diff options
| author | wren romano <2998727+wrengr@users.noreply.github.com> | 2023-05-17 13:09:53 -0700 |
|---|---|---|
| committer | wren romano <2998727+wrengr@users.noreply.github.com> | 2023-05-17 14:24:09 -0700 |
| commit | a0615d020a02e252196383439e2c8143c6525e05 (patch) | |
| tree | aa308ef0e4c62d7dba3450f0eb4f8f1dffc0f57c /mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir | |
| parent | 4dc205f016e3dd2eb1182886a77676f24e39e329 (diff) | |
| download | llvm-a0615d020a02e252196383439e2c8143c6525e05.tar.gz | |
[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
Diffstat (limited to 'mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir')
| -rw-r--r-- | mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir index b847a277859f..3045aea07f22 100644 --- a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir +++ b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir @@ -4,15 +4,15 @@ // RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT #SparseVector = #sparse_tensor.encoding<{ - dimLevelType = ["compressed"] + lvlTypes = ["compressed"] }> #SparseMatrix = #sparse_tensor.encoding<{ - dimLevelType = ["dense", "compressed"] + lvlTypes = ["dense", "compressed"] }> #SparseTensor = #sparse_tensor.encoding<{ - dimLevelType = ["dense", "compressed", "compressed"], + lvlTypes = ["dense", "compressed", "compressed"], dimOrdering = affine_map<(i,j,k) -> (k,i,j)> }> @@ -145,7 +145,7 @@ func.func @sparse_convert_1d_dyn(%arg0: tensor<?xi32, #SparseVector>) -> tensor< // CHECK: return %[[T]] : tensor<2x4xf64> // CHECK-RWT-LABEL: func.func @sparse_convert_2d( -// CHECK-RWT-SAME: %[[A:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<2x4xf64> { +// CHECK-RWT-SAME: %[[A:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<2x4xf64> { // CHECK-RWT: %[[F0:.*]] = arith.constant 0.000000e+00 : f64 // CHECK-RWT: %[[B:.*]] = memref.alloc() : memref<2x4xf64> // CHECK-RWT: linalg.fill ins(%[[F0]] : f64) outs(%[[B]] @@ -301,7 +301,7 @@ func.func @sparse_convert_2d_dyn1(%arg0: tensor<2x?xf64, #SparseMatrix>) -> tens // CHECK: return %[[T]] : tensor<?x?xf64> // CHECK-RWT-LABEL: func.func @sparse_convert_2d_dyn2( -// CHECK-RWT-SAME: %[[A:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<?x?xf64> { +// CHECK-RWT-SAME: %[[A:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<?x?xf64> { // CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index // CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index // CHECK-RWT-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f64 |