKernel, xpu>::Launch(s, nnr, idx);
+  });
+}
+
 // Fill a rsp NDArray with zeros by updating the aux shape.
 template<typename xpu>
 void FillZerosRspImpl(mshadow::Stream<xpu> *s, NDArray *dst) {
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index 18872d9cb52d..aecec1df9b84 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -351,6 +351,20 @@ def test_sparse_nd_output_fallback():
     mx.nd.random_normal(shape=shape, out=out)
     assert(np.sum(out.asnumpy()) != 0)
 
+def test_sparse_nd_random():
+    shape = (100, 100)
+    fns = [mx.nd.random_uniform, mx.nd.random_normal, mx.nd.random_gamma]
+    for fn in fns:
+        rsp_out = mx.nd.zeros(shape=shape, stype='row_sparse')
+        dns_out = mx.nd.zeros(shape=shape, stype='default')
+        mx.random.seed(0)
+        np.random.seed(0)
+        fn(shape=shape, out=dns_out)
+        mx.random.seed(0)
+        np.random.seed(0)
+        fn(shape=shape, out=rsp_out)
+        assert_almost_equal(dns_out.asnumpy(), rsp_out.asnumpy())
+
 
 def test_sparse_nd_astype():
     stypes = ['row_sparse', 'csr']