[Mlir-commits] [mlir] 02c6620 - [mlir][linalg][NFC] Move padding transformation to separate file
Matthias Springer
llvmlistbot at llvm.org
Tue Jun 27 05:52:18 PDT 2023
Author: Matthias Springer
Date: 2023-06-27T14:45:03+02:00
New Revision: 02c662077cff0f026edd174c514b75b88d67c3d4
URL: https://github.com/llvm/llvm-project/commit/02c662077cff0f026edd174c514b75b88d67c3d4
DIFF: https://github.com/llvm/llvm-project/commit/02c662077cff0f026edd174c514b75b88d67c3d4.diff
LOG: [mlir][linalg][NFC] Move padding transformation to separate file
Also remove `LinalgPaddingPattern`, which has no uses. (There is a transform dialect op that is used for testing instead.)
Differential Revision: https://reviews.llvm.org/D153512
Added:
mlir/lib/Dialect/Linalg/Transforms/Padding.cpp
Modified:
mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt
mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h b/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
index 9591f0b9b3ef2..cc7139e67c086 100644
--- a/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
+++ b/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
@@ -1048,24 +1048,6 @@ rewriteInIm2Col(RewriterBase &rewriter, linalg::Conv2DNchwFchwOp convOp);
// functional-stye API call.
//===----------------------------------------------------------------------===//
-///
-/// Linalg padding pattern.
-///
-/// Apply the `padding` transformation as a pattern.
-/// See `padding` for more details.
-struct LinalgPaddingPattern : public OpInterfaceRewritePattern<LinalgOp> {
- LinalgPaddingPattern(MLIRContext *context,
- LinalgPaddingOptions options = LinalgPaddingOptions(),
- PatternBenefit benefit = 1);
-
- LogicalResult matchAndRewrite(LinalgOp op,
- PatternRewriter &rewriter) const override;
-
-private:
- /// Options to control padding and hoisting.
- LinalgPaddingOptions options;
-};
-
/// Rewrites 2-D convolution ops with size-1 window dimensions into 1-D
/// convolution ops.
template <typename Conv2DOp, typename Conv1DOp>
diff --git a/mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt b/mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt
index 7877edee6d197..82787a3f70eb9 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt
+++ b/mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt
@@ -21,6 +21,7 @@ add_mlir_dialect_library(MLIRLinalgTransforms
Interchange.cpp
Loops.cpp
NamedOpConversions.cpp
+ Padding.cpp
Promotion.cpp
Split.cpp
SplitReduction.cpp
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Padding.cpp b/mlir/lib/Dialect/Linalg/Transforms/Padding.cpp
new file mode 100644
index 0000000000000..2c934b1cf46ee
--- /dev/null
+++ b/mlir/lib/Dialect/Linalg/Transforms/Padding.cpp
@@ -0,0 +1,243 @@
+//===- Padding.cpp - Padding of Linalg ops --------------------------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
+
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Interfaces/ValueBoundsOpInterface.h"
+
+#define DEBUG_TYPE "linalg-padding"
+
+using namespace mlir;
+using namespace mlir::linalg;
+
+#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
+#define DBGSNL() (llvm::dbgs() << "\n")
+
+/// Pad the `opOperand` in the `paddingDimensions` using the padding value and
+/// the nofold flag found in `paddingValues` and `packPaddings`, respectively.
+///
+/// Exit early and return the `opOperand` value if it already has the requested
+/// shape. I.e.:
+/// - static shape
+/// - nofold is not set
+/// - dim sizes are multiples of `padToMultipleOf`
+///
+/// Otherwise, try to pad the shape dimensions that match the iterator
+/// dimensions `paddingDimensions` and return the tensor::PadOp result if
+/// padding succeeds or failure otherwise.
+static FailureOr<Value> padOperandToSmallestStaticBoundingBox(
+ RewriterBase &rewriter, linalg::LinalgOp opToPad, OpOperand *opOperand,
+ ArrayRef<int64_t> paddingDimensions, ArrayRef<int64_t> padToMultipleOf,
+ ArrayRef<Attribute> paddingValues, ArrayRef<bool> packPaddings) {
+ assert(padToMultipleOf.size() == paddingDimensions.size() &&
+ "invalid number of elements in padToMultipleOf");
+
+ AffineMap indexingMap = opToPad.getMatchingIndexingMap(opOperand);
+ ArrayRef<int64_t> shape = opToPad.getShape(opOperand);
+
+ // Collect the shape dimensions that are a function of `paddingDimensions`,
+ // along with the multiple that they should be padded to ("1" if none).
+ bool alreadyHasRequestedShape = true;
+ DenseMap<int64_t, int64_t> shapeDimToMultiple;
+ for (const auto &dimEn : enumerate(paddingDimensions)) {
+ for (const auto &en : enumerate(indexingMap.getResults())) {
+ if (en.value().isFunctionOfDim(dimEn.value())) {
+ int64_t dimSize = shape[en.index()];
+ shapeDimToMultiple[en.index()] = padToMultipleOf[dimEn.index()];
+ if (ShapedType::isDynamic(dimSize)) {
+ alreadyHasRequestedShape = false;
+ } else if (dimSize % shapeDimToMultiple[en.index()] != 0) {
+ alreadyHasRequestedShape = false;
+ }
+ }
+ }
+ }
+
+ // Return the unpadded operand if padding to a static shape is not needed and
+ // if the nofold flag is not set.
+ bool nofold = opOperand->getOperandNumber() < packPaddings.size()
+ ? packPaddings[opOperand->getOperandNumber()]
+ : false;
+ if (!nofold && alreadyHasRequestedShape)
+ return opOperand->get();
+
+ // Fail if `paddingValues` specifies no padding value.
+ if (opOperand->getOperandNumber() >= paddingValues.size()) {
+ return rewriter.notifyMatchFailure(opToPad, "--no padding value specified");
+ }
+ Attribute paddingAttr = paddingValues[opOperand->getOperandNumber()];
+ Value paddingValue = rewriter.create<arith::ConstantOp>(
+ opToPad.getLoc(), cast<TypedAttr>(paddingAttr));
+
+ // Helper function to round a number up to a given multiple.
+ auto ceil = [](int64_t val, int64_t multiple) {
+ return ((val + multiple - 1) / multiple) * multiple;
+ };
+
+ // Upper bound the sizes to obtain a static bounding box.
+ SmallVector<int64_t> paddedShape(shape.begin(), shape.end());
+ for (int64_t i = 0, e = shape.size(); i < e; ++i) {
+ LLVM_DEBUG(DBGS() << "--compute padded size for dim " << i << "\n");
+ // Skip dimensions that do not require padding.
+ if (!shapeDimToMultiple.contains(i)) {
+ LLVM_DEBUG(DBGS() << "----dim does not require padding, SKIP\n");
+ continue;
+ }
+ // Otherwise, try to compute a constant upper bound for the size value.
+ FailureOr<int64_t> upperBound =
+ ValueBoundsConstraintSet::computeConstantBound(
+ presburger::BoundType::UB, opOperand->get(),
+ /*dim=*/i, /*stopCondition=*/nullptr, /*closedUB=*/true);
+ if (failed(upperBound)) {
+ LLVM_DEBUG(DBGS() << "----count not compute a bounding box for padding");
+ return rewriter.notifyMatchFailure(
+ opToPad, "count not compute a bounding box for padding");
+ }
+ paddedShape[i] = ceil(*upperBound, shapeDimToMultiple[i]);
+ LLVM_DEBUG(DBGS() << "----new dim size: " << paddedShape[i] << "\n");
+ }
+
+ // Pad the operand to the bounding box defined by `paddedShape`.
+ auto paddedTensorType = RankedTensorType::get(
+ paddedShape, getElementTypeOrSelf(opOperand->get()));
+ LLVM_DEBUG(DBGS() << "--SUCCESS, makeComposedPadHighOp with type: "
+ << paddedTensorType);
+ return makeComposedPadHighOp(rewriter, opToPad->getLoc(), paddedTensorType,
+ opOperand->get(), paddingValue, nofold);
+}
+
+FailureOr<SmallVector<Value>>
+linalg::rewriteAsPaddedOp(RewriterBase &rewriter, LinalgOp opToPad,
+ ArrayRef<int64_t> paddingDimensions,
+ ArrayRef<int64_t> padToMultipleOf,
+ ArrayRef<Attribute> paddingValues,
+ ArrayRef<bool> packPaddings, LinalgOp &paddedOp) {
+ LLVM_DEBUG(DBGS() << "Start rewriteAsPaddedOp : " << opToPad << "\n");
+ Location loc = opToPad->getLoc();
+
+ // TODO: there are cases where we may still want to pad to larger sizes.
+ if (!opToPad.hasTensorSemantics())
+ return rewriter.notifyMatchFailure(opToPad,
+ "expected operation on tensors");
+
+ OpBuilder::InsertionGuard g(rewriter);
+ // Set IP after op because we also take the dims of the original output.
+ rewriter.setInsertionPointAfter(opToPad);
+
+ // Make a copy of the shaped operands and update it.
+ SmallVector<Value> newOperands;
+ newOperands.reserve(opToPad->getNumOperands());
+ for (OpOperand &opOperand : opToPad->getOpOperands()) {
+ FailureOr<Value> paddedOperand = padOperandToSmallestStaticBoundingBox(
+ rewriter, opToPad, &opOperand, paddingDimensions, padToMultipleOf,
+ paddingValues, packPaddings);
+ // Exit if `paddingDimensions` cannot be bounded statically.
+ if (failed(paddedOperand)) {
+ LLVM_DEBUG(DBGS() << "--operand cannot be bound statically : "
+ << opOperand.get() << " -> FAIL\n");
+ return rewriter.notifyMatchFailure(opToPad,
+ "operand cannot be bound statically");
+ }
+ newOperands.push_back(*paddedOperand);
+ }
+
+ ReifiedRankedShapedTypeDims reifiedResultShapes;
+ if (failed(reifyResultShapes(rewriter, opToPad, reifiedResultShapes))) {
+ LLVM_DEBUG(DBGS() << "--failed to reify result shapes -> FAIL\n");
+ return rewriter.notifyMatchFailure(opToPad,
+ "failed to reify result shapes");
+ }
+ assert(reifiedResultShapes.size() == opToPad->getNumResults() &&
+ "expected same number of results");
+
+ // Clone `opToPad` to operate on the statically padded shapes.
+ auto resultTensorTypes =
+ ValueRange(newOperands).take_back(opToPad.getNumDpsInits()).getTypes();
+ // clone **should** properly notify the rewriter.
+ paddedOp = clone(rewriter, opToPad, resultTensorTypes, newOperands);
+ LLVM_DEBUG(DBGS() << "--cloned padded op: " << paddedOp << "\n");
+
+ // Recover the slice out of the new static results. This keeps the original
+ // linalg op around because it uses the dims of the original results.
+ SmallVector<Value> paddedSubtensorResults;
+ paddedSubtensorResults.reserve(opToPad->getNumResults());
+ for (const auto &en : llvm::enumerate(paddedOp->getResults())) {
+ Value paddedResult = en.value();
+ int64_t resultNumber = en.index();
+ int64_t rank = cast<RankedTensorType>(paddedResult.getType()).getRank();
+ SmallVector<OpFoldResult> offsets(rank, rewriter.getIndexAttr(0));
+ SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
+ paddedSubtensorResults.push_back(rewriter.create<tensor::ExtractSliceOp>(
+ loc, paddedResult, offsets, reifiedResultShapes[resultNumber],
+ strides));
+ }
+ return paddedSubtensorResults;
+}
+
+FailureOr<LinalgOp>
+mlir::linalg::padAndHoistLinalgOp(RewriterBase &rewriter, LinalgOp linalgOp,
+ LinalgPaddingOptions options) {
+ if (!linalgOp.hasTensorSemantics())
+ return rewriter.notifyMatchFailure(
+ linalgOp, "only applies to Linalg ops with tensor semantics");
+
+ // Pad the operation.
+ LinalgOp paddedOp;
+ SmallVector<int64_t> padToMultipleOf(options.paddingDimensions.size(), 1);
+ if (options.padToMultipleOf.has_value())
+ padToMultipleOf.assign(options.padToMultipleOf->begin(),
+ options.padToMultipleOf->end());
+ FailureOr<SmallVector<Value>> newResults = rewriteAsPaddedOp(
+ rewriter, linalgOp, options.paddingDimensions, padToMultipleOf,
+ options.paddingValues, options.packPaddings, paddedOp);
+ if (failed(newResults))
+ return rewriter.notifyMatchFailure(linalgOp,
+ "failed to rewrite as a padded op");
+
+ // Hoist the padding.
+ for (const auto &en : enumerate(options.hoistPaddings)) {
+ if (static_cast<int64_t>(en.index()) >= paddedOp->getNumOperands())
+ break;
+ OpOperand &opOperand = paddedOp->getOpOperand(en.index());
+ auto padOp = opOperand.get().getDefiningOp<tensor::PadOp>();
+ if (!padOp || en.value() == 0) {
+ (void)rewriter.notifyMatchFailure(linalgOp, "not a tensor.pad -- skip");
+ continue;
+ }
+
+ // Fail hoisting if the operand shape is not fully static.
+ if (llvm::any_of(paddedOp.getShape(&opOperand), ShapedType::isDynamic)) {
+ (void)rewriter.notifyMatchFailure(linalgOp,
+ "non static padding shape -- skip");
+ continue;
+ }
+
+ tensor::PadOp hoistedOp;
+ SmallVector<GenericOp> transposeOps;
+ SmallVector<int64_t> transposeVector =
+ en.index() < options.transposePaddings.size()
+ ? options.transposePaddings[en.index()]
+ : SmallVector<int64_t>{};
+
+ FailureOr<Value> newResult = hoistPaddingOnTensors(
+ padOp, en.value(), transposeVector, hoistedOp, transposeOps);
+ if (failed(newResult)) {
+ (void)rewriter.notifyMatchFailure(linalgOp,
+ "failed to apply hoistPadding");
+ continue;
+ }
+ rewriter.replaceOp(padOp, *newResult);
+ }
+
+ // Replace the original operation to pad.
+ rewriter.replaceOp(linalgOp, *newResults);
+
+ return paddedOp;
+}
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
index 9044fea4509ac..8727e8668cfed 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -27,7 +27,6 @@
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/Matchers.h"
-#include "mlir/Interfaces/ValueBoundsOpInterface.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
@@ -46,176 +45,10 @@ using namespace mlir::linalg;
#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
#define DBGSNL() (llvm::dbgs() << "\n")
-/// Pad the `opOperand` in the `paddingDimensions` using the padding value and
-/// the nofold flag found in `paddingValues` and `packPaddings`, respectively.
-///
-/// Exit early and return the `opOperand` value if it already has the requested
-/// shape. I.e.:
-/// - static shape
-/// - nofold is not set
-/// - dim sizes are multiples of `padToMultipleOf`
-///
-/// Otherwise, try to pad the shape dimensions that match the iterator
-/// dimensions `paddingDimensions` and return the tensor::PadOp result if
-/// padding succeeds or failure otherwise.
-static FailureOr<Value> padOperandToSmallestStaticBoundingBox(
- RewriterBase &rewriter, linalg::LinalgOp opToPad, OpOperand *opOperand,
- ArrayRef<int64_t> paddingDimensions, ArrayRef<int64_t> padToMultipleOf,
- ArrayRef<Attribute> paddingValues, ArrayRef<bool> packPaddings) {
- assert(padToMultipleOf.size() == paddingDimensions.size() &&
- "invalid number of elements in padToMultipleOf");
-
- AffineMap indexingMap = opToPad.getMatchingIndexingMap(opOperand);
- ArrayRef<int64_t> shape = opToPad.getShape(opOperand);
-
- // Collect the shape dimensions that are a function of `paddingDimensions`,
- // along with the multiple that they should be padded to ("1" if none).
- bool alreadyHasRequestedShape = true;
- DenseMap<int64_t, int64_t> shapeDimToMultiple;
- for (const auto &dimEn : enumerate(paddingDimensions)) {
- for (const auto &en : enumerate(indexingMap.getResults())) {
- if (en.value().isFunctionOfDim(dimEn.value())) {
- int64_t dimSize = shape[en.index()];
- shapeDimToMultiple[en.index()] = padToMultipleOf[dimEn.index()];
- if (ShapedType::isDynamic(dimSize)) {
- alreadyHasRequestedShape = false;
- } else if (dimSize % shapeDimToMultiple[en.index()] != 0) {
- alreadyHasRequestedShape = false;
- }
- }
- }
- }
-
- // Return the unpadded operand if padding to a static shape is not needed and
- // if the nofold flag is not set.
- bool nofold = opOperand->getOperandNumber() < packPaddings.size()
- ? packPaddings[opOperand->getOperandNumber()]
- : false;
- if (!nofold && alreadyHasRequestedShape)
- return opOperand->get();
-
- // Fail if `paddingValues` specifies no padding value.
- if (opOperand->getOperandNumber() >= paddingValues.size()) {
- return rewriter.notifyMatchFailure(opToPad, "--no padding value specified");
- }
- Attribute paddingAttr = paddingValues[opOperand->getOperandNumber()];
- Value paddingValue = rewriter.create<arith::ConstantOp>(
- opToPad.getLoc(), cast<TypedAttr>(paddingAttr));
-
- // Helper function to round a number up to a given multiple.
- auto ceil = [](int64_t val, int64_t multiple) {
- return ((val + multiple - 1) / multiple) * multiple;
- };
-
- // Upper bound the sizes to obtain a static bounding box.
- SmallVector<int64_t> paddedShape(shape.begin(), shape.end());
- for (int64_t i = 0, e = shape.size(); i < e; ++i) {
- LLVM_DEBUG(DBGS() << "--compute padded size for dim " << i << "\n");
- // Skip dimensions that do not require padding.
- if (!shapeDimToMultiple.contains(i)) {
- LLVM_DEBUG(DBGS() << "----dim does not require padding, SKIP\n");
- continue;
- }
- // Otherwise, try to compute a constant upper bound for the size value.
- FailureOr<int64_t> upperBound =
- ValueBoundsConstraintSet::computeConstantBound(
- presburger::BoundType::UB, opOperand->get(),
- /*dim=*/i, /*stopCondition=*/nullptr, /*closedUB=*/true);
- if (failed(upperBound)) {
- LLVM_DEBUG(DBGS() << "----count not compute a bounding box for padding");
- return rewriter.notifyMatchFailure(
- opToPad, "count not compute a bounding box for padding");
- }
- paddedShape[i] = ceil(*upperBound, shapeDimToMultiple[i]);
- LLVM_DEBUG(DBGS() << "----new dim size: " << paddedShape[i] << "\n");
- }
-
- // Pad the operand to the bounding box defined by `paddedShape`.
- auto paddedTensorType = RankedTensorType::get(
- paddedShape, getElementTypeOrSelf(opOperand->get()));
- LLVM_DEBUG(DBGS() << "--SUCCESS, makeComposedPadHighOp with type: "
- << paddedTensorType);
- return makeComposedPadHighOp(rewriter, opToPad->getLoc(), paddedTensorType,
- opOperand->get(), paddingValue, nofold);
-}
-
//===----------------------------------------------------------------------===//
// Transformations exposed as functional-style API calls.
//===----------------------------------------------------------------------===//
-//===----------------------------------------------------------------------===//
-// rewriteAsPaddedOp transformation.
-//===----------------------------------------------------------------------===//
-
-FailureOr<SmallVector<Value>>
-linalg::rewriteAsPaddedOp(RewriterBase &rewriter, LinalgOp opToPad,
- ArrayRef<int64_t> paddingDimensions,
- ArrayRef<int64_t> padToMultipleOf,
- ArrayRef<Attribute> paddingValues,
- ArrayRef<bool> packPaddings, LinalgOp &paddedOp) {
- LLVM_DEBUG(DBGS() << "Start rewriteAsPaddedOp : " << opToPad << "\n");
- Location loc = opToPad->getLoc();
-
- // TODO: there are cases where we may still want to pad to larger sizes.
- if (!opToPad.hasTensorSemantics())
- return rewriter.notifyMatchFailure(opToPad,
- "expected operation on tensors");
-
- OpBuilder::InsertionGuard g(rewriter);
- // Set IP after op because we also take the dims of the original output.
- rewriter.setInsertionPointAfter(opToPad);
-
- // Make a copy of the shaped operands and update it.
- SmallVector<Value> newOperands;
- newOperands.reserve(opToPad->getNumOperands());
- for (OpOperand &opOperand : opToPad->getOpOperands()) {
- FailureOr<Value> paddedOperand = padOperandToSmallestStaticBoundingBox(
- rewriter, opToPad, &opOperand, paddingDimensions, padToMultipleOf,
- paddingValues, packPaddings);
- // Exit if `paddingDimensions` cannot be bounded statically.
- if (failed(paddedOperand)) {
- LLVM_DEBUG(DBGS() << "--operand cannot be bound statically : "
- << opOperand.get() << " -> FAIL\n");
- return rewriter.notifyMatchFailure(opToPad,
- "operand cannot be bound statically");
- }
- newOperands.push_back(*paddedOperand);
- }
-
- ReifiedRankedShapedTypeDims reifiedResultShapes;
- if (failed(reifyResultShapes(rewriter, opToPad, reifiedResultShapes))) {
- LLVM_DEBUG(DBGS() << "--failed to reify result shapes -> FAIL\n");
- return rewriter.notifyMatchFailure(opToPad,
- "failed to reify result shapes");
- }
- assert(reifiedResultShapes.size() == opToPad->getNumResults() &&
- "expected same number of results");
-
- // Clone `opToPad` to operate on the statically padded shapes.
- auto resultTensorTypes =
- ValueRange(newOperands).take_back(opToPad.getNumDpsInits()).getTypes();
- // clone **should** properly notify the rewriter.
- paddedOp = clone(rewriter, opToPad, resultTensorTypes, newOperands);
- LLVM_DEBUG(DBGS() << "--cloned padded op: " << paddedOp << "\n");
-
- // Recover the slice out of the new static results. This keeps the original
- // linalg op around because it uses the dims of the original results.
- SmallVector<Value> paddedSubtensorResults;
- paddedSubtensorResults.reserve(opToPad->getNumResults());
- for (const auto &en : llvm::enumerate(paddedOp->getResults())) {
- Value paddedResult = en.value();
- int64_t resultNumber = en.index();
- int64_t rank = cast<RankedTensorType>(paddedResult.getType()).getRank();
- SmallVector<OpFoldResult> offsets(rank, rewriter.getIndexAttr(0));
- SmallVector<OpFoldResult> sizes;
- SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
- paddedSubtensorResults.push_back(rewriter.create<tensor::ExtractSliceOp>(
- loc, paddedResult, offsets, reifiedResultShapes[resultNumber],
- strides));
- }
- return paddedSubtensorResults;
-}
-
//===----------------------------------------------------------------------===//
// peelLoop transformation.
//===----------------------------------------------------------------------===//
@@ -245,71 +78,6 @@ void mlir::linalg::peelLoops(RewriterBase &rewriter,
peelLoop(rewriter, loopOp);
}
-//===----------------------------------------------------------------------===//
-// pad transformation.
-//===----------------------------------------------------------------------===//
-
-FailureOr<LinalgOp>
-mlir::linalg::padAndHoistLinalgOp(RewriterBase &rewriter, LinalgOp linalgOp,
- LinalgPaddingOptions options) {
- if (!linalgOp.hasTensorSemantics())
- return rewriter.notifyMatchFailure(
- linalgOp, "only applies to Linalg ops with tensor semantics");
-
- // Pad the operation.
- LinalgOp paddedOp;
- SmallVector<int64_t> padToMultipleOf(options.paddingDimensions.size(), 1);
- if (options.padToMultipleOf.has_value())
- padToMultipleOf.assign(options.padToMultipleOf->begin(),
- options.padToMultipleOf->end());
- FailureOr<SmallVector<Value>> newResults = rewriteAsPaddedOp(
- rewriter, linalgOp, options.paddingDimensions, padToMultipleOf,
- options.paddingValues, options.packPaddings, paddedOp);
- if (failed(newResults))
- return rewriter.notifyMatchFailure(linalgOp,
- "failed to rewrite as a padded op");
-
- // Hoist the padding.
- for (const auto &en : enumerate(options.hoistPaddings)) {
- if (static_cast<int64_t>(en.index()) >= paddedOp->getNumOperands())
- break;
- OpOperand &opOperand = paddedOp->getOpOperand(en.index());
- auto padOp = opOperand.get().getDefiningOp<tensor::PadOp>();
- if (!padOp || en.value() == 0) {
- (void)rewriter.notifyMatchFailure(linalgOp, "not a tensor.pad -- skip");
- continue;
- }
-
- // Fail hoisting if the operand shape is not fully static.
- if (llvm::any_of(paddedOp.getShape(&opOperand), ShapedType::isDynamic)) {
- (void)rewriter.notifyMatchFailure(linalgOp,
- "non static padding shape -- skip");
- continue;
- }
-
- tensor::PadOp hoistedOp;
- SmallVector<GenericOp> transposeOps;
- SmallVector<int64_t> transposeVector =
- en.index() < options.transposePaddings.size()
- ? options.transposePaddings[en.index()]
- : SmallVector<int64_t>{};
-
- FailureOr<Value> newResult = hoistPaddingOnTensors(
- padOp, en.value(), transposeVector, hoistedOp, transposeOps);
- if (failed(newResult)) {
- (void)rewriter.notifyMatchFailure(linalgOp,
- "failed to apply hoistPadding");
- continue;
- }
- rewriter.replaceOp(padOp, *newResult);
- }
-
- // Replace the original operation to pad.
- rewriter.replaceOp(linalgOp, *newResults);
-
- return paddedOp;
-}
-
//===----------------------------------------------------------------------===//
// pack transformation.
//===----------------------------------------------------------------------===//
@@ -1004,19 +772,6 @@ mlir::linalg::LinalgTilingOptions::setTileSizes(ArrayRef<int64_t> ts) {
return *this;
}
-///
-/// Padding pattern.
-///
-mlir::linalg::LinalgPaddingPattern::LinalgPaddingPattern(
- MLIRContext *context, LinalgPaddingOptions options, PatternBenefit benefit)
- : OpInterfaceRewritePattern<LinalgOp>(context, benefit),
- options(std::move(options)) {}
-
-LogicalResult mlir::linalg::LinalgPaddingPattern::matchAndRewrite(
- LinalgOp op, PatternRewriter &rewriter) const {
- return padAndHoistLinalgOp(rewriter, op, options);
-}
-
LogicalResult mlir::linalg::CopyVectorizationPattern::matchAndRewrite(
memref::CopyOp copyOp, PatternRewriter &rewriter) const {
return vectorizeCopy(rewriter, copyOp);
More information about the Mlir-commits
mailing list