diff --git a/mlir/lib/ExecutionEngine/SparseUtils.cpp b/mlir/lib/ExecutionEngine/SparseUtils.cpp
--- a/mlir/lib/ExecutionEngine/SparseUtils.cpp
+++ b/mlir/lib/ExecutionEngine/SparseUtils.cpp
@@ -151,12 +151,12 @@
 /// each differently annotated sparse tensor, this method provides a convenient
 /// "one-size-fits-all" solution that simply takes an input tensor and
 /// annotations to implement all required setup in a general manner.
-template <typename P, typename I, typename V, typename Ve>
+template <typename P, typename I, typename V>
 class SparseTensorStorage : public SparseTensorStorageBase {
 public:
   /// Constructs sparse tensor storage scheme following the given
   /// per-rank dimension dense/sparse annotations.
-  SparseTensorStorage(SparseTensor<Ve> *tensor, uint8_t *sparsity)
+  SparseTensorStorage(SparseTensor<V> *tensor, uint8_t *sparsity)
       : sizes(tensor->getSizes()), pointers(getRank()), indices(getRank()) {
     // Provide hints on capacity.
     // TODO: needs fine-tuning based on sparsity
@@ -191,14 +191,23 @@
   }
   void getValues(std::vector<V> **out) override { *out = &values; }
 
+  // Factory method.
+  static SparseTensorStorage<P, I, V> *newSparseTensor(SparseTensor<V> *t,
+                                                       uint8_t *s) {
+    t->sort(); // sort lexicographically
+    SparseTensorStorage<P, I, V> *n = new SparseTensorStorage<P, I, V>(t, s);
+    delete t;
+    return n;
+  }
+
 private:
   /// Initializes sparse tensor storage scheme from a memory-resident
   /// representation of an external sparse tensor. This method prepares
   /// the pointers and indices arrays under the given per-rank dimension
   /// dense/sparse annotations.
-  void traverse(SparseTensor<Ve> *tensor, uint8_t *sparsity, uint64_t lo,
+  void traverse(SparseTensor<V> *tensor, uint8_t *sparsity, uint64_t lo,
                 uint64_t hi, uint64_t d) {
-    const std::vector<Element<Ve>> &elements = tensor->getElements();
+    const std::vector<Element<V>> &elements = tensor->getElements();
     // Once dimensions are exhausted, insert the numerical values.
     if (d == getRank()) {
       values.push_back(lo < hi ? elements[lo].value : 0);
@@ -321,9 +330,9 @@
 }
 
 /// Reads a sparse tensor with the given filename into a memory-resident
-/// sparse tensor in coordinate scheme. The external formats always store
-/// the numerical values with the type double.
-static SparseTensor<double> *openTensor(char *filename, uint64_t *perm) {
+/// sparse tensor in coordinate scheme.
+template <typename V>
+static SparseTensor<V> *openTensor(char *filename, uint64_t *perm) {
   // Open the file.
   FILE *file = fopen(filename, "r");
   if (!file) {
@@ -347,7 +356,7 @@
   std::vector<uint64_t> indices(rank);
   for (uint64_t r = 0; r < rank; r++)
     indices[perm[r]] = idata[2 + r];
-  SparseTensor<double> *tensor = new SparseTensor<double>(indices, nnz);
+  SparseTensor<V> *tensor = new SparseTensor<V>(indices, nnz);
   // Read all nonzero elements.
   for (uint64_t k = 0; k < nnz; k++) {
     uint64_t idx = -1;
@@ -359,6 +368,8 @@
       // Add 0-based index.
       indices[perm[r]] = idx - 1;
     }
+    // The external formats always store the numerical values with the type
+    // double, but we cast these values to the sparse tensor object type.
     double value;
     if (fscanf(file, "%lg\n", &value) != 1) {
       fprintf(stderr, "Cannot find next value in %s\n", filename);
@@ -366,21 +377,8 @@
     }
     tensor->add(indices, value);
   }
-  // Close the file and return sorted tensor.
+  // Close the file and return tensor.
   fclose(file);
-  tensor->sort(); // sort lexicographically
-  return tensor;
-}
-
-/// Templated reader.
-template <typename P, typename I, typename V>
-void *newSparseTensor(char *filename, uint8_t *sparsity, uint64_t *perm,
-                      uint64_t size) {
-  SparseTensor<double> *t = openTensor(filename, perm);
-  assert(size == t->getRank()); // sparsity array must match rank
-  SparseTensorStorageBase *tensor =
-      new SparseTensorStorage<P, I, V, double>(t, sparsity);
-  delete t;
   return tensor;
 }
 
@@ -419,8 +417,11 @@
   }
 
 #define CASE(p, i, v, P, I, V)                                                 \
-  if (ptrTp == (p) && indTp == (i) && valTp == (v))                            \
-  return newSparseTensor<P, I, V>(filename, sparsity, perm, asize)
+  if (ptrTp == (p) && indTp == (i) && valTp == (v)) {                          \
+    SparseTensor<V> *tensor = openTensor<V>(filename, perm);                   \
+    assert(asize == tensor->getRank());                                        \
+    return SparseTensorStorage<P, I, V>::newSparseTensor(tensor, sparsity);    \
+  }
 
 #define IMPL1(RET, NAME, TYPE, LIB)                                            \
   RET NAME(void *tensor) {                                                     \