diff --git a/src/operator/tensor/control_flow_op.h b/src/operator/tensor/control_flow_op.h
index 07252963c874..9d0e8cf90817 100644
--- a/src/operator/tensor/control_flow_op.h
+++ b/src/operator/tensor/control_flow_op.h
@@ -46,7 +46,7 @@ struct where {
   // DType is the output data type
   // CType is condition data type
   template<typename DType, typename CType>
-  MSHADOW_XINLINE static void Map(int i, DType* out, const CType* cond,
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const CType* cond,
                                   const DType* x, const DType* y) {
     KERNEL_ASSIGN(out[i], req, (0 != cond[i]? x[i] : y[i]));
   }
@@ -64,7 +64,7 @@ struct where_csr {
   // CType is condition data type
   // i is for i-th row in the output
   template<typename DType, typename CType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, DType* out, const IType* cond_idx,
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const IType* cond_idx,
                                   const IType* cond_indptr, const CType* cond_data,
                                   const nnvm::dim_t num_cols, const DType* x) {
     using nnvm::dim_t;
@@ -92,8 +92,8 @@ struct where_batch {
   // DType is the output data type
   // CType is the condition data type
   template<typename DType, typename CType>
-  MSHADOW_XINLINE static void Map(int i, DType* out, const CType* cond,
-                                  const DType* x, const DType* y, int M) {
+  MSHADOW_XINLINE static void Map(index_t i, DType* out, const CType* cond,
+                                  const DType* x, const DType* y, index_t M) {
     KERNEL_ASSIGN(out[i], req, (0 != cond[i/M]? x[i] : y[i]));
   }
 };
@@ -109,7 +109,7 @@ struct where_backward {
   // DType is the output data type
   // CType is condition data type
   template<typename DType, typename CType>
-  MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+  MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
                                   const DType* grad_in,
                                   const CType* cond) {
     KERNEL_ASSIGN(grad_out[i], req,
@@ -130,7 +130,7 @@ struct where_backward_csr {
   // CType is condition data type
   // IType is condition aux data type
   template<typename DType, typename CType, typename IType>
-  MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+  MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
                                   const DType* grad_in,
                                   const CType* cond_data,
                                   const IType* cond_idx,
@@ -161,9 +161,9 @@ struct where_batch_backward {
   // DType is the output data type
   // CType is condition data type
   template<typename DType, typename CType>
-  MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+  MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
                                   const DType* grad_in,
-                                  const CType* cond, int M) {
+                                  const CType* cond, index_t M) {
     KERNEL_ASSIGN(grad_out[i], req,
       ((0 == cond[i/M])^negate)? grad_in[i] : static_cast<DType>(0));
   }
diff --git a/tests/nightly/test_large_array.py b/tests/nightly/test_large_array.py
index a301362f2db7..696fdb1d4175 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -134,6 +134,17 @@ def test_Dense(ctx=mx.cpu(0)):
     res.wait_to_read()
     assert res.shape == (50000000, 100)
 
+def test_where():
+    a = nd.ones(shape=(LARGE_X, SMALL_Y))
+    b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
+    b = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y))
+    res = nd.where(b > 100, a, b)
+    assert np.sum(res[-1].asnumpy() == 1) == b.shape[1]
+
+    csr_cond = nd.sparse.cast_storage(b < 10, 'csr')
+    res = nd.sparse.where(csr_cond, a, b)
+    assert np.sum(res[0].asnumpy() == 1) == b.shape[1]
+
 
 if __name__ == '__main__':
     import nose