[Mlir-commits] [mlir] 914eff5 - [mlir][sparse][vector] ensure loop peeling to remove vector masks works
Aart Bik
llvmlistbot at llvm.org
Mon Nov 21 16:51:13 PST 2022
Author: Aart Bik
Date: 2022-11-21T16:51:06-08:00
New Revision: 914eff5a8ca9a05cf14ca758c282e8ca8987967d
URL: https://github.com/llvm/llvm-project/commit/914eff5a8ca9a05cf14ca758c282e8ca8987967d
DIFF: https://github.com/llvm/llvm-project/commit/914eff5a8ca9a05cf14ca758c282e8ca8987967d.diff
LOG: [mlir][sparse][vector] ensure loop peeling to remove vector masks works
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D138343
Added:
mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir
Modified:
Removed:
################################################################################
diff --git a/mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir b/mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir
new file mode 100644
index 0000000000000..d379965ef01df
--- /dev/null
+++ b/mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir
@@ -0,0 +1,63 @@
+// RUN: mlir-opt %s --sparsification -cse -sparse-vectorization="vl=16" -scf-for-loop-peeling -canonicalize -cse | \
+// RUN: FileCheck %s
+
+#SparseVector = #sparse_tensor.encoding<{
+ dimLevelType = [ "compressed" ],
+ pointerBitWidth = 32,
+ indexBitWidth = 32
+}>
+
+#trait_mul_s = {
+ indexing_maps = [
+ affine_map<(i) -> (i)>, // a
+ affine_map<(i) -> (i)>, // b
+ affine_map<(i) -> (i)> // x (out)
+ ],
+ iterator_types = ["parallel"],
+ doc = "x(i) = a(i) * b(i)"
+}
+
+// CHECK-DAG: #[[$map0:.*]] = affine_map<()[s0, s1] -> (s0 + ((-s0 + s1) floordiv 16) * 16)>
+// CHECK-DAG: #[[$map1:.*]] = affine_map<(d0)[s0] -> (-d0 + s0)>
+// CHECK-LABEL: func @mul_s
+// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[c16:.*]] = arith.constant 16 : index
+// CHECK: %[[p:.*]] = memref.load %{{.*}}[%[[c0]]] : memref<?xi32>
+// CHECK: %[[a:.*]] = arith.extui %[[p]] : i32 to i64
+// CHECK: %[[q:.*]] = arith.index_cast %[[a]] : i64 to index
+// CHECK: %[[r:.*]] = memref.load %{{.*}}[%[[c1]]] : memref<?xi32>
+// CHECK: %[[b:.*]] = arith.extui %[[r]] : i32 to i64
+// CHECK: %[[s:.*]] = arith.index_cast %[[b]] : i64 to index
+// CHECK: %[[boundary:.*]] = affine.apply #[[$map0]]()[%[[q]], %[[s]]]
+// CHECK: scf.for %[[i:.*]] = %[[q]] to %[[boundary]] step %[[c16]] {
+// CHECK: %[[mask:.*]] = vector.constant_mask [16] : vector<16xi1>
+// CHECK: %[[li:.*]] = vector.load %{{.*}}[%[[i]]] : memref<?xi32>, vector<16xi32>
+// CHECK: %[[zi:.*]] = arith.extui %[[li]] : vector<16xi32> to vector<16xi64>
+// CHECK: %[[la:.*]] = vector.load %{{.*}}[%[[i]]] : memref<?xf32>, vector<16xf32>
+// CHECK: %[[lb:.*]] = vector.gather %{{.*}}[%[[c0]]] [%[[zi]]], %[[mask]], %{{.*}} : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> into vector<16xf32>
+// CHECK: %[[m:.*]] = arith.mulf %[[la]], %[[lb]] : vector<16xf32>
+// CHECK: vector.scatter %{{.*}}[%[[c0]]] [%[[zi]]], %[[mask]], %[[m]] : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32>
+// CHECK: }
+// CHECK: scf.for %[[i2:.*]] = %[[boundary]] to %[[s]] step %[[c16]] {
+// CHECK: %[[sub:.*]] = affine.apply #[[$map1]](%[[i2]])[%[[s]]]
+// CHECK: %[[mask2:.*]] = vector.create_mask %[[sub]] : vector<16xi1>
+// CHECK: %[[li2:.*]] = vector.maskedload %{{.*}}[%[[i2]]], %[[mask2]], %{{.*}} : memref<?xi32>, vector<16xi1>, vector<16xi32> into vector<16xi32>
+// CHECK: %[[zi2:.*]] = arith.extui %[[li2]] : vector<16xi32> to vector<16xi64>
+// CHECK: %[[la2:.*]] = vector.maskedload %{{.*}}[%[[i2]]], %[[mask2]], %{{.*}} : memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32>
+// CHECK: %[[lb2:.*]] = vector.gather %{{.*}}[%[[c0]]] [%[[zi2]]], %[[mask2]], %{{.*}} : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> into vector<16xf32>
+// CHECK: %[[m2:.*]] = arith.mulf %[[la2]], %[[lb2]] : vector<16xf32>
+// CHECK: vector.scatter %{{.*}}[%[[c0]]] [%[[zi2]]], %[[mask2]], %[[m2]] : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32>
+// CHECK: }
+// CHECK: return
+//
+func.func @mul_s(%arga: tensor<1024xf32, #SparseVector>, %argb: tensor<1024xf32>, %argx: tensor<1024xf32>) -> tensor<1024xf32> {
+ %0 = linalg.generic #trait_mul_s
+ ins(%arga, %argb: tensor<1024xf32, #SparseVector>, tensor<1024xf32>)
+ outs(%argx: tensor<1024xf32>) {
+ ^bb(%a: f32, %b: f32, %x: f32):
+ %0 = arith.mulf %a, %b : f32
+ linalg.yield %0 : f32
+ } -> tensor<1024xf32>
+ return %0 : tensor<1024xf32>
+}
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