[Mlir-commits] [mlir] 1f3c482 - [mlir][sparse] more test cases for linalg.index
Aart Bik
llvmlistbot at llvm.org
Tue Mar 15 10:36:03 PDT 2022
Author: Aart Bik
Date: 2022-03-15T10:30:54-07:00
New Revision: 1f3c482b76ef851c7a24f7787907b76382a2f432
URL: https://github.com/llvm/llvm-project/commit/1f3c482b76ef851c7a24f7787907b76382a2f432
DIFF: https://github.com/llvm/llvm-project/commit/1f3c482b76ef851c7a24f7787907b76382a2f432.diff
LOG: [mlir][sparse] more test cases for linalg.index
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D121660
Added:
Modified:
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir
Removed:
################################################################################
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir
index 36a052155a591..cd5a5aee75d2c 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir
@@ -3,30 +3,85 @@
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
+#SparseVector = #sparse_tensor.encoding<{
+ dimLevelType = ["compressed"]
+}>
+
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
-#trait = {
+#trait_1d = {
+ indexing_maps = [
+ affine_map<(i) -> (i)>, // a
+ affine_map<(i) -> (i)> // x (out)
+ ],
+ iterator_types = ["parallel"],
+ doc = "X(i) = a(i) op i"
+}
+
+#trait_2d = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
- doc = "X(i,j) = A(i,j) * i * j"
+ doc = "X(i,j) = A(i,j) op i op j"
}
+//
+// Test with indices. Note that a lot of results are actually
+// dense, but this is done to stress test all the operations.
+//
module {
//
- // Kernel that uses indices in the index notation.
+ // Kernel that uses index in the index notation (conjunction).
+ //
+ func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector> {
+ %d0 = arith.constant 8 : index
+ %init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
+ %r = linalg.generic #trait_1d
+ ins(%arga: tensor<8xi64, #SparseVector>)
+ outs(%init: tensor<8xi64, #SparseVector>) {
+ ^bb(%a: i64, %x: i64):
+ %i = linalg.index 0 : index
+ %ii = arith.index_cast %i : index to i64
+ %m1 = arith.muli %a, %ii : i64
+ linalg.yield %m1 : i64
+ } -> tensor<8xi64, #SparseVector>
+ return %r : tensor<8xi64, #SparseVector>
+ }
+
+ //
+ // Kernel that uses index in the index notation (disjunction).
+ //
+ func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector> {
+ %d0 = arith.constant 8 : index
+ %init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
+ %r = linalg.generic #trait_1d
+ ins(%arga: tensor<8xi64, #SparseVector>)
+ outs(%init: tensor<8xi64, #SparseVector>) {
+ ^bb(%a: i64, %x: i64):
+ %i = linalg.index 0 : index
+ %ii = arith.index_cast %i : index to i64
+ %m1 = arith.addi %a, %ii : i64
+ linalg.yield %m1 : i64
+ } -> tensor<8xi64, #SparseVector>
+ return %r : tensor<8xi64, #SparseVector>
+ }
+
+ //
+ // Kernel that uses indices in the index notation (conjunction).
//
- func @sparse_index(%arga: tensor<3x4xi64, #SparseMatrix>)
- -> tensor<3x4xi64, #SparseMatrix> {
+ func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix> {
%d0 = arith.constant 3 : index
%d1 = arith.constant 4 : index
%init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
- %r = linalg.generic #trait
+ %r = linalg.generic #trait_2d
ins(%arga: tensor<3x4xi64, #SparseMatrix>)
outs(%init: tensor<3x4xi64, #SparseMatrix>) {
^bb(%a: i64, %x: i64):
@@ -41,40 +96,122 @@ module {
return %r : tensor<3x4xi64, #SparseMatrix>
}
+ //
+ // Kernel that uses indices in the index notation (disjunction).
+ //
+ func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix> {
+ %d0 = arith.constant 3 : index
+ %d1 = arith.constant 4 : index
+ %init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
+ %r = linalg.generic #trait_2d
+ ins(%arga: tensor<3x4xi64, #SparseMatrix>)
+ outs(%init: tensor<3x4xi64, #SparseMatrix>) {
+ ^bb(%a: i64, %x: i64):
+ %i = linalg.index 0 : index
+ %j = linalg.index 1 : index
+ %ii = arith.index_cast %i : index to i64
+ %jj = arith.index_cast %j : index to i64
+ %m1 = arith.addi %ii, %a : i64
+ %m2 = arith.addi %jj, %m1 : i64
+ linalg.yield %m2 : i64
+ } -> tensor<3x4xi64, #SparseMatrix>
+ return %r : tensor<3x4xi64, #SparseMatrix>
+ }
+
//
// Main driver.
//
func @entry() {
%c0 = arith.constant 0 : index
- %c1 = arith.constant 1 : index
- %c4 = arith.constant 4 : index
%du = arith.constant -1 : i64
+ // Setup input sparse vector.
+ %v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64>
+ %sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector>
+
+ // Setup input "sparse" vector.
+ %v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64>
+ %dv = sparse_tensor.convert %v2 : tensor<8xi64> to tensor<8xi64, #SparseVector>
+
+ // Setup input sparse matrix.
+ %m1 = arith.constant sparse<[[1,1], [2,3]], [10, 20]> : tensor<3x4xi64>
+ %sm = sparse_tensor.convert %m1 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
+
// Setup input "sparse" matrix.
- %d = arith.constant dense <[
- [ 1, 1, 1, 1 ],
- [ 1, 1, 1, 1 ],
- [ 1, 1, 1, 1 ]
- ]> : tensor<3x4xi64>
- %a = sparse_tensor.convert %d : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
+ %m2 = arith.constant dense <[ [ 1, 1, 1, 1 ],
+ [ 1, 2, 1, 1 ],
+ [ 1, 1, 3, 4 ] ]> : tensor<3x4xi64>
+ %dm = sparse_tensor.convert %m2 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
- // Call the kernel.
- %0 = call @sparse_index(%a) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64, #SparseMatrix>
+ // Call the kernels.
+ %0 = call @sparse_index_1d_conj(%sv) : (tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector>
+ %1 = call @sparse_index_1d_disj(%sv) : (tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector>
+ %2 = call @sparse_index_1d_conj(%dv) : (tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector>
+ %3 = call @sparse_index_1d_disj(%dv) : (tensor<8xi64, #SparseVector>)
+ -> tensor<8xi64, #SparseVector>
+ %4 = call @sparse_index_2d_conj(%sm) : (tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix>
+ %5 = call @sparse_index_2d_disj(%sm) : (tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix>
+ %6 = call @sparse_index_2d_conj(%dm) : (tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix>
+ %7 = call @sparse_index_2d_disj(%dm) : (tensor<3x4xi64, #SparseMatrix>)
+ -> tensor<3x4xi64, #SparseMatrix>
//
// Verify result.
//
- // CHECK: ( ( 0, 0, 0, 0 ), ( 0, 1, 2, 3 ), ( 0, 2, 4, 6 ) )
+ // CHECK: ( 20, 80, -1, -1, -1, -1, -1, -1 )
+ // CHECK-NEXT: ( 0, 1, 12, 3, 24, 5, 6, 7 )
+ // CHECK-NEXT: ( 0, 2, 8, 24, 64, 160, 384, 896 )
+ // CHECK-NEXT: ( 1, 3, 6, 11, 20, 37, 70, 135 )
+ // CHECK-NEXT: ( 10, 120, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 )
+ // CHECK-NEXT: ( 0, 1, 2, 3, 1, 12, 3, 4, 2, 3, 4, 25 )
+ // CHECK-NEXT: ( 0, 0, 0, 0, 0, 2, 2, 3, 0, 2, 12, 24 )
+ // CHECK-NEXT: ( 1, 2, 3, 4, 2, 4, 4, 5, 3, 4, 7, 9 )
//
- %x = sparse_tensor.convert %0 : tensor<3x4xi64, #SparseMatrix> to tensor<3x4xi64>
- %m = bufferization.to_memref %x : memref<3x4xi64>
- %v = vector.transfer_read %m[%c0, %c0], %du: memref<3x4xi64>, vector<3x4xi64>
- vector.print %v : vector<3x4xi64>
+ %8 = sparse_tensor.values %0 : tensor<8xi64, #SparseVector> to memref<?xi64>
+ %9 = sparse_tensor.values %1 : tensor<8xi64, #SparseVector> to memref<?xi64>
+ %10 = sparse_tensor.values %2 : tensor<8xi64, #SparseVector> to memref<?xi64>
+ %11 = sparse_tensor.values %3 : tensor<8xi64, #SparseVector> to memref<?xi64>
+ %12 = sparse_tensor.values %4 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
+ %13 = sparse_tensor.values %5 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
+ %14 = sparse_tensor.values %6 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
+ %15 = sparse_tensor.values %7 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
+ %16 = vector.transfer_read %8[%c0], %du: memref<?xi64>, vector<8xi64>
+ %17 = vector.transfer_read %9[%c0], %du: memref<?xi64>, vector<8xi64>
+ %18 = vector.transfer_read %10[%c0], %du: memref<?xi64>, vector<8xi64>
+ %19 = vector.transfer_read %11[%c0], %du: memref<?xi64>, vector<8xi64>
+ %20 = vector.transfer_read %12[%c0], %du: memref<?xi64>, vector<12xi64>
+ %21 = vector.transfer_read %13[%c0], %du: memref<?xi64>, vector<12xi64>
+ %22 = vector.transfer_read %14[%c0], %du: memref<?xi64>, vector<12xi64>
+ %23 = vector.transfer_read %15[%c0], %du: memref<?xi64>, vector<12xi64>
+ vector.print %16 : vector<8xi64>
+ vector.print %17 : vector<8xi64>
+ vector.print %18 : vector<8xi64>
+ vector.print %19 : vector<8xi64>
+ vector.print %20 : vector<12xi64>
+ vector.print %21 : vector<12xi64>
+ vector.print %22 : vector<12xi64>
+ vector.print %23 : vector<12xi64>
// Release resources.
- sparse_tensor.release %a : tensor<3x4xi64, #SparseMatrix>
- sparse_tensor.release %0 : tensor<3x4xi64, #SparseMatrix>
- memref.dealloc %m : memref<3x4xi64>
+ sparse_tensor.release %sv : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %dv : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %0 : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %1 : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %2 : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %3 : tensor<8xi64, #SparseVector>
+ sparse_tensor.release %sm : tensor<3x4xi64, #SparseMatrix>
+ sparse_tensor.release %dm : tensor<3x4xi64, #SparseMatrix>
+ sparse_tensor.release %4 : tensor<3x4xi64, #SparseMatrix>
+ sparse_tensor.release %5 : tensor<3x4xi64, #SparseMatrix>
+ sparse_tensor.release %6 : tensor<3x4xi64, #SparseMatrix>
+ sparse_tensor.release %7 : tensor<3x4xi64, #SparseMatrix>
return
}
More information about the Mlir-commits
mailing list