0a8e3dd432
Rename interface functions as follows: * `hasTensorSemantics` -> `hasPureTensorSemantics` * `hasBufferSemantics` -> `hasPureBufferSemantics` These two functions return "true" if the op has tensor/buffer operands but not buffer/tensor operands. Also drop the "ranked" part from the interface, i.e., do not distinguish between ranked/unranked types. The new function names describe the functions more accurately. They also align their semantics with the notion of "tensor semantics" with the bufferization framework. (An op is supposed to be bufferized if it has tensor operands, and we don't care if it also has memref operands.) This change is in preparation of #75273, which adds `BufferizableOpInterface::hasTensorSemantics`. By renaming the functions in the `DestinationStyleOpInterface`, we can avoid name clashes between the two interfaces.
391 lines
17 KiB
C++
391 lines
17 KiB
C++
//===- DecomposeLinalgOps.cpp - Pattern to break up Linalg ops ------------===//
|
|
//
|
|
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
|
|
|
|
#include "mlir/Dialect/Affine/IR/AffineOps.h"
|
|
#include "mlir/Dialect/Linalg/IR/Linalg.h"
|
|
#include <optional>
|
|
|
|
using namespace mlir;
|
|
using namespace mlir::linalg;
|
|
|
|
namespace {
|
|
|
|
/// Pattern to decompose a GenericOp that has more than two statements
|
|
/// into one GenericOp with the first statement (i.e. peeled operation), and
|
|
/// a second GenericOp with the remaining statements (i.e. residual operations).
|
|
|
|
/// - The result of the first GenericOp has the same shape as the iteration
|
|
/// space of the GenericOp. The body of the op yields as many values as the
|
|
/// original op plus all the results of the peeled operation.
|
|
/// - The second GenericOp has as many operands as the original operation plus
|
|
/// all the results of the first Generic Op. It has the same number of yields as
|
|
/// the original op.
|
|
/// - If the result of the peeled operation was yielded by the original
|
|
/// GenericOp the uses of the corresponding results will be replaced with the
|
|
/// result of the first GenericOp created.
|
|
///
|
|
/// Example
|
|
///
|
|
/// ```mlir
|
|
/// %result:2 = linalg.generic ... ins(%arg0, %arg1, %arg2 : ...)
|
|
/// outs(%init0, %init1 : ...) {
|
|
/// ^bb0(%b0: ... , %b1: ... , %b2: ... , %b3: ..., %b4: ...):
|
|
/// %0 = <s0> %b0, %b1 : ...
|
|
/// %1 = <s1> %0, %b2 : ...
|
|
/// linalg.yield %0, %1 : ...
|
|
/// } -> (..., ...)
|
|
/// return %result#0, %result#1
|
|
/// ```
|
|
///
|
|
/// gets split into
|
|
///
|
|
/// ```mlir
|
|
/// %init = tensor.empty ...
|
|
/// %op0:3 = linalg.generic ... ins(%arg0, %arg1, %arg2 : ...)
|
|
/// outs(%init0, %init1, %init : ...)
|
|
/// ^bb0(%b0: ... , %b1: ... , %b2: ... , %b3: ..., %b4: ..., %b5: ...):
|
|
/// %0 = <s0> %b0, %b1 : ...
|
|
/// linalg.yield %0, %..., %0 : ...
|
|
/// } -> (..., ..., ...)
|
|
/// %op1:2 = linalg.generic ... ins(%arg0, %arg1, %arg2, %op0#2 : ...)
|
|
/// outs(%init0, %init1 : ...) {
|
|
/// ^bb0(%b0: ... , %b1: ... , %b2: ... , %b3: ..., %b4: ..., %b5: ...):
|
|
/// %1 = <s1> %b3, %b2 : ...
|
|
/// linalg.yield %..., %1 : ...
|
|
/// } -> (..., ...)
|
|
/// return %op0#0, %op1#1
|
|
/// ```
|
|
///
|
|
/// After canonicalization this is expected to be
|
|
///
|
|
/// ```mlir
|
|
/// %init = tensor.empty ...
|
|
/// %op0 = linalg.generic ... ins(%arg0, %arg1, : ...)
|
|
/// outs(%init : ...)
|
|
/// ^bb0(%b0: ... , %b1: ... , %b2: ...):
|
|
/// %0 = <s0> %b0, %b1 : ...
|
|
/// linalg.yield %0 : ...
|
|
/// } -> ...
|
|
/// %op1 = linalg.generic ... ins(%arg2, %op0#2 : ...)
|
|
/// outs(%init1 : ...) {
|
|
/// ^bb0(%b0: ... , %b1: ... , %b2: ...):
|
|
/// %1 = <s1> %b1, %b0 : ...
|
|
/// linalg.yield %..., %1 : ...
|
|
/// } -> ...
|
|
/// return %op0, %op1
|
|
/// ```
|
|
struct DecomposeLinalgOp : public OpRewritePattern<GenericOp> {
|
|
using OpRewritePattern<GenericOp>::OpRewritePattern;
|
|
|
|
LogicalResult matchAndRewrite(GenericOp genericOp,
|
|
PatternRewriter &rewriter) const override;
|
|
|
|
private:
|
|
/// Helper method to create a generic op for the peeled scalar operation. The
|
|
/// created op has an empty region.
|
|
GenericOp createPeeledGenericOp(GenericOp genericOp,
|
|
PatternRewriter &rewriter) const;
|
|
|
|
/// Helper method to create a generic op for the residual scalar operation.
|
|
/// The created op has the same region as the original op.
|
|
GenericOp createResidualGenericOp(GenericOp genericOp,
|
|
GenericOp peeledGenericOp,
|
|
PatternRewriter &rewriter) const;
|
|
};
|
|
} // namespace
|
|
|
|
/// Helper method to compute the range of a generic op.
|
|
static SmallVector<OpFoldResult> getGenericOpLoopRange(OpBuilder &b,
|
|
GenericOp op) {
|
|
OpBuilder::InsertionGuard g(b);
|
|
b.setInsertionPoint(op);
|
|
Location loc = op.getLoc();
|
|
auto allShapesSizes =
|
|
cast<LinalgOp>(op.getOperation()).createFlatListOfOperandDims(b, loc);
|
|
AffineMap map = op.getShapesToLoopsMap();
|
|
IRRewriter rewriter(b);
|
|
return affine::makeComposedFoldedMultiResultAffineApply(rewriter, loc, map,
|
|
allShapesSizes);
|
|
}
|
|
|
|
/// Helper method to permute the list of `values` based on the `map`.
|
|
SmallVector<OpFoldResult> permuteValues(ArrayRef<OpFoldResult> values,
|
|
AffineMap map) {
|
|
assert(map.isPermutation());
|
|
SmallVector<OpFoldResult> permutedValues(values.size());
|
|
for (const auto &position :
|
|
llvm::enumerate(llvm::map_range(map.getResults(), [](AffineExpr expr) {
|
|
return cast<AffineDimExpr>(expr).getPosition();
|
|
})))
|
|
permutedValues[position.value()] = values[position.index()];
|
|
return permutedValues;
|
|
}
|
|
|
|
/// Get zero value for an element type.
|
|
static Value getZero(OpBuilder &b, Location loc, Type elementType) {
|
|
assert(elementType.isIntOrIndexOrFloat() &&
|
|
"expected scalar type while computing zero value");
|
|
if (isa<IntegerType>(elementType))
|
|
return b.create<arith::ConstantIntOp>(loc, 0, elementType);
|
|
if (elementType.isIndex())
|
|
return b.create<arith::ConstantIndexOp>(loc, 0);
|
|
// Assume float.
|
|
auto floatType = cast<FloatType>(elementType);
|
|
return b.create<arith::ConstantFloatOp>(
|
|
loc, APFloat::getZero(floatType.getFloatSemantics()), floatType);
|
|
}
|
|
|
|
GenericOp
|
|
DecomposeLinalgOp::createPeeledGenericOp(GenericOp genericOp,
|
|
PatternRewriter &rewriter) const {
|
|
Block *body = genericOp.getBody();
|
|
Operation *peeledScalarOperation = &(*body->begin());
|
|
SmallVector<AffineMap> peeledGenericOpIndexingMaps =
|
|
genericOp.getIndexingMapsArray();
|
|
|
|
/// Compute the loop ranges for operation. This is the shape of the result of
|
|
/// the generic op for the peeled operation.
|
|
Location loc = genericOp.getLoc();
|
|
SmallVector<OpFoldResult> domain = getGenericOpLoopRange(rewriter, genericOp);
|
|
SmallVector<Value> newInitValues;
|
|
SmallVector<Type> newResultTypes;
|
|
|
|
// Add as many new results as the number of results of the peeled scalar op.
|
|
for (auto scalarOpResult : peeledScalarOperation->getResults()) {
|
|
// If the result is yielded by the original op, use the operand, indexing
|
|
// map and result type that correspond to the yielded value.
|
|
|
|
std::optional<unsigned> resultNumber;
|
|
for (auto *user : scalarOpResult.getUsers()) {
|
|
if (auto yieldOp = dyn_cast<YieldOp>(user)) {
|
|
// Find the first use of the `scalarOpResult` in the yield op.
|
|
for (OpOperand &yieldOperand : yieldOp->getOpOperands()) {
|
|
if (yieldOperand.get() == scalarOpResult) {
|
|
resultNumber = yieldOperand.getOperandNumber();
|
|
break;
|
|
}
|
|
}
|
|
assert(resultNumber && "unable to find use of a value in its user");
|
|
break;
|
|
}
|
|
}
|
|
if (resultNumber) {
|
|
newInitValues.push_back(
|
|
genericOp.getDpsInitOperand(*resultNumber)->get());
|
|
OpResult result = cast<OpResult>(genericOp.getResult(*resultNumber));
|
|
newResultTypes.push_back(result.getType());
|
|
peeledGenericOpIndexingMaps.push_back(
|
|
genericOp.getIndexingMapMatchingResult(result));
|
|
continue;
|
|
}
|
|
|
|
// Fall back path, use an `init_tensor` and identity indexing map.
|
|
AffineMap indexingMap = rewriter.getMultiDimIdentityMap(domain.size());
|
|
Value emptyTensor =
|
|
rewriter.create<tensor::EmptyOp>(loc, domain, scalarOpResult.getType());
|
|
newInitValues.push_back(emptyTensor);
|
|
newResultTypes.push_back(emptyTensor.getType());
|
|
peeledGenericOpIndexingMaps.push_back(indexingMap);
|
|
}
|
|
|
|
/// Create the peeled generic op with an empty body.
|
|
SmallVector<Value> outsOperands = genericOp.getOutputs();
|
|
outsOperands.append(newInitValues.begin(), newInitValues.end());
|
|
SmallVector<Type> resultTypes = llvm::to_vector(genericOp.getResultTypes());
|
|
resultTypes.append(newResultTypes.begin(), newResultTypes.end());
|
|
auto indexingMapAttr =
|
|
rewriter.getAffineMapArrayAttr(peeledGenericOpIndexingMaps);
|
|
return rewriter.create<GenericOp>(
|
|
loc, resultTypes, genericOp.getInputs(), outsOperands, indexingMapAttr,
|
|
genericOp.getIteratorTypes(), /*doc=*/nullptr, /*libraryCall=*/nullptr,
|
|
[](OpBuilder, Location, ValueRange) {});
|
|
}
|
|
|
|
GenericOp
|
|
DecomposeLinalgOp::createResidualGenericOp(GenericOp genericOp,
|
|
GenericOp peeledGenericOp,
|
|
PatternRewriter &rewriter) const {
|
|
/// Append all results from the peeledGenericOps as `ins` operand for the
|
|
/// residual generic op.
|
|
SmallVector<Value> residualGenericOpOperands = genericOp.getInputs();
|
|
unsigned origNumResults = genericOp.getNumResults();
|
|
unsigned peeledGenericOpNumResults = peeledGenericOp.getNumResults();
|
|
SmallVector<Value> extraIns;
|
|
for (auto resultNum :
|
|
llvm::seq<unsigned>(origNumResults, peeledGenericOpNumResults))
|
|
extraIns.push_back(peeledGenericOp->getResult(resultNum));
|
|
residualGenericOpOperands.append(extraIns);
|
|
|
|
/// Add indexing maps for the newly added operands. Use the same map
|
|
/// as those used for the new results of the peeledGenericOp.
|
|
auto indexingMaps = llvm::to_vector(
|
|
llvm::map_range(genericOp.getDpsInputOperands(), [&](OpOperand *operand) {
|
|
return genericOp.getMatchingIndexingMap(operand);
|
|
}));
|
|
for (auto resultNum :
|
|
llvm::seq<unsigned>(origNumResults, peeledGenericOpNumResults)) {
|
|
OpResult result = cast<OpResult>(peeledGenericOp.getResult(resultNum));
|
|
indexingMaps.push_back(
|
|
peeledGenericOp.getIndexingMapMatchingResult(result));
|
|
}
|
|
for (OpOperand &outOperand : genericOp.getDpsInitsMutable())
|
|
indexingMaps.push_back(genericOp.getMatchingIndexingMap(&outOperand));
|
|
|
|
auto indexingMapAttr = rewriter.getAffineMapArrayAttr(indexingMaps);
|
|
return rewriter.create<GenericOp>(
|
|
genericOp->getLoc(), genericOp->getResultTypes(),
|
|
residualGenericOpOperands, genericOp.getOutputs(), indexingMapAttr,
|
|
genericOp.getIteratorTypes(), /*doc=*/nullptr, /*libraryCall=*/nullptr,
|
|
[](OpBuilder, Location, ValueRange) {});
|
|
}
|
|
|
|
LogicalResult
|
|
DecomposeLinalgOp::matchAndRewrite(GenericOp genericOp,
|
|
PatternRewriter &rewriter) const {
|
|
/// For now only match on operations where the iterator types are all parallel
|
|
if (genericOp.getNumParallelLoops() != genericOp.getNumLoops()) {
|
|
return rewriter.notifyMatchFailure(genericOp,
|
|
"unhandled decomposition of operation "
|
|
"with non-parallel iterator types");
|
|
}
|
|
// TODO: this could be generalized to handle `linalg.generic` with buffer
|
|
// operands too but requires allocation for intermediates. Punt on this for
|
|
// now.
|
|
if (!genericOp.hasPureTensorSemantics()) {
|
|
return rewriter.notifyMatchFailure(
|
|
genericOp, "only operations with tensor semantics are handled");
|
|
}
|
|
|
|
if (llvm::any_of(genericOp.getDpsInitsMutable(), [&](OpOperand &outOperand) {
|
|
return !genericOp.getMatchingIndexingMap(&outOperand).isPermutation();
|
|
})) {
|
|
return rewriter.notifyMatchFailure(
|
|
genericOp, "unhandled decomposition of generic op with out operand not "
|
|
"accessed using a permutation");
|
|
}
|
|
|
|
/// If the op has only a single statement (apart from the yield), do nothing.
|
|
Block *body = genericOp.getBody();
|
|
if (body->getOperations().size() <= 2) {
|
|
return rewriter.notifyMatchFailure(genericOp,
|
|
"operation has less than 3 statements");
|
|
}
|
|
|
|
/// Check that the peeled statement has a scalar element type.
|
|
if (llvm::any_of(body->getOperations().begin()->getResultTypes(),
|
|
[](Type t) { return !t.isIntOrIndexOrFloat(); })) {
|
|
return rewriter.notifyMatchFailure(
|
|
&(*body->getOperations().begin()),
|
|
"expected return type to be only int, index or float");
|
|
}
|
|
|
|
GenericOp peeledGenericOp = createPeeledGenericOp(genericOp, rewriter);
|
|
GenericOp residualGenericOp =
|
|
createResidualGenericOp(genericOp, peeledGenericOp, rewriter);
|
|
|
|
/// Move the first statement of the original operation into the body of the
|
|
/// generic op for the peeled operation.
|
|
Block *peeledGenericOpBody = peeledGenericOp.getBody();
|
|
Block *residualGenericOpBody = residualGenericOp.getBody();
|
|
assert(peeledGenericOpBody->empty() && residualGenericOpBody->empty() &&
|
|
"expected split generic ops to have empty region");
|
|
peeledGenericOpBody->getOperations().splice(
|
|
peeledGenericOpBody->begin(), body->getOperations(), body->begin());
|
|
residualGenericOpBody->getOperations().splice(residualGenericOpBody->begin(),
|
|
body->getOperations());
|
|
|
|
Operation *peeledScalarOperation = &(*peeledGenericOpBody->begin());
|
|
auto *yieldOp = residualGenericOpBody->getTerminator();
|
|
{
|
|
// Yield all the result of the peeled scalar operation.
|
|
OpBuilder::InsertionGuard g(rewriter);
|
|
rewriter.setInsertionPointToEnd(peeledGenericOpBody);
|
|
SmallVector<Value> yieldedVals;
|
|
for (auto origYield : yieldOp->getOperands()) {
|
|
if (origYield.getDefiningOp() == peeledScalarOperation) {
|
|
yieldedVals.push_back(origYield);
|
|
} else {
|
|
// Do not materialize any new ops inside of the decomposed LinalgOp,
|
|
// as that would trigger another application of the rewrite pattern
|
|
// (infinite loop).
|
|
OpBuilder::InsertionGuard g(rewriter);
|
|
rewriter.setInsertionPoint(peeledGenericOp);
|
|
yieldedVals.push_back(
|
|
getZero(rewriter, genericOp.getLoc(), origYield.getType()));
|
|
}
|
|
}
|
|
yieldedVals.append(llvm::to_vector(
|
|
llvm::map_range(peeledScalarOperation->getResults(),
|
|
[](OpResult opr) -> Value { return opr; })));
|
|
rewriter.create<YieldOp>(genericOp.getLoc(), yieldedVals);
|
|
}
|
|
|
|
/// In the split operations, replace block arguments uses that refer to
|
|
/// original operation to the block arguments of the newly created operation.
|
|
unsigned origNumInputs = genericOp.getNumDpsInputs();
|
|
for (const auto &inputBlockArg :
|
|
llvm::enumerate(genericOp.getBody()->getArguments())) {
|
|
Value residualOpReplacementArg =
|
|
residualGenericOpBody->getArgument(inputBlockArg.index());
|
|
rewriter.replaceUsesWithIf(
|
|
inputBlockArg.value(), residualOpReplacementArg, [&](OpOperand &use) {
|
|
return use.getOwner()->getBlock() == residualGenericOpBody;
|
|
});
|
|
|
|
Value peeledOpReplacementArg =
|
|
peeledGenericOpBody->getArgument(inputBlockArg.index());
|
|
rewriter.replaceUsesWithIf(
|
|
inputBlockArg.value(), peeledOpReplacementArg, [&](OpOperand &use) {
|
|
return use.getOwner()->getBlock() == peeledGenericOpBody;
|
|
});
|
|
}
|
|
|
|
/// Before fixing up the residual operation, track what values are yielded. If
|
|
/// any of those are from the peeled scalar operation, the uses of the
|
|
/// corresponding result have to be remapped to result of the generic op for
|
|
/// the peeled operation.
|
|
SmallVector<Value> replacements;
|
|
for (const auto &yieldValue : llvm::enumerate(yieldOp->getOperands())) {
|
|
OpResult opr = dyn_cast<OpResult>(yieldValue.value());
|
|
if (!opr || opr.getOwner() != peeledScalarOperation)
|
|
replacements.push_back(residualGenericOp.getResult(yieldValue.index()));
|
|
else
|
|
replacements.push_back(peeledGenericOp->getResult(yieldValue.index()));
|
|
}
|
|
|
|
/// Update all uses of the peeled scalar operation results in the residual op
|
|
/// to the newly added arguments.
|
|
{
|
|
SmallVector<Value> scalarReplacements;
|
|
unsigned peeledScalarOpNumResults = peeledScalarOperation->getNumResults();
|
|
scalarReplacements.reserve(peeledScalarOpNumResults);
|
|
for (auto num : llvm::seq<unsigned>(0, peeledScalarOpNumResults))
|
|
scalarReplacements.push_back(
|
|
residualGenericOpBody->getArgument(num + origNumInputs));
|
|
bool allUsesReplaced = false;
|
|
rewriter.replaceOpWithinBlock(peeledScalarOperation, scalarReplacements,
|
|
residualGenericOpBody, &allUsesReplaced);
|
|
assert(!allUsesReplaced &&
|
|
"peeled scalar operation is erased when it wasnt expected to be");
|
|
}
|
|
|
|
// Replace the original operation
|
|
rewriter.replaceOp(genericOp, replacements);
|
|
return success();
|
|
}
|
|
|
|
void mlir::linalg::populateDecomposeLinalgOpsPattern(
|
|
RewritePatternSet &patterns, bool removeDeadArgsAndResults) {
|
|
patterns.insert<DecomposeLinalgOp>(patterns.getContext());
|
|
// Add the patterns to clean up the dead operands and results.
|
|
if (removeDeadArgsAndResults)
|
|
populateEraseUnusedOperandsAndResultsPatterns(patterns);
|
|
}
|