[mlir][sparse] Renaming the STEA field `dimLevelType` to `lvlTypes`

This commit is part of the migration of towards the new STEA syntax/design.  In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
  * `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
  * `Merger::{getDimLevelType => getLvlType}` (for consistency)
  * `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
  * the STEA parser and printer
  * the C and Python bindings
  * PyTACO

However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D150330

1 files changed, 6 insertions, 6 deletions

diff --git a/mlir/test/Dialect/SparseTensor/conversion.mlir b/mlir/test/Dialect/SparseTensor/conversion.mlir
index 5fdaf1bb7dbd..3fcbd829765a 100644
--- a/mlir/test/Dialect/SparseTensor/conversion.mlir
+++ b/mlir/test/Dialect/SparseTensor/conversion.mlir
@@ -1,32 +1,32 @@
 // RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{
-  dimLevelType = ["compressed"]
+  lvlTypes = ["compressed"]
 }>
 
 #SparseVector64 = #sparse_tensor.encoding<{
-  dimLevelType = ["compressed"],
+  lvlTypes = ["compressed"],
   posWidth = 64,
   crdWidth = 64
 }>
 
 #SparseVector32 = #sparse_tensor.encoding<{
-  dimLevelType = ["compressed"],
+  lvlTypes = ["compressed"],
   posWidth = 32,
   crdWidth = 32
 }>
 
 #CSR = #sparse_tensor.encoding<{
-  dimLevelType = ["dense", "compressed"]
+  lvlTypes = ["dense", "compressed"]
 }>
 
 #CSC = #sparse_tensor.encoding<{
-  dimLevelType = ["dense", "compressed"],
+  lvlTypes = ["dense", "compressed"],
   dimOrdering = affine_map<(i,j) -> (j,i)>
 }>
 
 #SparseTensor = #sparse_tensor.encoding<{
-  dimLevelType = ["dense", "compressed", "compressed"],
+  lvlTypes = ["dense", "compressed", "compressed"],
   dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
 }>