[mlir][sparse] Restyling macros in the runtime library

[wrengr]

Summary

In addition to reducing code repetition, this also helps ensure that the various API functions follow the naming convention of mlir::sparse_tensor::primaryTypeFunctionSuffix (e.g., due to typos in the repetitious code).

Depends On D125428

[wrengr]

(rebasing for dependencies having rerun git-clang-format)

[aartbik]

I like this a lot! You will have to rebase for my complex change (sorry), but good to submit without review after that.

[wrengr]

rebasing for D125596

[wrengr]

rerunning git-clang-format

mlir/lib/ExecutionEngine/SparseTensorUtils.cpp

 private:
   const std::vector<uint64_t> sizes; // per-dimension sizes
   std::vector<Element<V>> elements;  // all COO elements
   std::vector<uint64_t> indices;     // shared index pool
   bool iteratorLocked = false;
   unsigned iteratorPos = 0;
 };
 
+// See <https://en.wikipedia.org/wiki/X_Macro>
+#define FOREVERY_V(DO)                                                         \
+  DO(F64, double)                                                              \
+  DO(F32, float)                                                               \
+  DO(I64, int64_t)                                                             \
+  DO(I32, int32_t)                                                             \
+  DO(I16, int16_t)                                                             \
+  DO(I8, int8_t)
+
 // Forward.
 template <typename V>
 class SparseTensorEnumeratorBase;
 
 /// Abstract base class for `SparseTensorStorage<P,I,V>`.  This class
 /// takes responsibility for all the `<P,I,V>`-independent aspects
 /// of the tensor (e.g., shape, sparsity, permutation).  In addition,
 /// we use function overloading to implement "partial" method
public:
 
   /// Safely check if the (storage-order) dimension uses compressed storage.
   bool isCompressedDim(uint64_t d) const {
     assert(d < getRank());
     return (dimTypes[d] == DimLevelType::kCompressed);
   }
 
   /// Allocate a new enumerator.
-  virtual void newEnumerator(SparseTensorEnumeratorBase<double> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumf64");
-  }
-  virtual void newEnumerator(SparseTensorEnumeratorBase<float> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumf32");
-  }
-  virtual void newEnumerator(SparseTensorEnumeratorBase<int64_t> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumi64");
-  }
-  virtual void newEnumerator(SparseTensorEnumeratorBase<int32_t> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumi32");
-  }
-  virtual void newEnumerator(SparseTensorEnumeratorBase<int16_t> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumi16");
-  }
-  virtual void newEnumerator(SparseTensorEnumeratorBase<int8_t> **, uint64_t,
-                             const uint64_t *) const {
-    fatal("enumi8");
+#define DECL_NEWENUMERATOR(VNAME, V)                                           \
+  virtual void newEnumerator(SparseTensorEnumeratorBase<V> **, uint64_t,       \
+                             const uint64_t *) const {                         \
+    fatal("newEnumerator" #VNAME);                                             \
   }
+FOREVERY_V(DECL_NEWENUMERATOR)
+#undef DECL_NEWENUMERATOR
 
   /// Overhead storage.
   virtual void getPointers(std::vector<uint64_t> **, uint64_t) { fatal("p64"); }
   virtual void getPointers(std::vector<uint32_t> **, uint64_t) { fatal("p32"); }
   virtual void getPointers(std::vector<uint16_t> **, uint64_t) { fatal("p16"); }
   virtual void getPointers(std::vector<uint8_t> **, uint64_t) { fatal("p8"); }
   virtual void getIndices(std::vector<uint64_t> **, uint64_t) { fatal("i64"); }
   virtual void getIndices(std::vector<uint32_t> **, uint64_t) { fatal("i32"); }
   virtual void getIndices(std::vector<uint16_t> **, uint64_t) { fatal("i16"); }
   virtual void getIndices(std::vector<uint8_t> **, uint64_t) { fatal("i8"); }
 
   /// Primary storage.
-  virtual void getValues(std::vector<double> **) { fatal("valf64"); }
-  virtual void getValues(std::vector<float> **) { fatal("valf32"); }
-  virtual void getValues(std::vector<int64_t> **) { fatal("vali64"); }
-  virtual void getValues(std::vector<int32_t> **) { fatal("vali32"); }
-  virtual void getValues(std::vector<int16_t> **) { fatal("vali16"); }
-  virtual void getValues(std::vector<int8_t> **) { fatal("vali8"); }
+#define DECL_GETVALUES(VNAME, V)                                               \
+  virtual void getValues(std::vector<V> **) { fatal("getValues" #VNAME); }
+FOREVERY_V(DECL_GETVALUES)
+#undef DECL_GETVALUES
 
   /// Element-wise insertion in lexicographic index order.
-  virtual void lexInsert(const uint64_t *, double) { fatal("insf64"); }
-  virtual void lexInsert(const uint64_t *, float) { fatal("insf32"); }
-  virtual void lexInsert(const uint64_t *, int64_t) { fatal("insi64"); }
-  virtual void lexInsert(const uint64_t *, int32_t) { fatal("insi32"); }
-  virtual void lexInsert(const uint64_t *, int16_t) { fatal("ins16"); }
-  virtual void lexInsert(const uint64_t *, int8_t) { fatal("insi8"); }
+#define DECL_LEXINSERT(VNAME, V)                                               \
+  virtual void lexInsert(const uint64_t *, V) { fatal("lexInsert" #VNAME); }
+FOREVERY_V(DECL_LEXINSERT)
+#undef DECL_LEXINSERT
 
   /// Expanded insertion.
-  virtual void expInsert(uint64_t *, double *, bool *, uint64_t *, uint64_t) {
-    fatal("expf64");
-  }
-  virtual void expInsert(uint64_t *, float *, bool *, uint64_t *, uint64_t) {
-    fatal("expf32");
-  }
-  virtual void expInsert(uint64_t *, int64_t *, bool *, uint64_t *, uint64_t) {
-    fatal("expi64");
-  }
-  virtual void expInsert(uint64_t *, int32_t *, bool *, uint64_t *, uint64_t) {
-    fatal("expi32");
-  }
-  virtual void expInsert(uint64_t *, int16_t *, bool *, uint64_t *, uint64_t) {
-    fatal("expi16");
-  }
-  virtual void expInsert(uint64_t *, int8_t *, bool *, uint64_t *, uint64_t) {
-    fatal("expi8");
+#define DECL_EXPINSERT(VNAME, V)                                               \
+  virtual void expInsert(uint64_t *, V *, bool *, uint64_t *, uint64_t) {      \
+    fatal("expInsert" #VNAME);                                                 \
   }
+FOREVERY_V(DECL_EXPINSERT)
+#undef DECL_EXPINSERT
 
   /// Finishes insertion.
   virtual void endInsert() = 0;
 
 protected:
   // Since this class is virtual, we must disallow public copying in
   // order to avoid "slicing".  Since this class has data members,
   // that means making copying protected.
if (action == Action::kToIterator) {                                       \
       coo->startIterator();                                                    \
     } else {                                                                   \
       assert(action == Action::kToCOO);                                        \
     }                                                                          \
     return coo;                                                                \
   }
 
 #define CASE_SECSAME(p, v, P, V) CASE(p, p, v, P, P, V)
+// TODO(wrengr): move `_mlir_ciface_newSparseTensor` closer to these
+// macro definitions, but as a separate change so as not to muddy the diff.
 
-#define IMPL_SPARSEVALUES(NAME, TYPE, LIB)                                     \
-  void _mlir_ciface_##NAME(StridedMemRefType<TYPE, 1> *ref, void *tensor) {    \
+/// Methods that provide direct access to values.
+#define IMPL_SPARSEVALUES(VNAME, V)                                            \
+  void _mlir_ciface_sparseValues##VNAME(StridedMemRefType<V, 1> *ref,          \
+                                        void *tensor) {                        \
     assert(ref &&tensor);                                                      \
-    std::vector<TYPE> *v;                                                      \
-    static_cast<SparseTensorStorageBase *>(tensor)->LIB(&v);                   \
+    std::vector<V> *v;                                                         \
+    static_cast<SparseTensorStorageBase *>(tensor)->getValues(&v);             \
     ref->basePtr = ref->data = v->data();                                      \
     ref->offset = 0;                                                           \
     ref->sizes[0] = v->size();                                                 \
     ref->strides[0] = 1;                                                       \
   }
+FOREVERY_V(IMPL_SPARSEVALUES)
+#undef IMPL_SPARSEVALUES
 
 #define IMPL_GETOVERHEAD(NAME, TYPE, LIB)                                      \
   void _mlir_ciface_##NAME(StridedMemRefType<TYPE, 1> *ref, void *tensor,      \
                            index_type d) {                                     \
     assert(ref &&tensor);                                                      \
     std::vector<TYPE> *v;                                                      \
     static_cast<SparseTensorStorageBase *>(tensor)->LIB(&v, d);                \
     ref->basePtr = ref->data = v->data();                                      \
     ref->offset = 0;                                                           \
     ref->sizes[0] = v->size();                                                 \
     ref->strides[0] = 1;                                                       \
   }
+/// Methods that provide direct access to pointers.
+IMPL_GETOVERHEAD(sparsePointers, index_type, getPointers)
+IMPL_GETOVERHEAD(sparsePointers64, uint64_t, getPointers)
+IMPL_GETOVERHEAD(sparsePointers32, uint32_t, getPointers)
+IMPL_GETOVERHEAD(sparsePointers16, uint16_t, getPointers)
+IMPL_GETOVERHEAD(sparsePointers8, uint8_t, getPointers)
 
-#define IMPL_ADDELT(NAME, TYPE)                                                \
-  void *_mlir_ciface_##NAME(void *tensor, TYPE value,                          \
-                            StridedMemRefType<index_type, 1> *iref,            \
-                            StridedMemRefType<index_type, 1> *pref) {          \
-    assert(tensor &&iref &&pref);                                              \
+/// Methods that provide direct access to indices.
+IMPL_GETOVERHEAD(sparseIndices, index_type, getIndices)
+IMPL_GETOVERHEAD(sparseIndices64, uint64_t, getIndices)
+IMPL_GETOVERHEAD(sparseIndices32, uint32_t, getIndices)
+IMPL_GETOVERHEAD(sparseIndices16, uint16_t, getIndices)
+IMPL_GETOVERHEAD(sparseIndices8, uint8_t, getIndices)
+#undef IMPL_GETOVERHEAD
+
+/// Helper to add value to coordinate scheme, one per value type.
+#define IMPL_ADDELT(VNAME, V)                                                  \
+  void *_mlir_ciface_addElt##VNAME(void *coo, V value,                         \
+                                   StridedMemRefType<index_type, 1> *iref,     \
+                                   StridedMemRefType<index_type, 1> *pref) {   \
+    assert(coo &&iref &&pref);                                                 \
     assert(iref->strides[0] == 1 && pref->strides[0] == 1);                    \
     assert(iref->sizes[0] == pref->sizes[0]);                                  \
     const index_type *indx = iref->data + iref->offset;                        \
     const index_type *perm = pref->data + pref->offset;                        \
     uint64_t isize = iref->sizes[0];                                           \
     std::vector<index_type> indices(isize);                                    \
     for (uint64_t r = 0; r < isize; r++)                                       \
       indices[perm[r]] = indx[r];                                              \
-    static_cast<SparseTensorCOO<TYPE> *>(tensor)->add(indices, value);         \
-    return tensor;                                                             \
+    static_cast<SparseTensorCOO<V> *>(coo)->add(indices, value);               \
+    return coo;                                                                \
   }
+FOREVERY_V(IMPL_ADDELT)
+#undef IMPL_ADDELT
 
-#define IMPL_GETNEXT(NAME, V)                                                  \
-  bool _mlir_ciface_##NAME(void *tensor,                                       \
-                           StridedMemRefType<index_type, 1> *iref,             \
-                           StridedMemRefType<V, 0> *vref) {                    \
-    assert(tensor &&iref &&vref);                                              \
+/// Helper to enumerate elements of coordinate scheme, one per value type.
+#define IMPL_GETNEXT(VNAME, V)                                                 \
+  bool _mlir_ciface_getNext##VNAME(void *coo,                                  \
+                                   StridedMemRefType<index_type, 1> *iref,     \
+                                   StridedMemRefType<V, 0> *vref) {            \
+    assert(coo &&iref &&vref);                                                 \
     assert(iref->strides[0] == 1);                                             \
     index_type *indx = iref->data + iref->offset;                              \
     V *value = vref->data + vref->offset;                                      \
     const uint64_t isize = iref->sizes[0];                                     \
-    auto iter = static_cast<SparseTensorCOO<V> *>(tensor);                     \
-    const Element<V> *elem = iter->getNext();                                  \
+    const Element<V> *elem = static_cast<SparseTensorCOO<V> *>(coo)->getNext();\
     if (elem == nullptr)                                                       \
       return false;                                                            \
     for (uint64_t r = 0; r < isize; r++)                                       \
       indx[r] = elem->indices[r];                                              \
     *value = elem->value;                                                      \
     return true;                                                               \
   }
+FOREVERY_V(IMPL_GETNEXT)
+#undef IMPL_GETNEXT
 
-#define IMPL_LEXINSERT(NAME, V)                                                \
-  void _mlir_ciface_##NAME(void *tensor,                                       \
-                           StridedMemRefType<index_type, 1> *cref, V val) {    \
+/// Insert elements in lexicographical index order, one per value type.
+#define IMPL_LEXINSERT(VNAME, V)                                               \
+  void _mlir_ciface_lexInsert##VNAME(void *tensor,                             \
+                                     StridedMemRefType<index_type, 1> *cref,   \
+                                     V val) {                                  \
     assert(tensor &&cref);                                                     \
     assert(cref->strides[0] == 1);                                             \
     index_type *cursor = cref->data + cref->offset;                            \
     assert(cursor);                                                            \
     static_cast<SparseTensorStorageBase *>(tensor)->lexInsert(cursor, val);    \
   }
+FOREVERY_V(IMPL_LEXINSERT)
+#undef IMPL_LEXINSERT
 
-#define IMPL_EXPINSERT(NAME, V)                                                \
-  void _mlir_ciface_##NAME(                                                    \
+/// Insert using expansion, one per value type.
+#define IMPL_EXPINSERT(VNAME, V)                                               \
+  void _mlir_ciface_expInsert##VNAME(                                          \
       void *tensor, StridedMemRefType<index_type, 1> *cref,                    \
       StridedMemRefType<V, 1> *vref, StridedMemRefType<bool, 1> *fref,         \
       StridedMemRefType<index_type, 1> *aref, index_type count) {              \
     assert(tensor &&cref &&vref &&fref &&aref);                                \
     assert(cref->strides[0] == 1);                                             \
     assert(vref->strides[0] == 1);                                             \
     assert(fref->strides[0] == 1);                                             \
     assert(aref->strides[0] == 1);                                             \
     assert(vref->sizes[0] == fref->sizes[0]);                                  \
     index_type *cursor = cref->data + cref->offset;                            \
     V *values = vref->data + vref->offset;                                     \
     bool *filled = fref->data + fref->offset;                                  \
     index_type *added = aref->data + aref->offset;                             \
     static_cast<SparseTensorStorageBase *>(tensor)->expInsert(                 \
         cursor, values, filled, added, count);                                 \
   }
+FOREVERY_V(IMPL_EXPINSERT)
+#undef IMPL_EXPINSERT
 
 // Assume index_type is in fact uint64_t, so that _mlir_ciface_newSparseTensor
 // can safely rewrite kIndex to kU64.  We make this assertion to guarantee
 // that this file cannot get out of sync with its header.
 static_assert(std::is_same<index_type, uint64_t>::value,
               "Expected index_type == uint64_t");
 
 /// Constructs a new sparse tensor. This is the "swiss army knife"
_mlir_ciface_newSparseTensor(StridedMemRefType<DimLevelType, 1> *aref, // NOLINT
   CASE_SECSAME(OverheadType::kU8, PrimaryType::kI32, uint8_t, int32_t);
   CASE_SECSAME(OverheadType::kU8, PrimaryType::kI16, uint8_t, int16_t);
   CASE_SECSAME(OverheadType::kU8, PrimaryType::kI8, uint8_t, int8_t);
 
   // Unsupported case (add above if needed).
   fputs("unsupported combination of types\n", stderr);
   exit(1);
 }
-
-/// Methods that provide direct access to pointers.
-IMPL_GETOVERHEAD(sparsePointers, index_type, getPointers)
-IMPL_GETOVERHEAD(sparsePointers64, uint64_t, getPointers)
-IMPL_GETOVERHEAD(sparsePointers32, uint32_t, getPointers)
-IMPL_GETOVERHEAD(sparsePointers16, uint16_t, getPointers)
-IMPL_GETOVERHEAD(sparsePointers8, uint8_t, getPointers)
-
-/// Methods that provide direct access to indices.
-IMPL_GETOVERHEAD(sparseIndices, index_type, getIndices)
-IMPL_GETOVERHEAD(sparseIndices64, uint64_t, getIndices)
-IMPL_GETOVERHEAD(sparseIndices32, uint32_t, getIndices)
-IMPL_GETOVERHEAD(sparseIndices16, uint16_t, getIndices)
-IMPL_GETOVERHEAD(sparseIndices8, uint8_t, getIndices)
-
-/// Methods that provide direct access to values.
-IMPL_SPARSEVALUES(sparseValuesF64, double, getValues)
-IMPL_SPARSEVALUES(sparseValuesF32, float, getValues)
-IMPL_SPARSEVALUES(sparseValuesI64, int64_t, getValues)
-IMPL_SPARSEVALUES(sparseValuesI32, int32_t, getValues)
-IMPL_SPARSEVALUES(sparseValuesI16, int16_t, getValues)
-IMPL_SPARSEVALUES(sparseValuesI8, int8_t, getValues)
-
-/// Helper to add value to coordinate scheme, one per value type.
-IMPL_ADDELT(addEltF64, double)
-IMPL_ADDELT(addEltF32, float)
-IMPL_ADDELT(addEltI64, int64_t)
-IMPL_ADDELT(addEltI32, int32_t)
-IMPL_ADDELT(addEltI16, int16_t)
-IMPL_ADDELT(addEltI8, int8_t)
-
-/// Helper to enumerate elements of coordinate scheme, one per value type.
-IMPL_GETNEXT(getNextF64, double)
-IMPL_GETNEXT(getNextF32, float)
-IMPL_GETNEXT(getNextI64, int64_t)
-IMPL_GETNEXT(getNextI32, int32_t)
-IMPL_GETNEXT(getNextI16, int16_t)
-IMPL_GETNEXT(getNextI8, int8_t)
-
-/// Insert elements in lexicographical index order, one per value type.
-IMPL_LEXINSERT(lexInsertF64, double)
-IMPL_LEXINSERT(lexInsertF32, float)
-IMPL_LEXINSERT(lexInsertI64, int64_t)
-IMPL_LEXINSERT(lexInsertI32, int32_t)
-IMPL_LEXINSERT(lexInsertI16, int16_t)
-IMPL_LEXINSERT(lexInsertI8, int8_t)
-
-/// Insert using expansion, one per value type.
-IMPL_EXPINSERT(expInsertF64, double)
-IMPL_EXPINSERT(expInsertF32, float)
-IMPL_EXPINSERT(expInsertI64, int64_t)
-IMPL_EXPINSERT(expInsertI32, int32_t)
-IMPL_EXPINSERT(expInsertI16, int16_t)
-IMPL_EXPINSERT(expInsertI8, int8_t)
-
 #undef CASE
-#undef IMPL_SPARSEVALUES
-#undef IMPL_GETOVERHEAD
-#undef IMPL_ADDELT
-#undef IMPL_GETNEXT
-#undef IMPL_LEXINSERT
-#undef IMPL_EXPINSERT
+#undef CASE_SECSAME
 
 /// Output a sparse tensor, one per value type.
-void outSparseTensorF64(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<double>(tensor, dest, sort);
-}
-void outSparseTensorF32(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<float>(tensor, dest, sort);
-}
-void outSparseTensorI64(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<int64_t>(tensor, dest, sort);
-}
-void outSparseTensorI32(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<int32_t>(tensor, dest, sort);
-}
-void outSparseTensorI16(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<int16_t>(tensor, dest, sort);
-}
-void outSparseTensorI8(void *tensor, void *dest, bool sort) {
-  return outSparseTensor<int8_t>(tensor, dest, sort);
+#define IMPL_OUTSPARSETENSOR(VNAME, V)                                         \
+  void outSparseTensor##VNAME(void *coo, void *dest, bool sort) {              \
+    return outSparseTensor<V>(coo, dest, sort);                                \
   }
+FOREVERY_V(IMPL_OUTSPARSETENSOR)
+#undef IMPL_OUTSPARSETENSOR
 
 //===----------------------------------------------------------------------===//
 //
 // Public API with methods that accept C-style data structures to interact
 // with sparse tensors, which are only visible as opaque pointers externally.
 // These methods can be used both by MLIR compiler-generated code as well as by
 // an external runtime that wants to interact with MLIR compiler-generated code.
 //
void delSparseTensor(void *tensor) {
   delete static_cast<SparseTensorStorageBase *>(tensor);
 }
 
 /// Releases sparse tensor coordinate scheme.
 #define IMPL_DELCOO(VNAME, V)                                                  \
   void delSparseTensorCOO##VNAME(void *coo) {                                  \
     delete static_cast<SparseTensorCOO<V> *>(coo);                             \
   }
-IMPL_DELCOO(F64, double)
-IMPL_DELCOO(F32, float)
-IMPL_DELCOO(I64, int64_t)
-IMPL_DELCOO(I32, int32_t)
-IMPL_DELCOO(I16, int16_t)
-IMPL_DELCOO(I8, int8_t)
+FOREVERY_V(IMPL_DELCOO)
 #undef IMPL_DELCOO
 
 /// Initializes sparse tensor from a COO-flavored format expressed using C-style
 /// data structures. The expected parameters are:
 ///
 ///   rank:    rank of tensor
 ///   nse:     number of specified elements (usually the nonzeros)
 ///   shape:   array with dimension size for each rank

 ///      rank    = 2
 ///      nse     = 3
 ///      shape   = [2, 3]
 ///      values  = [1.0, 5.0, 3.0]
 ///      indices = [ 0, 0,  1, 1,  1, 2]
 //
 // TODO: generalize beyond 64-bit indices.
 //
-void *convertToMLIRSparseTensorF64(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                   double *values, uint64_t *indices,
-                                   uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<double>(rank, nse, shape, values, indices, perm,
-                                    sparse);
-}
-void *convertToMLIRSparseTensorF32(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                   float *values, uint64_t *indices,
-                                   uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<float>(rank, nse, shape, values, indices, perm,
-                                   sparse);
-}
-void *convertToMLIRSparseTensorI64(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                   int64_t *values, uint64_t *indices,
-                                   uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<int64_t>(rank, nse, shape, values, indices, perm,
-                                     sparse);
-}
-void *convertToMLIRSparseTensorI32(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                   int32_t *values, uint64_t *indices,
-                                   uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<int32_t>(rank, nse, shape, values, indices, perm,
-                                     sparse);
-}
-void *convertToMLIRSparseTensorI16(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                   int16_t *values, uint64_t *indices,
-                                   uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<int16_t>(rank, nse, shape, values, indices, perm,
-                                     sparse);
-}
-void *convertToMLIRSparseTensorI8(uint64_t rank, uint64_t nse, uint64_t *shape,
-                                  int8_t *values, uint64_t *indices,
-                                  uint64_t *perm, uint8_t *sparse) {
-  return toMLIRSparseTensor<int8_t>(rank, nse, shape, values, indices, perm,
-                                    sparse);
+#define IMPL_CONVERTTOMLIRSPARSETENSOR(VNAME, V)                               \
+  void *convertToMLIRSparseTensor##VNAME(uint64_t rank, uint64_t nse,          \
+                                         uint64_t *shape, V *values,           \
+                                         uint64_t *indices, uint64_t *perm,    \
+                                         uint8_t *sparse) {                    \
+    return toMLIRSparseTensor<V>(rank, nse, shape, values, indices, perm,      \
+                                 sparse);                                      \
 }
+FOREVERY_V(IMPL_CONVERTTOMLIRSPARSETENSOR)
+#undef IMPL_CONVERTTOMLIRSPARSETENSOR
 
 /// Converts a sparse tensor to COO-flavored format expressed using C-style
 /// data structures. The expected output parameters are pointers for these
 /// values:
 ///
 ///   rank:    rank of tensor
 ///   nse:     number of specified elements (usually the nonzeros)
 ///   shape:   array with dimension size for each rank
 ///   values:  a "nse" array with values for all specified elements
 ///   indices: a flat "nse x rank" array with indices for all specified elements
 ///
 /// The input is a pointer to SparseTensorStorage<P, I, V>, typically returned
 /// from convertToMLIRSparseTensor.
 ///
 //  TODO: Currently, values are copied from SparseTensorStorage to
 //  SparseTensorCOO, then to the output. We may want to reduce the number of
 //  copies.
 //
 // TODO: generalize beyond 64-bit indices, no dim ordering, all dimensions
 // compressed
 //
-void convertFromMLIRSparseTensorF64(void *tensor, uint64_t *pRank,
-                                    uint64_t *pNse, uint64_t **pShape,
-                                    double **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<double>(tensor, pRank, pNse, pShape, pValues, pIndices);
-}
-void convertFromMLIRSparseTensorF32(void *tensor, uint64_t *pRank,
-                                    uint64_t *pNse, uint64_t **pShape,
-                                    float **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<float>(tensor, pRank, pNse, pShape, pValues, pIndices);
-}
-void convertFromMLIRSparseTensorI64(void *tensor, uint64_t *pRank,
-                                    uint64_t *pNse, uint64_t **pShape,
-                                    int64_t **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<int64_t>(tensor, pRank, pNse, pShape, pValues, pIndices);
-}
-void convertFromMLIRSparseTensorI32(void *tensor, uint64_t *pRank,
-                                    uint64_t *pNse, uint64_t **pShape,
-                                    int32_t **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<int32_t>(tensor, pRank, pNse, pShape, pValues, pIndices);
-}
-void convertFromMLIRSparseTensorI16(void *tensor, uint64_t *pRank,
-                                    uint64_t *pNse, uint64_t **pShape,
-                                    int16_t **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<int16_t>(tensor, pRank, pNse, pShape, pValues, pIndices);
-}
-void convertFromMLIRSparseTensorI8(void *tensor, uint64_t *pRank,
-                                   uint64_t *pNse, uint64_t **pShape,
-                                   int8_t **pValues, uint64_t **pIndices) {
-  fromMLIRSparseTensor<int8_t>(tensor, pRank, pNse, pShape, pValues, pIndices);
+#define IMPL_CONVERTFROMMLIRSPARSETENSOR(VNAME, V)                             \
+  void convertFromMLIRSparseTensor##VNAME(void *tensor, uint64_t *pRank,       \
+                                          uint64_t *pNse, uint64_t **pShape,   \
+                                          V **pValues, uint64_t **pIndices) {  \
+    fromMLIRSparseTensor<V>(tensor, pRank, pNse, pShape, pValues, pIndices);   \
   }
+FOREVERY_V(IMPL_CONVERTFROMMLIRSPARSETENSOR)
+#undef IMPL_CONVERTFROMMLIRSPARSETENSOR
 
 } // extern "C"
 
 #endif // MLIR_CRUNNERUTILS_DEFINE_FUNCTIONS