[Mlir-commits] [mlir] 5879da4 - [mlir][sparse] replace experimental flag with inplace attribute
Aart Bik
llvmlistbot at llvm.org
Mon May 17 11:44:00 PDT 2021
Author: Aart Bik
Date: 2021-05-17T11:43:44-07:00
New Revision: 5879da496cdf44e50cbd8ea062aa36370e218fdb
URL: https://github.com/llvm/llvm-project/commit/5879da496cdf44e50cbd8ea062aa36370e218fdb
DIFF: https://github.com/llvm/llvm-project/commit/5879da496cdf44e50cbd8ea062aa36370e218fdb.diff
LOG: [mlir][sparse] replace experimental flag with inplace attribute
The experimental flag for "inplace" bufferization in the sparse
compiler can be replaced with the new inplace attribute. This gives
a uniform way of expressing the more efficient way of bufferization.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D102538
Added:
mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir
Modified:
mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp
mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
mlir/test/Dialect/SparseTensor/sparse_lower.mlir
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h
index 12720a38f1b62..ec5dcfea8afe3 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h
+++ b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h
@@ -48,16 +48,15 @@ enum class SparseVectorizationStrategy {
/// Sparsification options.
struct SparsificationOptions {
SparsificationOptions(SparseParallelizationStrategy p,
- SparseVectorizationStrategy v, unsigned vl, bool fo)
- : parallelizationStrategy(p), vectorizationStrategy(v), vectorLength(vl),
- fastOutput(fo) {}
+ SparseVectorizationStrategy v, unsigned vl)
+ : parallelizationStrategy(p), vectorizationStrategy(v), vectorLength(vl) {
+ }
SparsificationOptions()
: SparsificationOptions(SparseParallelizationStrategy::kNone,
- SparseVectorizationStrategy::kNone, 1u, false) {}
+ SparseVectorizationStrategy::kNone, 1u) {}
SparseParallelizationStrategy parallelizationStrategy;
SparseVectorizationStrategy vectorizationStrategy;
unsigned vectorLength;
- bool fastOutput; // experimental: fast output buffers
};
/// Sets up sparsification rewriting rules with the given options.
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp
index 05fd7537a474e..7159c6a2745c0 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp
@@ -45,10 +45,6 @@ struct SparsificationPass : public SparsificationBase<SparsificationPass> {
Option<int32_t> vectorLength{
*this, "vl", llvm::cl::desc("Set the vector length"), llvm::cl::init(1)};
- Option<bool> fastOutput{*this, "fast-output",
- llvm::cl::desc("Allows fast output buffers"),
- llvm::cl::init(false)};
-
/// Returns parallelization strategy given on command line.
SparseParallelizationStrategy parallelOption() {
switch (parallelization) {
@@ -82,7 +78,7 @@ struct SparsificationPass : public SparsificationBase<SparsificationPass> {
RewritePatternSet patterns(ctx);
// Translate strategy flags to strategy options.
SparsificationOptions options(parallelOption(), vectorOption(),
- vectorLength, fastOutput);
+ vectorLength);
// Apply rewriting.
populateSparsificationPatterns(patterns, options);
vector::populateVectorToVectorCanonicalizationPatterns(patterns);
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
index 2cc6825af79f1..7348a7391f3ae 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
@@ -506,6 +506,16 @@ static Type genIntType(PatternRewriter &rewriter, unsigned width) {
return rewriter.getIntegerType(width);
}
+/// Detects in-place annotation on tensor argument.
+static bool getInPlace(Value val) {
+ if (auto arg = val.dyn_cast<BlockArgument>())
+ if (auto funcOp = dyn_cast<FuncOp>(arg.getOwner()->getParentOp()))
+ if (auto attr = funcOp.getArgAttrOfType<BoolAttr>(
+ arg.getArgNumber(), linalg::LinalgDialect::kInplaceableAttrName))
+ return attr.getValue();
+ return false;
+}
+
/// Generates buffer for the output tensor.
static Value genOutputBuffer(CodeGen &codegen, PatternRewriter &rewriter,
linalg::GenericOp op, MemRefType denseTp,
@@ -515,9 +525,8 @@ static Value genOutputBuffer(CodeGen &codegen, PatternRewriter &rewriter,
// The output tensor simply could materialize from the buffer that will
// be generated for the tensor present in the outs() clause. This has
// the major advantage that the sparse kernel only updates the nonzero
- // positions for the output tensor. Currently this results in functional,
- // but slightly imprecise IR, so it is put under an experimental option.
- if (codegen.options.fastOutput)
+ // positions for the output tensor.
+ if (getInPlace(tensor))
return rewriter.create<memref::BufferCastOp>(loc, denseTp, tensor);
// By default, a new buffer is allocated which is initialized to the
// tensor defined in the outs() clause. This is always correct but
diff --git a/mlir/test/Dialect/SparseTensor/sparse_lower.mlir b/mlir/test/Dialect/SparseTensor/sparse_lower.mlir
index 7daee6a0ba50f..ce2395ff7112f 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_lower.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_lower.mlir
@@ -7,11 +7,6 @@
// RUN: --convert-linalg-to-loops --func-bufferize --tensor-constant-bufferize \
// RUN: --tensor-bufferize --finalizing-bufferize | \
// RUN: FileCheck %s --check-prefix=CHECK-LIR
-//
-// RUN: mlir-opt %s -sparsification="fast-output" --sparse-tensor-conversion \
-// RUN: --convert-linalg-to-loops --func-bufferize --tensor-constant-bufferize \
-// RUN: --tensor-bufferize --finalizing-bufferize | \
-// RUN: FileCheck %s --check-prefix=CHECK-FAST
#CSR = #sparse_tensor.encoding<{dimLevelType = [ "dense", "compressed" ]}>
@@ -32,9 +27,9 @@
// CHECK-HIR: %[[VAL_3:.*]] = constant 64 : index
// CHECK-HIR: %[[VAL_4:.*]] = constant 0 : index
// CHECK-HIR: %[[VAL_5:.*]] = constant 1 : index
-// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #{{.*}}> to memref<?xindex>
-// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #{{.*}}> to memref<?xindex>
-// CHECK-HIR: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<64x64xf64, #{{.*}}> to memref<?xf64>
+// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-HIR: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xf64>
// CHECK-HIR: %[[VAL_10:.*]] = memref.buffer_cast %[[VAL_1]] : memref<64xf64>
// CHECK-HIR: %[[VAL_11:.*]] = memref.buffer_cast %[[VAL_2]] : memref<64xf64>
// CHECK-HIR: %[[VAL_12:.*]] = memref.alloc() : memref<64xf64>
@@ -127,35 +122,9 @@
// CHECK-LIR: return %[[VAL_9]] : memref<64xf64>
// CHECK-LIR: }
-// CHECK-FAST-LABEL: func @matvec(
-// CHECK-FAST-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
-// CHECK-FAST-SAME: %[[VAL_1:.*]]: memref<64xf64>,
-// CHECK-FAST-SAME: %[[VAL_2:.*]]: memref<64xf64>) -> memref<64xf64> {
-// CHECK-FAST: %[[VAL_3:.*]] = constant 64 : index
-// CHECK-FAST: %[[VAL_4:.*]] = constant 0 : index
-// CHECK-FAST: %[[VAL_5:.*]] = constant 1 : index
-// CHECK-FAST: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-FAST: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-FAST: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
-// CHECK-FAST: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
-// CHECK-FAST: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xindex>
-// CHECK-FAST: %[[VAL_11:.*]] = addi %[[VAL_9]], %[[VAL_5]] : index
-// CHECK-FAST: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
-// CHECK-FAST: %[[VAL_13:.*]] = memref.load %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
-// CHECK-FAST: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f64) {
-// CHECK-FAST: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
-// CHECK-FAST: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf64>
-// CHECK-FAST: %[[VAL_19:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_17]]] : memref<64xf64>
-// CHECK-FAST: %[[VAL_20:.*]] = mulf %[[VAL_18]], %[[VAL_19]] : f64
-// CHECK-FAST: %[[VAL_21:.*]] = addf %[[VAL_16]], %[[VAL_20]] : f64
-// CHECK-FAST: scf.yield %[[VAL_21]] : f64
-// CHECK-FAST: }
-// CHECK-FAST: store %[[VAL_22:.*]], %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
-// CHECK-FAST: }
-// CHECK-FAST: return %[[VAL_2]] : memref<64xf64>
-// CHECK-FAST: }
-
-func @matvec(%arga: tensor<64x64xf64, #CSR>, %argb: tensor<64xf64>, %argx: tensor<64xf64>) -> tensor<64xf64> {
+func @matvec(%arga: tensor<64x64xf64, #CSR>,
+ %argb: tensor<64xf64>,
+ %argx: tensor<64xf64>) -> tensor<64xf64> {
%0 = linalg.generic #trait_matvec
ins(%arga, %argb : tensor<64x64xf64, #CSR>, tensor<64xf64>)
outs(%argx: tensor<64xf64>) {
diff --git a/mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir b/mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir
new file mode 100644
index 0000000000000..810cef0c24787
--- /dev/null
+++ b/mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir
@@ -0,0 +1,125 @@
+// RUN: mlir-opt %s -sparsification | FileCheck %s --check-prefix=CHECK-HIR
+//
+// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion \
+// RUN: --convert-linalg-to-loops | FileCheck %s --check-prefix=CHECK-MIR
+//
+// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion \
+// RUN: --convert-linalg-to-loops --func-bufferize --tensor-constant-bufferize \
+// RUN: --tensor-bufferize --finalizing-bufferize | \
+// RUN: FileCheck %s --check-prefix=CHECK-LIR
+
+#CSR = #sparse_tensor.encoding<{dimLevelType = [ "dense", "compressed" ]}>
+
+#trait_matvec = {
+ indexing_maps = [
+ affine_map<(i,j) -> (i,j)>, // A
+ affine_map<(i,j) -> (j)>, // b
+ affine_map<(i,j) -> (i)> // x (out)
+ ],
+ iterator_types = ["parallel","reduction"],
+ doc = "x(i) += A(i,j) * b(j)"
+}
+
+// CHECK-HIR-LABEL: func @matvec(
+// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>>,
+// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
+// CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<64xf64> {linalg.inplaceable = true}) -> tensor<64xf64> {
+// CHECK-HIR: %[[VAL_3:.*]] = constant 64 : index
+// CHECK-HIR: %[[VAL_4:.*]] = constant 0 : index
+// CHECK-HIR: %[[VAL_5:.*]] = constant 1 : index
+// CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<64x64xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xf64>
+// CHECK-HIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<64xf64>
+// CHECK-HIR: %[[VAL_10:.*]] = memref.buffer_cast %[[VAL_2]] : memref<64xf64>
+// CHECK-HIR: scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
+// CHECK-HIR: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
+// CHECK-HIR: %[[VAL_13:.*]] = addi %[[VAL_11]], %[[VAL_5]] : index
+// CHECK-HIR: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
+// CHECK-HIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<64xf64>
+// CHECK-HIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_5]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f64) {
+// CHECK-HIR: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>
+// CHECK-HIR: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xf64>
+// CHECK-HIR: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<64xf64>
+// CHECK-HIR: %[[VAL_22:.*]] = mulf %[[VAL_20]], %[[VAL_21]] : f64
+// CHECK-HIR: %[[VAL_23:.*]] = addf %[[VAL_18]], %[[VAL_22]] : f64
+// CHECK-HIR: scf.yield %[[VAL_23]] : f64
+// CHECK-HIR: }
+// CHECK-HIR: memref.store %[[VAL_24:.*]], %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<64xf64>
+// CHECK-HIR: }
+// CHECK-HIR: %[[VAL_25:.*]] = memref.tensor_load %[[VAL_10]] : memref<64xf64>
+// CHECK-HIR: return %[[VAL_25]] : tensor<64xf64>
+// CHECK-HIR: }
+
+// CHECK-MIR-LABEL: func @matvec(
+// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
+// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
+// CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<64xf64> {linalg.inplaceable = true}) -> tensor<64xf64> {
+// CHECK-MIR: %[[VAL_3:.*]] = constant 64 : index
+// CHECK-MIR: %[[VAL_4:.*]] = constant 0 : index
+// CHECK-MIR: %[[VAL_5:.*]] = constant 1 : index
+// CHECK-MIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
+// CHECK-MIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<64xf64>
+// CHECK-MIR: %[[VAL_10:.*]] = memref.buffer_cast %[[VAL_2]] : memref<64xf64>
+// CHECK-MIR: scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
+// CHECK-MIR: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
+// CHECK-MIR: %[[VAL_13:.*]] = addi %[[VAL_11]], %[[VAL_5]] : index
+// CHECK-MIR: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
+// CHECK-MIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<64xf64>
+// CHECK-MIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_5]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f64) {
+// CHECK-MIR: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>
+// CHECK-MIR: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xf64>
+// CHECK-MIR: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<64xf64>
+// CHECK-MIR: %[[VAL_22:.*]] = mulf %[[VAL_20]], %[[VAL_21]] : f64
+// CHECK-MIR: %[[VAL_23:.*]] = addf %[[VAL_18]], %[[VAL_22]] : f64
+// CHECK-MIR: scf.yield %[[VAL_23]] : f64
+// CHECK-MIR: }
+// CHECK-MIR: memref.store %[[VAL_24:.*]], %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<64xf64>
+// CHECK-MIR: }
+// CHECK-MIR: %[[VAL_25:.*]] = memref.tensor_load %[[VAL_10]] : memref<64xf64>
+// CHECK-MIR: return %[[VAL_25]] : tensor<64xf64>
+// CHECK-MIR: }
+
+// CHECK-LIR-LABEL: func @matvec(
+// CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
+// CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>,
+// CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<64xf64> {linalg.inplaceable = true}) -> memref<64xf64> {
+// CHECK-LIR: %[[VAL_3:.*]] = constant 64 : index
+// CHECK-LIR: %[[VAL_4:.*]] = constant 0 : index
+// CHECK-LIR: %[[VAL_5:.*]] = constant 1 : index
+// CHECK-LIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
+// CHECK-LIR: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
+// CHECK-LIR: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xindex>
+// CHECK-LIR: %[[VAL_11:.*]] = addi %[[VAL_9]], %[[VAL_5]] : index
+// CHECK-LIR: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
+// CHECK-LIR: %[[VAL_13:.*]] = memref.load %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
+// CHECK-LIR: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f64) {
+// CHECK-LIR: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
+// CHECK-LIR: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf64>
+// CHECK-LIR: %[[VAL_19:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_17]]] : memref<64xf64>
+// CHECK-LIR: %[[VAL_20:.*]] = mulf %[[VAL_18]], %[[VAL_19]] : f64
+// CHECK-LIR: %[[VAL_21:.*]] = addf %[[VAL_16]], %[[VAL_20]] : f64
+// CHECK-LIR: scf.yield %[[VAL_21]] : f64
+// CHECK-LIR: }
+// CHECK-LIR: memref.store %[[VAL_22:.*]], %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
+// CHECK-LIR: }
+// CHECK-LIR: return %[[VAL_2]] : memref<64xf64>
+// CHECK-LIR: }
+
+func @matvec(%arga: tensor<64x64xf64, #CSR>,
+ %argb: tensor<64xf64>,
+ %argx: tensor<64xf64> {linalg.inplaceable = true}) -> tensor<64xf64> {
+ %0 = linalg.generic #trait_matvec
+ ins(%arga, %argb : tensor<64x64xf64, #CSR>, tensor<64xf64>)
+ outs(%argx: tensor<64xf64>) {
+ ^bb(%A: f64, %b: f64, %x: f64):
+ %0 = mulf %A, %b : f64
+ %1 = addf %x, %0 : f64
+ linalg.yield %1 : f64
+ } -> tensor<64xf64>
+ return %0 : tensor<64xf64>
+}
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir
index 17d37e1839908..ae22e2896d701 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir
@@ -1,5 +1,5 @@
// RUN: mlir-opt %s \
-// RUN: --sparsification="fast-output" --sparse-tensor-conversion \
+// RUN: --sparsification --sparse-tensor-conversion \
// RUN: --convert-linalg-to-loops --convert-vector-to-scf --convert-scf-to-std \
// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN: --std-bufferize --finalizing-bufferize \
@@ -41,7 +41,7 @@ module {
func @sampled_dense_dense(%args: tensor<?x?xf32, #SparseMatrix>,
%arga: tensor<?x?xf32>,
%argb: tensor<?x?xf32>,
- %argx: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %argx: tensor<?x?xf32> {linalg.inplaceable = true}) -> tensor<?x?xf32> {
%0 = linalg.generic #trait_sampled_dense_dense
ins(%args, %arga, %argb: tensor<?x?xf32, #SparseMatrix>, tensor<?x?xf32>, tensor<?x?xf32>)
outs(%argx: tensor<?x?xf32>) {
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