[mlir][linalg][elementwise] Fold transpose into new elementwise #130207
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javedabsar1
requested review from
dcaballe,
nicolasvasilache and
rengolin
as code owners
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@llvm/pr-subscribers-mlir Author: Javed Absar (javedabsar1) ChangesFull diff: https://github.com/llvm/llvm-project/pull/130207.diff 3 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
index e4dd458eaff84..f7b1d2c9dfcb3 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
@@ -601,12 +601,24 @@ def ElementwiseOp : LinalgStructuredBase_Op<"elementwise", [
[{
buildStructuredOp($_builder, $_state, std::nullopt, inputs, outputs,
attributes, ElementwiseOp::getRegionBuilder());
- }]>
+ }]>,
+
+ OpBuilder<(ins "ValueRange":$inputs, "ValueRange":$outputs,
+ "ElementwiseKindAttr":$kind,
+ "ArrayAttr":$indexingMaps,
+ CArg<"ArrayRef<NamedAttribute>", "{}">:$attributes),
+ [{
+ $_state.addAttribute("kind", kind);
+ $_state.addAttribute("indexing_maps", indexingMaps);
+ buildStructuredOp($_builder, $_state, std::nullopt, inputs, outputs,
+ attributes, ElementwiseOp::getRegionBuilder());
+ }]>
];
let hasCustomAssemblyFormat = 1;
let hasFolder = 1;
let hasVerifier = 1;
+ let hasCanonicalizer = 1;
let extraClassDeclaration = structuredOpsBaseDecls # [{
/// Get the arity enum corresponding to the kind of op, e.g. if arg is
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 07b19e5cb1a89..f6b7c32659bb5 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -17,6 +17,7 @@
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
+#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
@@ -4285,6 +4286,47 @@ Speculation::Speculatability ElementwiseOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
+namespace {
+struct FoldTranspose : public OpRewritePattern<ElementwiseOp> {
+ using OpRewritePattern<ElementwiseOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(ElementwiseOp op,
+ PatternRewriter &rewriter) const override {
+ bool changed = false;
+ SmallVector<Value> newIns;
+ SmallVector<AffineMap> newMaps;
+ for (OpOperand *operand : op.getDpsInputOperands()) {
+ AffineMap map = op.getMatchingIndexingMap(operand);
+ auto transposeOp = operand->get().getDefiningOp<TransposeOp>();
+
+ if (!map.isIdentity() || !transposeOp) {
+ // push in original operand and its map.
+ newIns.push_back(operand->get());
+ newMaps.push_back(map);
+ continue;
+ }
+ newIns.push_back(transposeOp.getInput());
+ // push in transposeOp's inverse permutation map.
+ newMaps.push_back(transposeOp.getMatchingIndexingMap(
+ transposeOp.getDpsInputOperand(0)));
+ changed = true;
+ }
+ if (!changed)
+ return failure();
+ newMaps.push_back(op.getIndexingMapsArray().back());
+
+ rewriter.replaceOpWithNewOp<ElementwiseOp>(
+ op, newIns, op.getDpsInits()[0], op.getKindAttr(),
+ rewriter.getAffineMapArrayAttr(newMaps));
+ return success();
+ }
+};
+} // namespace
+void ElementwiseOp::getCanonicalizationPatterns(RewritePatternSet &results,
+ MLIRContext *context) {
+ results.add<FoldTranspose>(context);
+}
+
//===----------------------------------------------------------------------===//
// PackOp/UnPackOp Common
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Linalg/elementwise/fold.mlir b/mlir/test/Dialect/Linalg/elementwise/fold.mlir
new file mode 100644
index 0000000000000..7b2ff0b6de12e
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/elementwise/fold.mlir
@@ -0,0 +1,43 @@
+// RUN: mlir-opt %s -canonicalize -split-input-file | FileCheck %s
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[TRANSPOSED:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+//
+// CHECK: func.func @unary_transpose(%[[A:.+]]: tensor<16x8x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+// CHECK-NEXT: %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+// CHECK-SAME: indexing_maps = [#[[TRANSPOSED]], #[[IDENTITY]]]
+// CHECK-SAME: ins(%[[A]] : tensor<16x8x32xf32>) outs(%[[B]] : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+// CHECK-NEXT: return %[[RES]] : tensor<8x16x32xf32>
+//
+func.func @unary_transpose(%A : tensor<16x8x32xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+ %empty = tensor.empty() : tensor<8x16x32xf32>
+ %transposed_A = linalg.transpose ins(%A : tensor<16x8x32xf32>) outs(%empty : tensor<8x16x32xf32>) permutation = [1, 0, 2]
+ %result = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+ ins(%transposed_A : tensor<8x16x32xf32>) outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+ return %result : tensor<8x16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[TRANSPOSED:.+]] = affine_map<(d0, d1) -> (d1, d0)>
+//
+// CHECK: func.func @binary_transposed(%[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?x?xf32>, %[[C:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK-NEXT: %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<add>
+// CHECK-SAME: indexing_maps = [#[[IDENTITY]], #[[TRANSPOSED]], #[[IDENTITY]]]
+// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[C]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NEXT: return %[[RES]] : tensor<?x?xf32>
+//
+func.func @binary_transposed(%A : tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %c0 = arith.constant 0 : index
+ %c1 = arith.constant 1 : index
+ %dim0 = tensor.dim %A, %c0 : tensor<?x?xf32>
+ %dim1 = tensor.dim %A, %c1 : tensor<?x?xf32>
+
+ %empty = tensor.empty(%dim1, %dim0) : tensor<?x?xf32>
+ %transposed_B = linalg.transpose ins(%B : tensor<?x?xf32>) outs(%empty : tensor<?x?xf32>) permutation = [1, 0]
+ %result = linalg.elementwise kind=#linalg.elementwise_kind<add>
+ ins(%A, %transposed_B : tensor<?x?xf32>, tensor<?x?xf32>)
+ outs(%C: tensor<?x?xf32>) -> tensor<?x?xf32>
+ return %result : tensor<?x?xf32>
+}
|
|
@llvm/pr-subscribers-mlir-linalg Author: Javed Absar (javedabsar1) ChangesFull diff: https://github.com/llvm/llvm-project/pull/130207.diff 3 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
index e4dd458eaff84..f7b1d2c9dfcb3 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
@@ -601,12 +601,24 @@ def ElementwiseOp : LinalgStructuredBase_Op<"elementwise", [
[{
buildStructuredOp($_builder, $_state, std::nullopt, inputs, outputs,
attributes, ElementwiseOp::getRegionBuilder());
- }]>
+ }]>,
+
+ OpBuilder<(ins "ValueRange":$inputs, "ValueRange":$outputs,
+ "ElementwiseKindAttr":$kind,
+ "ArrayAttr":$indexingMaps,
+ CArg<"ArrayRef<NamedAttribute>", "{}">:$attributes),
+ [{
+ $_state.addAttribute("kind", kind);
+ $_state.addAttribute("indexing_maps", indexingMaps);
+ buildStructuredOp($_builder, $_state, std::nullopt, inputs, outputs,
+ attributes, ElementwiseOp::getRegionBuilder());
+ }]>
];
let hasCustomAssemblyFormat = 1;
let hasFolder = 1;
let hasVerifier = 1;
+ let hasCanonicalizer = 1;
let extraClassDeclaration = structuredOpsBaseDecls # [{
/// Get the arity enum corresponding to the kind of op, e.g. if arg is
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 07b19e5cb1a89..f6b7c32659bb5 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -17,6 +17,7 @@
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
+#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
@@ -4285,6 +4286,47 @@ Speculation::Speculatability ElementwiseOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
+namespace {
+struct FoldTranspose : public OpRewritePattern<ElementwiseOp> {
+ using OpRewritePattern<ElementwiseOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(ElementwiseOp op,
+ PatternRewriter &rewriter) const override {
+ bool changed = false;
+ SmallVector<Value> newIns;
+ SmallVector<AffineMap> newMaps;
+ for (OpOperand *operand : op.getDpsInputOperands()) {
+ AffineMap map = op.getMatchingIndexingMap(operand);
+ auto transposeOp = operand->get().getDefiningOp<TransposeOp>();
+
+ if (!map.isIdentity() || !transposeOp) {
+ // push in original operand and its map.
+ newIns.push_back(operand->get());
+ newMaps.push_back(map);
+ continue;
+ }
+ newIns.push_back(transposeOp.getInput());
+ // push in transposeOp's inverse permutation map.
+ newMaps.push_back(transposeOp.getMatchingIndexingMap(
+ transposeOp.getDpsInputOperand(0)));
+ changed = true;
+ }
+ if (!changed)
+ return failure();
+ newMaps.push_back(op.getIndexingMapsArray().back());
+
+ rewriter.replaceOpWithNewOp<ElementwiseOp>(
+ op, newIns, op.getDpsInits()[0], op.getKindAttr(),
+ rewriter.getAffineMapArrayAttr(newMaps));
+ return success();
+ }
+};
+} // namespace
+void ElementwiseOp::getCanonicalizationPatterns(RewritePatternSet &results,
+ MLIRContext *context) {
+ results.add<FoldTranspose>(context);
+}
+
//===----------------------------------------------------------------------===//
// PackOp/UnPackOp Common
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Linalg/elementwise/fold.mlir b/mlir/test/Dialect/Linalg/elementwise/fold.mlir
new file mode 100644
index 0000000000000..7b2ff0b6de12e
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/elementwise/fold.mlir
@@ -0,0 +1,43 @@
+// RUN: mlir-opt %s -canonicalize -split-input-file | FileCheck %s
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[TRANSPOSED:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+//
+// CHECK: func.func @unary_transpose(%[[A:.+]]: tensor<16x8x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+// CHECK-NEXT: %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+// CHECK-SAME: indexing_maps = [#[[TRANSPOSED]], #[[IDENTITY]]]
+// CHECK-SAME: ins(%[[A]] : tensor<16x8x32xf32>) outs(%[[B]] : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+// CHECK-NEXT: return %[[RES]] : tensor<8x16x32xf32>
+//
+func.func @unary_transpose(%A : tensor<16x8x32xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+ %empty = tensor.empty() : tensor<8x16x32xf32>
+ %transposed_A = linalg.transpose ins(%A : tensor<16x8x32xf32>) outs(%empty : tensor<8x16x32xf32>) permutation = [1, 0, 2]
+ %result = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+ ins(%transposed_A : tensor<8x16x32xf32>) outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+ return %result : tensor<8x16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[TRANSPOSED:.+]] = affine_map<(d0, d1) -> (d1, d0)>
+//
+// CHECK: func.func @binary_transposed(%[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?x?xf32>, %[[C:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK-NEXT: %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<add>
+// CHECK-SAME: indexing_maps = [#[[IDENTITY]], #[[TRANSPOSED]], #[[IDENTITY]]]
+// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[C]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NEXT: return %[[RES]] : tensor<?x?xf32>
+//
+func.func @binary_transposed(%A : tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %c0 = arith.constant 0 : index
+ %c1 = arith.constant 1 : index
+ %dim0 = tensor.dim %A, %c0 : tensor<?x?xf32>
+ %dim1 = tensor.dim %A, %c1 : tensor<?x?xf32>
+
+ %empty = tensor.empty(%dim1, %dim0) : tensor<?x?xf32>
+ %transposed_B = linalg.transpose ins(%B : tensor<?x?xf32>) outs(%empty : tensor<?x?xf32>) permutation = [1, 0]
+ %result = linalg.elementwise kind=#linalg.elementwise_kind<add>
+ ins(%A, %transposed_B : tensor<?x?xf32>, tensor<?x?xf32>)
+ outs(%C: tensor<?x?xf32>) -> tensor<?x?xf32>
+ return %result : tensor<?x?xf32>
+}
|
javedabsar1
requested review from
MaheshRavishankar,
banach-space,
rolfmorel and
matthias-springer
rengolin
reviewed
Mar 7, 2025
rengolin
approved these changes
Mar 11, 2025
frederik-h
pushed a commit
to frederik-h/llvm-project
that referenced
this pull request
Mar 18, 2025
…#130207) Fold transpose into new elementwise Op which has affine-map attached. Will add broadcast folding in next diff.
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Fold transpose into new elementwise Op which has affine-map attached.
Will add broadcast folding in next diff.