[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, 7 insertions, 7 deletions

diff --git a/mlir/test/Dialect/SparseTensor/sparse_nd.mlir b/mlir/test/Dialect/SparseTensor/sparse_nd.mlir
index c99a34b5ce38..742d42be3f8c 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_nd.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_nd.mlir
@@ -5,7 +5,7 @@
 // but an acyclic iteration graph using sparse constraints only.
 
 #SparseTensor = #sparse_tensor.encoding<{
-  dimLevelType = [ "dense", "dense", "dense", "compressed",
+  lvlTypes = [ "dense", "dense", "dense", "compressed",
                    "compressed", "dense", "dense", "dense" ]
 }>
 
@@ -22,7 +22,7 @@
 
 // CHECK-LABEL:   func @mul(
 // CHECK-SAME:              %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>,
-// CHECK-SAME:              %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>>,
+// CHECK-SAME:              %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>>,
 // CHECK-SAME:              %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 10 : index
@@ -34,11 +34,11 @@
 // CHECK-DAG:       %[[VAL_11:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_12:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xf32>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] {