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| 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/sparse_vector_chain.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/sparse_vector_chain.mlir')
| -rw-r--r-- | mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir b/mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir index 37545618bd58..01342c5d1991 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir @@ -1,7 +1,7 @@ // RUN: mlir-opt %s -sparsification -cse -sparse-vectorization="vl=8" -cse | \ // RUN: FileCheck %s -#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}> +#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["dense","compressed"]}> #trait = { indexing_maps = [ @@ -18,19 +18,19 @@ // // CHECK-LABEL: func.func @sparse_matrix_sum( // CHECK-SAME: %[[VAL_0:.*]]: tensor<f64>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> { +// CHECK-SAME: %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<f64> { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64> // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 64 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex> -// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64> -// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex> -// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex> -// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64> +// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex> +// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex> +// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64> +// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex> +// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex> +// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64> // CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref<f64> // CHECK: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f64) { |