3100 lines
125 KiB
C++
3100 lines
125 KiB
C++
//===- LinalgTransformOps.cpp - Implementation of Linalg transform ops ----===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.h"
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#include "mlir/AsmParser/AsmParser.h"
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/GPU/IR/GPUDialect.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
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#include "mlir/Dialect/Linalg/Utils/Utils.h"
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#include "mlir/Dialect/PDL/IR/PDL.h"
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#include "mlir/Dialect/PDL/IR/PDLTypes.h"
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#include "mlir/Dialect/SCF/Transforms/TileUsingInterface.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tensor/Utils/Utils.h"
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#include "mlir/Dialect/Transform/IR/TransformDialect.h"
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#include "mlir/Dialect/Transform/IR/TransformInterfaces.h"
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#include "mlir/Dialect/Transform/IR/TransformTypes.h"
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#include "mlir/Dialect/Transform/IR/TransformUtils.h"
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#include "mlir/Dialect/Transform/Utils/Utils.h"
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#include "mlir/Dialect/Utils/IndexingUtils.h"
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#include "mlir/IR/AffineMap.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Matchers.h"
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#include "mlir/IR/OpDefinition.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/Interfaces/TilingInterface.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/ScopeExit.h"
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#include "llvm/ADT/SetOperations.h"
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#include "llvm/ADT/SmallVector.h"
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#include "llvm/ADT/StringSet.h"
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#include "llvm/ADT/TypeSwitch.h"
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#include "llvm/Support/Debug.h"
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using namespace mlir;
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using namespace mlir::linalg;
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using namespace mlir::transform;
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#define DEBUG_TYPE "linalg-transforms"
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#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE "]: ")
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#define DBGSNL() (llvm::dbgs() << "\n")
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/// Attempts to apply the pattern specified as template argument to the given
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/// operation. The pattern is expected to have a `returningMatchAndRewrite`
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/// function that returns the "main" result or failure. Returns failure if the
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/// pattern failed to apply. Extra arguments are forwarded to the pattern
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/// constructor.
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template <typename PatternTy, typename... Args>
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static FailureOr<LinalgOp> tryApply(Operation *operation, Args &&...args) {
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// Check if the given operation has the type expected by the pattern.
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using OpTy = typename llvm::function_traits<
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decltype(&PatternTy::returningMatchAndRewrite)>::template arg_t<0>;
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auto op = dyn_cast<OpTy>(operation);
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if (!op)
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return failure();
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// Apply the pattern directly to the op.
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PatternTy pattern(operation->getContext(), std::forward<Args>(args)...);
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TrivialPatternRewriter rewriter(operation->getContext());
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rewriter.setInsertionPoint(operation);
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auto result = pattern.returningMatchAndRewrite(op, rewriter);
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if (failed(result))
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return failure();
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return cast<LinalgOp>(result->getOperation());
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}
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/// Assuming that `ofr` is an index attr or a transform dialect handle mapped
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/// to exactly one op with one index result, return that value.
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static DiagnosedSilenceableFailure unpackSingleIndexResultPDLOperations(
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transform::TransformState &state, TransformOpInterface transformOp,
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SmallVector<OpFoldResult> &result, ArrayRef<OpFoldResult> ofrs) {
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for (OpFoldResult ofr : ofrs) {
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if (ofr.is<Attribute>()) {
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if (!ofr.get<Attribute>().isa<IntegerAttr>())
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return transformOp.emitDefiniteFailure() << "expected IntegerAttr";
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result.push_back(ofr);
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continue;
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}
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ArrayRef<Operation *> payloadOps = state.getPayloadOps(ofr.get<Value>());
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if (payloadOps.size() != 1) {
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DiagnosedSilenceableFailure diag =
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transformOp.emitSilenceableError()
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<< "handle must be mapped to exactly one payload op";
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diag.attachNote(ofr.get<Value>().getLoc())
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<< "mapped to " << payloadOps.size() << " payload ops";
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return diag;
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}
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Operation *op = payloadOps[0];
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if (op->getNumResults() != 1 || !op->getResult(0).getType().isIndex()) {
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DiagnosedSilenceableFailure diag =
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transformOp.emitSilenceableError()
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<< "payload op must have exactly 1 index result";
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diag.attachNote(op->getLoc())
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<< "has " << op->getNumResults() << " results";
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return diag;
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}
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result.push_back(op->getResult(0));
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}
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return DiagnosedSilenceableFailure::success();
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}
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// Given a list of OpFoldResults that are either index attrs or op
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// handles, return a list of OpFoldResults where all op handles are
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// replaced with the first (and only) OpResult of that payload op. (There
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// must be exactly one mapped payload op and it must have exactly one
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// index result.)
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static DiagnosedSilenceableFailure unpackSingleIndexResultPDLOperations(
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transform::TransformState &state, TransformOpInterface transformOp,
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SmallVector<OpFoldResult> &result, Value packedHandle) {
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ArrayRef<Operation *> payloadOps = state.getPayloadOps(packedHandle);
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for (Operation *op : payloadOps) {
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if (op->getNumResults() != 1 || !op->getResult(0).getType().isIndex()) {
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DiagnosedSilenceableFailure diag =
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transformOp.emitSilenceableError()
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<< "payload op must have exactly 1 index result";
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diag.attachNote(op->getLoc())
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<< "has " << op->getNumResults() << " results";
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return diag;
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}
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result.push_back(op->getResult(0));
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}
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return DiagnosedSilenceableFailure::success();
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}
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/// Return a permutation vector of size permSize that would result in moving
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/// positions into desiredPositions.
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///
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/// For example, permSize == 5, positions = {2, 4}, desiredPositions = {1, 0}
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/// would result in a {4, 2, 0, 1, 3} permutation vector.
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static SmallVector<int64_t>
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computePermutationVector(int64_t permSize, ArrayRef<int64_t> positions,
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ArrayRef<int64_t> desiredPositions) {
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SmallVector<int64_t> res(permSize, -1);
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DenseSet<int64_t> seen;
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for (auto [pos, desiredPos] : llvm::zip_equal(positions, desiredPositions)) {
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res[desiredPos] = pos;
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seen.insert(pos);
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}
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int64_t nextPos = 0;
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for (int64_t &entry : res) {
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if (entry != -1)
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continue;
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while (seen.contains(nextPos))
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++nextPos;
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entry = nextPos;
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++nextPos;
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}
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return res;
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}
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struct PackingMetadata {
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SmallVector<int64_t> insertPositions;
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SmallVector<ReassociationIndices> reassociations;
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};
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/// Given a vector of `positions` indices representing desired packing insertion
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/// points into a target vector (i.e. pack/unpack.inner_dim_pos), compute the
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/// final positions in the target shape as well as the reshape reassociations.
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// Note: This should not be called with a large positions array (or the
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// implementation needs to be updated to use an N.log N sort instead of
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// repeated N^2 counts).
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static PackingMetadata computePackingMetadata(int64_t packedRank,
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ArrayRef<int64_t> innerDimPos) {
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PackingMetadata res;
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res.insertPositions.reserve(innerDimPos.size());
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// The pack insert position is the position + the number of previously
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// inserted positions + offset.
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// The offset controls whether the packing dimension is the first or last.
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//
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// Example
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// =======
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// Consider packing from a hypothetical ABCD layout to ABCDba whose
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// pack.inner_dims is [1, 0]. The first step consists in undoing the
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// permutation and producing AaBbCD. This is achieved purely by computing the
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// insert positions of `b` and `a` into `ABCD`, starting from [1, 0]. One
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// possibility, is to produce insert positions [2, 0], this would result in an
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// aAbBCD layout (i.e. offset 0). The other possibility, is to produce insert
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// positions [3, 1], this would result in an AaBbCD layout (i.e. offset 1).
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// The latter is what we expect from packing.
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int64_t offset = 1;
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for (int64_t pos : innerDimPos) {
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int64_t numInsertedBefore = llvm::count_if(
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innerDimPos, [&pos](int64_t pos2) { return pos > pos2; });
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res.insertPositions.push_back(pos + numInsertedBefore + offset);
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}
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DenseSet<int64_t> posSet(res.insertPositions.begin(),
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res.insertPositions.end());
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res.reassociations.reserve(packedRank);
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for (int64_t i = 1; i <= packedRank; ++i) {
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if (!posSet.contains(i)) {
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res.reassociations.push_back(ReassociationIndices{i - 1});
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continue;
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}
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res.reassociations.push_back(ReassociationIndices{i - 1, i});
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++i;
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}
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return res;
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}
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//===----------------------------------------------------------------------===//
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// BufferizeToAllocationOp
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//===----------------------------------------------------------------------===//
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DiagnosedSilenceableFailure
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transform::BufferizeToAllocationOp::apply(transform::TransformResults &results,
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transform::TransformState &state) {
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Attribute memorySpace =
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getMemorySpace().has_value() ? getMemorySpace().value() : Attribute();
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IRRewriter rewriter(getContext());
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auto transformed = llvm::to_vector(
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llvm::map_range(state.getPayloadValues(getTarget()), [&](Value v) {
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return linalg::bufferizeToAllocation(rewriter, v, memorySpace);
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}));
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results.setValues(getTransformed().cast<OpResult>(), transformed);
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return DiagnosedSilenceableFailure::success();
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}
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void transform::BufferizeToAllocationOp::getEffects(
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SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
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consumesHandle(getTarget(), effects);
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producesHandle(getTransformed(), effects);
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modifiesPayload(effects);
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}
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//===----------------------------------------------------------------------===//
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// DecomposeOp
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//===----------------------------------------------------------------------===//
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DiagnosedSilenceableFailure
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transform::DecomposeOp::applyToOne(LinalgOp target,
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transform::ApplyToEachResultList &results,
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transform::TransformState &state) {
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#define DOWNSCALE(trans) \
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{ \
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FailureOr<LinalgOp> res = tryApply<trans>(target); \
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if (succeeded(res)) { \
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results.push_back(*res); \
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return DiagnosedSilenceableFailure::success(); \
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} \
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}
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#define DOWNSCALE_CALL(a, b) DownscaleSizeOneWindowed2DConvolution<a, b>
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#define DOWNSCALE_NORMAL(a, b) DOWNSCALE(DOWNSCALE_CALL(a, b))
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DOWNSCALE_NORMAL(Conv2DNhwcHwcfOp, Conv1DNwcWcfOp)
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DOWNSCALE_NORMAL(Conv2DNchwFchwOp, Conv1DNcwFcwOp)
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DOWNSCALE_NORMAL(PoolingNhwcSumOp, PoolingNwcSumOp)
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DOWNSCALE_NORMAL(PoolingNchwSumOp, PoolingNcwSumOp)
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DOWNSCALE_NORMAL(PoolingNhwcMaxOp, PoolingNwcMaxOp)
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DOWNSCALE_NORMAL(PoolingNhwcMaxUnsignedOp, PoolingNwcMaxUnsignedOp)
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DOWNSCALE_NORMAL(PoolingNhwcMinOp, PoolingNwcMinOp)
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DOWNSCALE_NORMAL(PoolingNhwcMinUnsignedOp, PoolingNwcMinUnsignedOp)
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DOWNSCALE_NORMAL(PoolingNchwMaxOp, PoolingNcwMaxOp)
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DOWNSCALE(DownscaleDepthwiseConv2DNhwcHwcOp)
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#undef DOWNSCALE_NORMAL
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#undef DOWNSCALE_CALL
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#undef DOWNSCALE
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return emitDefaultSilenceableFailure(target);
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}
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//===----------------------------------------------------------------------===//
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// FuseOp
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//===----------------------------------------------------------------------===//
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/// Apply a tiling transformation to all payload ops and store both the
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/// tiled operation as well as the created tile loops.
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static LogicalResult applyTilingToAll(
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Operation *transformOp, ArrayRef<Operation *> payloadOps, unsigned numLoops,
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transform::TransformResults &transformResults,
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function_ref<FailureOr<scf::SCFTileAndFuseResult>(TilingInterface)>
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applyFn) {
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SmallVector<Operation *> tiledLinalgOps;
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SmallVector<SmallVector<Operation *>> loopOps(numLoops);
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for (unsigned int i = 0; i < numLoops; ++i)
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loopOps[i].reserve(payloadOps.size());
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for (Operation *target : payloadOps) {
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auto tilingInterfaceOp = dyn_cast<TilingInterface>(target);
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if (!tilingInterfaceOp)
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return transformOp->emitError("only TilingInterface ops are supported");
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TrivialPatternRewriter rewriter(target->getContext());
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rewriter.setInsertionPoint(target);
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FailureOr<scf::SCFTileAndFuseResult> tiledResults =
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applyFn(tilingInterfaceOp);
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if (failed(tiledResults))
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return failure();
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// Perform the replacement of tiled and fused values.
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SmallVector<Operation *> opsToReplace{target};
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llvm::append_range(opsToReplace, tiledResults->fusedProducers);
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for (Operation *toReplace : opsToReplace) {
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SmallVector<Value> replacements;
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replacements.reserve(toReplace->getNumResults());
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for (OpResult res : toReplace->getResults()) {
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auto it = tiledResults->replacements.find(res);
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if (it == tiledResults->replacements.end())
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replacements.push_back(res);
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else
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replacements.push_back(it->getSecond());
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}
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rewriter.replaceOp(toReplace, replacements);
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}
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// Report back the relevant handles to the transform op.
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tiledLinalgOps.push_back(tiledResults->tiledAndFusedOps.front());
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assert(tiledResults->loops.size() == numLoops &&
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"Mismatched number of loops, tile and fuse transform should have "
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"failed");
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for (unsigned int i = 0; i < numLoops; ++i)
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loopOps[i].push_back(tiledResults->loops[i]);
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}
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transformResults.set(transformOp->getOpResult(0), tiledLinalgOps);
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for (unsigned int i = 0; i < numLoops; ++i)
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transformResults.set(transformOp->getOpResult(i + 1), loopOps[i]);
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return success();
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}
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/// Parse a tiling-like operation that returns the tiled op as well as the
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/// created tile loops. The function counts the non-zero tile sizes to compute
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/// the number of results.
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static ParseResult parseTileLikeOp(OpAsmParser &parser, OperationState &result,
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StringRef sizesAttrName) {
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OpAsmParser::UnresolvedOperand targetOperand;
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SMLoc opLoc = parser.getCurrentLocation();
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if (parser.parseOperand(targetOperand) ||
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parser.parseOptionalAttrDict(result.attributes))
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return failure();
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Attribute sizesAttr = result.attributes.get(sizesAttrName);
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if (!sizesAttr)
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return parser.emitError(opLoc)
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<< "expected '" << sizesAttrName << "' attribute";
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auto sizesArrayAttr = sizesAttr.dyn_cast<ArrayAttr>();
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if (!sizesArrayAttr)
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return parser.emitError(opLoc)
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<< "'" << sizesAttrName << "' attribute must be an array";
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Type pdlOpType = parser.getBuilder().getType<pdl::OperationType>();
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size_t numExpectedLoops =
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sizesArrayAttr.size() -
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llvm::count(extractFromI64ArrayAttr(sizesArrayAttr), 0);
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result.addTypes(SmallVector<Type>(numExpectedLoops + 1, pdlOpType));
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if (parser.resolveOperand(targetOperand, pdlOpType, result.operands))
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return failure();
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return success();
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}
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DiagnosedSilenceableFailure
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transform::FuseOp::apply(mlir::transform::TransformResults &transformResults,
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mlir::transform::TransformState &state) {
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SmallVector<int64_t> tileSizes = extractFromI64ArrayAttr(getTileSizes());
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SmallVector<int64_t> tileInterchange =
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extractFromI64ArrayAttr(getTileInterchange());
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scf::SCFTilingOptions tilingOptions;
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tilingOptions.interchangeVector = tileInterchange;
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tilingOptions = tilingOptions.setTileSizes(tileSizes);
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scf::SCFTileAndFuseOptions tileAndFuseOptions;
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tileAndFuseOptions.tilingOptions = tilingOptions;
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LogicalResult result = applyTilingToAll(
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getOperation(), state.getPayloadOps(getTarget()),
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tileSizes.size() - llvm::count(tileSizes, 0), transformResults,
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[&](TilingInterface tilingInterfaceOp)
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-> FailureOr<scf::SCFTileAndFuseResult> {
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TrivialPatternRewriter rewriter(getContext());
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return tileConsumerAndFuseProducerGreedilyUsingSCFForOp(
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rewriter, tilingInterfaceOp, tileAndFuseOptions);
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});
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return failed(result) ? DiagnosedSilenceableFailure::definiteFailure()
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: DiagnosedSilenceableFailure::success();
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}
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ParseResult transform::FuseOp::parse(OpAsmParser &parser,
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OperationState &result) {
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return parseTileLikeOp(
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parser, result,
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transform::FuseOp::getTileSizesAttrName(result.name).getValue());
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}
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void transform::FuseOp::print(OpAsmPrinter &p) {
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p << ' ';
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p << getTarget();
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p.printOptionalAttrDict((*this)->getAttrs());
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}
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LogicalResult transform::FuseOp::verify() {
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SmallVector<int64_t> permutation =
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extractFromI64ArrayAttr(getTileInterchange());
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auto sequence = llvm::to_vector(llvm::seq<int64_t>(0, permutation.size()));
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if (!std::is_permutation(sequence.begin(), sequence.end(),
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permutation.begin(), permutation.end())) {
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return emitOpError() << "expects interchange to be a permutation, found "
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<< getTileInterchange();
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}
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return success();
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}
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//===----------------------------------------------------------------------===//
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// FuseIntoContainingOp
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//===----------------------------------------------------------------------===//
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void transform::FuseIntoContainingOp::build(OpBuilder &builder,
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OperationState &result,
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Value producerOp,
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Value containingOp) {
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result.addOperands({producerOp, containingOp});
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result.addTypes(pdl::OperationType::get(builder.getContext()));
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}
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/// Find the first "extract" user of `producerOp` and tile it right before its
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/// use. The tiled op is fused under the `containingOp`.
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/// Return this fused op on success or nullptr if anything fails.
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static Operation *tileAndFuseFirstExtractUse(RewriterBase &rewriter,
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Diagnostic &diag,
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Operation *producerOp,
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Operation *containingOp) {
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LLVM_DEBUG(DBGS() << "Try to fuse a direct extract use\n");
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auto tileableProducer = dyn_cast<TilingInterface>(producerOp);
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if (!tileableProducer) {
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diag.attachNote(producerOp->getLoc())
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<< "producer is not a TileableInterface: " << *producerOp;
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return nullptr;
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}
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// Search the producer slices accessed within the containing operation.
|
|
// TODO: Generalize to more extract/insert/parallel_insert triples, maybe
|
|
// evolve into an interface.
|
|
auto it = llvm::find_if(tileableProducer->getUsers(), [&](Operation *user) {
|
|
auto sliceOp = dyn_cast<tensor::ExtractSliceOp>(user);
|
|
return sliceOp && containingOp->isProperAncestor(sliceOp);
|
|
});
|
|
|
|
// Find a fusion opportunity.
|
|
if (it == tileableProducer->getUsers().end()) {
|
|
diag.attachNote(tileableProducer->getLoc())
|
|
<< "could not find fusion opportunity for: " << *tileableProducer;
|
|
return nullptr;
|
|
}
|
|
auto sliceOpToTile = cast<tensor::ExtractSliceOp>(*it);
|
|
|
|
// Try to fuse the producer in-place.
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
rewriter.setInsertionPoint(sliceOpToTile);
|
|
|
|
// Tile the producer.
|
|
int64_t resultNumber =
|
|
sliceOpToTile.getSource().cast<OpResult>().getResultNumber();
|
|
LLVM_DEBUG(DBGS() << "resultNumber: " << resultNumber << "\n");
|
|
|
|
FailureOr<Value> tiledProducer = tileableProducer.generateResultTileValue(
|
|
rewriter, resultNumber, sliceOpToTile.getMixedOffsets(),
|
|
sliceOpToTile.getMixedSizes());
|
|
if (failed(tiledProducer)) {
|
|
diag.attachNote(tileableProducer->getLoc())
|
|
<< "failed to tile producer op: " << *tileableProducer;
|
|
return nullptr;
|
|
}
|
|
LLVM_DEBUG(DBGS() << "tiledProducer: " << *tiledProducer << "\n");
|
|
|
|
// Replace the extract op.
|
|
Operation *fusedOp = tiledProducer->getDefiningOp();
|
|
auto maybeRankReduced = tensor::ExtractSliceOp::rankReduceIfNeeded(
|
|
rewriter, sliceOpToTile->getLoc(), fusedOp->getResult(resultNumber),
|
|
sliceOpToTile->getResult(0)
|
|
.getType()
|
|
.cast<RankedTensorType>()
|
|
.getShape());
|
|
assert(succeeded(maybeRankReduced) && "unexpected shape");
|
|
rewriter.replaceOp(sliceOpToTile, *maybeRankReduced);
|
|
return fusedOp;
|
|
}
|
|
|
|
/// First, find the first "scf::ForallOp" user of `producerOp` and ensure
|
|
/// it is exactly the `containingOp`, otherwise bail.
|
|
/// Then, find the first "extract" user of the tied block argument and tile it
|
|
/// right before its "extract" use. The tiled op is fused under the
|
|
/// `containingOp`.
|
|
/// Return this fused op on success or nullptr if anything fails.
|
|
static Operation *tileAndFuseFirstExtractUseThroughContainingOpBlockArgument(
|
|
RewriterBase &rewriter, Diagnostic &diag, Operation *producerOp,
|
|
Operation *containingOp) {
|
|
LLVM_DEBUG(DBGS() << "Try to fuse an extract use through block argument\n");
|
|
|
|
auto tileableProducer = dyn_cast<TilingInterface>(producerOp);
|
|
if (!tileableProducer) {
|
|
diag.attachNote(producerOp->getLoc())
|
|
<< "producer is not a TileableInterface: " << *producerOp;
|
|
return nullptr;
|
|
}
|
|
|
|
// Search the first use by a "scf::ForallOp" user.
|
|
scf::ForallOp forallOp;
|
|
auto itProducerUses =
|
|
llvm::find_if(tileableProducer->getUses(), [&](OpOperand &use) {
|
|
forallOp = dyn_cast<scf::ForallOp>(use.getOwner());
|
|
return forallOp;
|
|
});
|
|
// If it's not from the containing op, return.
|
|
if (!forallOp || forallOp != containingOp) {
|
|
diag.attachNote(tileableProducer->getLoc())
|
|
<< "could not find a use by the containing op: " << *tileableProducer;
|
|
return nullptr;
|
|
}
|
|
|
|
// Search the producer slices accessed within the containing
|
|
// operation.
|
|
// TODO: Generalize to more extract/insert/parallel_insert triples.
|
|
// Maybe evolve into an interface.
|
|
OpOperand *pUse = &(*itProducerUses);
|
|
BlockArgument bbArg = forallOp.getTiedBlockArgument(pUse);
|
|
|
|
// Search the producer slices accessed within the containing operation.
|
|
// TODO: Generalize to more extract/insert/parallel_insert triples, maybe
|
|
// evolve into an interface.
|
|
auto itBBArgUsers = llvm::find_if(bbArg.getUsers(), [&](Operation *user) {
|
|
auto sliceOp = dyn_cast<tensor::ExtractSliceOp>(user);
|
|
return sliceOp && containingOp->isProperAncestor(sliceOp);
|
|
});
|
|
|
|
// Find a fusion opportunity.
|
|
if (itBBArgUsers == bbArg.getUsers().end()) {
|
|
diag.attachNote(containingOp->getLoc())
|
|
<< "could not find fusion opportunity for bbArg: " << bbArg;
|
|
return nullptr;
|
|
}
|
|
auto sliceOpToTile = cast<tensor::ExtractSliceOp>(*itBBArgUsers);
|
|
|
|
// Try to fuse the producer in-place.
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
rewriter.setInsertionPoint(sliceOpToTile);
|
|
|
|
// Replace the use in the tileableProducer before tiling: clone, replace and
|
|
// then tile.
|
|
int64_t resultNumber = pUse->get().cast<OpResult>().getResultNumber();
|
|
LLVM_DEBUG(DBGS() << "resultNumber: " << resultNumber << "\n");
|
|
|
|
// Gather destination tensors.
|
|
SmallVector<Value> destinationTensors;
|
|
if (failed(tensor::getOrCreateDestinations(
|
|
rewriter, tileableProducer->getLoc(), tileableProducer,
|
|
destinationTensors))) {
|
|
diag.attachNote(tileableProducer->getLoc())
|
|
<< "failed to get destination tensors for: " << *tileableProducer;
|
|
return nullptr;
|
|
}
|
|
|
|
IRMapping bvm;
|
|
bvm.map(destinationTensors[resultNumber], bbArg);
|
|
auto tileableProducerClone =
|
|
cast<TilingInterface>(rewriter.clone(*tileableProducer, bvm));
|
|
auto scopeGuard =
|
|
llvm::make_scope_exit([&]() { rewriter.eraseOp(tileableProducerClone); });
|
|
|
|
// Tile the producer.
|
|
FailureOr<Value> tiledProducer =
|
|
tileableProducerClone.generateResultTileValue(
|
|
rewriter, resultNumber, sliceOpToTile.getMixedOffsets(),
|
|
sliceOpToTile.getMixedSizes());
|
|
if (failed(tiledProducer)) {
|
|
diag.attachNote(tileableProducer->getLoc())
|
|
<< "failed to tile producer op: " << *tileableProducer;
|
|
return nullptr;
|
|
}
|
|
LLVM_DEBUG(DBGS() << "tiledProducer: " << *tiledProducer << "\n");
|
|
|
|
// Replace the extract op.
|
|
Operation *fusedOp = tiledProducer->getDefiningOp();
|
|
auto maybeRankReduced = tensor::ExtractSliceOp::rankReduceIfNeeded(
|
|
rewriter, sliceOpToTile->getLoc(), fusedOp->getResult(resultNumber),
|
|
sliceOpToTile->getResult(0)
|
|
.getType()
|
|
.cast<RankedTensorType>()
|
|
.getShape());
|
|
assert(succeeded(maybeRankReduced) && "unexpected shape");
|
|
rewriter.replaceOp(sliceOpToTile, *maybeRankReduced);
|
|
|
|
// Replace the use in containingOp.
|
|
rewriter.updateRootInPlace(containingOp, [&]() {
|
|
containingOp->setOperand(pUse->getOperandNumber(),
|
|
destinationTensors.front());
|
|
});
|
|
|
|
return fusedOp;
|
|
}
|
|
|
|
static Operation *cloneAndFuseFirstUse(RewriterBase &rewriter, Diagnostic &diag,
|
|
Operation *producerOp,
|
|
Operation *containingOp) {
|
|
LLVM_DEBUG(DBGS() << "Try to fuse an use by cloning\n");
|
|
|
|
// Gather all uses inside the containing op.
|
|
SmallVector<OpOperand *> uses;
|
|
for (OpResult result : producerOp->getOpResults()) {
|
|
for (OpOperand &use : result.getUses()) {
|
|
if (containingOp->isProperAncestor(use.getOwner())) {
|
|
uses.push_back(&use);
|
|
continue;
|
|
}
|
|
// Cannot clone and fuse if the use is by the containing op itself: fail
|
|
// immediately.
|
|
if (containingOp == use.getOwner()) {
|
|
diag.attachNote(producerOp->getLoc())
|
|
<< "producer op use by containing op cannot be fused by cloning";
|
|
return nullptr;
|
|
}
|
|
}
|
|
}
|
|
|
|
// Check for a non-empty list of fusion opportunities.
|
|
if (uses.empty()) {
|
|
diag.attachNote(producerOp->getLoc()) << "no fusion opportunity by cloning";
|
|
return nullptr;
|
|
}
|
|
|
|
// Clone and fuse inside the containing op.
|
|
Operation *fusedOp = nullptr;
|
|
OpOperand *use = uses.front();
|
|
// Parallel insert slice is not a valid clone destination.
|
|
// TODO: Generalize to other type of ops.
|
|
assert(!isa<tensor::ParallelInsertSliceOp>(use->getOwner()) &&
|
|
"Parallel insert slice is not a valid clone destination");
|
|
unsigned resultNumber = use->get().cast<OpResult>().getResultNumber();
|
|
LLVM_DEBUG(DBGS() << "resultNumber: " << resultNumber << "\n");
|
|
|
|
OpBuilder::InsertionGuard guard(rewriter);
|
|
rewriter.setInsertionPoint(use->getOwner());
|
|
fusedOp = rewriter.clone(*producerOp);
|
|
rewriter.updateRootInPlace(
|
|
use->getOwner(), [&] { use->set(fusedOp->getOpResult(resultNumber)); });
|
|
|
|
return fusedOp;
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::FuseIntoContainingOp::apply(transform::TransformResults &results,
|
|
transform::TransformState &state) {
|
|
SmallVector<Operation *> fusedOps;
|
|
ArrayRef<Operation *> producerOps = state.getPayloadOps(getProducerOp());
|
|
// If nothing to fuse, propagate success.
|
|
if (producerOps.empty()) {
|
|
results.set(getFusedOp().cast<OpResult>(),
|
|
SmallVector<mlir::Operation *>{});
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
ArrayRef<Operation *> containingOps = state.getPayloadOps(getContainingOp());
|
|
if (containingOps.size() != 1) {
|
|
return emitDefiniteFailure()
|
|
<< "requires exactly one containing_op handle (got "
|
|
<< containingOps.size() << ")";
|
|
}
|
|
Operation *containingOp = containingOps.front();
|
|
|
|
// Helper function to find the next producer that should be fused. Take any
|
|
// producer that has a use inside the containing op.
|
|
SmallVector<Operation *> remainingProducers(producerOps.begin(),
|
|
producerOps.end());
|
|
auto getNextProducer = [&]() -> FailureOr<Operation *> {
|
|
for (const auto &it : enumerate(remainingProducers)) {
|
|
Operation *producerOp = it.value();
|
|
// The containing op may be a user of producerOp: use isAncestor.
|
|
int64_t numUsesInContainingOp =
|
|
llvm::count_if(producerOp->getUsers(), [&](Operation *op) {
|
|
return containingOp->isAncestor(op);
|
|
});
|
|
// TODO: When resolving the TODO below (no duplicate ops), take an op
|
|
// that has no use among the remaining producers. This is a topological
|
|
// sorting.
|
|
if (numUsesInContainingOp > 0) {
|
|
if (numUsesInContainingOp == 1)
|
|
remainingProducers.erase(remainingProducers.begin() + it.index());
|
|
return producerOp;
|
|
}
|
|
}
|
|
return failure();
|
|
};
|
|
|
|
IRRewriter rewriter(getContext());
|
|
while (!remainingProducers.empty()) {
|
|
auto nextProducer = getNextProducer();
|
|
if (failed(nextProducer)) {
|
|
return mlir::emitSilenceableFailure(containingOp->getLoc())
|
|
<< "could not find next producer to fuse into container";
|
|
}
|
|
|
|
Operation *producerOp = *nextProducer;
|
|
|
|
// Default diagnostic, to be complemented with more failure information.
|
|
Diagnostic diag(producerOp->getLoc(), DiagnosticSeverity::Remark);
|
|
diag << "could not fuse " << *producerOp << " into " << *containingOp;
|
|
|
|
// TODO: If there are multiple uses of the producer in the containing op,
|
|
// we currently tile/clone the op multiple times (once per use). In some
|
|
// cases, we can tile/clone once and reuse the value for each use.
|
|
// Futhermore, producers should then be traversed according to a
|
|
// topological sorting.
|
|
Operation *tiled =
|
|
tileAndFuseFirstExtractUse(rewriter, diag, producerOp, containingOp);
|
|
if (tiled) {
|
|
LLVM_DEBUG(DBGS() << "\nFused a direct extract use\n" << *containingOp);
|
|
fusedOps.push_back(tiled);
|
|
continue;
|
|
}
|
|
|
|
Operation *tiledContainingOpOperand =
|
|
tileAndFuseFirstExtractUseThroughContainingOpBlockArgument(
|
|
rewriter, diag, producerOp, containingOp);
|
|
if (tiledContainingOpOperand) {
|
|
LLVM_DEBUG(DBGS() << "\nFused an extract use through block argument\n"
|
|
<< *containingOp);
|
|
fusedOps.push_back(tiledContainingOpOperand);
|
|
continue;
|
|
}
|
|
|
|
Operation *cloned =
|
|
cloneAndFuseFirstUse(rewriter, diag, producerOp, containingOp);
|
|
if (cloned) {
|
|
LLVM_DEBUG(DBGS() << "\nFused an use by cloning\n" << *containingOp);
|
|
fusedOps.push_back(cloned);
|
|
continue;
|
|
}
|
|
return DiagnosedSilenceableFailure::silenceableFailure(std::move(diag));
|
|
}
|
|
|
|
results.set(getFusedOp().cast<OpResult>(), fusedOps);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::FuseIntoContainingOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getProducerOp(), effects);
|
|
onlyReadsHandle(getContainingOp(), effects);
|
|
producesHandle(getFusedOp(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// GeneralizeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::GeneralizeOp::applyToOne(LinalgOp target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
// Exit early if no transformation is needed.
|
|
if (isa<GenericOp>(target)) {
|
|
results.push_back(target);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
FailureOr<LinalgOp> generic = tryApply<LinalgGeneralizationPattern>(target);
|
|
if (succeeded(generic)) {
|
|
results.push_back(generic->getOperation());
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
return emitDefaultSilenceableFailure(target);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// InterchangeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::InterchangeOp::applyToOne(GenericOp target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
ArrayRef<int64_t> interchangeVector = getIteratorInterchange();
|
|
// Exit early if no transformation is needed.
|
|
if (interchangeVector.empty()) {
|
|
results.push_back(target);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
TrivialPatternRewriter rewriter(target->getContext());
|
|
FailureOr<GenericOp> res =
|
|
interchangeGenericOp(rewriter, target,
|
|
SmallVector<unsigned>(interchangeVector.begin(),
|
|
interchangeVector.end()));
|
|
if (failed(res))
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
results.push_back(res->getOperation());
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
LogicalResult transform::InterchangeOp::verify() {
|
|
ArrayRef<int64_t> permutation = getIteratorInterchange();
|
|
auto sequence = llvm::to_vector(llvm::seq<int64_t>(0, permutation.size()));
|
|
if (!std::is_permutation(sequence.begin(), sequence.end(),
|
|
permutation.begin(), permutation.end())) {
|
|
return emitOpError()
|
|
<< "expects iterator_interchange to be a permutation, found "
|
|
<< getIteratorInterchange();
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// LowerPackOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
struct LowerPackResult {
|
|
tensor::PadOp padOp;
|
|
tensor::ExpandShapeOp expandShapeOp;
|
|
linalg::TransposeOp transposeOp;
|
|
};
|
|
|
|
/// Rewrite pack as pad + reshape + transpose.
|
|
static FailureOr<LowerPackResult> lowerPack(RewriterBase &rewriter,
|
|
tensor::PackOp packOp) {
|
|
// 1. Filter out NYI cases.
|
|
if (!packOp.getOuterDimsPerm().empty())
|
|
return rewriter.notifyMatchFailure(packOp, "outer dims perm NYI");
|
|
|
|
auto packedTensorType =
|
|
packOp->getResultTypes().front().cast<RankedTensorType>();
|
|
if (!packedTensorType.hasStaticShape()) {
|
|
return rewriter.notifyMatchFailure(
|
|
packOp,
|
|
"non-static shape NYI, needs a more powerful tensor.expand_shape op");
|
|
}
|
|
|
|
Location loc = packOp->getLoc();
|
|
OpBuilder::InsertionGuard g(rewriter);
|
|
rewriter.setInsertionPoint(packOp);
|
|
|
|
// 2. Compute the permutation vector to move the last `numPackedDims` into the
|
|
// `innerPosDims` of a shape of rank `packedRank`.
|
|
int64_t numPackedDims = packOp.getInnerDimsPos().size();
|
|
int64_t packedRank = packedTensorType.getRank();
|
|
auto lastDims = llvm::to_vector(
|
|
llvm::seq<int64_t>(packedRank - numPackedDims, packedRank));
|
|
PackingMetadata packingMetadata = computePackingMetadata(
|
|
packedTensorType.getRank(), packOp.getInnerDimsPos());
|
|
SmallVector<int64_t> lastDimsToInsertPositionsPerm = computePermutationVector(
|
|
packedRank, lastDims, packingMetadata.insertPositions);
|
|
|
|
// 3. Compute the stripMinedShape: this is the packed shape before any outer
|
|
// or inner permutations have been applied.
|
|
SmallVector<int64_t> stripMinedShape(packedTensorType.getShape());
|
|
applyPermutationToVector(stripMinedShape, lastDimsToInsertPositionsPerm);
|
|
|
|
// 4. Pad the source of packOp to a shape we can expand into stripMinedShape.
|
|
RankedTensorType collapsed = tensor::CollapseShapeOp::inferCollapsedType(
|
|
RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape),
|
|
packingMetadata.reassociations);
|
|
Value paddingValue = packOp.getPaddingValue();
|
|
if (!paddingValue) {
|
|
rewriter.create<arith::ConstantOp>(
|
|
loc, rewriter.getZeroAttr(getElementTypeOrSelf(collapsed)));
|
|
}
|
|
auto padOp =
|
|
tensor::createPadHighOp(collapsed, packOp.getSource(), paddingValue,
|
|
/*nofold=*/false, loc, rewriter);
|
|
|
|
LLVM_DEBUG(
|
|
DBGSNL(); DBGSNL(); llvm::interleaveComma(packingMetadata.insertPositions,
|
|
DBGS() << "insertPositions: ");
|
|
DBGSNL(); llvm::interleaveComma(packedTensorType.getShape(),
|
|
DBGS() << "packedShape: ");
|
|
DBGSNL();
|
|
llvm::interleaveComma(lastDimsToInsertPositionsPerm,
|
|
DBGS() << "lastDimsToInsertPositionsPerm: ");
|
|
DBGSNL(); llvm::interleaveComma(
|
|
packingMetadata.reassociations, DBGS() << "reassociations: ",
|
|
[&](ReassociationIndices ri) {
|
|
llvm::interleaveComma(ri, llvm::dbgs() << "|");
|
|
});
|
|
DBGSNL();
|
|
llvm::interleaveComma(stripMinedShape, DBGS() << "stripMinedShape: ");
|
|
DBGSNL(); DBGS() << "collapsed type: " << collapsed; DBGSNL(););
|
|
|
|
// 5. Expand from the padded result to the stripMinedShape.
|
|
auto reshapeOp = rewriter.create<tensor::ExpandShapeOp>(
|
|
loc,
|
|
RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape),
|
|
padOp.getResult(), packingMetadata.reassociations);
|
|
|
|
// 6. Transpose stripMinedShape to packedShape.
|
|
SmallVector<int64_t> insertPositionsToLastDimsPerm = computePermutationVector(
|
|
packedRank, packingMetadata.insertPositions, lastDims);
|
|
auto transposeOp = rewriter.create<linalg::TransposeOp>(
|
|
loc, reshapeOp.getResult(), packOp.getDest(),
|
|
insertPositionsToLastDimsPerm);
|
|
|
|
LLVM_DEBUG(DBGSNL(); DBGSNL(); DBGSNL();
|
|
DBGS() << "reshape op: " << reshapeOp; DBGSNL();
|
|
llvm::interleaveComma(insertPositionsToLastDimsPerm,
|
|
DBGS() << "insertPositionsToLastDimsPerm: ");
|
|
DBGSNL(); DBGS() << "transpose op: " << transposeOp; DBGSNL(););
|
|
|
|
// 7. Replace packOp by transposeOp.
|
|
rewriter.replaceOp(packOp, transposeOp->getResults());
|
|
|
|
return LowerPackResult{padOp, reshapeOp, transposeOp};
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::LowerPackOp::applyToOne(
|
|
tensor::PackOp target, transform::ApplyToEachResultList &transformResults,
|
|
transform::TransformState &state) {
|
|
IRRewriter rewriter(target->getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<LowerPackResult> res = lowerPack(rewriter, target);
|
|
if (failed(res)) {
|
|
return mlir::emitSilenceableFailure(target->getLoc())
|
|
<< "cannot lower to pad + expand + transpose";
|
|
}
|
|
transformResults.push_back(res->padOp);
|
|
transformResults.push_back(res->expandShapeOp);
|
|
transformResults.push_back(res->transposeOp);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// LowerUnPackOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
struct LowerUnPackOpResult {
|
|
tensor::EmptyOp emptyOp;
|
|
linalg::TransposeOp transposeOp;
|
|
tensor::CollapseShapeOp collapseShapeOp;
|
|
tensor::ExtractSliceOp extractSliceOp;
|
|
};
|
|
|
|
/// Rewrite pack as empty + transpose + reshape + extract_slice.
|
|
static FailureOr<LowerUnPackOpResult> lowerUnPack(RewriterBase &rewriter,
|
|
tensor::UnPackOp unPackOp) {
|
|
// 1. Filter out NYI cases.
|
|
if (!unPackOp.getOuterDimsPerm().empty())
|
|
return rewriter.notifyMatchFailure(unPackOp, "outer dims perm NYI");
|
|
|
|
RankedTensorType packedTensorType = unPackOp.getSourceType();
|
|
if (!packedTensorType.hasStaticShape()) {
|
|
return rewriter.notifyMatchFailure(
|
|
unPackOp,
|
|
"non-static shape NYI, needs a more powerful tensor.expand_shape op");
|
|
}
|
|
|
|
Location loc = unPackOp->getLoc();
|
|
OpBuilder::InsertionGuard g(rewriter);
|
|
rewriter.setInsertionPoint(unPackOp);
|
|
|
|
// 2. Compute the permutation vector to move the last `numPackedDims` into the
|
|
// `innerPosDims` of a shape of rank `packedRank`.
|
|
int64_t numPackedDims = unPackOp.getInnerDimsPos().size();
|
|
int64_t packedRank = packedTensorType.getRank();
|
|
auto lastDims = llvm::to_vector(
|
|
llvm::seq<int64_t>(packedRank - numPackedDims, packedRank));
|
|
PackingMetadata packingMetadata =
|
|
computePackingMetadata(packedRank, unPackOp.getInnerDimsPos());
|
|
SmallVector<int64_t> lastDimsToInsertPositionsPerm = computePermutationVector(
|
|
packedRank, lastDims, packingMetadata.insertPositions);
|
|
|
|
// 3. Compute the stripMinedShape: this is the packed shape without outer and
|
|
// inner permutations.
|
|
SmallVector<int64_t> stripMinedShape(packedTensorType.getShape());
|
|
applyPermutationToVector(stripMinedShape, lastDimsToInsertPositionsPerm);
|
|
|
|
// 4. Transpose packedShape to stripMinedShape.
|
|
RankedTensorType stripMinedTensorType =
|
|
RankedTensorType::Builder(packedTensorType).setShape(stripMinedShape);
|
|
RankedTensorType collapsedType = tensor::CollapseShapeOp::inferCollapsedType(
|
|
stripMinedTensorType, packingMetadata.reassociations);
|
|
auto emptyOp =
|
|
rewriter.create<tensor::EmptyOp>(loc, stripMinedTensorType, ValueRange{});
|
|
auto transposeOp = rewriter.create<linalg::TransposeOp>(
|
|
loc, unPackOp.getSource(), emptyOp, lastDimsToInsertPositionsPerm);
|
|
|
|
LLVM_DEBUG(
|
|
DBGSNL(); DBGSNL(); llvm::interleaveComma(packingMetadata.insertPositions,
|
|
DBGS() << "insertPositions: ");
|
|
DBGSNL(); llvm::interleaveComma(packedTensorType.getShape(),
|
|
DBGS() << "packedShape: ");
|
|
DBGSNL();
|
|
llvm::interleaveComma(lastDimsToInsertPositionsPerm,
|
|
DBGS() << "lastDimsToInsertPositionsPerm: ");
|
|
DBGSNL(); llvm::interleaveComma(
|
|
packingMetadata.reassociations, DBGS() << "reassociations: ",
|
|
[&](ReassociationIndices ri) {
|
|
llvm::interleaveComma(ri, llvm::dbgs() << "|");
|
|
});
|
|
DBGSNL();
|
|
llvm::interleaveComma(stripMinedShape, DBGS() << "stripMinedShape: ");
|
|
DBGSNL(); DBGS() << "collapsed type: " << collapsedType; DBGSNL(););
|
|
|
|
// 5. Collapse from the stripMinedShape to the padded result.
|
|
auto reshapeOp = rewriter.create<tensor::CollapseShapeOp>(
|
|
loc, collapsedType, transposeOp->getResult(0),
|
|
packingMetadata.reassociations);
|
|
|
|
// 6. ExtractSlice
|
|
auto destTensorType = unPackOp.getDest().getType().cast<RankedTensorType>();
|
|
int64_t destRank = destTensorType.getRank();
|
|
OpFoldResult zero = rewriter.getIndexAttr(0), one = rewriter.getIndexAttr(1);
|
|
auto extractSliceOp = rewriter.create<tensor::ExtractSliceOp>(
|
|
loc, destTensorType, reshapeOp->getResult(0),
|
|
SmallVector<OpFoldResult>(destRank, zero),
|
|
tensor::getMixedSizes(rewriter, loc, unPackOp->getResult(0)),
|
|
SmallVector<OpFoldResult>(destRank, one));
|
|
|
|
// 7. Replace unPackOp by transposeOp.
|
|
rewriter.replaceOp(unPackOp, extractSliceOp->getResults());
|
|
|
|
return LowerUnPackOpResult{emptyOp, transposeOp, reshapeOp, extractSliceOp};
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::LowerUnPackOp::applyToOne(
|
|
tensor::UnPackOp target, transform::ApplyToEachResultList &transformResults,
|
|
transform::TransformState &state) {
|
|
IRRewriter rewriter(target->getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<LowerUnPackOpResult> res = lowerUnPack(rewriter, target);
|
|
if (failed(res)) {
|
|
return mlir::emitSilenceableFailure(target->getLoc())
|
|
<< "cannot rewrite to pad + expand + transpose";
|
|
}
|
|
transformResults.push_back(res->emptyOp);
|
|
transformResults.push_back(res->transposeOp);
|
|
transformResults.push_back(res->collapseShapeOp);
|
|
transformResults.push_back(res->extractSliceOp);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// MatchOp
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
void transform::MatchOp::build(OpBuilder &builder, OperationState &result,
|
|
Value target, ArrayRef<StringRef> opNames) {
|
|
result.addOperands(target);
|
|
result.addAttribute(MatchOp::getOpsAttrName(result.name),
|
|
builder.getStrArrayAttr(opNames));
|
|
result.addTypes(pdl::OperationType::get(builder.getContext()));
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::MatchOp::apply(transform::TransformResults &results,
|
|
transform::TransformState &state) {
|
|
llvm::StringSet<> strs;
|
|
if (getOps().has_value())
|
|
strs.insert(getOps()->getAsValueRange<StringAttr>().begin(),
|
|
getOps()->getAsValueRange<StringAttr>().end());
|
|
|
|
ArrayRef<Operation *> payloadOps = state.getPayloadOps(getTarget());
|
|
if (payloadOps.size() != 1) {
|
|
return emitDefiniteFailure("requires exactly one target handle");
|
|
}
|
|
|
|
SmallVector<Operation *> res;
|
|
auto matchFun = [&](Operation *op) {
|
|
if (getOps().has_value() && !strs.contains(op->getName().getStringRef()))
|
|
return;
|
|
|
|
// Interfaces cannot be matched by name, just by ID.
|
|
// So we specifically encode the interfaces we care about for this op.
|
|
if (getInterface().has_value()) {
|
|
auto iface = getInterface().value();
|
|
if (iface == transform::MatchInterfaceEnum::LinalgOp &&
|
|
!isa<LinalgOp>(op))
|
|
return;
|
|
if (iface == transform::MatchInterfaceEnum::TilingInterface &&
|
|
isa<TilingInterface>(op))
|
|
return;
|
|
}
|
|
|
|
// Check if all specified attributes match.
|
|
if (getOpAttrs().has_value()) {
|
|
DictionaryAttr opAttrs = getOpAttrs().value();
|
|
for (NamedAttribute attr : opAttrs) {
|
|
if (attr.getName() == getInterfaceAttrName() ||
|
|
attr.getName() == getOpsAttrName())
|
|
continue;
|
|
if (!op->hasAttr(attr.getName()))
|
|
return;
|
|
if (op->getAttr(attr.getName()) != attr.getValue())
|
|
return;
|
|
}
|
|
}
|
|
|
|
if (getFilterResultType().has_value()) {
|
|
Type t = getFilterResultType().value();
|
|
if (op->getNumResults() != 1 || op->getResultTypes().front() != t)
|
|
return;
|
|
}
|
|
|
|
// All constraints are satisfied.
|
|
res.push_back(op);
|
|
return;
|
|
};
|
|
|
|
payloadOps.front()->walk(matchFun);
|
|
results.set(getResult().cast<OpResult>(), res);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// MultiTileSizesOp
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
static void printMultitileSizesTypes(OpAsmPrinter &printer, Operation *op,
|
|
Type targetType, Type lowSizeType, Type,
|
|
Type) {
|
|
printer.printFunctionalType(TypeRange{targetType}, TypeRange{lowSizeType});
|
|
}
|
|
|
|
static ParseResult parseMultitileSizesTypes(OpAsmParser &parser,
|
|
Type &targetType, Type &lowSizeType,
|
|
Type &highSizeType,
|
|
Type &splitPointType) {
|
|
FunctionType funcType;
|
|
llvm::SMLoc typeLoc = parser.getCurrentLocation();
|
|
if (failed(parser.parseType<FunctionType>(funcType)))
|
|
return failure();
|
|
|
|
if (funcType.getNumInputs() != 1 || funcType.getNumResults() != 1) {
|
|
parser.emitError(typeLoc) << "expects a trailing functional type with one "
|
|
"argument and one result";
|
|
}
|
|
targetType = funcType.getInput(0);
|
|
lowSizeType = highSizeType = splitPointType = funcType.getResult(0);
|
|
|
|
return success();
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::MultiTileSizesOp::applyToOne(
|
|
LinalgOp target, transform::ApplyToEachResultList &results,
|
|
TransformState &state) {
|
|
if (getLowSize().getType().isa<TransformParamTypeInterface>()) {
|
|
if (target.hasDynamicShape()) {
|
|
auto diag = emitSilenceableError()
|
|
<< "cannot compute parametric tile sizes for dynamically "
|
|
"shaped payload op";
|
|
diag.attachNote(target->getLoc()) << "payload op";
|
|
return diag;
|
|
}
|
|
|
|
FailureOr<StaticMultiSizeSpecification> spec = computeStaticMultiTileSizes(
|
|
target, getDimension(), getTargetSize(), getDivisor());
|
|
if (failed(spec)) {
|
|
return emitSilenceableError()
|
|
<< "failed to compute multi-size tiling sizes";
|
|
}
|
|
|
|
Builder builder(target.getContext());
|
|
results.assign(llvm::map_range(
|
|
ArrayRef<int64_t>({spec->lowTileSize, spec->highTileSize,
|
|
spec->lowTileSize * spec->lowTripCount}),
|
|
[&builder, this](int64_t value) {
|
|
return builder.getIntegerAttr(
|
|
getLowSize().getType().cast<ParamType>().getType(), value);
|
|
}));
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
OpBuilder builder(target.getContext());
|
|
builder.setInsertionPoint(target);
|
|
OpFoldResult targetSize = builder.getIndexAttr(getTargetSize());
|
|
OpFoldResult divisor = builder.getIndexAttr(getDivisor());
|
|
FailureOr<MultiSizeSpecification> spec = computeMultiTileSizes(
|
|
builder, target, getDimension(), targetSize, divisor);
|
|
if (failed(spec)) {
|
|
return emitSilenceableError() << "could not generate tile size computation";
|
|
}
|
|
|
|
AffineExpr s0 = builder.getAffineSymbolExpr(0);
|
|
AffineExpr s1 = builder.getAffineSymbolExpr(1);
|
|
Operation *splitPoint =
|
|
makeComposedAffineApply(builder, target.getLoc(), s0 * s1,
|
|
{spec->lowTileSize, spec->lowTripCount});
|
|
Operation *lowTileSize = spec->lowTileSize.getDefiningOp();
|
|
Operation *highTileSize = spec->highTileSize.getDefiningOp();
|
|
assert(lowTileSize && highTileSize && splitPoint &&
|
|
"tile sizes are not produced by operations");
|
|
results.reserve(results.size() + 3);
|
|
results.push_back(lowTileSize);
|
|
results.push_back(highTileSize);
|
|
results.push_back(splitPoint);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::MultiTileSizesOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
onlyReadsHandle(getTarget(), effects);
|
|
producesHandle(getResults(), effects);
|
|
if (getLowSize().getType().isa<TransformParamTypeInterface>())
|
|
onlyReadsPayload(effects);
|
|
else
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
LogicalResult transform::MultiTileSizesOp::verify() {
|
|
if (getLowSize().getType() != getHighSize().getType() ||
|
|
getLowSize().getType() != getSplitPoint().getType()) {
|
|
return emitOpError() << "expects all results type to be the same";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// PackOp
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
void transform::PackOp::build(OpBuilder &builder, OperationState &result,
|
|
Value target,
|
|
ArrayRef<OpFoldResult> mixedPackedSizes) {
|
|
SmallVector<int64_t> staticPackedSizes;
|
|
SmallVector<Value> dynamicPackedSizes;
|
|
dispatchIndexOpFoldResults(mixedPackedSizes, dynamicPackedSizes,
|
|
staticPackedSizes);
|
|
// Call the default builder which sets up the proper operands segment sizes
|
|
// attributes for multiple variadic operands. In the absence of this, horrible
|
|
// bugs ensue.
|
|
Type linalgOpHType = transform::OperationType::get(
|
|
builder.getContext(), GenericOp::getOperationName());
|
|
build(builder, result,
|
|
/*resultType=*/linalgOpHType,
|
|
/*target=*/target,
|
|
/*dynamic_sizes=*/dynamicPackedSizes,
|
|
/*static_sizes=*/builder.getDenseI64ArrayAttr(staticPackedSizes));
|
|
}
|
|
|
|
SmallVector<OpFoldResult> transform::PackOp::getMixedPackedSizes() {
|
|
Builder b(getContext());
|
|
return getMixedValues(getStaticPackedSizes(), getPackedSizes(), b);
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::PackOp::apply(transform::TransformResults &transformResults,
|
|
transform::TransformState &state) {
|
|
ArrayRef<Operation *> targetOps = state.getPayloadOps(getTarget());
|
|
// If nothing to pack, propagate success.
|
|
if (targetOps.empty()) {
|
|
transformResults.set(getPackedOp().cast<OpResult>(), {});
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
// Fail on multi-op handles.
|
|
auto linalgOp = dyn_cast<LinalgOp>(targetOps.front());
|
|
if (targetOps.size() != 1 || !linalgOp) {
|
|
return emitSilenceableError()
|
|
<< "requires target to map to exactly 1 LinalgOp (got "
|
|
<< targetOps.size() << ")";
|
|
}
|
|
// Fail on mismatched number of pack sizes.
|
|
if (getMixedPackedSizes().size() != linalgOp.getNumLoops()) {
|
|
return emitSilenceableError()
|
|
<< "requires number of packed sizes match the number of loops ("
|
|
<< getMixedPackedSizes().size() << " vs " << linalgOp.getNumLoops()
|
|
<< ")";
|
|
}
|
|
|
|
// Unpack handles to constants or actual SSA index values.
|
|
SmallVector<OpFoldResult> packedSizes;
|
|
DiagnosedSilenceableFailure status = unpackSingleIndexResultPDLOperations(
|
|
state, *this, packedSizes, getMixedPackedSizes());
|
|
|
|
IRRewriter rewriter(linalgOp->getContext());
|
|
rewriter.setInsertionPoint(linalgOp);
|
|
FailureOr<PackResult> maybeResult = pack(rewriter, linalgOp, packedSizes);
|
|
if (failed(maybeResult))
|
|
return emitDefiniteFailure("data tiling failed");
|
|
|
|
transformResults.set(getPackedOp().cast<OpResult>(),
|
|
maybeResult->packedLinalgOp.getOperation());
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::PackOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
transform::consumesHandle(getTarget(), effects);
|
|
transform::onlyReadsHandle(getPackedSizes(), effects);
|
|
transform::producesHandle(getPackedOp(), effects);
|
|
transform::modifiesPayload(effects);
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// PackGreedilyOp.
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
LogicalResult transform::PackGreedilyOp::verify() {
|
|
if (!isPermutationVector(getGemmInnerDimsOrder())) {
|
|
return emitOpError() << getGemmInnerDimsOrderAttrName()
|
|
<< " is not a valid permutation";
|
|
}
|
|
// TODO: relax to allow empty once we have another strategy than just gemm.
|
|
if (getGemmInnerDimsOrder().size() != 3 ||
|
|
getMixedGemmPackedSizes().size() != 3) {
|
|
return emitOpError() << " needs 3 entries for gemm_packed_sizes and "
|
|
<< getGemmInnerDimsOrderAttrName()
|
|
<< " order for the gemm strategy";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
auto par = utils::IteratorType::parallel;
|
|
auto red = utils::IteratorType::reduction;
|
|
} // namespace
|
|
|
|
DenseSet<int64_t> transform::findPermutationsIndexingOperand(
|
|
LinalgOp linalgOp, OpOperand *opOperand, utils::IteratorType iter) {
|
|
DenseSet<int64_t> res;
|
|
assert(linalgOp == opOperand->getOwner() && "expected linalgOp owner");
|
|
AffineMap indexingMap = linalgOp.getMatchingIndexingMap(opOperand);
|
|
for (AffineExpr e : indexingMap.getResults()) {
|
|
if (auto d = e.dyn_cast<AffineDimExpr>()) {
|
|
if (linalgOp.getIteratorTypesArray()[d.getPosition()] == iter &&
|
|
llvm::count_if(indexingMap.getResults(), [d](AffineExpr e) {
|
|
return e.isFunctionOfDim(d.getPosition());
|
|
}) == 1)
|
|
res.insert(d.getPosition());
|
|
}
|
|
}
|
|
return res;
|
|
}
|
|
|
|
FailureOr<GemmDimsForPacking> transform::inferGemmDims(LinalgOp linalgOp) {
|
|
assert(linalgOp.getNumDpsInits() == 1 && "wrong number of dps inits");
|
|
assert(linalgOp.getNumDpsInputs() == 2 && "wrong number of dps inputs");
|
|
|
|
DenseSet<int64_t> a = findPermutationsIndexingOperand(
|
|
linalgOp, linalgOp.getDpsInputOperand(0), par);
|
|
DenseSet<int64_t> b = findPermutationsIndexingOperand(
|
|
linalgOp, linalgOp.getDpsInputOperand(1), par);
|
|
DenseSet<int64_t> c = findPermutationsIndexingOperand(
|
|
linalgOp, linalgOp.getDpsInitOperand(0), par);
|
|
|
|
// A & C - B are the iterators involved in an outer-product along A (the LHS).
|
|
DenseSet<int64_t> ac = a;
|
|
llvm::set_intersect(ac, c);
|
|
llvm::set_subtract(ac, b);
|
|
// B & C - A are the iterators involved in an outer-product along B (the RHS).
|
|
DenseSet<int64_t> bc = b;
|
|
llvm::set_intersect(bc, c);
|
|
llvm::set_subtract(bc, a);
|
|
|
|
// Note: if we ever need them, A & B & C would be "batch" dimensions.
|
|
|
|
// A & B red are the reduction dimensions.
|
|
DenseSet<int64_t> ra = findPermutationsIndexingOperand(
|
|
linalgOp, linalgOp.getDpsInputOperand(0), red);
|
|
DenseSet<int64_t> rb = findPermutationsIndexingOperand(
|
|
linalgOp, linalgOp.getDpsInputOperand(1), red);
|
|
llvm::set_intersect(ra, rb);
|
|
|
|
if (ac.empty() || bc.empty() || ra.empty())
|
|
return failure();
|
|
|
|
// Pick the first one in each set.
|
|
// TODO: Better heuristic (e.g pick dims based on packing-based metric).
|
|
return GemmDimsForPacking{ac, bc, ra};
|
|
}
|
|
|
|
bool transform::containsMostMinorGemm(LinalgOp linalgOp) {
|
|
FailureOr<GemmDimsForPacking> res = inferGemmDims(linalgOp);
|
|
if (failed(res))
|
|
return false;
|
|
int64_t numLoops = linalgOp.getNumLoops();
|
|
for (const DenseSet<int64_t> &s : {res->mPos, res->nPos, res->kPos}) {
|
|
if (s.contains(numLoops - 3) || s.contains(numLoops - 2) ||
|
|
s.contains(numLoops - 1))
|
|
continue;
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
/// Pack a LinalgOp by greedily inferring gemm dimensions (m, n, k) where m
|
|
/// and n are proper parallel dimensions and k is a proper reduction
|
|
/// dimension. Packing occurs by rewriting the op as a linalg.generic and
|
|
/// calling linalg::pack by `mnkPackedSizes`. The order of the packed
|
|
/// dimensions is customizable: the `mnkOrder` is a permutation of {0, 1, 2}
|
|
/// to reorder {m, n, k} into one of the 8 possible forms. The outer
|
|
/// dimensions of the operands are not permuted at this time, this is left for
|
|
/// future work.
|
|
static FailureOr<PackResult>
|
|
packGemmGreedily(RewriterBase &rewriter, LinalgOp linalgOp,
|
|
ArrayRef<OpFoldResult> mnkPackedSizes,
|
|
ArrayRef<int64_t> mnkOrder) {
|
|
assert(mnkPackedSizes.size() == 3 && "unexpected num of packing sizes");
|
|
assert(mnkOrder.size() == 3 && "unexpected mnkOrder size");
|
|
assert(isPermutationVector(mnkOrder) && "expected a permutation");
|
|
|
|
int64_t numLoops = linalgOp.getNumLoops();
|
|
if (numLoops <= 2) {
|
|
return rewriter.notifyMatchFailure(linalgOp,
|
|
"need 3+ loops to find a gemm to pack");
|
|
}
|
|
|
|
// Locally adjust the desired iterator position of mnk and packing sizes.
|
|
int64_t numPackedDims = mnkPackedSizes.size();
|
|
SmallVector<int64_t> mmnnkkPos(numPackedDims);
|
|
for (int64_t i = 0, e = numPackedDims; i < e; ++i)
|
|
mmnnkkPos[i] = numLoops - numPackedDims + mnkOrder[i];
|
|
SmallVector<OpFoldResult> packedSizes(mnkPackedSizes.size());
|
|
for (int64_t i = 0, e = numPackedDims; i < e; ++i)
|
|
packedSizes[mnkOrder[i]] = mnkPackedSizes[i];
|
|
|
|
// 1. Infer dims that are important for gemm.
|
|
FailureOr<GemmDimsForPacking> res = inferGemmDims(linalgOp);
|
|
if (failed(res)) {
|
|
return rewriter.notifyMatchFailure(linalgOp,
|
|
"couldn't infer gemm iterators");
|
|
}
|
|
|
|
// 2. Normalize linalgOp to an kmn-matmul-like with [red, par, par] most
|
|
// minor iterators. If we wanted a different normalization order, this is
|
|
// where it would have to plug a heuristic.
|
|
int64_t mPos = *(res->mPos.begin()), nPos = *(res->nPos.begin()),
|
|
kPos = *(res->kPos.begin());
|
|
LLVM_DEBUG(DBGSNL(); DBGSNL(); DBGSNL();
|
|
DBGS() << "Start packing generic op greedily with (m@" << mPos
|
|
<< ", n@" << nPos << ", k@" << kPos << "): " << linalgOp
|
|
<< "\n";);
|
|
|
|
// 2.a. Rewrite as a generic.
|
|
auto genericOp = dyn_cast<GenericOp>(linalgOp.getOperation());
|
|
if (!genericOp) {
|
|
FailureOr<GenericOp> generalizeResult =
|
|
generalizeNamedOp(rewriter, linalgOp);
|
|
assert(succeeded(generalizeResult) && "unexpected failure generalizing op");
|
|
genericOp = *generalizeResult;
|
|
}
|
|
|
|
// 2.b. Interchange to move the dimensions (k, m, n) as most-minor
|
|
// iterators. Note that this only normalized the iteration order and does
|
|
// not change the indexings of any operand.
|
|
SmallVector<int64_t> permutation =
|
|
computePermutationVector(numLoops, {mPos, nPos, kPos}, mmnnkkPos);
|
|
LLVM_DEBUG(llvm::interleaveComma(permutation, DBGS() << "perm: "); DBGSNL(););
|
|
// Sign .. unsigned pollution.
|
|
SmallVector<unsigned> unsignedPerm(permutation.begin(), permutation.end());
|
|
FailureOr<GenericOp> interchangeResult =
|
|
interchangeGenericOp(rewriter, genericOp, unsignedPerm);
|
|
assert(succeeded(interchangeResult) && "unexpected failure interchanging op");
|
|
genericOp = *interchangeResult;
|
|
LLVM_DEBUG(DBGS() << "Generalized Op to pack: " << genericOp << "\n";);
|
|
|
|
// At this point, the op iterators are normalized to {leading, k, m, n}.
|
|
// The layouts induced by packing will always be:
|
|
// - LHS{leading_lhs, kk, mm}
|
|
// - RHS{leading_rhs, kk, nn}
|
|
// - RES{leading_res, mm, nn}
|
|
// If we wanted to change the packed order, we would reorder (k, m, n) to
|
|
// something else above.
|
|
//
|
|
// Additional permutations of the outer dims of the operands (i.e.
|
|
// leading_lhs, leading_rhs and leading_res) could follow by computing the
|
|
// desired outerPerm for each operand.
|
|
// This is left for future work.
|
|
|
|
// Add leading zeros to match numLoops.
|
|
SmallVector<OpFoldResult> adjustedPackedSizes(numLoops - packedSizes.size(),
|
|
rewriter.getIndexAttr(0));
|
|
llvm::append_range(adjustedPackedSizes, packedSizes);
|
|
|
|
// TODO: If we wanted to give the genericOp a name after packing, after
|
|
// calling `pack` would be a good time.
|
|
auto packingRes = linalg::pack(rewriter, genericOp, adjustedPackedSizes);
|
|
assert(containsMostMinorGemm(packingRes->packedLinalgOp) &&
|
|
"failed to pack to a most minor gemm");
|
|
return packingRes;
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
PackGreedilyOp::apply(transform::TransformResults &transformResults,
|
|
transform::TransformState &state) {
|
|
ArrayRef<Operation *> targetOps = state.getPayloadOps(getTarget());
|
|
|
|
SmallVector<Operation *> results;
|
|
IRRewriter rewriter(getContext());
|
|
for (Operation *op : targetOps) {
|
|
auto linalgOp = dyn_cast<LinalgOp>(op);
|
|
if (!linalgOp)
|
|
continue;
|
|
// linalgOp will be replaced and the insertion point may be invalidated if
|
|
// we set it before -> set it after.
|
|
rewriter.setInsertionPointAfter(linalgOp);
|
|
// Failing to pack greedily is perfectly fine.
|
|
// In the future we will want to order packings according to some metric.
|
|
FailureOr<PackResult> packResult = packGemmGreedily(
|
|
/*rewriter=*/rewriter,
|
|
/*linalgOp=*/linalgOp,
|
|
/*mnkPackedSizes=*/getMixedGemmPackedSizes(),
|
|
/*mnkOrder=*/getGemmInnerDimsOrder());
|
|
if (succeeded(packResult)) {
|
|
results.push_back(packResult->packedLinalgOp);
|
|
continue;
|
|
}
|
|
results.push_back(linalgOp);
|
|
}
|
|
transformResults.set(getPackedOp().cast<OpResult>(), results);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
SmallVector<OpFoldResult> PackGreedilyOp::getMixedGemmPackedSizes() {
|
|
Builder b(getContext());
|
|
return getMixedValues(getStaticGemmPackedSizes(), getGemmPackedSizes(), b);
|
|
}
|
|
|
|
void transform::PackGreedilyOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
transform::consumesHandle(getTarget(), effects);
|
|
transform::onlyReadsHandle(getGemmPackedSizes(), effects);
|
|
transform::producesHandle(getPackedOp(), effects);
|
|
transform::modifiesPayload(effects);
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// PackTransposeOp
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
LogicalResult transform::PackTransposeOp::verify() {
|
|
if (!isPermutationVector(getInnerPerm())) {
|
|
return emitOpError() << getInnerPermAttrName()
|
|
<< " is not a valid permutation";
|
|
}
|
|
if (!isPermutationVector(getOuterPerm())) {
|
|
return emitOpError() << getOuterPermAttrName()
|
|
<< " is not a valid permutation";
|
|
}
|
|
if (getInnerPerm().empty() && getOuterPerm().empty()) {
|
|
return emitOpError() << " at least one of " << getInnerPermAttrName()
|
|
<< " or " << getOuterPermAttrName()
|
|
<< " must be specified";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
namespace {
|
|
enum class OuterOrInnerPerm { Outer = 0, Inner = 1 };
|
|
} // namespace
|
|
|
|
/// Return true if `permutation` is a valid permutation of the
|
|
/// `outer_dims_perm` (case OuterOrInnerPerm::Outer) or `inner_dims_pos`
|
|
/// (OuterOrInnerPerm::Inner) of the `tensor.pack` or `tensor.unpack` `op.
|
|
/// This is the case when the `permutation` rank matches the rank expected by
|
|
/// `op` and `permutation` is itself a permutation vector.
|
|
/// Return true if either `op` or `permutation` are empty to allow a simpler
|
|
/// polymorphic implementation.
|
|
template <typename RelayoutOpTy>
|
|
bool isValidPackingPermutation(
|
|
RelayoutOpTy op, ArrayRef<int64_t> permutation,
|
|
OuterOrInnerPerm outerOrInnerPerm = OuterOrInnerPerm::Outer) {
|
|
static_assert(
|
|
llvm::is_one_of<RelayoutOpTy, tensor::PackOp, tensor::UnPackOp>::value,
|
|
"applies to only pack or unpack operations");
|
|
if (!op || permutation.empty())
|
|
return true;
|
|
size_t innerRank = op.getInnerDimsPos().size();
|
|
if (outerOrInnerPerm == OuterOrInnerPerm::Inner)
|
|
return permutation.size() == innerRank && isPermutationVector(permutation);
|
|
// op.getOuterDimsPerm() may be empty, in which case it is identity.
|
|
// Don't rely on it.
|
|
if (std::is_same<RelayoutOpTy, tensor::PackOp>::value) {
|
|
return permutation.size() == op.getSourceRank() &&
|
|
isPermutationVector(permutation);
|
|
}
|
|
return permutation.size() == op.getDestRank() &&
|
|
isPermutationVector(permutation);
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::PackTransposeOp::apply(transform::TransformResults &transformResults,
|
|
transform::TransformState &state) {
|
|
ArrayRef<Operation *> packOrUnpackOps =
|
|
state.getPayloadOps(getTargetPackOrUnPackOp());
|
|
ArrayRef<Operation *> linalgOps = state.getPayloadOps(getTargetLinalgOp());
|
|
// Step 1. If nothing to pack, propagate success.
|
|
if (packOrUnpackOps.empty()) {
|
|
transformResults.set(getPackedOp().cast<OpResult>(), {});
|
|
transformResults.set(getPackOp().cast<OpResult>(), {});
|
|
transformResults.set(getUnPackOp().cast<OpResult>(), {});
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
// Step 2. Bunch of runtime sanity check and error messages.
|
|
// Step 2.1. Fail on multi-op handles.
|
|
if (packOrUnpackOps.size() != 1 || linalgOps.size() != 1) {
|
|
return emitSilenceableError() << "requires target to map to exactly 1 "
|
|
"packing op and 1 packed op ("
|
|
<< "got " << packOrUnpackOps.size() << " and "
|
|
<< linalgOps.size() << ")";
|
|
}
|
|
|
|
// Step 2.2. Fail on wrong type.
|
|
auto packOp = dyn_cast<tensor::PackOp>(packOrUnpackOps.front());
|
|
auto unPackOp = dyn_cast<tensor::UnPackOp>(packOrUnpackOps.front());
|
|
if ((!packOp && !unPackOp)) {
|
|
return emitSilenceableError() << "requires target to map to a "
|
|
"tensor.pack or tensor.unpack";
|
|
}
|
|
LinalgOp linalgOpTarget = dyn_cast<LinalgOp>(linalgOps.front());
|
|
if (!linalgOpTarget)
|
|
return emitSilenceableError() << "requires a LinalgOp target";
|
|
|
|
// Step 2.3. Fail if we can't get the producer / consumer Linalg op.
|
|
LinalgOp linalgOp;
|
|
if (packOp && packOp.getResult().hasOneUse())
|
|
linalgOp = dyn_cast<LinalgOp>(*(packOp.getResult().getUsers().begin()));
|
|
else if (unPackOp)
|
|
linalgOp = unPackOp.getSource().getDefiningOp<LinalgOp>();
|
|
if (linalgOp != linalgOpTarget) {
|
|
auto errorMsg =
|
|
packOp ? StringLiteral{"not a single use by the LinalgOp target"}
|
|
: StringLiteral{"not produced by the LinalgOp target"};
|
|
return emitSilenceableError() << errorMsg;
|
|
}
|
|
|
|
// Step 2.4. If we have an UnPackOp, we need to fetch the symmetrical
|
|
// PackOp.
|
|
if (unPackOp) {
|
|
assert(!packOp && "packOp must be null on entry when unPackOp is not null");
|
|
OpOperand *packUse = linalgOp.getDpsInitOperand(
|
|
unPackOp.getSource().cast<OpResult>().getResultNumber());
|
|
packOp = dyn_cast_or_null<tensor::PackOp>(packUse->get().getDefiningOp());
|
|
if (!packOp || !packOp.getResult().hasOneUse())
|
|
return emitSilenceableError() << "could not find matching pack op";
|
|
}
|
|
|
|
// Step 2.5. Fail if any permutation does not validate.
|
|
for (auto permType : {OuterOrInnerPerm::Outer, OuterOrInnerPerm::Inner}) {
|
|
ArrayRef<int64_t> perm =
|
|
(permType == OuterOrInnerPerm::Outer) ? getOuterPerm() : getInnerPerm();
|
|
auto errorMsg = (permType == OuterOrInnerPerm::Outer)
|
|
? StringLiteral{"invalid outer_perm"}
|
|
: StringLiteral{"invalid inner_perm"};
|
|
if (!isValidPackingPermutation(packOp, perm, permType) ||
|
|
!isValidPackingPermutation(unPackOp, perm, permType)) {
|
|
Operation *packOrUnpackOp =
|
|
unPackOp ? unPackOp.getOperation() : packOp.getOperation();
|
|
return emitSilenceableError() << errorMsg << ": " << *packOrUnpackOp;
|
|
}
|
|
}
|
|
|
|
// From here on, packOp and linalgOp are always present, unPackOp may or may
|
|
// not be present.
|
|
assert(packOp && linalgOp && "unexpected null op");
|
|
|
|
// Step 3. Actually transpose the ops.
|
|
IRRewriter rewriter(getContext());
|
|
FailureOr<PackTransposeResult> res = packTranspose(
|
|
rewriter, packOp, linalgOp, unPackOp, getOuterPerm(), getInnerPerm());
|
|
// Preconditions have been checked, it is an error to fail here.
|
|
assert(succeeded(res) && "unexpected packTranspose failure");
|
|
|
|
// Step 4. Return results.
|
|
transformResults.set(getPackOp().cast<OpResult>(), {res->transposedPackOp});
|
|
transformResults.set(getPackedOp().cast<OpResult>(),
|
|
{res->transposedLinalgOp});
|
|
if (unPackOp) {
|
|
transformResults.set(getUnPackOp().cast<OpResult>(),
|
|
{res->transposedUnPackOp});
|
|
} else {
|
|
transformResults.set(getUnPackOp().cast<OpResult>(), {});
|
|
}
|
|
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===---------------------------------------------------------------------===//
|
|
// PadOp
|
|
//===---------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::PadOp::applyToOne(LinalgOp target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
// Convert the integer packing flags to booleans.
|
|
SmallVector<bool> packPaddings;
|
|
for (int64_t packPadding : extractFromI64ArrayAttr(getPackPaddings()))
|
|
packPaddings.push_back(static_cast<bool>(packPadding));
|
|
|
|
// Convert the padding values to attributes.
|
|
SmallVector<Attribute> paddingValues;
|
|
for (auto const &it :
|
|
llvm::zip(getPaddingValues(), target->getOperandTypes())) {
|
|
auto attr = std::get<0>(it).dyn_cast<TypedAttr>();
|
|
if (!attr) {
|
|
emitOpError("expects padding values to be typed attributes");
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
Type elementType = getElementTypeOrSelf(std::get<1>(it));
|
|
// Try to parse string attributes to obtain an attribute of element type.
|
|
if (auto stringAttr = attr.dyn_cast<StringAttr>()) {
|
|
paddingValues.push_back(
|
|
parseAttribute(attr.cast<StringAttr>(), elementType));
|
|
if (!paddingValues.back()) {
|
|
auto diag = this->emitOpError("expects a padding that parses to ")
|
|
<< elementType << ", got " << std::get<0>(it);
|
|
diag.attachNote(target.getLoc()) << "when applied to this op";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
continue;
|
|
}
|
|
// Otherwise, add the attribute directly.
|
|
if (attr.getType() != elementType) {
|
|
auto diag = this->emitOpError("expects a padding value of type ")
|
|
<< elementType << ", got " << attr;
|
|
diag.attachNote(target.getLoc()) << "when applied to this op";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
paddingValues.push_back(attr);
|
|
}
|
|
|
|
// Extract the transpose vectors.
|
|
SmallVector<SmallVector<int64_t>> transposePaddings;
|
|
for (Attribute transposeVector : getTransposePaddings().cast<ArrayAttr>())
|
|
transposePaddings.push_back(
|
|
extractFromI64ArrayAttr(transposeVector.cast<ArrayAttr>()));
|
|
|
|
LinalgPaddingOptions paddingOptions;
|
|
paddingOptions.setPaddingValues(paddingValues);
|
|
paddingOptions.setPaddingDimensions(
|
|
extractFromI64ArrayAttr(getPaddingDimensions()));
|
|
paddingOptions.setPackPaddings(packPaddings);
|
|
paddingOptions.setHoistPaddings(extractFromI64ArrayAttr(getHoistPaddings()));
|
|
paddingOptions.setTransposePaddings(transposePaddings);
|
|
|
|
FailureOr<LinalgOp> result =
|
|
tryApply<LinalgPaddingPattern>(target, paddingOptions);
|
|
if (succeeded(result)) {
|
|
results.push_back(result->getOperation());
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
return emitDefaultSilenceableFailure(target);
|
|
}
|
|
|
|
LogicalResult transform::PadOp::verify() {
|
|
SmallVector<int64_t> packPaddings =
|
|
extractFromI64ArrayAttr(getPackPaddings());
|
|
if (any_of(packPaddings, [](int64_t packPadding) {
|
|
return packPadding != 0 && packPadding != 1;
|
|
})) {
|
|
return emitOpError()
|
|
<< "expects pack_paddings to contain booleans (0/1), found "
|
|
<< getPackPaddings();
|
|
}
|
|
|
|
SmallVector<int64_t> paddingDimensions =
|
|
extractFromI64ArrayAttr(getPaddingDimensions());
|
|
if (any_of(paddingDimensions,
|
|
[](int64_t paddingDimension) { return paddingDimension < 0; })) {
|
|
return emitOpError() << "expects padding_dimensions to contain positive "
|
|
"integers, found "
|
|
<< getPaddingDimensions();
|
|
}
|
|
|
|
SmallVector<int64_t> hoistPaddings =
|
|
extractFromI64ArrayAttr(getHoistPaddings());
|
|
if (any_of(hoistPaddings,
|
|
[](int64_t hoistPadding) { return hoistPadding < 0; })) {
|
|
return emitOpError()
|
|
<< "expects hoist_paddings to contain positive integers, found "
|
|
<< getHoistPaddings();
|
|
}
|
|
|
|
ArrayAttr transposes = getTransposePaddings();
|
|
for (Attribute attr : transposes) {
|
|
SmallVector<int64_t> transpose = extractFromI64ArrayAttr(attr);
|
|
auto sequence = llvm::to_vector(llvm::seq<int64_t>(0, transpose.size()));
|
|
if (!std::is_permutation(sequence.begin(), sequence.end(),
|
|
transpose.begin(), transpose.end())) {
|
|
return emitOpError()
|
|
<< "expects transpose_paddings to be a permutation, found "
|
|
<< attr;
|
|
}
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// PromoteOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::PromoteOp::applyToOne(LinalgOp target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
LinalgPromotionOptions promotionOptions;
|
|
if (!getOperandsToPromote().empty())
|
|
promotionOptions = promotionOptions.setOperandsToPromote(
|
|
extractFromI64ArrayAttr(getOperandsToPromote()));
|
|
if (getUseFullTilesByDefault())
|
|
promotionOptions = promotionOptions.setUseFullTileBuffersByDefault(
|
|
getUseFullTilesByDefault());
|
|
if (getUseAlloca())
|
|
promotionOptions = promotionOptions.setUseAlloca(getUseAlloca());
|
|
if (!getUseFullTileBuffers().empty())
|
|
promotionOptions = promotionOptions.setUseFullTileBuffers(
|
|
llvm::to_vector(getUseFullTileBuffers().getAsValueRange<BoolAttr>()));
|
|
if (getAlignment().has_value())
|
|
promotionOptions = promotionOptions.setAlignment(*getAlignment());
|
|
|
|
if (failed(promoteSubviewsPrecondition(target, promotionOptions)))
|
|
return emitDefaultDefiniteFailure(target);
|
|
|
|
TrivialPatternRewriter rewriter(target->getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<LinalgOp> res = promoteSubViews(rewriter, target, promotionOptions);
|
|
if (failed(res))
|
|
return emitDefaultDefiniteFailure(target);
|
|
results.push_back(target);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ReplaceOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::ReplaceOp::apply(TransformResults &transformResults,
|
|
TransformState &state) {
|
|
ArrayRef<Operation *> payload = state.getPayloadOps(getTarget());
|
|
|
|
// Check for invalid targets.
|
|
for (Operation *target : payload) {
|
|
if (target->getNumOperands() > 0)
|
|
return emitDefiniteFailure() << "expected target without operands";
|
|
if (!target->hasTrait<OpTrait::IsIsolatedFromAbove>() &&
|
|
target->getNumRegions() > 0)
|
|
return emitDefiniteFailure()
|
|
<< "expected target that is isolated from above";
|
|
}
|
|
|
|
// Clone and replace.
|
|
IRRewriter rewriter(getContext());
|
|
Operation *pattern = &getBodyRegion().front().front();
|
|
SmallVector<Operation *> replacements;
|
|
for (Operation *target : payload) {
|
|
if (getOperation()->isAncestor(target))
|
|
continue;
|
|
rewriter.setInsertionPoint(target);
|
|
Operation *replacement = rewriter.clone(*pattern);
|
|
rewriter.replaceOp(target, replacement->getResults());
|
|
replacements.push_back(replacement);
|
|
}
|
|
transformResults.set(getReplacement().cast<OpResult>(), replacements);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::ReplaceOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
producesHandle(getReplacement(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
LogicalResult transform::ReplaceOp::verify() {
|
|
if (!getBodyRegion().hasOneBlock())
|
|
return emitOpError() << "expected one block";
|
|
if (std::distance(getBodyRegion().front().begin(),
|
|
getBodyRegion().front().end()) != 1)
|
|
return emitOpError() << "expected one operation in block";
|
|
Operation *replacement = &getBodyRegion().front().front();
|
|
if (replacement->getNumOperands() > 0)
|
|
return replacement->emitOpError()
|
|
<< "expected replacement without operands";
|
|
if (!replacement->hasTrait<OpTrait::IsIsolatedFromAbove>() &&
|
|
replacement->getNumRegions() > 0)
|
|
return replacement->emitOpError()
|
|
<< "expect op that is isolated from above";
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// ScalarizeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::ScalarizeOp::applyToOne(LinalgOp target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
scf::SCFTilingOptions tilingOptions;
|
|
tilingOptions.setTileSizeComputationFunction([&](OpBuilder &b, Operation *) {
|
|
SmallVector<Value, 4> tileSizes;
|
|
Location loc = target.getLoc();
|
|
SmallVector<OpFoldResult> allShapeSizes =
|
|
target.createFlatListOfOperandDims(b, loc);
|
|
AffineMap map = target.getShapesToLoopsMap();
|
|
if (!map)
|
|
return tileSizes;
|
|
IRRewriter rewriter(b);
|
|
SmallVector<OpFoldResult> shapeSizes =
|
|
makeComposedFoldedMultiResultAffineApply(rewriter, loc, map,
|
|
allShapeSizes);
|
|
// If the shape size is dynamic, tile by 1.
|
|
// Otherwise, do not tile (i.e. tile size 0).
|
|
for (OpFoldResult shapeSize : shapeSizes) {
|
|
tileSizes.push_back(getConstantIntValue(shapeSize)
|
|
? b.create<arith::ConstantIndexOp>(loc, 0)
|
|
: b.create<arith::ConstantIndexOp>(loc, 1));
|
|
}
|
|
return tileSizes;
|
|
});
|
|
SmallVector<int64_t> emptyTileSizes;
|
|
TrivialPatternRewriter rewriter(getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<scf::SCFTilingResult> maybeTilingResult = tileUsingSCFForOp(
|
|
rewriter, cast<TilingInterface>(target.getOperation()), tilingOptions);
|
|
if (failed(maybeTilingResult))
|
|
return emitDefaultDefiniteFailure(target);
|
|
|
|
if (target->getNumResults())
|
|
rewriter.replaceOp(target, maybeTilingResult->replacements);
|
|
else
|
|
rewriter.eraseOp(target);
|
|
|
|
results.reserve(maybeTilingResult->tiledOps.size());
|
|
for (Operation *tiled : maybeTilingResult->tiledOps)
|
|
results.push_back(tiled);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// RewriteInDestinationPassingStyleOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::RewriteInDestinationPassingStyleOp::apply(
|
|
transform::TransformResults &results, transform::TransformState &state) {
|
|
SmallVector<Operation *> res;
|
|
ArrayRef<Operation *> targetOps = state.getPayloadOps(getTarget());
|
|
for (Operation *target : targetOps) {
|
|
IRRewriter rewriter(target->getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<Operation *> maybeResult =
|
|
TypeSwitch<Operation *, FailureOr<Operation *>>(target)
|
|
.Case<tensor::FromElementsOp, tensor::GenerateOp, tensor::PadOp>(
|
|
[&rewriter](auto op) {
|
|
return rewriteInDestinationPassingStyle(rewriter, op);
|
|
});
|
|
if (failed(maybeResult))
|
|
return emitDefaultSilenceableFailure(target);
|
|
res.push_back(*maybeResult);
|
|
}
|
|
results.set(getResult().cast<OpResult>(), res);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SplitOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure SplitOp::apply(TransformResults &results,
|
|
TransformState &state) {
|
|
// Collect the dynamic split points if provided.
|
|
ArrayRef<Operation *> payload = state.getPayloadOps(getTarget());
|
|
TrivialPatternRewriter rewriter(getContext());
|
|
SmallVector<OpFoldResult> splitPoints;
|
|
splitPoints.reserve(payload.size());
|
|
if (getDynamicSplitPoint()) {
|
|
auto diag = DiagnosedSilenceableFailure::success();
|
|
if (getDynamicSplitPoint().getType().isa<TransformHandleTypeInterface>()) {
|
|
splitPoints = llvm::to_vector(llvm::map_range(
|
|
state.getPayloadOps(getDynamicSplitPoint()), [&](Operation *op) {
|
|
if (op->getNumResults() != 1 ||
|
|
!op->getResult(0).getType().isIndex()) {
|
|
diag = emitSilenceableError()
|
|
<< "expected dynamic split point handle to point to a "
|
|
"single-result index-typed op";
|
|
diag.attachNote(op->getLoc()) << "dynamic split point";
|
|
}
|
|
return OpFoldResult(op->getResult(0));
|
|
}));
|
|
} else {
|
|
splitPoints = llvm::to_vector(
|
|
llvm::map_range(state.getParams(getDynamicSplitPoint()),
|
|
[](Attribute attr) { return OpFoldResult(attr); }));
|
|
}
|
|
if (diag.isSilenceableFailure())
|
|
return diag;
|
|
|
|
if (splitPoints.size() != payload.size()) {
|
|
return emitDefiniteFailure()
|
|
<< "expected the dynamic split point handle to point to as "
|
|
"many operations ("
|
|
<< splitPoints.size() << ") as the target handle ("
|
|
<< payload.size() << ")";
|
|
}
|
|
} else {
|
|
splitPoints.resize(payload.size(),
|
|
rewriter.getIndexAttr(getStaticSplitPoint()));
|
|
}
|
|
|
|
// Split each target operation.
|
|
SmallVector<Operation *> first, second;
|
|
Operation *noSecondPart = nullptr;
|
|
for (const auto &pair : llvm::zip(payload, splitPoints)) {
|
|
Operation *target = std::get<0>(pair);
|
|
auto linalgOp = dyn_cast<LinalgOp>(target);
|
|
if (!linalgOp) {
|
|
auto diag = emitSilenceableError() << "only applies to structured ops";
|
|
diag.attachNote(target->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
|
|
if (getDimension() >= linalgOp.getNumLoops()) {
|
|
auto diag = emitSilenceableError() << "dimension " << getDimension()
|
|
<< " does not exist in target op";
|
|
diag.attachNote(target->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
|
|
rewriter.setInsertionPoint(linalgOp);
|
|
std::tie(first.emplace_back(), second.emplace_back()) = linalg::splitOp(
|
|
rewriter, cast<TilingInterface>(linalgOp.getOperation()),
|
|
getDimension(), std::get<1>(pair));
|
|
|
|
// Propagate errors.
|
|
if (!first.back() && !second.back()) {
|
|
auto diag = emitDefiniteFailure() << "internal failure in splitting";
|
|
diag.attachNote(target->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
|
|
// Do not add null second parts.
|
|
if (!second.back()) {
|
|
noSecondPart = target;
|
|
second.pop_back();
|
|
}
|
|
}
|
|
|
|
if (second.size() != first.size() && !second.empty()) {
|
|
auto diag = emitSilenceableError()
|
|
<< "splitting does not produce the second part for a subset "
|
|
"of targets";
|
|
diag.attachNote() << "expected splitting to produce the second part of all "
|
|
"or none of the targets";
|
|
diag.attachNote(noSecondPart->getLoc())
|
|
<< "first target with no second part";
|
|
return diag;
|
|
}
|
|
|
|
results.set(getFirst().cast<OpResult>(), first);
|
|
results.set(getSecond().cast<OpResult>(), second);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void SplitOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
if (getDynamicSplitPoint())
|
|
onlyReadsHandle(getDynamicSplitPoint(), effects);
|
|
producesHandle(getResults(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
ParseResult SplitOp::parse(OpAsmParser &parser, OperationState &result) {
|
|
OpAsmParser::UnresolvedOperand target, dynamicSplitPoint;
|
|
IntegerAttr staticSplitPoint;
|
|
if (parser.parseOperand(target) || parser.parseKeyword("after"))
|
|
return failure();
|
|
|
|
OptionalParseResult dynamicPointParseResult =
|
|
parser.parseOptionalOperand(dynamicSplitPoint);
|
|
if (!dynamicPointParseResult.has_value()) {
|
|
int64_t staticSplitPointValue;
|
|
if (failed(parser.parseInteger(staticSplitPointValue)))
|
|
return failure();
|
|
|
|
staticSplitPoint =
|
|
parser.getBuilder().getI64IntegerAttr(staticSplitPointValue);
|
|
}
|
|
|
|
Type targetType;
|
|
if (parser.parseOptionalAttrDict(result.attributes) ||
|
|
parser.parseColonType(targetType) ||
|
|
parser.resolveOperand(target, targetType, result.operands)) {
|
|
return failure();
|
|
}
|
|
if (dynamicPointParseResult.has_value()) {
|
|
Type splitPointType;
|
|
if (failed(*dynamicPointParseResult) || parser.parseComma() ||
|
|
parser.parseType(splitPointType) ||
|
|
parser.resolveOperand(dynamicSplitPoint, splitPointType,
|
|
result.operands)) {
|
|
return failure();
|
|
}
|
|
|
|
staticSplitPoint =
|
|
parser.getBuilder().getI64IntegerAttr(ShapedType::kDynamic);
|
|
}
|
|
|
|
result.addAttribute(
|
|
SplitOp::getStaticSplitPointAttrName(result.name).getValue(),
|
|
staticSplitPoint);
|
|
result.addTypes({targetType, targetType});
|
|
return success();
|
|
}
|
|
|
|
void SplitOp::print(OpAsmPrinter &printer) {
|
|
printer << " " << getTarget() << " after ";
|
|
int64_t staticSplitSize = static_cast<int64_t>(getStaticSplitPoint());
|
|
if (staticSplitSize != ShapedType::kDynamic)
|
|
printer << staticSplitSize;
|
|
else
|
|
printer << getDynamicSplitPoint();
|
|
printer << " ";
|
|
printer.printOptionalAttrDict(getOperation()->getAttrs(),
|
|
{getStaticSplitPointAttrName()});
|
|
printer << " : " << getTarget().getType();
|
|
if (staticSplitSize == ShapedType::kDynamic)
|
|
printer << ", " << getDynamicSplitPoint().getType();
|
|
}
|
|
|
|
LogicalResult SplitOp::verify() {
|
|
if ((static_cast<int64_t>(getStaticSplitPoint()) != ShapedType::kDynamic) ^
|
|
(getDynamicSplitPoint() == nullptr)) {
|
|
return emitOpError() << "expects either a dynamic or a static split "
|
|
"point to be provided";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// SplitReductionOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::SplitReductionOp::build(
|
|
OpBuilder &builder, OperationState &result, Value target,
|
|
int64_t splitFactor, int64_t insertSplitDimension, bool innerParallel,
|
|
bool useScalingAlgorithm, bool useAlloc) {
|
|
MLIRContext *ctx = builder.getContext();
|
|
result.addOperands(target);
|
|
result.addAttribute(SplitReductionOp::getSplitFactorAttrName(result.name),
|
|
builder.getI64IntegerAttr(splitFactor));
|
|
result.addAttribute(
|
|
SplitReductionOp::getInsertSplitDimensionAttrName(result.name),
|
|
builder.getI64IntegerAttr(insertSplitDimension));
|
|
if (innerParallel) {
|
|
result.addAttribute(SplitReductionOp::getInnerParallelAttrName(result.name),
|
|
builder.getUnitAttr());
|
|
}
|
|
if (useScalingAlgorithm) {
|
|
result.addAttribute(
|
|
SplitReductionOp::getUseScalingAlgorithmAttrName(result.name),
|
|
builder.getUnitAttr());
|
|
}
|
|
if (useAlloc) {
|
|
result.addAttribute(SplitReductionOp::getUseAllocAttrName(result.name),
|
|
builder.getUnitAttr());
|
|
}
|
|
auto resultType = pdl::OperationType::get(ctx);
|
|
result.addTypes({resultType, resultType, resultType, resultType});
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::SplitReductionOp::applyToOne(
|
|
LinalgOp target, transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
ControlSplitReductionFn splitFn = [&](LinalgOp) {
|
|
return linalg::SplitReductionOptions{int64_t(getSplitFactor()),
|
|
unsigned(getInsertSplitDimension()),
|
|
bool(getInnerParallel())};
|
|
};
|
|
TrivialPatternRewriter rewriter(getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<SplitReductionResult> splitResult =
|
|
(getUseScalingAlgorithm())
|
|
? splitReductionByScaling(rewriter, target, splitFn, getUseAlloc())
|
|
: splitReduction(rewriter, target, splitFn, getUseAlloc());
|
|
if (failed(splitResult))
|
|
return emitDefaultDefiniteFailure(target);
|
|
|
|
results.push_back(splitResult->initOrAlloc);
|
|
results.push_back(splitResult->fillOp);
|
|
results.push_back(splitResult->splitLinalgOp);
|
|
results.push_back(splitResult->resultCombiningLinalgOp);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TileReductionUsingScfOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::TileReductionUsingScfOp::build(
|
|
OpBuilder &builder, OperationState &result, Value target,
|
|
ArrayRef<int64_t> staticTileSizes) {
|
|
// Call the default builder.
|
|
// This is future-proof re mixed static-dynamic and setting up the proper
|
|
// operands segment sizes attributes for multiple variadic operands.
|
|
// In the absence of this, horrible bugs ensue.
|
|
// TODO: support mixed static-dynamic (see TileToForallOp).
|
|
MLIRContext *ctx = builder.getContext();
|
|
auto opTy = pdl::OperationType::get(ctx);
|
|
auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
|
|
build(builder, result,
|
|
/*resultTypes=*/TypeRange{opTy, opTy, opTy, opTy},
|
|
/*target=*/target,
|
|
/*tile_sizes=*/staticTileSizesAttr);
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::TileReductionUsingScfOp::applyToOne(
|
|
LinalgOp target, transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
TrivialPatternRewriter rewriter(getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
FailureOr<scf::SCFReductionTilingResult> result = scf::tileReductionUsingScf(
|
|
rewriter, cast<PartialReductionOpInterface>(target.getOperation()),
|
|
getAsOpFoldResult(rewriter.getI64ArrayAttr(getTileSizes())));
|
|
|
|
if (failed(result))
|
|
return emitDefaultSilenceableFailure(target);
|
|
results.push_back(result->loops.front());
|
|
results.push_back(result->initialOp);
|
|
results.push_back(result->parallelTiledOp);
|
|
results.push_back(result->mergeOp);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TileReductionUsingForallOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::TileReductionUsingForallOp::build(
|
|
OpBuilder &builder, OperationState &result, Value target,
|
|
ArrayRef<int64_t> staticNumThreads, ArrayRef<int64_t> staticTileSizes,
|
|
ArrayAttr mapping) {
|
|
// Call the default builder.
|
|
// This is future-proof re mixed static-dynamic and setting up the proper
|
|
// operands segment sizes attributes for multiple variadic operands.
|
|
// In the absence of this, horrible bugs ensue.
|
|
// TODO: support mixed static-dynamic (see TileToForallOp).
|
|
MLIRContext *ctx = builder.getContext();
|
|
auto opTy = pdl::OperationType::get(ctx);
|
|
auto staticNumThreadsAttr = builder.getDenseI64ArrayAttr(staticNumThreads);
|
|
auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
|
|
build(builder, result,
|
|
/*resultTypes=*/TypeRange{opTy, opTy, opTy, opTy},
|
|
/*target=*/target,
|
|
/*num_threads=*/staticNumThreadsAttr,
|
|
/*tile_sizes=*/staticTileSizesAttr,
|
|
/*mapping=*/mapping);
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::TileReductionUsingForallOp::applyToOne(
|
|
LinalgOp target, transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
TrivialPatternRewriter rewriter(getContext());
|
|
rewriter.setInsertionPoint(target);
|
|
SmallVector<OpFoldResult> numThreads =
|
|
getAsOpFoldResult(rewriter.getI64ArrayAttr(getNumThreads()));
|
|
SmallVector<OpFoldResult> tileSizes =
|
|
getAsOpFoldResult(rewriter.getI64ArrayAttr(getTileSizes()));
|
|
FailureOr<linalg::ForallReductionTilingResult> result =
|
|
linalg::tileReductionUsingForall(
|
|
rewriter, cast<PartialReductionOpInterface>(target.getOperation()),
|
|
numThreads, tileSizes, getMapping());
|
|
|
|
if (failed(result)) {
|
|
auto diag = emitSilenceableError() << "could not tile reduction";
|
|
diag.attachNote(target.getLoc()) << "target operation";
|
|
return diag;
|
|
}
|
|
results.push_back(result->loops);
|
|
results.push_back(result->initialOp);
|
|
results.push_back(result->parallelTiledOp);
|
|
results.push_back(result->mergeOp);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TileOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::TileOp::build(OpBuilder &builder, OperationState &result,
|
|
TypeRange loopTypes, Value target,
|
|
ArrayRef<int64_t> staticTileSizes,
|
|
ArrayRef<int64_t> interchange) {
|
|
return build(builder, result, loopTypes,
|
|
/*target=*/target,
|
|
/*mixedTileSizes=*/
|
|
getAsOpFoldResult(builder.getI64ArrayAttr(staticTileSizes)),
|
|
interchange);
|
|
}
|
|
|
|
void transform::TileOp::build(OpBuilder &builder, OperationState &result,
|
|
Value target, ArrayRef<int64_t> staticTileSizes,
|
|
ArrayRef<int64_t> interchange) {
|
|
build(builder, result, target,
|
|
getAsOpFoldResult(builder.getI64ArrayAttr(staticTileSizes)),
|
|
interchange);
|
|
}
|
|
|
|
void transform::TileOp::build(OpBuilder &builder, OperationState &result,
|
|
Value target,
|
|
ArrayRef<OpFoldResult> mixedTileSizes,
|
|
ArrayRef<int64_t> interchange) {
|
|
// Loop types are automaticaly splat by the callee, setting up one is
|
|
// enough.
|
|
SmallVector<Type> loopTypes(1, builder.getType<transform::AnyOpType>());
|
|
build(builder, result, loopTypes, target, mixedTileSizes, interchange);
|
|
}
|
|
|
|
void transform::TileOp::build(OpBuilder &builder, OperationState &result,
|
|
TypeRange loopTypes, Value target,
|
|
ArrayRef<OpFoldResult> mixedTileSizes,
|
|
ArrayRef<int64_t> interchange) {
|
|
SmallVector<int64_t> staticTileSizes;
|
|
SmallVector<Value> dynamicTileSizes;
|
|
dispatchIndexOpFoldResults(mixedTileSizes, dynamicTileSizes, staticTileSizes);
|
|
// Call the default builder which sets up the proper operands segment sizes
|
|
// attributes for multiple variadic operands. In the absence of this,
|
|
// horrible bugs ensue.
|
|
auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
|
|
unsigned numExpectedLoops =
|
|
staticTileSizes.size() - llvm::count(staticTileSizes, 0);
|
|
SmallVector<Type> resultTypes;
|
|
resultTypes.reserve(numExpectedLoops);
|
|
assert((loopTypes.size() == 1 || loopTypes.size() == numExpectedLoops) &&
|
|
"expected one loop type or as many as loops");
|
|
if (loopTypes.size() == 1)
|
|
resultTypes.append(numExpectedLoops, loopTypes[0]);
|
|
else
|
|
llvm::append_range(resultTypes, loopTypes);
|
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build(builder, result, /*tiled_linalg_op=*/target.getType(),
|
|
/*loops=*/resultTypes,
|
|
/*target=*/target,
|
|
/*dynamic_sizes=*/dynamicTileSizes,
|
|
/*static_sizes=*/staticTileSizesAttr,
|
|
/*interchange=*/builder.getDenseI64ArrayAttr(interchange));
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::TileOp::apply(TransformResults &transformResults,
|
|
TransformState &state) {
|
|
ArrayRef<int64_t> tileSizes = getStaticSizes();
|
|
|
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ArrayRef<Operation *> targets = state.getPayloadOps(getTarget());
|
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SmallVector<ArrayRef<Operation *>> dynamicSizeProducers;
|
|
SmallVector<SmallVector<int64_t>> paramSizes;
|
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dynamicSizeProducers.reserve(getDynamicSizes().size());
|
|
paramSizes.reserve(getDynamicSizes().size());
|
|
for (Value transformValue : getDynamicSizes()) {
|
|
if (transformValue.getType().isa<ParamType>()) {
|
|
dynamicSizeProducers.push_back({});
|
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ArrayRef<Attribute> params = state.getParams(transformValue);
|
|
paramSizes.push_back(
|
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llvm::to_vector(llvm::map_range(params, [](Attribute attr) {
|
|
return attr.cast<IntegerAttr>().getValue().getSExtValue();
|
|
})));
|
|
|
|
if (paramSizes.back().size() != targets.size()) {
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError()
|
|
<< "expected as many parameter values ("
|
|
<< dynamicSizeProducers.back().size() << ") as target ops ("
|
|
<< targets.size() << ")";
|
|
diag.attachNote(transformValue.getLoc()) << "for this parameter";
|
|
return diag;
|
|
}
|
|
|
|
continue;
|
|
}
|
|
paramSizes.push_back({});
|
|
dynamicSizeProducers.push_back(state.getPayloadOps(transformValue));
|
|
|
|
if (dynamicSizeProducers.back().size() != targets.size()) {
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError()
|
|
<< "expected as many dynamic size-producing operations ("
|
|
<< dynamicSizeProducers.back().size() << ") as target ops ("
|
|
<< targets.size() << ")";
|
|
diag.attachNote(transformValue.getLoc()) << "for this handle";
|
|
return diag;
|
|
}
|
|
|
|
for (Operation *op : dynamicSizeProducers.back()) {
|
|
if (op->getNumResults() == 1 &&
|
|
op->getResult(0).getType().isa<IndexType>())
|
|
continue;
|
|
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError() << "expected sizes to be produced by ops "
|
|
"with a single index-type result";
|
|
diag.attachNote(op->getLoc()) << "size producer op";
|
|
diag.attachNote(transformValue.getLoc()) << "for this handle";
|
|
return diag;
|
|
}
|
|
}
|
|
|
|
SmallVector<Operation *> tiled;
|
|
SmallVector<SmallVector<Operation *, 4>, 4> loops;
|
|
loops.resize(getLoops().size());
|
|
for (auto &[i, op] : llvm::enumerate(targets)) {
|
|
auto tilingInterface = dyn_cast<TilingInterface>(op);
|
|
auto dpsInterface = dyn_cast<DestinationStyleOpInterface>(op);
|
|
if (!tilingInterface || !dpsInterface) {
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError() << "only ops implementing TilingInterface and "
|
|
"DestinationStyleOpInterface are supported";
|
|
diag.attachNote(op->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
|
|
scf::SCFTilingOptions tilingOptions;
|
|
if (!tileSizes.empty()) {
|
|
tilingOptions.setTileSizeComputationFunction([&, index = i](OpBuilder &b,
|
|
Operation *) {
|
|
SmallVector<Value, 4> sizes;
|
|
sizes.reserve(tileSizes.size());
|
|
unsigned dynamicIdx = 0;
|
|
for (OpFoldResult ofr : getMixedSizes()) {
|
|
if (auto attr = ofr.dyn_cast<Attribute>()) {
|
|
sizes.push_back(b.create<arith::ConstantIndexOp>(
|
|
getLoc(), attr.cast<IntegerAttr>().getInt()));
|
|
continue;
|
|
}
|
|
ArrayRef<Operation *> dynamicSizes = dynamicSizeProducers[dynamicIdx];
|
|
ArrayRef<int64_t> params = paramSizes[dynamicIdx];
|
|
++dynamicIdx;
|
|
assert((dynamicSizes.empty() ^ params.empty()) &&
|
|
"expected either dynamic sizes or parameters");
|
|
if (!params.empty()) {
|
|
sizes.push_back(
|
|
b.create<arith::ConstantIndexOp>(getLoc(), params[index]));
|
|
} else {
|
|
sizes.push_back(dynamicSizes[index]->getResult(0));
|
|
}
|
|
}
|
|
return sizes;
|
|
});
|
|
}
|
|
|
|
tilingOptions.setInterchange(getInterchange());
|
|
TrivialPatternRewriter rewriter(op->getContext());
|
|
FailureOr<scf::SCFTilingResult> maybeTilingResult =
|
|
tileUsingSCFForOp(rewriter, tilingInterface, tilingOptions);
|
|
if (failed(maybeTilingResult))
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
|
|
if (dpsInterface.hasBufferSemantics())
|
|
rewriter.eraseOp(op);
|
|
else
|
|
rewriter.replaceOp(op, maybeTilingResult->loops.front()->getResults());
|
|
|
|
tiled.append(maybeTilingResult->tiledOps);
|
|
for (const auto &en2 : llvm::enumerate(maybeTilingResult->loops))
|
|
loops[en2.index()].push_back(en2.value());
|
|
}
|
|
|
|
transformResults.set(getTiledLinalgOp().cast<OpResult>(), tiled);
|
|
for (const auto &en : llvm::enumerate(loops))
|
|
transformResults.set(getLoops()[en.index()].cast<OpResult>(), en.value());
|
|
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
SmallVector<OpFoldResult> transform::TileOp::getMixedSizes() {
|
|
ValueRange dynamic = getDynamicSizes();
|
|
ArrayRef<int64_t> tileSizes = getStaticSizes();
|
|
SmallVector<OpFoldResult> results;
|
|
results.reserve(tileSizes.size());
|
|
unsigned dynamicPos = 0;
|
|
Builder builder(getContext());
|
|
for (int64_t size : tileSizes) {
|
|
if (size == ShapedType::kDynamic) {
|
|
results.push_back(dynamic[dynamicPos++]);
|
|
} else {
|
|
results.push_back(builder.getIndexAttr(size));
|
|
}
|
|
}
|
|
return results;
|
|
}
|
|
|
|
// We want to parse `DenseI64ArrayAttr` using the short form without the
|
|
// `array` prefix to be consistent in the IR with `parseDynamicIndexList`.
|
|
ParseResult parseOptionalInterchange(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
if (succeeded(parser.parseOptionalLBrace())) {
|
|
if (failed(parser.parseKeyword("interchange")))
|
|
return parser.emitError(parser.getNameLoc()) << "expect `interchange`";
|
|
if (failed(parser.parseEqual()))
|
|
return parser.emitError(parser.getNameLoc()) << "expect `=`";
|
|
result.addAttribute("interchange",
|
|
DenseI64ArrayAttr::parse(parser, Type{}));
|
|
if (failed(parser.parseRBrace()))
|
|
return parser.emitError(parser.getNameLoc()) << "expect `}`";
|
|
}
|
|
return success();
|
|
}
|
|
|
|
void printOptionalInterchange(OpAsmPrinter &p,
|
|
ArrayRef<int64_t> interchangeVals) {
|
|
if (!interchangeVals.empty()) {
|
|
p << " {interchange = [";
|
|
llvm::interleaveComma(interchangeVals, p,
|
|
[&](int64_t integer) { p << integer; });
|
|
p << "]}";
|
|
}
|
|
}
|
|
|
|
ParseResult transform::TileOp::parse(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
OpAsmParser::UnresolvedOperand target;
|
|
SmallVector<OpAsmParser::UnresolvedOperand> dynamicSizes;
|
|
DenseI64ArrayAttr staticSizes;
|
|
FunctionType functionalType;
|
|
llvm::SMLoc operandLoc;
|
|
if (parser.parseOperand(target) || parser.getCurrentLocation(&operandLoc) ||
|
|
parseDynamicIndexList(parser, dynamicSizes, staticSizes) ||
|
|
parseOptionalInterchange(parser, result) ||
|
|
parser.parseColonType(functionalType))
|
|
return ParseResult::failure();
|
|
|
|
size_t numExpectedLoops =
|
|
staticSizes.size() - llvm::count(staticSizes.asArrayRef(), 0);
|
|
if (functionalType.getNumResults() != numExpectedLoops + 1) {
|
|
return parser.emitError(parser.getNameLoc())
|
|
<< "expected " << (numExpectedLoops + 1) << " result type(s)";
|
|
}
|
|
if (functionalType.getNumInputs() != dynamicSizes.size() + 1) {
|
|
return parser.emitError(operandLoc)
|
|
<< "expected " << dynamicSizes.size() + 1 << " operand type(s)";
|
|
}
|
|
if (parser.resolveOperand(target, functionalType.getInputs().front(),
|
|
result.operands) ||
|
|
parser.resolveOperands(dynamicSizes,
|
|
functionalType.getInputs().drop_front(),
|
|
operandLoc, result.operands)) {
|
|
return failure();
|
|
}
|
|
|
|
result.addAttribute(getStaticSizesAttrName(result.name), staticSizes);
|
|
result.addTypes(functionalType.getResults());
|
|
return success();
|
|
}
|
|
|
|
void TileOp::print(OpAsmPrinter &p) {
|
|
p << ' ' << getTarget();
|
|
printDynamicIndexList(p, getOperation(), getDynamicSizes(), getStaticSizes());
|
|
printOptionalInterchange(p, getInterchange());
|
|
p << " : ";
|
|
p.printFunctionalType(getOperands().getTypes(), getResults().getTypes());
|
|
}
|
|
|
|
void transform::TileOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
onlyReadsHandle(getDynamicSizes(), effects);
|
|
producesHandle(getTiledLinalgOp(), effects);
|
|
producesHandle(getLoops(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TileToForallOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::TileToForallOp::build(OpBuilder &builder,
|
|
OperationState &result, Value target,
|
|
ArrayRef<int64_t> staticTileSizes,
|
|
transform::TileSizesSpec,
|
|
ArrayAttr mapping) {
|
|
return build(builder, result,
|
|
/*target=*/target,
|
|
/*mixedTileSizes=*/
|
|
getAsOpFoldResult(builder.getI64ArrayAttr(staticTileSizes)),
|
|
/*_=*/TileSizesSpec(),
|
|
/*mapping=*/mapping);
|
|
}
|
|
|
|
void transform::TileToForallOp::build(OpBuilder &builder,
|
|
OperationState &result, Value target,
|
|
ArrayRef<OpFoldResult> mixedTileSizes,
|
|
transform::TileSizesSpec,
|
|
ArrayAttr mapping) {
|
|
SmallVector<int64_t> staticTileSizes;
|
|
SmallVector<Value> dynamicTileSizes;
|
|
dispatchIndexOpFoldResults(mixedTileSizes, dynamicTileSizes, staticTileSizes);
|
|
// Call the default builder which sets up the proper operands segment sizes
|
|
// attributes for multiple variadic operands. In the absence of this,
|
|
// horrible bugs ensue.
|
|
MLIRContext *ctx = builder.getContext();
|
|
auto operationType = pdl::OperationType::get(ctx);
|
|
auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
|
|
build(builder, result,
|
|
/*resultTypes=*/TypeRange{operationType, operationType},
|
|
/*target=*/target,
|
|
/*num_threads=*/ValueRange{},
|
|
/*tile_sizes=*/dynamicTileSizes,
|
|
/*packed_num_threads=*/Value(),
|
|
/*packed_tile_sizes=*/Value(),
|
|
/*static_num_threads=*/builder.getDenseI64ArrayAttr({}),
|
|
/*static_tile_sizes=*/staticTileSizesAttr,
|
|
/*mapping=*/mapping);
|
|
}
|
|
|
|
void transform::TileToForallOp::build(OpBuilder &builder,
|
|
OperationState &result, Value target,
|
|
ArrayRef<int64_t> staticNumThreads,
|
|
transform::NumThreadsSpec,
|
|
ArrayAttr mapping) {
|
|
return build(builder, result, target,
|
|
getAsOpFoldResult(builder.getI64ArrayAttr(staticNumThreads)),
|
|
NumThreadsSpec(), mapping);
|
|
}
|
|
|
|
void transform::TileToForallOp::build(OpBuilder &builder,
|
|
OperationState &result, Value target,
|
|
ArrayRef<OpFoldResult> mixedNumThreads,
|
|
transform::NumThreadsSpec,
|
|
ArrayAttr mapping) {
|
|
SmallVector<int64_t> staticNumThreads;
|
|
SmallVector<Value> dynamicNumThreads;
|
|
dispatchIndexOpFoldResults(mixedNumThreads, dynamicNumThreads,
|
|
staticNumThreads);
|
|
// Call the default builder which sets up the proper operands segment sizes
|
|
// attributes for multiple variadic operands. In the absence of this,
|
|
// horrible bugs ensue.
|
|
MLIRContext *ctx = builder.getContext();
|
|
auto operationType = pdl::OperationType::get(ctx);
|
|
auto staticNumThreadsAttr = builder.getDenseI64ArrayAttr(staticNumThreads);
|
|
build(builder, result,
|
|
/*resultTypes=*/TypeRange{operationType, operationType},
|
|
/*target=*/target,
|
|
/*num_threads=*/dynamicNumThreads,
|
|
/*tile_sizes=*/ValueRange{},
|
|
/*packed_num_threads=*/Value(),
|
|
/*packed_tile_sizes=*/Value(),
|
|
/*static_num_threads=*/staticNumThreadsAttr,
|
|
/*static_tile_sizes=*/builder.getDenseI64ArrayAttr({}),
|
|
/*mapping=*/mapping);
|
|
}
|
|
|
|
DiagnosedSilenceableFailure transform::tileToForallOpImpl(
|
|
RewriterBase &rewriter, transform::TransformState &state,
|
|
TransformOpInterface transformOp, ArrayRef<Operation *> targets,
|
|
ArrayRef<OpFoldResult> mixedNumThreads,
|
|
ArrayRef<OpFoldResult> mixedTileSizes, std::optional<ArrayAttr> mapping,
|
|
SmallVector<Operation *> &tileOps, SmallVector<Operation *> &tiledOps) {
|
|
if (targets.empty())
|
|
return DiagnosedSilenceableFailure::success();
|
|
|
|
// Transform all targets one by one.
|
|
for (Operation *target : targets) {
|
|
auto tileableOp = dyn_cast<TilingInterface>(target);
|
|
if (!tileableOp) {
|
|
DiagnosedSilenceableFailure diag =
|
|
transformOp.emitSilenceableError()
|
|
<< "only TilingInterface ops are supported";
|
|
diag.attachNote(target->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
rewriter.setInsertionPoint(tileableOp);
|
|
FailureOr<linalg::ForallTilingResult> tilingResult = failure();
|
|
if (!mixedNumThreads.empty()) {
|
|
tilingResult = linalg::tileToForallOp(rewriter, tileableOp,
|
|
mixedNumThreads, mapping);
|
|
} else {
|
|
tilingResult = linalg::tileToForallOpUsingTileSizes(
|
|
rewriter, tileableOp, mixedTileSizes, mapping);
|
|
}
|
|
|
|
if (failed(tilingResult))
|
|
return transformOp.emitDefaultSilenceableFailure(tileableOp);
|
|
rewriter.replaceOp(tileableOp, tilingResult->tileOp->getResults());
|
|
|
|
tileOps.push_back(tilingResult->tileOp);
|
|
tiledOps.push_back(tilingResult->tiledOp);
|
|
}
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::TileToForallOp::apply(transform::TransformResults &transformResults,
|
|
transform::TransformState &state) {
|
|
IRRewriter rewriter(getContext());
|
|
auto transformOp = cast<TransformOpInterface>(getOperation());
|
|
ArrayRef<Operation *> targets = state.getPayloadOps(getTarget());
|
|
|
|
// Result payload ops.
|
|
SmallVector<Operation *> tileOps;
|
|
SmallVector<Operation *> tiledOps;
|
|
|
|
// Unpack handles.
|
|
SmallVector<OpFoldResult> mixedNumThreads;
|
|
DiagnosedSilenceableFailure status =
|
|
getPackedNumThreads()
|
|
? unpackSingleIndexResultPDLOperations(
|
|
state, transformOp, mixedNumThreads, getPackedNumThreads())
|
|
: unpackSingleIndexResultPDLOperations(
|
|
state, transformOp, mixedNumThreads, getMixedNumThreads());
|
|
if (!status.succeeded())
|
|
return status;
|
|
SmallVector<OpFoldResult> mixedTileSizes;
|
|
status = getPackedTileSizes()
|
|
? unpackSingleIndexResultPDLOperations(
|
|
state, transformOp, mixedTileSizes, getPackedTileSizes())
|
|
: unpackSingleIndexResultPDLOperations(
|
|
state, transformOp, mixedTileSizes, getMixedTileSizes());
|
|
if (!status.succeeded())
|
|
return status;
|
|
|
|
DiagnosedSilenceableFailure diag =
|
|
tileToForallOpImpl(rewriter, state, transformOp, targets, mixedNumThreads,
|
|
mixedTileSizes, getMapping(), tileOps, tiledOps);
|
|
|
|
if (!diag.succeeded())
|
|
return diag;
|
|
|
|
transformResults.set(getForallOp().cast<OpResult>(), tileOps);
|
|
transformResults.set(getTiledOp().cast<OpResult>(), tiledOps);
|
|
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::TileToForallOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
onlyReadsHandle(getTileSizes(), effects);
|
|
onlyReadsHandle(getNumThreads(), effects);
|
|
onlyReadsHandle(getPackedNumThreads(), effects);
|
|
onlyReadsHandle(getPackedTileSizes(), effects);
|
|
producesHandle(getResults(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
SmallVector<OpFoldResult> TileToForallOp::getMixedNumThreads() {
|
|
Builder b(getContext());
|
|
return getMixedValues(getStaticNumThreads(), getNumThreads(), b);
|
|
}
|
|
|
|
SmallVector<OpFoldResult> TileToForallOp::getMixedTileSizes() {
|
|
Builder b(getContext());
|
|
return getMixedValues(getStaticTileSizes(), getTileSizes(), b);
|
|
}
|
|
|
|
LogicalResult TileToForallOp::verify() {
|
|
int numThreadsSpec = static_cast<int>(!getMixedNumThreads().empty()) +
|
|
static_cast<int>(getPackedNumThreads() != Value());
|
|
if (numThreadsSpec > 1)
|
|
return emitOpError(
|
|
"num_threads and packed_num_threads are mutually exclusive");
|
|
int tileSizesSpec = static_cast<int>(!getMixedTileSizes().empty()) +
|
|
static_cast<int>(getPackedTileSizes() != Value());
|
|
if (tileSizesSpec > 1)
|
|
return emitOpError(
|
|
"tile_sizes and packed_tile_sizes are mutually exclusive");
|
|
if (numThreadsSpec == 0 && tileSizesSpec == 0)
|
|
return emitOpError("either (packed_)num_threads or (packed_)tile_sizes "
|
|
"must be specified");
|
|
return success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// TileToScfForOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::TileToScfForOp::apply(TransformResults &transformResults,
|
|
TransformState &state) {
|
|
ArrayRef<int64_t> tileSizes = getStaticSizes();
|
|
|
|
ArrayRef<Operation *> targets = state.getPayloadOps(getTarget());
|
|
SmallVector<ArrayRef<Operation *>> dynamicSizeProducers;
|
|
dynamicSizeProducers.reserve(getDynamicSizes().size());
|
|
for (Value dynamicSizeProducerHandle : getDynamicSizes()) {
|
|
dynamicSizeProducers.push_back(
|
|
state.getPayloadOps(dynamicSizeProducerHandle));
|
|
|
|
if (dynamicSizeProducers.back().size() != targets.size()) {
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError()
|
|
<< "expected as many dynamic size-producing operations ("
|
|
<< dynamicSizeProducers.back().size() << ") as target ops ("
|
|
<< targets.size() << ")";
|
|
diag.attachNote(dynamicSizeProducerHandle.getLoc()) << "for this handle";
|
|
return diag;
|
|
}
|
|
|
|
for (Operation *op : dynamicSizeProducers.back()) {
|
|
if (op->getNumResults() == 1 &&
|
|
op->getResult(0).getType().isa<IndexType>())
|
|
continue;
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError() << "expected sizes to be produced by ops "
|
|
"with a single index-type result";
|
|
diag.attachNote(op->getLoc()) << "size producer op";
|
|
diag.attachNote(dynamicSizeProducerHandle.getLoc()) << "for this handle";
|
|
return diag;
|
|
}
|
|
}
|
|
|
|
SmallVector<Operation *> tiled;
|
|
SmallVector<SmallVector<Operation *, 4>, 4> loops;
|
|
loops.resize(getLoops().size());
|
|
for (auto &en : llvm::enumerate(targets)) {
|
|
auto tilingInterfaceOp = dyn_cast<TilingInterface>(en.value());
|
|
if (!tilingInterfaceOp) {
|
|
DiagnosedSilenceableFailure diag =
|
|
emitSilenceableError() << "only TilingInterface ops are supported";
|
|
diag.attachNote(en.value()->getLoc()) << "target op";
|
|
return diag;
|
|
}
|
|
|
|
scf::SCFTilingOptions tilingOptions;
|
|
unsigned index = en.index();
|
|
if (!tileSizes.empty()) {
|
|
tilingOptions.setTileSizeComputationFunction(
|
|
[&, index](OpBuilder &b, Operation *) {
|
|
SmallVector<Value, 4> sizes;
|
|
sizes.reserve(tileSizes.size());
|
|
unsigned dynamicIdx = 0;
|
|
for (OpFoldResult ofr : getMixedSizes()) {
|
|
if (auto attr = ofr.dyn_cast<Attribute>()) {
|
|
sizes.push_back(b.create<arith::ConstantIndexOp>(
|
|
getLoc(), attr.cast<IntegerAttr>().getInt()));
|
|
} else {
|
|
sizes.push_back(
|
|
dynamicSizeProducers[dynamicIdx++][index]->getResult(0));
|
|
}
|
|
}
|
|
return sizes;
|
|
});
|
|
}
|
|
|
|
tilingOptions.setInterchange(getInterchange());
|
|
TrivialPatternRewriter rewriter(tilingInterfaceOp.getContext());
|
|
FailureOr<scf::SCFTilingResult> tilingResult =
|
|
tileUsingSCFForOp(rewriter, tilingInterfaceOp, tilingOptions);
|
|
if (failed(tilingResult))
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
|
|
rewriter.replaceOp(tilingInterfaceOp, tilingResult->replacements);
|
|
|
|
tiled.append(tilingResult->tiledOps);
|
|
for (const auto &en2 : llvm::enumerate(tilingResult->loops))
|
|
loops[en2.index()].push_back(en2.value());
|
|
}
|
|
|
|
transformResults.set(getTiledLinalgOp().cast<OpResult>(), tiled);
|
|
for (const auto &en : llvm::enumerate(loops))
|
|
transformResults.set(getLoops()[en.index()].cast<OpResult>(), en.value());
|
|
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
SmallVector<OpFoldResult> transform::TileToScfForOp::getMixedSizes() {
|
|
ValueRange dynamic = getDynamicSizes();
|
|
ArrayRef<int64_t> tileSizes = getStaticSizes();
|
|
SmallVector<OpFoldResult> results;
|
|
results.reserve(tileSizes.size());
|
|
unsigned dynamicPos = 0;
|
|
Builder builder(getContext());
|
|
for (int64_t size : tileSizes) {
|
|
if (size == ShapedType::kDynamic) {
|
|
results.push_back(dynamic[dynamicPos++]);
|
|
} else {
|
|
results.push_back(builder.getIndexAttr(size));
|
|
}
|
|
}
|
|
return results;
|
|
}
|
|
|
|
ParseResult transform::TileToScfForOp::parse(OpAsmParser &parser,
|
|
OperationState &result) {
|
|
OpAsmParser::UnresolvedOperand target;
|
|
SmallVector<OpAsmParser::UnresolvedOperand> dynamicSizes;
|
|
DenseI64ArrayAttr staticSizes;
|
|
auto pdlOperationType = pdl::OperationType::get(parser.getContext());
|
|
if (parser.parseOperand(target) ||
|
|
parser.resolveOperand(target, pdlOperationType, result.operands) ||
|
|
parseDynamicIndexList(parser, dynamicSizes, staticSizes) ||
|
|
parser.resolveOperands(dynamicSizes, pdlOperationType, result.operands))
|
|
return ParseResult::failure();
|
|
|
|
// Parse optional interchange.
|
|
if (failed(parseOptionalInterchange(parser, result)))
|
|
return ParseResult::failure();
|
|
result.addAttribute(getStaticSizesAttrName(result.name), staticSizes);
|
|
size_t numExpectedLoops =
|
|
staticSizes.size() - llvm::count(staticSizes.asArrayRef(), 0);
|
|
result.addTypes(SmallVector<Type>(numExpectedLoops + 1, pdlOperationType));
|
|
return success();
|
|
}
|
|
|
|
void TileToScfForOp::print(OpAsmPrinter &p) {
|
|
p << ' ' << getTarget();
|
|
printDynamicIndexList(p, getOperation(), getDynamicSizes(), getStaticSizes());
|
|
printOptionalInterchange(p, getInterchange());
|
|
}
|
|
|
|
void transform::TileToScfForOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
onlyReadsHandle(getDynamicSizes(), effects);
|
|
producesHandle(getTiledLinalgOp(), effects);
|
|
producesHandle(getLoops(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// VectorizeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void transform::VectorizeOp::build(OpBuilder &builder, OperationState &result,
|
|
Value target, bool vectorizePadding,
|
|
bool vectorizeExtract) {
|
|
result.addOperands(target);
|
|
if (vectorizePadding) {
|
|
result.addAttribute(VectorizeOp::getVectorizePaddingAttrName(result.name),
|
|
builder.getUnitAttr());
|
|
}
|
|
if (vectorizeExtract) {
|
|
result.addAttribute(VectorizeOp::getVectorizeNdExtractAttrName(result.name),
|
|
builder.getUnitAttr());
|
|
}
|
|
result.addTypes(pdl::OperationType::get(builder.getContext()));
|
|
}
|
|
|
|
namespace {
|
|
/// This is an helper only to call vectorize via a pattern inside of
|
|
/// VectorizeOp::applyToOne.
|
|
struct VectorizationPattern : public RewritePattern {
|
|
explicit VectorizationPattern(MLIRContext *context,
|
|
bool vectorizeExtract = false)
|
|
: RewritePattern(MatchAnyOpTypeTag(), /*benefit=*/1, context),
|
|
vectorizeNDExtract(vectorizeExtract) {}
|
|
LogicalResult matchAndRewrite(Operation *op,
|
|
PatternRewriter &rewriter) const override {
|
|
LinalgOp linalgOp = dyn_cast<LinalgOp>(op);
|
|
if (!linalgOp)
|
|
return rewriter.notifyMatchFailure(op, "expected Linalg Op");
|
|
return vectorize(rewriter, linalgOp, /*inputVectorSizes=*/{},
|
|
vectorizeNDExtract);
|
|
}
|
|
|
|
private:
|
|
/// Controls whether to vectorize `tensor.extract` when the input tensor is
|
|
/// rank >= 2.
|
|
bool vectorizeNDExtract = false;
|
|
};
|
|
} // namespace
|
|
|
|
DiagnosedSilenceableFailure
|
|
transform::VectorizeOp::applyToOne(Operation *target,
|
|
transform::ApplyToEachResultList &results,
|
|
transform::TransformState &state) {
|
|
if (!target->hasTrait<OpTrait::IsIsolatedFromAbove>()) {
|
|
auto diag = this->emitOpError("requires isolated-from-above targets");
|
|
diag.attachNote(target->getLoc()) << "non-isolated target";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
|
|
MLIRContext *ctx = getContext();
|
|
RewritePatternSet patterns(ctx);
|
|
patterns.add<VectorizationPattern>(ctx, getVectorizeNdExtract());
|
|
|
|
if (!getDisableTransferPermutationMapLoweringPatterns())
|
|
vector::populateVectorTransferPermutationMapLoweringPatterns(patterns);
|
|
|
|
if (!getDisableMultiReductionToContractPatterns())
|
|
vector::populateVectorReductionToContractPatterns(patterns);
|
|
|
|
patterns.add<linalg::LinalgCopyVTRForwardingPattern,
|
|
linalg::LinalgCopyVTWForwardingPattern>(ctx,
|
|
/*benefit=*/2);
|
|
vector::TransferReadOp::getCanonicalizationPatterns(patterns, ctx);
|
|
vector::TransferWriteOp::getCanonicalizationPatterns(patterns, ctx);
|
|
|
|
patterns.add<CopyVectorizationPattern>(ctx);
|
|
|
|
if (getVectorizePadding())
|
|
linalg::populatePadOpVectorizationPatterns(patterns);
|
|
|
|
if (failed(applyPatternsAndFoldGreedily(target, std::move(patterns))))
|
|
return emitDefaultDefiniteFailure(target);
|
|
|
|
results.push_back(target);
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// MaskedVectorizeOp
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
DiagnosedSilenceableFailure transform::MaskedVectorizeOp::apply(
|
|
mlir::transform::TransformResults &transformResults,
|
|
mlir::transform::TransformState &state) {
|
|
IRRewriter rewriter(getContext());
|
|
ArrayRef<Operation *> targets = state.getPayloadOps(getTarget());
|
|
if (targets.empty())
|
|
return DiagnosedSilenceableFailure::success();
|
|
|
|
SmallVector<int64_t> vectorSizes;
|
|
for (OpFoldResult sz : getMixedVectorSizes()) {
|
|
if (sz.is<Attribute>()) {
|
|
auto attr = sz.get<Attribute>();
|
|
vectorSizes.push_back(attr.cast<IntegerAttr>().getInt());
|
|
continue;
|
|
}
|
|
|
|
ArrayRef<Operation *> szPayloads = state.getPayloadOps(sz.get<Value>());
|
|
if (szPayloads.size() != 1) {
|
|
auto diag = this->emitOpError(
|
|
"requires vector size handle that is mapped to 1 payload op");
|
|
diag.attachNote(sz.get<Value>().getLoc())
|
|
<< "mapped to " << szPayloads.size() << " payload ops";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
|
|
Operation *szPayloadOp = szPayloads[0];
|
|
if (szPayloadOp->getNumResults() != 1 ||
|
|
!szPayloadOp->getResult(0).getType().isIndex()) {
|
|
auto diag = this->emitOpError(
|
|
"requires vector size payload op with 1 index result");
|
|
diag.attachNote(szPayloadOp->getLoc()) << "vector size payload op";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
|
|
IntegerAttr attr;
|
|
if (!matchPattern(szPayloadOp->getResult(0), m_Constant(&attr))) {
|
|
auto diag = this->emitOpError("requires constant vector size");
|
|
diag.attachNote(szPayloadOp->getLoc()) << "vector size payload op";
|
|
return DiagnosedSilenceableFailure::definiteFailure();
|
|
}
|
|
|
|
vectorSizes.push_back(attr.getInt());
|
|
}
|
|
|
|
// TODO: Check that the correct number of vectorSizes was provided.
|
|
|
|
for (Operation *target : targets) {
|
|
auto linalgOp = dyn_cast<LinalgOp>(target);
|
|
if (!linalgOp) {
|
|
return mlir::emitSilenceableFailure(target->getLoc())
|
|
<< "cannot vectorize non-Linalg op";
|
|
}
|
|
|
|
if (failed(linalg::vectorize(rewriter, linalgOp, vectorSizes,
|
|
getVectorizeNdExtract()))) {
|
|
return mlir::emitSilenceableFailure(target->getLoc())
|
|
<< "failed to vectorize op";
|
|
}
|
|
}
|
|
|
|
return DiagnosedSilenceableFailure::success();
|
|
}
|
|
|
|
void transform::MaskedVectorizeOp::getEffects(
|
|
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
|
|
consumesHandle(getTarget(), effects);
|
|
onlyReadsHandle(getVectorSizes(), effects);
|
|
modifiesPayload(effects);
|
|
}
|
|
|
|
SmallVector<OpFoldResult> MaskedVectorizeOp::getMixedVectorSizes() {
|
|
OpBuilder b(getContext());
|
|
return getMixedValues(getStaticVectorSizes(), getVectorSizes(), b);
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Transform op registration
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace {
|
|
/// Registers new ops and declares PDL as dependent dialect since the
|
|
/// additional ops are using PDL types for operands and results.
|
|
class LinalgTransformDialectExtension
|
|
: public transform::TransformDialectExtension<
|
|
LinalgTransformDialectExtension> {
|
|
public:
|
|
using Base::Base;
|
|
|
|
void init() {
|
|
declareDependentDialect<pdl::PDLDialect>();
|
|
declareDependentDialect<LinalgDialect>();
|
|
declareGeneratedDialect<AffineDialect>();
|
|
declareGeneratedDialect<arith::ArithDialect>();
|
|
declareGeneratedDialect<scf::SCFDialect>();
|
|
declareGeneratedDialect<vector::VectorDialect>();
|
|
declareGeneratedDialect<gpu::GPUDialect>();
|
|
|
|
registerTransformOps<
|
|
#define GET_OP_LIST
|
|
#include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp.inc"
|
|
>();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
#include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOpsEnums.cpp.inc"
|
|
|
|
#define GET_OP_CLASSES
|
|
#include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp.inc"
|
|
|
|
void mlir::linalg::registerTransformDialectExtension(
|
|
DialectRegistry ®istry) {
|
|
registry.addExtensions<LinalgTransformDialectExtension>();
|
|
}
|