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rust / rust-lang / llvm-project / refs/heads/rustc/12.0-2021-07-10 / . / mlir / lib / Dialect / StandardOps / IR / Ops.cpp
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//===- Ops.cpp - Standard MLIR Operations ---------------------------------===//
//
// 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/StandardOps/IR/Ops.h"
#include "mlir/Dialect/CommonFolders.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/IR/Value.h"
#include "mlir/Support/MathExtras.h"
#include "mlir/Transforms/InliningUtils.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/StringSwitch.h"
#include "llvm/Support/FormatVariadic.h"
#include "llvm/Support/raw_ostream.h"
// Pull in all enum type definitions and utility function declarations.
#include "mlir/Dialect/StandardOps/IR/OpsEnums.cpp.inc"
using namespace mlir;
/// Helper function to dispatch an OpFoldResult into either the `dynamicVec` if
/// it is a Value or into `staticVec` if it is an IntegerAttr.
/// In the case of a Value, a copy of the `sentinel` value is also pushed to
/// `staticVec`. This is useful to extract mixed static and dynamic entries that
/// come from an AttrSizedOperandSegments trait.
static void dispatchIndexOpFoldResult(OpFoldResult ofr,
SmallVectorImpl<Value> &dynamicVec,
SmallVectorImpl<int64_t> &staticVec,
int64_t sentinel) {
if (auto v = ofr.dyn_cast<Value>()) {
dynamicVec.push_back(v);
staticVec.push_back(sentinel);
return;
}
APInt apInt = ofr.dyn_cast<Attribute>().cast<IntegerAttr>().getValue();
staticVec.push_back(apInt.getSExtValue());
}
static void dispatchIndexOpFoldResults(ArrayRef<OpFoldResult> ofrs,
SmallVectorImpl<Value> &dynamicVec,
SmallVectorImpl<int64_t> &staticVec,
int64_t sentinel) {
for (auto ofr : ofrs)
dispatchIndexOpFoldResult(ofr, dynamicVec, staticVec, sentinel);
}
//===----------------------------------------------------------------------===//
// StandardOpsDialect Interfaces
//===----------------------------------------------------------------------===//
namespace {
/// This class defines the interface for handling inlining with standard
/// operations.
struct StdInlinerInterface : public DialectInlinerInterface {
using DialectInlinerInterface::DialectInlinerInterface;
//===--------------------------------------------------------------------===//
// Analysis Hooks
//===--------------------------------------------------------------------===//
/// All call operations within standard ops can be inlined.
bool isLegalToInline(Operation *call, Operation *callable,
bool wouldBeCloned) const final {
return true;
}
/// All operations within standard ops can be inlined.
bool isLegalToInline(Operation *, Region *, bool,
BlockAndValueMapping &) const final {
return true;
}
//===--------------------------------------------------------------------===//
// Transformation Hooks
//===--------------------------------------------------------------------===//
/// Handle the given inlined terminator by replacing it with a new operation
/// as necessary.
void handleTerminator(Operation *op, Block *newDest) const final {
// Only "std.return" needs to be handled here.
auto returnOp = dyn_cast<ReturnOp>(op);
if (!returnOp)
return;
// Replace the return with a branch to the dest.
OpBuilder builder(op);
builder.create<BranchOp>(op->getLoc(), newDest, returnOp.getOperands());
op->erase();
}
/// Handle the given inlined terminator by replacing it with a new operation
/// as necessary.
void handleTerminator(Operation *op,
ArrayRef<Value> valuesToRepl) const final {
// Only "std.return" needs to be handled here.
auto returnOp = cast<ReturnOp>(op);
// Replace the values directly with the return operands.
assert(returnOp.getNumOperands() == valuesToRepl.size());
for (const auto &it : llvm::enumerate(returnOp.getOperands()))
valuesToRepl[it.index()].replaceAllUsesWith(it.value());
}
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// StandardOpsDialect
//===----------------------------------------------------------------------===//
/// A custom unary operation printer that omits the "std." prefix from the
/// operation names.
static void printStandardUnaryOp(Operation *op, OpAsmPrinter &p) {
assert(op->getNumOperands() == 1 && "unary op should have one operand");
assert(op->getNumResults() == 1 && "unary op should have one result");
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
<< op->getOperand(0);
p.printOptionalAttrDict(op->getAttrs());
p << " : " << op->getOperand(0).getType();
}
/// A custom binary operation printer that omits the "std." prefix from the
/// operation names.
static void printStandardBinaryOp(Operation *op, OpAsmPrinter &p) {
assert(op->getNumOperands() == 2 && "binary op should have two operands");
assert(op->getNumResults() == 1 && "binary op should have one result");
// If not all the operand and result types are the same, just use the
// generic assembly form to avoid omitting information in printing.
auto resultType = op->getResult(0).getType();
if (op->getOperand(0).getType() != resultType ||
op->getOperand(1).getType() != resultType) {
p.printGenericOp(op);
return;
}
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
<< op->getOperand(0) << ", " << op->getOperand(1);
p.printOptionalAttrDict(op->getAttrs());
// Now we can output only one type for all operands and the result.
p << " : " << op->getResult(0).getType();
}
/// A custom cast operation printer that omits the "std." prefix from the
/// operation names.
static void printStandardCastOp(Operation *op, OpAsmPrinter &p) {
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' '
<< op->getOperand(0) << " : " << op->getOperand(0).getType() << " to "
<< op->getResult(0).getType();
}
void StandardOpsDialect::initialize() {
getContext()->loadDialect<tensor::TensorDialect>();
addOperations<DmaStartOp, DmaWaitOp,
#define GET_OP_LIST
#include "mlir/Dialect/StandardOps/IR/Ops.cpp.inc"
>();
addInterfaces<StdInlinerInterface>();
}
/// Materialize a single constant operation from a given attribute value with
/// the desired resultant type.
Operation *StandardOpsDialect::materializeConstant(OpBuilder &builder,
Attribute value, Type type,
Location loc) {
return builder.create<ConstantOp>(loc, type, value);
}
/// Matches a ConstantIndexOp.
/// TODO: This should probably just be a general matcher that uses m_Constant
/// and checks the operation for an index type.
static detail::op_matcher<ConstantIndexOp> m_ConstantIndex() {
return detail::op_matcher<ConstantIndexOp>();
}
//===----------------------------------------------------------------------===//
// Common canonicalization pattern support logic
//===----------------------------------------------------------------------===//
/// This is a common class used for patterns of the form
/// "someop(memrefcast) -> someop". It folds the source of any memref_cast
/// into the root operation directly.
static LogicalResult foldMemRefCast(Operation *op) {
bool folded = false;
for (OpOperand &operand : op->getOpOperands()) {
auto cast = operand.get().getDefiningOp<MemRefCastOp>();
if (cast && !cast.getOperand().getType().isa<UnrankedMemRefType>()) {
operand.set(cast.getOperand());
folded = true;
}
}
return success(folded);
}
//===----------------------------------------------------------------------===//
// Common cast compatibility check for vector types.
//===----------------------------------------------------------------------===//
/// This method checks for cast compatibility of vector types.
/// If 'a' and 'b' are vector types, and they are cast compatible,
/// it calls the 'areElementsCastCompatible' function to check for
/// element cast compatibility.
/// Returns 'true' if the vector types are cast compatible, and 'false'
/// otherwise.
static bool areVectorCastSimpleCompatible(
Type a, Type b,
function_ref<bool(TypeRange, TypeRange)> areElementsCastCompatible) {
if (auto va = a.dyn_cast<VectorType>())
if (auto vb = b.dyn_cast<VectorType>())
return va.getShape().equals(vb.getShape()) &&
areElementsCastCompatible(va.getElementType(),
vb.getElementType());
return false;
}
//===----------------------------------------------------------------------===//
// Helpers for Tensor[Load|Store]Op, TensorToMemrefOp, and GlobalMemrefOp
//===----------------------------------------------------------------------===//
static Type getTensorTypeFromMemRefType(Type type) {
if (auto memref = type.dyn_cast<MemRefType>())
return RankedTensorType::get(memref.getShape(), memref.getElementType());
if (auto memref = type.dyn_cast<UnrankedMemRefType>())
return UnrankedTensorType::get(memref.getElementType());
return NoneType::get(type.getContext());
}
//===----------------------------------------------------------------------===//
// AddFOp
//===----------------------------------------------------------------------===//
OpFoldResult AddFOp::fold(ArrayRef<Attribute> operands) {
return constFoldBinaryOp<FloatAttr>(
operands, [](APFloat a, APFloat b) { return a + b; });
}
//===----------------------------------------------------------------------===//
// AddIOp
//===----------------------------------------------------------------------===//
OpFoldResult AddIOp::fold(ArrayRef<Attribute> operands) {
/// addi(x, 0) -> x
if (matchPattern(rhs(), m_Zero()))
return lhs();
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a + b; });
}
/// Extract int64_t values from the assumed ArrayAttr of IntegerAttr.
static SmallVector<int64_t, 4> extractFromI64ArrayAttr(Attribute attr) {
return llvm::to_vector<4>(
llvm::map_range(attr.cast<ArrayAttr>(), [](Attribute a) -> int64_t {
return a.cast<IntegerAttr>().getInt();
}));
}
//===----------------------------------------------------------------------===//
// AllocOp / AllocaOp
//===----------------------------------------------------------------------===//
template <typename AllocLikeOp>
static LogicalResult verifyAllocLikeOp(AllocLikeOp op) {
static_assert(llvm::is_one_of<AllocLikeOp, AllocOp, AllocaOp>::value,
"applies to only alloc or alloca");
auto memRefType = op.getResult().getType().template dyn_cast<MemRefType>();
if (!memRefType)
return op.emitOpError("result must be a memref");
if (static_cast<int64_t>(op.dynamicSizes().size()) !=
memRefType.getNumDynamicDims())
return op.emitOpError("dimension operand count does not equal memref "
"dynamic dimension count");
unsigned numSymbols = 0;
if (!memRefType.getAffineMaps().empty())
numSymbols = memRefType.getAffineMaps().front().getNumSymbols();
if (op.symbolOperands().size() != numSymbols)
return op.emitOpError(
"symbol operand count does not equal memref symbol count");
return success();
}
static LogicalResult verify(AllocOp op) { return verifyAllocLikeOp(op); }
static LogicalResult verify(AllocaOp op) {
// An alloca op needs to have an ancestor with an allocation scope trait.
if (!op->getParentWithTrait<OpTrait::AutomaticAllocationScope>())
return op.emitOpError(
"requires an ancestor op with AutomaticAllocationScope trait");
return verifyAllocLikeOp(op);
}
namespace {
/// Fold constant dimensions into an alloc like operation.
template <typename AllocLikeOp>
struct SimplifyAllocConst : public OpRewritePattern<AllocLikeOp> {
using OpRewritePattern<AllocLikeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(AllocLikeOp alloc,
PatternRewriter &rewriter) const override {
// Check to see if any dimensions operands are constants. If so, we can
// substitute and drop them.
if (llvm::none_of(alloc.getOperands(), [](Value operand) {
return matchPattern(operand, m_ConstantIndex());
}))
return failure();
auto memrefType = alloc.getType();
// Ok, we have one or more constant operands. Collect the non-constant ones
// and keep track of the resultant memref type to build.
SmallVector<int64_t, 4> newShapeConstants;
newShapeConstants.reserve(memrefType.getRank());
SmallVector<Value, 4> newOperands;
unsigned dynamicDimPos = 0;
for (unsigned dim = 0, e = memrefType.getRank(); dim < e; ++dim) {
int64_t dimSize = memrefType.getDimSize(dim);
// If this is already static dimension, keep it.
if (dimSize != -1) {
newShapeConstants.push_back(dimSize);
continue;
}
auto *defOp = alloc.getOperand(dynamicDimPos).getDefiningOp();
if (auto constantIndexOp = dyn_cast_or_null<ConstantIndexOp>(defOp)) {
// Dynamic shape dimension will be folded.
newShapeConstants.push_back(constantIndexOp.getValue());
} else {
// Dynamic shape dimension not folded; copy operand from old memref.
newShapeConstants.push_back(-1);
newOperands.push_back(alloc.getOperand(dynamicDimPos));
}
dynamicDimPos++;
}
// Create new memref type (which will have fewer dynamic dimensions).
MemRefType newMemRefType =
MemRefType::Builder(memrefType).setShape(newShapeConstants);
assert(static_cast<int64_t>(newOperands.size()) ==
newMemRefType.getNumDynamicDims());
// Create and insert the alloc op for the new memref.
auto newAlloc = rewriter.create<AllocLikeOp>(alloc.getLoc(), newMemRefType,
newOperands, IntegerAttr());
// Insert a cast so we have the same type as the old alloc.
auto resultCast = rewriter.create<MemRefCastOp>(alloc.getLoc(), newAlloc,
alloc.getType());
rewriter.replaceOp(alloc, {resultCast});
return success();
}
};
/// Fold alloc operations with no uses. Alloc has side effects on the heap,
/// but can still be deleted if it has zero uses.
struct SimplifyDeadAlloc : public OpRewritePattern<AllocOp> {
using OpRewritePattern<AllocOp>::OpRewritePattern;
LogicalResult matchAndRewrite(AllocOp alloc,
PatternRewriter &rewriter) const override {
if (alloc.use_empty()) {
rewriter.eraseOp(alloc);
return success();
}
return failure();
}
};
} // end anonymous namespace.
void AllocOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<SimplifyAllocConst<AllocOp>, SimplifyDeadAlloc>(context);
}
void AllocaOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<SimplifyAllocConst<AllocaOp>>(context);
}
//===----------------------------------------------------------------------===//
// AndOp
//===----------------------------------------------------------------------===//
OpFoldResult AndOp::fold(ArrayRef<Attribute> operands) {
/// and(x, 0) -> 0
if (matchPattern(rhs(), m_Zero()))
return rhs();
/// and(x, allOnes) -> x
APInt intValue;
if (matchPattern(rhs(), m_ConstantInt(&intValue)) &&
intValue.isAllOnesValue())
return lhs();
/// and(x,x) -> x
if (lhs() == rhs())
return rhs();
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a & b; });
}
//===----------------------------------------------------------------------===//
// AssertOp
//===----------------------------------------------------------------------===//
namespace {
struct EraseRedundantAssertions : public OpRewritePattern<AssertOp> {
using OpRewritePattern<AssertOp>::OpRewritePattern;
LogicalResult matchAndRewrite(AssertOp op,
PatternRewriter &rewriter) const override {
// Erase assertion if argument is constant true.
if (matchPattern(op.arg(), m_One())) {
rewriter.eraseOp(op);
return success();
}
return failure();
}
};
} // namespace
void AssertOp::getCanonicalizationPatterns(OwningRewritePatternList &patterns,
MLIRContext *context) {
patterns.insert<EraseRedundantAssertions>(context);
}
//===----------------------------------------------------------------------===//
// AssumeAlignmentOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(AssumeAlignmentOp op) {
unsigned alignment = op.alignment();
if (!llvm::isPowerOf2_32(alignment))
return op.emitOpError("alignment must be power of 2");
return success();
}
//===----------------------------------------------------------------------===//
// AtomicRMWOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(AtomicRMWOp op) {
if (op.getMemRefType().getRank() != op.getNumOperands() - 2)
return op.emitOpError(
"expects the number of subscripts to be equal to memref rank");
switch (op.kind()) {
case AtomicRMWKind::addf:
case AtomicRMWKind::maxf:
case AtomicRMWKind::minf:
case AtomicRMWKind::mulf:
if (!op.value().getType().isa<FloatType>())
return op.emitOpError()
<< "with kind '" << stringifyAtomicRMWKind(op.kind())
<< "' expects a floating-point type";
break;
case AtomicRMWKind::addi:
case AtomicRMWKind::maxs:
case AtomicRMWKind::maxu:
case AtomicRMWKind::mins:
case AtomicRMWKind::minu:
case AtomicRMWKind::muli:
if (!op.value().getType().isa<IntegerType>())
return op.emitOpError()
<< "with kind '" << stringifyAtomicRMWKind(op.kind())
<< "' expects an integer type";
break;
default:
break;
}
return success();
}
//===----------------------------------------------------------------------===//
// GenericAtomicRMWOp
//===----------------------------------------------------------------------===//
void GenericAtomicRMWOp::build(OpBuilder &builder, OperationState &result,
Value memref, ValueRange ivs) {
result.addOperands(memref);
result.addOperands(ivs);
if (auto memrefType = memref.getType().dyn_cast<MemRefType>()) {
Type elementType = memrefType.getElementType();
result.addTypes(elementType);
Region *bodyRegion = result.addRegion();
bodyRegion->push_back(new Block());
bodyRegion->addArgument(elementType);
}
}
static LogicalResult verify(GenericAtomicRMWOp op) {
auto &body = op.body();
if (body.getNumArguments() != 1)
return op.emitOpError("expected single number of entry block arguments");
if (op.getResult().getType() != body.getArgument(0).getType())
return op.emitOpError(
"expected block argument of the same type result type");
bool hasSideEffects =
body.walk([&](Operation *nestedOp) {
if (MemoryEffectOpInterface::hasNoEffect(nestedOp))
return WalkResult::advance();
nestedOp->emitError("body of 'generic_atomic_rmw' should contain "
"only operations with no side effects");
return WalkResult::interrupt();
})
.wasInterrupted();
return hasSideEffects ? failure() : success();
}
static ParseResult parseGenericAtomicRMWOp(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType memref;
Type memrefType;
SmallVector<OpAsmParser::OperandType, 4> ivs;
Type indexType = parser.getBuilder().getIndexType();
if (parser.parseOperand(memref) ||
parser.parseOperandList(ivs, OpAsmParser::Delimiter::Square) ||
parser.parseColonType(memrefType) ||
parser.resolveOperand(memref, memrefType, result.operands) ||
parser.resolveOperands(ivs, indexType, result.operands))
return failure();
Region *body = result.addRegion();
if (parser.parseRegion(*body, llvm::None, llvm::None) ||
parser.parseOptionalAttrDict(result.attributes))
return failure();
result.types.push_back(memrefType.cast<MemRefType>().getElementType());
return success();
}
static void print(OpAsmPrinter &p, GenericAtomicRMWOp op) {
p << op.getOperationName() << ' ' << op.memref() << "[" << op.indices()
<< "] : " << op.memref().getType();
p.printRegion(op.body());
p.printOptionalAttrDict(op.getAttrs());
}
//===----------------------------------------------------------------------===//
// AtomicYieldOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(AtomicYieldOp op) {
Type parentType = op->getParentOp()->getResultTypes().front();
Type resultType = op.result().getType();
if (parentType != resultType)
return op.emitOpError() << "types mismatch between yield op: " << resultType
<< " and its parent: " << parentType;
return success();
}
//===----------------------------------------------------------------------===//
// BranchOp
//===----------------------------------------------------------------------===//
/// Given a successor, try to collapse it to a new destination if it only
/// contains a passthrough unconditional branch. If the successor is
/// collapsable, `successor` and `successorOperands` are updated to reference
/// the new destination and values. `argStorage` is an optional storage to use
/// if operands to the collapsed successor need to be remapped.
static LogicalResult collapseBranch(Block *&successor,
ValueRange &successorOperands,
SmallVectorImpl<Value> &argStorage) {
// Check that the successor only contains a unconditional branch.
if (std::next(successor->begin()) != successor->end())
return failure();
// Check that the terminator is an unconditional branch.
BranchOp successorBranch = dyn_cast<BranchOp>(successor->getTerminator());
if (!successorBranch)
return failure();
// Check that the arguments are only used within the terminator.
for (BlockArgument arg : successor->getArguments()) {
for (Operation *user : arg.getUsers())
if (user != successorBranch)
return failure();
}
// Don't try to collapse branches to infinite loops.
Block *successorDest = successorBranch.getDest();
if (successorDest == successor)
return failure();
// Update the operands to the successor. If the branch parent has no
// arguments, we can use the branch operands directly.
OperandRange operands = successorBranch.getOperands();
if (successor->args_empty()) {
successor = successorDest;
successorOperands = operands;
return success();
}
// Otherwise, we need to remap any argument operands.
for (Value operand : operands) {
BlockArgument argOperand = operand.dyn_cast<BlockArgument>();
if (argOperand && argOperand.getOwner() == successor)
argStorage.push_back(successorOperands[argOperand.getArgNumber()]);
else
argStorage.push_back(operand);
}
successor = successorDest;
successorOperands = argStorage;
return success();
}
namespace {
/// Simplify a branch to a block that has a single predecessor. This effectively
/// merges the two blocks.
struct SimplifyBrToBlockWithSinglePred : public OpRewritePattern<BranchOp> {
using OpRewritePattern<BranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(BranchOp op,
PatternRewriter &rewriter) const override {
// Check that the successor block has a single predecessor.
Block *succ = op.getDest();
Block *opParent = op->getBlock();
if (succ == opParent || !llvm::hasSingleElement(succ->getPredecessors()))
return failure();
// Merge the successor into the current block and erase the branch.
rewriter.mergeBlocks(succ, opParent, op.getOperands());
rewriter.eraseOp(op);
return success();
}
};
/// br ^bb1
/// ^bb1
/// br ^bbN(...)
///
/// -> br ^bbN(...)
///
struct SimplifyPassThroughBr : public OpRewritePattern<BranchOp> {
using OpRewritePattern<BranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(BranchOp op,
PatternRewriter &rewriter) const override {
Block *dest = op.getDest();
ValueRange destOperands = op.getOperands();
SmallVector<Value, 4> destOperandStorage;
// Try to collapse the successor if it points somewhere other than this
// block.
if (dest == op->getBlock() ||
failed(collapseBranch(dest, destOperands, destOperandStorage)))
return failure();
// Create a new branch with the collapsed successor.
rewriter.replaceOpWithNewOp<BranchOp>(op, dest, destOperands);
return success();
}
};
} // end anonymous namespace.
Block *BranchOp::getDest() { return getSuccessor(); }
void BranchOp::setDest(Block *block) { return setSuccessor(block); }
void BranchOp::eraseOperand(unsigned index) { (*this)->eraseOperand(index); }
void BranchOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<SimplifyBrToBlockWithSinglePred, SimplifyPassThroughBr>(
context);
}
Optional<MutableOperandRange>
BranchOp::getMutableSuccessorOperands(unsigned index) {
assert(index == 0 && "invalid successor index");
return destOperandsMutable();
}
Block *BranchOp::getSuccessorForOperands(ArrayRef<Attribute>) { return dest(); }
//===----------------------------------------------------------------------===//
// CallOp
//===----------------------------------------------------------------------===//
LogicalResult CallOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
// Check that the callee attribute was specified.
auto fnAttr = (*this)->getAttrOfType<FlatSymbolRefAttr>("callee");
if (!fnAttr)
return emitOpError("requires a 'callee' symbol reference attribute");
FuncOp fn = symbolTable.lookupNearestSymbolFrom<FuncOp>(*this, fnAttr);
if (!fn)
return emitOpError() << "'" << fnAttr.getValue()
<< "' does not reference a valid function";
// Verify that the operand and result types match the callee.
auto fnType = fn.getType();
if (fnType.getNumInputs() != getNumOperands())
return emitOpError("incorrect number of operands for callee");
for (unsigned i = 0, e = fnType.getNumInputs(); i != e; ++i)
if (getOperand(i).getType() != fnType.getInput(i))
return emitOpError("operand type mismatch: expected operand type ")
<< fnType.getInput(i) << ", but provided "
<< getOperand(i).getType() << " for operand number " << i;
if (fnType.getNumResults() != getNumResults())
return emitOpError("incorrect number of results for callee");
for (unsigned i = 0, e = fnType.getNumResults(); i != e; ++i)
if (getResult(i).getType() != fnType.getResult(i))
return emitOpError("result type mismatch");
return success();
}
FunctionType CallOp::getCalleeType() {
return FunctionType::get(getContext(), getOperandTypes(), getResultTypes());
}
//===----------------------------------------------------------------------===//
// CallIndirectOp
//===----------------------------------------------------------------------===//
namespace {
/// Fold indirect calls that have a constant function as the callee operand.
struct SimplifyIndirectCallWithKnownCallee
: public OpRewritePattern<CallIndirectOp> {
using OpRewritePattern<CallIndirectOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CallIndirectOp indirectCall,
PatternRewriter &rewriter) const override {
// Check that the callee is a constant callee.
SymbolRefAttr calledFn;
if (!matchPattern(indirectCall.getCallee(), m_Constant(&calledFn)))
return failure();
// Replace with a direct call.
rewriter.replaceOpWithNewOp<CallOp>(indirectCall, calledFn,
indirectCall.getResultTypes(),
indirectCall.getArgOperands());
return success();
}
};
} // end anonymous namespace.
void CallIndirectOp::getCanonicalizationPatterns(
OwningRewritePatternList &results, MLIRContext *context) {
results.insert<SimplifyIndirectCallWithKnownCallee>(context);
}
//===----------------------------------------------------------------------===//
// General helpers for comparison ops
//===----------------------------------------------------------------------===//
// Return the type of the same shape (scalar, vector or tensor) containing i1.
static Type getI1SameShape(Type type) {
auto i1Type = IntegerType::get(type.getContext(), 1);
if (auto tensorType = type.dyn_cast<RankedTensorType>())
return RankedTensorType::get(tensorType.getShape(), i1Type);
if (type.isa<UnrankedTensorType>())
return UnrankedTensorType::get(i1Type);
if (auto vectorType = type.dyn_cast<VectorType>())
return VectorType::get(vectorType.getShape(), i1Type);
return i1Type;
}
//===----------------------------------------------------------------------===//
// CmpIOp
//===----------------------------------------------------------------------===//
static void buildCmpIOp(OpBuilder &build, OperationState &result,
CmpIPredicate predicate, Value lhs, Value rhs) {
result.addOperands({lhs, rhs});
result.types.push_back(getI1SameShape(lhs.getType()));
result.addAttribute(CmpIOp::getPredicateAttrName(),
build.getI64IntegerAttr(static_cast<int64_t>(predicate)));
}
// Compute `lhs` `pred` `rhs`, where `pred` is one of the known integer
// comparison predicates.
bool mlir::applyCmpPredicate(CmpIPredicate predicate, const APInt &lhs,
const APInt &rhs) {
switch (predicate) {
case CmpIPredicate::eq:
return lhs.eq(rhs);
case CmpIPredicate::ne:
return lhs.ne(rhs);
case CmpIPredicate::slt:
return lhs.slt(rhs);
case CmpIPredicate::sle:
return lhs.sle(rhs);
case CmpIPredicate::sgt:
return lhs.sgt(rhs);
case CmpIPredicate::sge:
return lhs.sge(rhs);
case CmpIPredicate::ult:
return lhs.ult(rhs);
case CmpIPredicate::ule:
return lhs.ule(rhs);
case CmpIPredicate::ugt:
return lhs.ugt(rhs);
case CmpIPredicate::uge:
return lhs.uge(rhs);
}
llvm_unreachable("unknown comparison predicate");
}
// Returns true if the predicate is true for two equal operands.
static bool applyCmpPredicateToEqualOperands(CmpIPredicate predicate) {
switch (predicate) {
case CmpIPredicate::eq:
case CmpIPredicate::sle:
case CmpIPredicate::sge:
case CmpIPredicate::ule:
case CmpIPredicate::uge:
return true;
case CmpIPredicate::ne:
case CmpIPredicate::slt:
case CmpIPredicate::sgt:
case CmpIPredicate::ult:
case CmpIPredicate::ugt:
return false;
}
llvm_unreachable("unknown comparison predicate");
}
// Constant folding hook for comparisons.
OpFoldResult CmpIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "cmpi takes two arguments");
if (lhs() == rhs()) {
auto val = applyCmpPredicateToEqualOperands(getPredicate());
return BoolAttr::get(val, getContext());
}
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
if (!lhs || !rhs)
return {};
auto val = applyCmpPredicate(getPredicate(), lhs.getValue(), rhs.getValue());
return BoolAttr::get(val, getContext());
}
//===----------------------------------------------------------------------===//
// CmpFOp
//===----------------------------------------------------------------------===//
static void buildCmpFOp(OpBuilder &build, OperationState &result,
CmpFPredicate predicate, Value lhs, Value rhs) {
result.addOperands({lhs, rhs});
result.types.push_back(getI1SameShape(lhs.getType()));
result.addAttribute(CmpFOp::getPredicateAttrName(),
build.getI64IntegerAttr(static_cast<int64_t>(predicate)));
}
/// Compute `lhs` `pred` `rhs`, where `pred` is one of the known floating point
/// comparison predicates.
bool mlir::applyCmpPredicate(CmpFPredicate predicate, const APFloat &lhs,
const APFloat &rhs) {
auto cmpResult = lhs.compare(rhs);
switch (predicate) {
case CmpFPredicate::AlwaysFalse:
return false;
case CmpFPredicate::OEQ:
return cmpResult == APFloat::cmpEqual;
case CmpFPredicate::OGT:
return cmpResult == APFloat::cmpGreaterThan;
case CmpFPredicate::OGE:
return cmpResult == APFloat::cmpGreaterThan ||
cmpResult == APFloat::cmpEqual;
case CmpFPredicate::OLT:
return cmpResult == APFloat::cmpLessThan;
case CmpFPredicate::OLE:
return cmpResult == APFloat::cmpLessThan || cmpResult == APFloat::cmpEqual;
case CmpFPredicate::ONE:
return cmpResult != APFloat::cmpUnordered && cmpResult != APFloat::cmpEqual;
case CmpFPredicate::ORD:
return cmpResult != APFloat::cmpUnordered;
case CmpFPredicate::UEQ:
return cmpResult == APFloat::cmpUnordered || cmpResult == APFloat::cmpEqual;
case CmpFPredicate::UGT:
return cmpResult == APFloat::cmpUnordered ||
cmpResult == APFloat::cmpGreaterThan;
case CmpFPredicate::UGE:
return cmpResult == APFloat::cmpUnordered ||
cmpResult == APFloat::cmpGreaterThan ||
cmpResult == APFloat::cmpEqual;
case CmpFPredicate::ULT:
return cmpResult == APFloat::cmpUnordered ||
cmpResult == APFloat::cmpLessThan;
case CmpFPredicate::ULE:
return cmpResult == APFloat::cmpUnordered ||
cmpResult == APFloat::cmpLessThan || cmpResult == APFloat::cmpEqual;
case CmpFPredicate::UNE:
return cmpResult != APFloat::cmpEqual;
case CmpFPredicate::UNO:
return cmpResult == APFloat::cmpUnordered;
case CmpFPredicate::AlwaysTrue:
return true;
}
llvm_unreachable("unknown comparison predicate");
}
// Constant folding hook for comparisons.
OpFoldResult CmpFOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "cmpf takes two arguments");
auto lhs = operands.front().dyn_cast_or_null<FloatAttr>();
auto rhs = operands.back().dyn_cast_or_null<FloatAttr>();
// TODO: We could actually do some intelligent things if we know only one
// of the operands, but it's inf or nan.
if (!lhs || !rhs)
return {};
auto val = applyCmpPredicate(getPredicate(), lhs.getValue(), rhs.getValue());
return IntegerAttr::get(IntegerType::get(getContext(), 1), APInt(1, val));
}
//===----------------------------------------------------------------------===//
// CondBranchOp
//===----------------------------------------------------------------------===//
namespace {
/// cond_br true, ^bb1, ^bb2
/// -> br ^bb1
/// cond_br false, ^bb1, ^bb2
/// -> br ^bb2
///
struct SimplifyConstCondBranchPred : public OpRewritePattern<CondBranchOp> {
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CondBranchOp condbr,
PatternRewriter &rewriter) const override {
if (matchPattern(condbr.getCondition(), m_NonZero())) {
// True branch taken.
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getTrueDest(),
condbr.getTrueOperands());
return success();
} else if (matchPattern(condbr.getCondition(), m_Zero())) {
// False branch taken.
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.getFalseDest(),
condbr.getFalseOperands());
return success();
}
return failure();
}
};
/// cond_br %cond, ^bb1, ^bb2
/// ^bb1
/// br ^bbN(...)
/// ^bb2
/// br ^bbK(...)
///
/// -> cond_br %cond, ^bbN(...), ^bbK(...)
///
struct SimplifyPassThroughCondBranch : public OpRewritePattern<CondBranchOp> {
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CondBranchOp condbr,
PatternRewriter &rewriter) const override {
Block *trueDest = condbr.trueDest(), *falseDest = condbr.falseDest();
ValueRange trueDestOperands = condbr.getTrueOperands();
ValueRange falseDestOperands = condbr.getFalseOperands();
SmallVector<Value, 4> trueDestOperandStorage, falseDestOperandStorage;
// Try to collapse one of the current successors.
LogicalResult collapsedTrue =
collapseBranch(trueDest, trueDestOperands, trueDestOperandStorage);
LogicalResult collapsedFalse =
collapseBranch(falseDest, falseDestOperands, falseDestOperandStorage);
if (failed(collapsedTrue) && failed(collapsedFalse))
return failure();
// Create a new branch with the collapsed successors.
rewriter.replaceOpWithNewOp<CondBranchOp>(condbr, condbr.getCondition(),
trueDest, trueDestOperands,
falseDest, falseDestOperands);
return success();
}
};
/// cond_br %cond, ^bb1(A, ..., N), ^bb1(A, ..., N)
/// -> br ^bb1(A, ..., N)
///
/// cond_br %cond, ^bb1(A), ^bb1(B)
/// -> %select = select %cond, A, B
/// br ^bb1(%select)
///
struct SimplifyCondBranchIdenticalSuccessors
: public OpRewritePattern<CondBranchOp> {
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CondBranchOp condbr,
PatternRewriter &rewriter) const override {
// Check that the true and false destinations are the same and have the same
// operands.
Block *trueDest = condbr.trueDest();
if (trueDest != condbr.falseDest())
return failure();
// If all of the operands match, no selects need to be generated.
OperandRange trueOperands = condbr.getTrueOperands();
OperandRange falseOperands = condbr.getFalseOperands();
if (trueOperands == falseOperands) {
rewriter.replaceOpWithNewOp<BranchOp>(condbr, trueDest, trueOperands);
return success();
}
// Otherwise, if the current block is the only predecessor insert selects
// for any mismatched branch operands.
if (trueDest->getUniquePredecessor() != condbr->getBlock())
return failure();
// Generate a select for any operands that differ between the two.
SmallVector<Value, 8> mergedOperands;
mergedOperands.reserve(trueOperands.size());
Value condition = condbr.getCondition();
for (auto it : llvm::zip(trueOperands, falseOperands)) {
if (std::get<0>(it) == std::get<1>(it))
mergedOperands.push_back(std::get<0>(it));
else
mergedOperands.push_back(rewriter.create<SelectOp>(
condbr.getLoc(), condition, std::get<0>(it), std::get<1>(it)));
}
rewriter.replaceOpWithNewOp<BranchOp>(condbr, trueDest, mergedOperands);
return success();
}
};
/// ...
/// cond_br %cond, ^bb1(...), ^bb2(...)
/// ...
/// ^bb1: // has single predecessor
/// ...
/// cond_br %cond, ^bb3(...), ^bb4(...)
///
/// ->
///
/// ...
/// cond_br %cond, ^bb1(...), ^bb2(...)
/// ...
/// ^bb1: // has single predecessor
/// ...
/// br ^bb3(...)
///
struct SimplifyCondBranchFromCondBranchOnSameCondition
: public OpRewritePattern<CondBranchOp> {
using OpRewritePattern<CondBranchOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CondBranchOp condbr,
PatternRewriter &rewriter) const override {
// Check that we have a single distinct predecessor.
Block *currentBlock = condbr->getBlock();
Block *predecessor = currentBlock->getSinglePredecessor();
if (!predecessor)
return failure();
// Check that the predecessor terminates with a conditional branch to this
// block and that it branches on the same condition.
auto predBranch = dyn_cast<CondBranchOp>(predecessor->getTerminator());
if (!predBranch || condbr.getCondition() != predBranch.getCondition())
return failure();
// Fold this branch to an unconditional branch.
if (currentBlock == predBranch.trueDest())
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.trueDest(),
condbr.trueDestOperands());
else
rewriter.replaceOpWithNewOp<BranchOp>(condbr, condbr.falseDest(),
condbr.falseDestOperands());
return success();
}
};
} // end anonymous namespace
void CondBranchOp::getCanonicalizationPatterns(
OwningRewritePatternList &results, MLIRContext *context) {
results.insert<SimplifyConstCondBranchPred, SimplifyPassThroughCondBranch,
SimplifyCondBranchIdenticalSuccessors,
SimplifyCondBranchFromCondBranchOnSameCondition>(context);
}
Optional<MutableOperandRange>
CondBranchOp::getMutableSuccessorOperands(unsigned index) {
assert(index < getNumSuccessors() && "invalid successor index");
return index == trueIndex ? trueDestOperandsMutable()
: falseDestOperandsMutable();
}
Block *CondBranchOp::getSuccessorForOperands(ArrayRef<Attribute> operands) {
if (IntegerAttr condAttr = operands.front().dyn_cast_or_null<IntegerAttr>())
return condAttr.getValue().isOneValue() ? trueDest() : falseDest();
return nullptr;
}
//===----------------------------------------------------------------------===//
// Constant*Op
//===----------------------------------------------------------------------===//
static void print(OpAsmPrinter &p, ConstantOp &op) {
p << "constant ";
p.printOptionalAttrDict(op.getAttrs(), /*elidedAttrs=*/{"value"});
if (op.getAttrs().size() > 1)
p << ' ';
p << op.getValue();
// If the value is a symbol reference, print a trailing type.
if (op.getValue().isa<SymbolRefAttr>())
p << " : " << op.getType();
}
static ParseResult parseConstantOp(OpAsmParser &parser,
OperationState &result) {
Attribute valueAttr;
if (parser.parseOptionalAttrDict(result.attributes) ||
parser.parseAttribute(valueAttr, "value", result.attributes))
return failure();
// If the attribute is a symbol reference, then we expect a trailing type.
Type type;
if (!valueAttr.isa<SymbolRefAttr>())
type = valueAttr.getType();
else if (parser.parseColonType(type))
return failure();
// Add the attribute type to the list.
return parser.addTypeToList(type, result.types);
}
/// The constant op requires an attribute, and furthermore requires that it
/// matches the return type.
static LogicalResult verify(ConstantOp &op) {
auto value = op.getValue();
if (!value)
return op.emitOpError("requires a 'value' attribute");
Type type = op.getType();
if (!value.getType().isa<NoneType>() && type != value.getType())
return op.emitOpError() << "requires attribute's type (" << value.getType()
<< ") to match op's return type (" << type << ")";
if (type.isa<IndexType>() || value.isa<BoolAttr>())
return success();
if (auto intAttr = value.dyn_cast<IntegerAttr>()) {
IntegerType intType = type.cast<IntegerType>();
if (!intType.isSignless())
return op.emitOpError("requires integer result types to be signless");
// If the type has a known bitwidth we verify that the value can be
// represented with the given bitwidth.
unsigned bitwidth = intType.getWidth();
APInt intVal = intAttr.getValue();
if (!intVal.isSignedIntN(bitwidth) && !intVal.isIntN(bitwidth))
return op.emitOpError("requires 'value' to be an integer within the "
"range of the integer result type");
return success();
}
if (type.isa<FloatType>()) {
if (!value.isa<FloatAttr>())
return op.emitOpError("requires 'value' to be a floating point constant");
return success();
}
if (type.isa<ShapedType>()) {
if (!value.isa<ElementsAttr>())
return op.emitOpError("requires 'value' to be a shaped constant");
return success();
}
if (type.isa<FunctionType>()) {
auto fnAttr = value.dyn_cast<FlatSymbolRefAttr>();
if (!fnAttr)
return op.emitOpError("requires 'value' to be a function reference");
// Try to find the referenced function.
auto fn =
op->getParentOfType<ModuleOp>().lookupSymbol<FuncOp>(fnAttr.getValue());
if (!fn)
return op.emitOpError()
<< "reference to undefined function '" << fnAttr.getValue() << "'";
// Check that the referenced function has the correct type.
if (fn.getType() != type)
return op.emitOpError("reference to function with mismatched type");
return success();
}
if (type.isa<NoneType>() && value.isa<UnitAttr>())
return success();
return op.emitOpError("unsupported 'value' attribute: ") << value;
}
OpFoldResult ConstantOp::fold(ArrayRef<Attribute> operands) {
assert(operands.empty() && "constant has no operands");
return getValue();
}
void ConstantOp::getAsmResultNames(
function_ref<void(Value, StringRef)> setNameFn) {
Type type = getType();
if (auto intCst = getValue().dyn_cast<IntegerAttr>()) {
IntegerType intTy = type.dyn_cast<IntegerType>();
// Sugar i1 constants with 'true' and 'false'.
if (intTy && intTy.getWidth() == 1)
return setNameFn(getResult(), (intCst.getInt() ? "true" : "false"));
// Otherwise, build a complex name with the value and type.
SmallString<32> specialNameBuffer;
llvm::raw_svector_ostream specialName(specialNameBuffer);
specialName << 'c' << intCst.getInt();
if (intTy)
specialName << '_' << type;
setNameFn(getResult(), specialName.str());
} else if (type.isa<FunctionType>()) {
setNameFn(getResult(), "f");
} else {
setNameFn(getResult(), "cst");
}
}
/// Returns true if a constant operation can be built with the given value and
/// result type.
bool ConstantOp::isBuildableWith(Attribute value, Type type) {
// SymbolRefAttr can only be used with a function type.
if (value.isa<SymbolRefAttr>())
return type.isa<FunctionType>();
// The attribute must have the same type as 'type'.
if (value.getType() != type)
return false;
// If the type is an integer type, it must be signless.
if (IntegerType integerTy = type.dyn_cast<IntegerType>())
if (!integerTy.isSignless())
return false;
// Finally, check that the attribute kind is handled.
return value.isa<IntegerAttr, FloatAttr, ElementsAttr, UnitAttr>();
}
void ConstantFloatOp::build(OpBuilder &builder, OperationState &result,
const APFloat &value, FloatType type) {
ConstantOp::build(builder, result, type, builder.getFloatAttr(type, value));
}
bool ConstantFloatOp::classof(Operation *op) {
return ConstantOp::classof(op) && op->getResult(0).getType().isa<FloatType>();
}
/// ConstantIntOp only matches values whose result type is an IntegerType.
bool ConstantIntOp::classof(Operation *op) {
return ConstantOp::classof(op) &&
op->getResult(0).getType().isSignlessInteger();
}
void ConstantIntOp::build(OpBuilder &builder, OperationState &result,
int64_t value, unsigned width) {
Type type = builder.getIntegerType(width);
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
}
/// Build a constant int op producing an integer with the specified type,
/// which must be an integer type.
void ConstantIntOp::build(OpBuilder &builder, OperationState &result,
int64_t value, Type type) {
assert(type.isSignlessInteger() &&
"ConstantIntOp can only have signless integer type");
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
}
/// ConstantIndexOp only matches values whose result type is Index.
bool ConstantIndexOp::classof(Operation *op) {
return ConstantOp::classof(op) && op->getResult(0).getType().isIndex();
}
void ConstantIndexOp::build(OpBuilder &builder, OperationState &result,
int64_t value) {
Type type = builder.getIndexType();
ConstantOp::build(builder, result, type, builder.getIntegerAttr(type, value));
}
//===----------------------------------------------------------------------===//
// DeallocOp
//===----------------------------------------------------------------------===//
namespace {
/// Fold Dealloc operations that are deallocating an AllocOp that is only used
/// by other Dealloc operations.
struct SimplifyDeadDealloc : public OpRewritePattern<DeallocOp> {
using OpRewritePattern<DeallocOp>::OpRewritePattern;
LogicalResult matchAndRewrite(DeallocOp dealloc,
PatternRewriter &rewriter) const override {
// Check that the memref operand's defining operation is an AllocOp.
Value memref = dealloc.memref();
if (!isa_and_nonnull<AllocOp>(memref.getDefiningOp()))
return failure();
// Check that all of the uses of the AllocOp are other DeallocOps.
for (auto *user : memref.getUsers())
if (!isa<DeallocOp>(user))
return failure();
// Erase the dealloc operation.
rewriter.eraseOp(dealloc);
return success();
}
};
} // end anonymous namespace.
static LogicalResult verify(DeallocOp op) {
if (!op.memref().getType().isa<MemRefType>())
return op.emitOpError("operand must be a memref");
return success();
}
void DeallocOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<SimplifyDeadDealloc>(context);
}
LogicalResult DeallocOp::fold(ArrayRef<Attribute> cstOperands,
SmallVectorImpl<OpFoldResult> &results) {
/// dealloc(memrefcast) -> dealloc
return foldMemRefCast(*this);
}
//===----------------------------------------------------------------------===//
// DimOp
//===----------------------------------------------------------------------===//
void DimOp::build(OpBuilder &builder, OperationState &result,
Value memrefOrTensor, int64_t index) {
auto loc = result.location;
Value indexValue = builder.create<ConstantIndexOp>(loc, index);
build(builder, result, memrefOrTensor, indexValue);
}
void DimOp::build(OpBuilder &builder, OperationState &result,
Value memrefOrTensor, Value index) {
auto indexTy = builder.getIndexType();
build(builder, result, indexTy, memrefOrTensor, index);
}
Optional<int64_t> DimOp::getConstantIndex() {
if (auto constantOp = index().getDefiningOp<ConstantOp>())
return constantOp.getValue().cast<IntegerAttr>().getInt();
return {};
}
static LogicalResult verify(DimOp op) {
// Assume unknown index to be in range.
Optional<int64_t> index = op.getConstantIndex();
if (!index.hasValue())
return success();
// Check that constant index is not knowingly out of range.
auto type = op.memrefOrTensor().getType();
if (auto tensorType = type.dyn_cast<RankedTensorType>()) {
if (index.getValue() >= tensorType.getRank())
return op.emitOpError("index is out of range");
} else if (auto memrefType = type.dyn_cast<MemRefType>()) {
if (index.getValue() >= memrefType.getRank())
return op.emitOpError("index is out of range");
} else if (type.isa<UnrankedTensorType>() || type.isa<UnrankedMemRefType>()) {
// Assume index to be in range.
} else {
llvm_unreachable("expected operand with tensor or memref type");
}
return success();
}
OpFoldResult DimOp::fold(ArrayRef<Attribute> operands) {
auto index = operands[1].dyn_cast_or_null<IntegerAttr>();
// All forms of folding require a known index.
if (!index)
return {};
auto argTy = memrefOrTensor().getType();
// Fold if the shape extent along the given index is known.
if (auto shapedTy = argTy.dyn_cast<ShapedType>()) {
// Folding for unranked types (UnrankedMemRefType, UnrankedTensorType) is
// not supported.
if (!shapedTy.hasRank())
return {};
if (!shapedTy.isDynamicDim(index.getInt())) {
Builder builder(getContext());
return builder.getIndexAttr(shapedTy.getShape()[index.getInt()]);
}
}
Operation *definingOp = memrefOrTensor().getDefiningOp();
// dim(tensor_load(memref)) -> dim(memref)
if (auto tensorLoadOp = dyn_cast_or_null<TensorLoadOp>(definingOp)) {
setOperand(0, tensorLoadOp.memref());
return getResult();
}
// Fold dim to the operand of tensor.generate.
if (auto fromElements = dyn_cast_or_null<tensor::GenerateOp>(definingOp)) {
auto resultType =
fromElements.getResult().getType().cast<RankedTensorType>();
// The case where the type encodes the size of the dimension is handled
// above.
assert(resultType.getShape()[index.getInt()] ==
RankedTensorType::kDynamicSize);
// Find the operand of the fromElements that corresponds to this index.
auto dynExtents = fromElements.dynamicExtents().begin();
for (auto dim : resultType.getShape().take_front(index.getInt()))
if (dim == RankedTensorType::kDynamicSize)
dynExtents++;
return Value{*dynExtents};
}
// The size at the given index is now known to be a dynamic size.
unsigned unsignedIndex = index.getValue().getZExtValue();
if (auto subtensor = dyn_cast_or_null<SubTensorOp>(definingOp)) {
assert(subtensor.isDynamicSize(unsignedIndex) &&
"Expected dynamic subtensor size");
return subtensor.getDynamicSize(unsignedIndex);
}
// Fold dim to the size argument for an `AllocOp`, `ViewOp`, or `SubViewOp`.
auto memrefType = argTy.dyn_cast<MemRefType>();
if (!memrefType)
return {};
if (auto alloc = dyn_cast_or_null<AllocOp>(definingOp))
return *(alloc.getDynamicSizes().begin() +
memrefType.getDynamicDimIndex(unsignedIndex));
if (auto view = dyn_cast_or_null<ViewOp>(definingOp))
return *(view.getDynamicSizes().begin() +
memrefType.getDynamicDimIndex(unsignedIndex));
if (auto subview = dyn_cast_or_null<SubViewOp>(definingOp)) {
assert(subview.isDynamicSize(unsignedIndex) &&
"Expected dynamic subview size");
return subview.getDynamicSize(unsignedIndex);
}
// dim(memrefcast) -> dim
if (succeeded(foldMemRefCast(*this)))
return getResult();
return {};
}
namespace {
/// Fold dim of a memref reshape operation to a load into the reshape's shape
/// operand.
struct DimOfMemRefReshape : public OpRewritePattern<DimOp> {
using OpRewritePattern<DimOp>::OpRewritePattern;
LogicalResult matchAndRewrite(DimOp dim,
PatternRewriter &rewriter) const override {
auto reshape = dim.memrefOrTensor().getDefiningOp<MemRefReshapeOp>();
if (!reshape)
return failure();
// Place the load directly after the reshape to ensure that the shape memref
// was not mutated.
rewriter.setInsertionPointAfter(reshape);
rewriter.replaceOpWithNewOp<LoadOp>(dim, reshape.shape(),
llvm::makeArrayRef({dim.index()}));
return success();
}
};
/// Fold dim of a dim of a cast into the dim of the source of the tensor cast.
template <typename CastOpTy>
struct DimOfCastOp : public OpRewritePattern<DimOp> {
using OpRewritePattern<DimOp>::OpRewritePattern;
LogicalResult matchAndRewrite(DimOp dimOp,
PatternRewriter &rewriter) const override {
auto castOp = dimOp.memrefOrTensor().getDefiningOp<CastOpTy>();
if (!castOp)
return failure();
Value newSource = castOp.getOperand();
rewriter.replaceOpWithNewOp<DimOp>(dimOp, newSource, dimOp.index());
return success();
}
};
} // end anonymous namespace.
void DimOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<DimOfMemRefReshape, DimOfCastOp<TensorToMemrefOp>,
DimOfCastOp<tensor::CastOp>>(context);
}
// ---------------------------------------------------------------------------
// DivFOp
// ---------------------------------------------------------------------------
OpFoldResult DivFOp::fold(ArrayRef<Attribute> operands) {
return constFoldBinaryOp<FloatAttr>(
operands, [](APFloat a, APFloat b) { return a / b; });
}
// ---------------------------------------------------------------------------
// DmaStartOp
// ---------------------------------------------------------------------------
void DmaStartOp::build(OpBuilder &builder, OperationState &result,
Value srcMemRef, ValueRange srcIndices, Value destMemRef,
ValueRange destIndices, Value numElements,
Value tagMemRef, ValueRange tagIndices, Value stride,
Value elementsPerStride) {
result.addOperands(srcMemRef);
result.addOperands(srcIndices);
result.addOperands(destMemRef);
result.addOperands(destIndices);
result.addOperands({numElements, tagMemRef});
result.addOperands(tagIndices);
if (stride)
result.addOperands({stride, elementsPerStride});
}
void DmaStartOp::print(OpAsmPrinter &p) {
p << "dma_start " << getSrcMemRef() << '[' << getSrcIndices() << "], "
<< getDstMemRef() << '[' << getDstIndices() << "], " << getNumElements()
<< ", " << getTagMemRef() << '[' << getTagIndices() << ']';
if (isStrided())
p << ", " << getStride() << ", " << getNumElementsPerStride();
p.printOptionalAttrDict(getAttrs());
p << " : " << getSrcMemRef().getType() << ", " << getDstMemRef().getType()
<< ", " << getTagMemRef().getType();
}
// Parse DmaStartOp.
// Ex:
// %dma_id = dma_start %src[%i, %j], %dst[%k, %l], %size,
// %tag[%index], %stride, %num_elt_per_stride :
// : memref<3076 x f32, 0>,
// memref<1024 x f32, 2>,
// memref<1 x i32>
//
ParseResult DmaStartOp::parse(OpAsmParser &parser, OperationState &result) {
OpAsmParser::OperandType srcMemRefInfo;
SmallVector<OpAsmParser::OperandType, 4> srcIndexInfos;
OpAsmParser::OperandType dstMemRefInfo;
SmallVector<OpAsmParser::OperandType, 4> dstIndexInfos;
OpAsmParser::OperandType numElementsInfo;
OpAsmParser::OperandType tagMemrefInfo;
SmallVector<OpAsmParser::OperandType, 4> tagIndexInfos;
SmallVector<OpAsmParser::OperandType, 2> strideInfo;
SmallVector<Type, 3> types;
auto indexType = parser.getBuilder().getIndexType();
// Parse and resolve the following list of operands:
// *) source memref followed by its indices (in square brackets).
// *) destination memref followed by its indices (in square brackets).
// *) dma size in KiB.
if (parser.parseOperand(srcMemRefInfo) ||
parser.parseOperandList(srcIndexInfos, OpAsmParser::Delimiter::Square) ||
parser.parseComma() || parser.parseOperand(dstMemRefInfo) ||
parser.parseOperandList(dstIndexInfos, OpAsmParser::Delimiter::Square) ||
parser.parseComma() || parser.parseOperand(numElementsInfo) ||
parser.parseComma() || parser.parseOperand(tagMemrefInfo) ||
parser.parseOperandList(tagIndexInfos, OpAsmParser::Delimiter::Square))
return failure();
// Parse optional stride and elements per stride.
if (parser.parseTrailingOperandList(strideInfo))
return failure();
bool isStrided = strideInfo.size() == 2;
if (!strideInfo.empty() && !isStrided) {
return parser.emitError(parser.getNameLoc(),
"expected two stride related operands");
}
if (parser.parseColonTypeList(types))
return failure();
if (types.size() != 3)
return parser.emitError(parser.getNameLoc(), "fewer/more types expected");
if (parser.resolveOperand(srcMemRefInfo, types[0], result.operands) ||
parser.resolveOperands(srcIndexInfos, indexType, result.operands) ||
parser.resolveOperand(dstMemRefInfo, types[1], result.operands) ||
parser.resolveOperands(dstIndexInfos, indexType, result.operands) ||
// size should be an index.
parser.resolveOperand(numElementsInfo, indexType, result.operands) ||
parser.resolveOperand(tagMemrefInfo, types[2], result.operands) ||
// tag indices should be index.
parser.resolveOperands(tagIndexInfos, indexType, result.operands))
return failure();
if (isStrided) {
if (parser.resolveOperands(strideInfo, indexType, result.operands))
return failure();
}
return success();
}
LogicalResult DmaStartOp::verify() {
unsigned numOperands = getNumOperands();
// Mandatory non-variadic operands are: src memref, dst memref, tag memref and
// the number of elements.
if (numOperands < 4)
return emitOpError("expected at least 4 operands");
// Check types of operands. The order of these calls is important: the later
// calls rely on some type properties to compute the operand position.
// 1. Source memref.
if (!getSrcMemRef().getType().isa<MemRefType>())
return emitOpError("expected source to be of memref type");
if (numOperands < getSrcMemRefRank() + 4)
return emitOpError() << "expected at least " << getSrcMemRefRank() + 4
<< " operands";
if (!getSrcIndices().empty() &&
!llvm::all_of(getSrcIndices().getTypes(),
[](Type t) { return t.isIndex(); }))
return emitOpError("expected source indices to be of index type");
// 2. Destination memref.
if (!getDstMemRef().getType().isa<MemRefType>())
return emitOpError("expected destination to be of memref type");
unsigned numExpectedOperands = getSrcMemRefRank() + getDstMemRefRank() + 4;
if (numOperands < numExpectedOperands)
return emitOpError() << "expected at least " << numExpectedOperands
<< " operands";
if (!getDstIndices().empty() &&
!llvm::all_of(getDstIndices().getTypes(),
[](Type t) { return t.isIndex(); }))
return emitOpError("expected destination indices to be of index type");
// 3. Number of elements.
if (!getNumElements().getType().isIndex())
return emitOpError("expected num elements to be of index type");
// 4. Tag memref.
if (!getTagMemRef().getType().isa<MemRefType>())
return emitOpError("expected tag to be of memref type");
numExpectedOperands += getTagMemRefRank();
if (numOperands < numExpectedOperands)
return emitOpError() << "expected at least " << numExpectedOperands
<< " operands";
if (!getTagIndices().empty() &&
!llvm::all_of(getTagIndices().getTypes(),
[](Type t) { return t.isIndex(); }))
return emitOpError("expected tag indices to be of index type");
// DMAs from different memory spaces supported.
if (getSrcMemorySpace() == getDstMemorySpace())
return emitOpError("DMA should be between different memory spaces");
// Optional stride-related operands must be either both present or both
// absent.
if (numOperands != numExpectedOperands &&
numOperands != numExpectedOperands + 2)
return emitOpError("incorrect number of operands");
// 5. Strides.
if (isStrided()) {
if (!getStride().getType().isIndex() ||
!getNumElementsPerStride().getType().isIndex())
return emitOpError(
"expected stride and num elements per stride to be of type index");
}
return success();
}
LogicalResult DmaStartOp::fold(ArrayRef<Attribute> cstOperands,
SmallVectorImpl<OpFoldResult> &results) {
/// dma_start(memrefcast) -> dma_start
return foldMemRefCast(*this);
}
// ---------------------------------------------------------------------------
// DmaWaitOp
// ---------------------------------------------------------------------------
void DmaWaitOp::build(OpBuilder &builder, OperationState &result,
Value tagMemRef, ValueRange tagIndices,
Value numElements) {
result.addOperands(tagMemRef);
result.addOperands(tagIndices);
result.addOperands(numElements);
}
void DmaWaitOp::print(OpAsmPrinter &p) {
p << "dma_wait " << getTagMemRef() << '[' << getTagIndices() << "], "
<< getNumElements();
p.printOptionalAttrDict(getAttrs());
p << " : " << getTagMemRef().getType();
}
// Parse DmaWaitOp.
// Eg:
// dma_wait %tag[%index], %num_elements : memref<1 x i32, (d0) -> (d0), 4>
//
ParseResult DmaWaitOp::parse(OpAsmParser &parser, OperationState &result) {
OpAsmParser::OperandType tagMemrefInfo;
SmallVector<OpAsmParser::OperandType, 2> tagIndexInfos;
Type type;
auto indexType = parser.getBuilder().getIndexType();
OpAsmParser::OperandType numElementsInfo;
// Parse tag memref, its indices, and dma size.
if (parser.parseOperand(tagMemrefInfo) ||
parser.parseOperandList(tagIndexInfos, OpAsmParser::Delimiter::Square) ||
parser.parseComma() || parser.parseOperand(numElementsInfo) ||
parser.parseColonType(type) ||
parser.resolveOperand(tagMemrefInfo, type, result.operands) ||
parser.resolveOperands(tagIndexInfos, indexType, result.operands) ||
parser.resolveOperand(numElementsInfo, indexType, result.operands))
return failure();
return success();
}
LogicalResult DmaWaitOp::fold(ArrayRef<Attribute> cstOperands,
SmallVectorImpl<OpFoldResult> &results) {
/// dma_wait(memrefcast) -> dma_wait
return foldMemRefCast(*this);
}
LogicalResult DmaWaitOp::verify() {
// Mandatory non-variadic operands are tag and the number of elements.
if (getNumOperands() < 2)
return emitOpError() << "expected at least 2 operands";
// Check types of operands. The order of these calls is important: the later
// calls rely on some type properties to compute the operand position.
if (!getTagMemRef().getType().isa<MemRefType>())
return emitOpError() << "expected tag to be of memref type";
if (getNumOperands() != 2 + getTagMemRefRank())
return emitOpError() << "expected " << 2 + getTagMemRefRank()
<< " operands";
if (!getTagIndices().empty() &&
!llvm::all_of(getTagIndices().getTypes(),
[](Type t) { return t.isIndex(); }))
return emitOpError() << "expected tag indices to be of index type";
if (!getNumElements().getType().isIndex())
return emitOpError()
<< "expected the number of elements to be of index type";
return success();
}
//===----------------------------------------------------------------------===//
// FPExtOp
//===----------------------------------------------------------------------===//
bool FPExtOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (auto fa = a.dyn_cast<FloatType>())
if (auto fb = b.dyn_cast<FloatType>())
return fa.getWidth() < fb.getWidth();
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// FPToSIOp
//===----------------------------------------------------------------------===//
bool FPToSIOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (a.isa<FloatType>() && b.isSignlessInteger())
return true;
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// FPToUIOp
//===----------------------------------------------------------------------===//
bool FPToUIOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (a.isa<FloatType>() && b.isSignlessInteger())
return true;
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// FPTruncOp
//===----------------------------------------------------------------------===//
bool FPTruncOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (auto fa = a.dyn_cast<FloatType>())
if (auto fb = b.dyn_cast<FloatType>())
return fa.getWidth() > fb.getWidth();
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// GlobalMemrefOp
//===----------------------------------------------------------------------===//
static void printGlobalMemrefOpTypeAndInitialValue(OpAsmPrinter &p,
GlobalMemrefOp op,
TypeAttr type,
Attribute initialValue) {
p << type;
if (!op.isExternal()) {
p << " = ";
if (op.isUninitialized())
p << "uninitialized";
else
p.printAttributeWithoutType(initialValue);
}
}
static ParseResult
parseGlobalMemrefOpTypeAndInitialValue(OpAsmParser &parser, TypeAttr &typeAttr,
Attribute &initialValue) {
Type type;
if (parser.parseType(type))
return failure();
auto memrefType = type.dyn_cast<MemRefType>();
if (!memrefType || !memrefType.hasStaticShape())
return parser.emitError(parser.getNameLoc())
<< "type should be static shaped memref, but got " << type;
typeAttr = TypeAttr::get(type);
if (parser.parseOptionalEqual())
return success();
if (succeeded(parser.parseOptionalKeyword("uninitialized"))) {
initialValue = UnitAttr::get(parser.getBuilder().getContext());
return success();
}
Type tensorType = getTensorTypeFromMemRefType(memrefType);
if (parser.parseAttribute(initialValue, tensorType))
return failure();
if (!initialValue.isa<ElementsAttr>())
return parser.emitError(parser.getNameLoc())
<< "initial value should be a unit or elements attribute";
return success();
}
static LogicalResult verify(GlobalMemrefOp op) {
auto memrefType = op.type().dyn_cast<MemRefType>();
if (!memrefType || !memrefType.hasStaticShape())
return op.emitOpError("type should be static shaped memref, but got ")
<< op.type();
// Verify that the initial value, if present, is either a unit attribute or
// an elements attribute.
if (op.initial_value().hasValue()) {
Attribute initValue = op.initial_value().getValue();
if (!initValue.isa<UnitAttr>() && !initValue.isa<ElementsAttr>())
return op.emitOpError("initial value should be a unit or elements "
"attribute, but got ")
<< initValue;
// Check that the type of the initial value is compatible with the type of
// the global variable.
if (initValue.isa<ElementsAttr>()) {
Type initType = initValue.getType();
Type tensorType = getTensorTypeFromMemRefType(memrefType);
if (initType != tensorType)
return op.emitOpError("initial value expected to be of type ")
<< tensorType << ", but was of type " << initType;
}
}
// TODO: verify visibility for declarations.
return success();
}
//===----------------------------------------------------------------------===//
// GetGlobalMemrefOp
//===----------------------------------------------------------------------===//
LogicalResult
GetGlobalMemrefOp::verifySymbolUses(SymbolTableCollection &symbolTable) {
// Verify that the result type is same as the type of the referenced
// global_memref op.
auto global =
symbolTable.lookupNearestSymbolFrom<GlobalMemrefOp>(*this, nameAttr());
if (!global)
return emitOpError("'")
<< name() << "' does not reference a valid global memref";
Type resultType = result().getType();
if (global.type() != resultType)
return emitOpError("result type ")
<< resultType << " does not match type " << global.type()
<< " of the global memref @" << name();
return success();
}
//===----------------------------------------------------------------------===//
// IndexCastOp
//===----------------------------------------------------------------------===//
// Index cast is applicable from index to integer and backwards.
bool IndexCastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (a.isa<ShapedType>() && b.isa<ShapedType>()) {
auto aShaped = a.cast<ShapedType>();
auto bShaped = b.cast<ShapedType>();
return (aShaped.getShape() == bShaped.getShape()) &&
areCastCompatible(aShaped.getElementType(),
bShaped.getElementType());
}
return (a.isIndex() && b.isSignlessInteger()) ||
(a.isSignlessInteger() && b.isIndex());
}
OpFoldResult IndexCastOp::fold(ArrayRef<Attribute> cstOperands) {
// Fold IndexCast(IndexCast(x)) -> x
auto cast = getOperand().getDefiningOp<IndexCastOp>();
if (cast && cast.getOperand().getType() == getType())
return cast.getOperand();
// Fold IndexCast(constant) -> constant
// A little hack because we go through int. Otherwise, the size
// of the constant might need to change.
if (auto value = cstOperands[0].dyn_cast_or_null<IntegerAttr>())
return IntegerAttr::get(getType(), value.getInt());
return {};
}
//===----------------------------------------------------------------------===//
// LoadOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(LoadOp op) {
if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
return op.emitOpError("incorrect number of indices for load");
return success();
}
OpFoldResult LoadOp::fold(ArrayRef<Attribute> cstOperands) {
/// load(memrefcast) -> load
if (succeeded(foldMemRefCast(*this)))
return getResult();
return OpFoldResult();
}
namespace {
/// Fold a load on a tensor_to_memref operation into an tensor.extract on the
/// corresponding tensor.
struct LoadOfTensorToMemref : public OpRewritePattern<LoadOp> {
using OpRewritePattern<LoadOp>::OpRewritePattern;
LogicalResult matchAndRewrite(LoadOp load,
PatternRewriter &rewriter) const override {
auto tensorToMemref = load.memref().getDefiningOp<TensorToMemrefOp>();
if (!tensorToMemref)
return failure();
rewriter.replaceOpWithNewOp<tensor::ExtractOp>(
load, tensorToMemref.tensor(), load.indices());
return success();
}
};
} // end anonymous namespace.
void LoadOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<LoadOfTensorToMemref>(context);
}
//===----------------------------------------------------------------------===//
// MemRefCastOp
//===----------------------------------------------------------------------===//
Value MemRefCastOp::getViewSource() { return source(); }
bool MemRefCastOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
auto aT = a.dyn_cast<MemRefType>();
auto bT = b.dyn_cast<MemRefType>();
auto uaT = a.dyn_cast<UnrankedMemRefType>();
auto ubT = b.dyn_cast<UnrankedMemRefType>();
if (aT && bT) {
if (aT.getElementType() != bT.getElementType())
return false;
if (aT.getAffineMaps() != bT.getAffineMaps()) {
int64_t aOffset, bOffset;
SmallVector<int64_t, 4> aStrides, bStrides;
if (failed(getStridesAndOffset(aT, aStrides, aOffset)) ||
failed(getStridesAndOffset(bT, bStrides, bOffset)) ||
aStrides.size() != bStrides.size())
return false;
// Strides along a dimension/offset are compatible if the value in the
// source memref is static and the value in the target memref is the
// same. They are also compatible if either one is dynamic (see
// description of MemRefCastOp for details).
auto checkCompatible = [](int64_t a, int64_t b) {
return (a == MemRefType::getDynamicStrideOrOffset() ||
b == MemRefType::getDynamicStrideOrOffset() || a == b);
};
if (!checkCompatible(aOffset, bOffset))
return false;
for (auto aStride : enumerate(aStrides))
if (!checkCompatible(aStride.value(), bStrides[aStride.index()]))
return false;
}
if (aT.getMemorySpace() != bT.getMemorySpace())
return false;
// They must have the same rank, and any specified dimensions must match.
if (aT.getRank() != bT.getRank())
return false;
for (unsigned i = 0, e = aT.getRank(); i != e; ++i) {
int64_t aDim = aT.getDimSize(i), bDim = bT.getDimSize(i);
if (aDim != -1 && bDim != -1 && aDim != bDim)
return false;
}
return true;
} else {
if (!aT && !uaT)
return false;
if (!bT && !ubT)
return false;
// Unranked to unranked casting is unsupported
if (uaT && ubT)
return false;
auto aEltType = (aT) ? aT.getElementType() : uaT.getElementType();
auto bEltType = (bT) ? bT.getElementType() : ubT.getElementType();
if (aEltType != bEltType)
return false;
auto aMemSpace = (aT) ? aT.getMemorySpace() : uaT.getMemorySpace();
auto bMemSpace = (bT) ? bT.getMemorySpace() : ubT.getMemorySpace();
if (aMemSpace != bMemSpace)
return false;
return true;
}
return false;
}
OpFoldResult MemRefCastOp::fold(ArrayRef<Attribute> operands) {
return succeeded(foldMemRefCast(*this)) ? getResult() : Value();
}
//===----------------------------------------------------------------------===//
// MemRefReinterpretCastOp
//===----------------------------------------------------------------------===//
/// Build a MemRefReinterpretCastOp with all dynamic entries: `staticOffsets`,
/// `staticSizes` and `staticStrides` are automatically filled with
/// source-memref-rank sentinel values that encode dynamic entries.
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
OpFoldResult offset,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
dispatchIndexOpFoldResults(offset, dynamicOffsets, staticOffsets,
ShapedType::kDynamicStrideOrOffset);
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
ShapedType::kDynamicSize);
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
ShapedType::kDynamicStrideOrOffset);
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
result.addAttributes(attrs);
}
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
int64_t offset,
ArrayRef<int64_t> sizes,
ArrayRef<int64_t> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> sizeValues =
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
build(b, result, resultType, source, b.getI64IntegerAttr(offset), sizeValues,
strideValues, attrs);
}
void mlir::MemRefReinterpretCastOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
Value offset, ValueRange sizes,
ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
build(b, result, resultType, source, offset, sizeValues, strideValues, attrs);
}
/// Print a memref_reinterpret_cast op of the form:
/// ```
/// `memref_reinterpret_cast` ssa-name to
/// offset: `[` offset `]`
/// sizes: `[` size-list `]`
/// strides:`[` stride-list `]`
/// `:` any-memref-type to strided-memref-type
/// ```
static void print(OpAsmPrinter &p, MemRefReinterpretCastOp op) {
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' ';
p << op.source() << " ";
printOffsetsSizesAndStrides(
p, op, /*offsetPrefix=*/"to offset: ", /*sizePrefix=*/", sizes: ",
/*stridePrefix=*/", strides: ");
p << ": " << op.source().getType() << " to " << op.getType();
}
/// Parse a memref_reinterpret_cast op of the form:
/// ```
/// `memref_reinterpret_cast` ssa-name to
/// offset: `[` offset `]`
/// sizes: `[` size-list `]`
/// strides:`[` stride-list `]`
/// `:` any-memref-type to strided-memref-type
/// ```
static ParseResult parseMemRefReinterpretCastOp(OpAsmParser &parser,
OperationState &result) {
// Parse `operand`
OpAsmParser::OperandType srcInfo;
if (parser.parseOperand(srcInfo))
return failure();
auto parseOffsetPrefix = [](OpAsmParser &parser) {
return failure(parser.parseKeyword("to") || parser.parseKeyword("offset") ||
parser.parseColon());
};
auto parseSizePrefix = [](OpAsmParser &parser) {
return failure(parser.parseComma() || parser.parseKeyword("sizes") ||
parser.parseColon());
};
auto parseStridePrefix = [](OpAsmParser &parser) {
return failure(parser.parseComma() || parser.parseKeyword("strides") ||
parser.parseColon());
};
Type srcType, dstType;
auto preResolutionFn = [&](OpAsmParser &parser, OperationState &result) {
return failure(parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.parseKeywordType("to", dstType) ||
parser.resolveOperand(srcInfo, srcType, result.operands));
};
if (failed(parseOffsetsSizesAndStrides(parser, result,
/*segmentSizes=*/{1}, // source memref
preResolutionFn, parseOffsetPrefix,
parseSizePrefix, parseStridePrefix)))
return failure();
return parser.addTypeToList(dstType, result.types);
}
static LogicalResult verify(MemRefReinterpretCastOp op) {
// The source and result memrefs should be in the same memory space.
auto srcType = op.source().getType().cast<BaseMemRefType>();
auto resultType = op.getType().cast<MemRefType>();
if (srcType.getMemorySpace() != resultType.getMemorySpace())
return op.emitError("different memory spaces specified for source type ")
<< srcType << " and result memref type " << resultType;
if (srcType.getElementType() != resultType.getElementType())
return op.emitError("different element types specified for source type ")
<< srcType << " and result memref type " << resultType;
// Match sizes in result memref type and in static_sizes attribute.
for (auto &en :
llvm::enumerate(llvm::zip(resultType.getShape(),
extractFromI64ArrayAttr(op.static_sizes())))) {
int64_t resultSize = std::get<0>(en.value());
int64_t expectedSize = std::get<1>(en.value());
if (resultSize != expectedSize)
return op.emitError("expected result type with size = ")
<< expectedSize << " instead of " << resultSize
<< " in dim = " << en.index();
}
// Match offset and strides in static_offset and static_strides attributes if
// result memref type has an affine map specified.
if (!resultType.getAffineMaps().empty()) {
int64_t resultOffset;
SmallVector<int64_t, 4> resultStrides;
if (failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
return failure();
// Match offset in result memref type and in static_offsets attribute.
int64_t expectedOffset =
extractFromI64ArrayAttr(op.static_offsets()).front();
if (resultOffset != expectedOffset)
return op.emitError("expected result type with offset = ")
<< resultOffset << " instead of " << expectedOffset;
// Match strides in result memref type and in static_strides attribute.
for (auto &en : llvm::enumerate(llvm::zip(
resultStrides, extractFromI64ArrayAttr(op.static_strides())))) {
int64_t resultStride = std::get<0>(en.value());
int64_t expectedStride = std::get<1>(en.value());
if (resultStride != expectedStride)
return op.emitError("expected result type with stride = ")
<< expectedStride << " instead of " << resultStride
<< " in dim = " << en.index();
}
}
return success();
}
//===----------------------------------------------------------------------===//
// MemRefReshapeOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(MemRefReshapeOp op) {
Type operandType = op.source().getType();
Type resultType = op.result().getType();
Type operandElementType = operandType.cast<ShapedType>().getElementType();
Type resultElementType = resultType.cast<ShapedType>().getElementType();
if (operandElementType != resultElementType)
return op.emitOpError("element types of source and destination memref "
"types should be the same");
if (auto operandMemRefType = operandType.dyn_cast<MemRefType>())
if (!operandMemRefType.getAffineMaps().empty())
return op.emitOpError(
"source memref type should have identity affine map");
int64_t shapeSize = op.shape().getType().cast<MemRefType>().getDimSize(0);
auto resultMemRefType = resultType.dyn_cast<MemRefType>();
if (resultMemRefType) {
if (!resultMemRefType.getAffineMaps().empty())
return op.emitOpError(
"result memref type should have identity affine map");
if (shapeSize == ShapedType::kDynamicSize)
return op.emitOpError("cannot use shape operand with dynamic length to "
"reshape to statically-ranked memref type");
if (shapeSize != resultMemRefType.getRank())
return op.emitOpError(
"length of shape operand differs from the result's memref rank");
}
return success();
}
//===----------------------------------------------------------------------===//
// MulFOp
//===----------------------------------------------------------------------===//
OpFoldResult MulFOp::fold(ArrayRef<Attribute> operands) {
return constFoldBinaryOp<FloatAttr>(
operands, [](APFloat a, APFloat b) { return a * b; });
}
//===----------------------------------------------------------------------===//
// MulIOp
//===----------------------------------------------------------------------===//
OpFoldResult MulIOp::fold(ArrayRef<Attribute> operands) {
/// muli(x, 0) -> 0
if (matchPattern(rhs(), m_Zero()))
return rhs();
/// muli(x, 1) -> x
if (matchPattern(rhs(), m_One()))
return getOperand(0);
// TODO: Handle the overflow case.
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a * b; });
}
//===----------------------------------------------------------------------===//
// OrOp
//===----------------------------------------------------------------------===//
OpFoldResult OrOp::fold(ArrayRef<Attribute> operands) {
/// or(x, 0) -> x
if (matchPattern(rhs(), m_Zero()))
return lhs();
/// or(x,x) -> x
if (lhs() == rhs())
return rhs();
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a | b; });
}
//===----------------------------------------------------------------------===//
// PrefetchOp
//===----------------------------------------------------------------------===//
static void print(OpAsmPrinter &p, PrefetchOp op) {
p << PrefetchOp::getOperationName() << " " << op.memref() << '[';
p.printOperands(op.indices());
p << ']' << ", " << (op.isWrite() ? "write" : "read");
p << ", locality<" << op.localityHint();
p << ">, " << (op.isDataCache() ? "data" : "instr");
p.printOptionalAttrDict(
op.getAttrs(),
/*elidedAttrs=*/{"localityHint", "isWrite", "isDataCache"});
p << " : " << op.getMemRefType();
}
static ParseResult parsePrefetchOp(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType memrefInfo;
SmallVector<OpAsmParser::OperandType, 4> indexInfo;
IntegerAttr localityHint;
MemRefType type;
StringRef readOrWrite, cacheType;
auto indexTy = parser.getBuilder().getIndexType();
auto i32Type = parser.getBuilder().getIntegerType(32);
if (parser.parseOperand(memrefInfo) ||
parser.parseOperandList(indexInfo, OpAsmParser::Delimiter::Square) ||
parser.parseComma() || parser.parseKeyword(&readOrWrite) ||
parser.parseComma() || parser.parseKeyword("locality") ||
parser.parseLess() ||
parser.parseAttribute(localityHint, i32Type, "localityHint",
result.attributes) ||
parser.parseGreater() || parser.parseComma() ||
parser.parseKeyword(&cacheType) || parser.parseColonType(type) ||
parser.resolveOperand(memrefInfo, type, result.operands) ||
parser.resolveOperands(indexInfo, indexTy, result.operands))
return failure();
if (!readOrWrite.equals("read") && !readOrWrite.equals("write"))
return parser.emitError(parser.getNameLoc(),
"rw specifier has to be 'read' or 'write'");
result.addAttribute(
PrefetchOp::getIsWriteAttrName(),
parser.getBuilder().getBoolAttr(readOrWrite.equals("write")));
if (!cacheType.equals("data") && !cacheType.equals("instr"))
return parser.emitError(parser.getNameLoc(),
"cache type has to be 'data' or 'instr'");
result.addAttribute(
PrefetchOp::getIsDataCacheAttrName(),
parser.getBuilder().getBoolAttr(cacheType.equals("data")));
return success();
}
static LogicalResult verify(PrefetchOp op) {
if (op.getNumOperands() != 1 + op.getMemRefType().getRank())
return op.emitOpError("too few indices");
return success();
}
LogicalResult PrefetchOp::fold(ArrayRef<Attribute> cstOperands,
SmallVectorImpl<OpFoldResult> &results) {
// prefetch(memrefcast) -> prefetch
return foldMemRefCast(*this);
}
//===----------------------------------------------------------------------===//
// RankOp
//===----------------------------------------------------------------------===//
OpFoldResult RankOp::fold(ArrayRef<Attribute> operands) {
// Constant fold rank when the rank of the operand is known.
auto type = getOperand().getType();
if (auto shapedType = type.dyn_cast<ShapedType>())
if (shapedType.hasRank())
return IntegerAttr::get(IndexType::get(getContext()),
shapedType.getRank());
return IntegerAttr();
}
//===----------------------------------------------------------------------===//
// ReturnOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(ReturnOp op) {
auto function = cast<FuncOp>(op->getParentOp());
// The operand number and types must match the function signature.
const auto &results = function.getType().getResults();
if (op.getNumOperands() != results.size())
return op.emitOpError("has ")
<< op.getNumOperands() << " operands, but enclosing function (@"
<< function.getName() << ") returns " << results.size();
for (unsigned i = 0, e = results.size(); i != e; ++i)
if (op.getOperand(i).getType() != results[i])
return op.emitError()
<< "type of return operand " << i << " ("
<< op.getOperand(i).getType()
<< ") doesn't match function result type (" << results[i] << ")"
<< " in function @" << function.getName();
return success();
}
//===----------------------------------------------------------------------===//
// SelectOp
//===----------------------------------------------------------------------===//
OpFoldResult SelectOp::fold(ArrayRef<Attribute> operands) {
auto condition = getCondition();
// select true, %0, %1 => %0
if (matchPattern(condition, m_One()))
return getTrueValue();
// select false, %0, %1 => %1
if (matchPattern(condition, m_Zero()))
return getFalseValue();
return nullptr;
}
static void print(OpAsmPrinter &p, SelectOp op) {
p << "select " << op.getOperands();
p.printOptionalAttrDict(op.getAttrs());
p << " : ";
if (ShapedType condType = op.getCondition().getType().dyn_cast<ShapedType>())
p << condType << ", ";
p << op.getType();
}
static ParseResult parseSelectOp(OpAsmParser &parser, OperationState &result) {
Type conditionType, resultType;
SmallVector<OpAsmParser::OperandType, 3> operands;
if (parser.parseOperandList(operands, /*requiredOperandCount=*/3) ||
parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(resultType))
return failure();
// Check for the explicit condition type if this is a masked tensor or vector.
if (succeeded(parser.parseOptionalComma())) {
conditionType = resultType;
if (parser.parseType(resultType))
return failure();
} else {
conditionType = parser.getBuilder().getI1Type();
}
result.addTypes(resultType);
return parser.resolveOperands(operands,
{conditionType, resultType, resultType},
parser.getNameLoc(), result.operands);
}
static LogicalResult verify(SelectOp op) {
Type conditionType = op.getCondition().getType();
if (conditionType.isSignlessInteger(1))
return success();
// If the result type is a vector or tensor, the type can be a mask with the
// same elements.
Type resultType = op.getType();
if (!resultType.isa<TensorType, VectorType>())
return op.emitOpError()
<< "expected condition to be a signless i1, but got "
<< conditionType;
Type shapedConditionType = getI1SameShape(resultType);
if (conditionType != shapedConditionType)
return op.emitOpError()
<< "expected condition type to have the same shape "
"as the result type, expected "
<< shapedConditionType << ", but got " << conditionType;
return success();
}
//===----------------------------------------------------------------------===//
// SignExtendIOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(SignExtendIOp op) {
// Get the scalar type (which is either directly the type of the operand
// or the vector's/tensor's element type.
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
auto dstType = getElementTypeOrSelf(op.getType());
// For now, index is forbidden for the source and the destination type.
if (srcType.isa<IndexType>())
return op.emitError() << srcType << " is not a valid operand type";
if (dstType.isa<IndexType>())
return op.emitError() << dstType << " is not a valid result type";
if (srcType.cast<IntegerType>().getWidth() >=
dstType.cast<IntegerType>().getWidth())
return op.emitError("result type ")
<< dstType << " must be wider than operand type " << srcType;
return success();
}
//===----------------------------------------------------------------------===//
// SignedDivIOp
//===----------------------------------------------------------------------===//
OpFoldResult SignedDivIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "binary operation takes two operands");
// Don't fold if it would overflow or if it requires a division by zero.
bool overflowOrDiv0 = false;
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
if (overflowOrDiv0 || !b) {
overflowOrDiv0 = true;
return a;
}
return a.sdiv_ov(b, overflowOrDiv0);
});
// Fold out division by one. Assumes all tensors of all ones are splats.
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
if (rhs.getValue() == 1)
return lhs();
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
return lhs();
}
return overflowOrDiv0 ? Attribute() : result;
}
//===----------------------------------------------------------------------===//
// SignedFloorDivIOp
//===----------------------------------------------------------------------===//
static APInt signedCeilNonnegInputs(APInt a, APInt b, bool &overflow) {
// Returns (a-1)/b + 1
APInt one(a.getBitWidth(), 1, true); // Signed value 1.
APInt val = a.ssub_ov(one, overflow).sdiv_ov(b, overflow);
return val.sadd_ov(one, overflow);
}
OpFoldResult SignedFloorDivIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "binary operation takes two operands");
// Don't fold if it would overflow or if it requires a division by zero.
bool overflowOrDiv0 = false;
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
if (overflowOrDiv0 || !b) {
overflowOrDiv0 = true;
return a;
}
unsigned bits = a.getBitWidth();
APInt zero = APInt::getNullValue(bits);
if (a.sge(zero) && b.sgt(zero)) {
// Both positive (or a is zero), return a / b.
return a.sdiv_ov(b, overflowOrDiv0);
} else if (a.sle(zero) && b.slt(zero)) {
// Both negative (or a is zero), return -a / -b.
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
return posA.sdiv_ov(posB, overflowOrDiv0);
} else if (a.slt(zero) && b.sgt(zero)) {
// A is negative, b is positive, return - ceil(-a, b).
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
APInt ceil = signedCeilNonnegInputs(posA, b, overflowOrDiv0);
return zero.ssub_ov(ceil, overflowOrDiv0);
} else {
// A is positive, b is negative, return - ceil(a, -b).
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
APInt ceil = signedCeilNonnegInputs(a, posB, overflowOrDiv0);
return zero.ssub_ov(ceil, overflowOrDiv0);
}
});
// Fold out floor division by one. Assumes all tensors of all ones are
// splats.
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
if (rhs.getValue() == 1)
return lhs();
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
return lhs();
}
return overflowOrDiv0 ? Attribute() : result;
}
//===----------------------------------------------------------------------===//
// SignedCeilDivIOp
//===----------------------------------------------------------------------===//
OpFoldResult SignedCeilDivIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "binary operation takes two operands");
// Don't fold if it would overflow or if it requires a division by zero.
bool overflowOrDiv0 = false;
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
if (overflowOrDiv0 || !b) {
overflowOrDiv0 = true;
return a;
}
unsigned bits = a.getBitWidth();
APInt zero = APInt::getNullValue(bits);
if (a.sgt(zero) && b.sgt(zero)) {
// Both positive, return ceil(a, b).
return signedCeilNonnegInputs(a, b, overflowOrDiv0);
} else if (a.slt(zero) && b.slt(zero)) {
// Both negative, return ceil(-a, -b).
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
return signedCeilNonnegInputs(posA, posB, overflowOrDiv0);
} else if (a.slt(zero) && b.sgt(zero)) {
// A is negative, b is positive, return - ( -a / b).
APInt posA = zero.ssub_ov(a, overflowOrDiv0);
APInt div = posA.sdiv_ov(b, overflowOrDiv0);
return zero.ssub_ov(div, overflowOrDiv0);
} else {
// A is positive (or zero), b is negative, return - (a / -b).
APInt posB = zero.ssub_ov(b, overflowOrDiv0);
APInt div = a.sdiv_ov(posB, overflowOrDiv0);
return zero.ssub_ov(div, overflowOrDiv0);
}
});
// Fold out floor division by one. Assumes all tensors of all ones are
// splats.
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
if (rhs.getValue() == 1)
return lhs();
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
return lhs();
}
return overflowOrDiv0 ? Attribute() : result;
}
//===----------------------------------------------------------------------===//
// SignedRemIOp
//===----------------------------------------------------------------------===//
OpFoldResult SignedRemIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "remi_signed takes two operands");
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
if (!rhs)
return {};
auto rhsValue = rhs.getValue();
// x % 1 = 0
if (rhsValue.isOneValue())
return IntegerAttr::get(rhs.getType(), APInt(rhsValue.getBitWidth(), 0));
// Don't fold if it requires division by zero.
if (rhsValue.isNullValue())
return {};
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
if (!lhs)
return {};
return IntegerAttr::get(lhs.getType(), lhs.getValue().srem(rhsValue));
}
//===----------------------------------------------------------------------===//
// SIToFPOp
//===----------------------------------------------------------------------===//
// sitofp is applicable from integer types to float types.
bool SIToFPOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (a.isSignlessInteger() && b.isa<FloatType>())
return true;
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// SplatOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(SplatOp op) {
// TODO: we could replace this by a trait.
if (op.getOperand().getType() !=
op.getType().cast<ShapedType>().getElementType())
return op.emitError("operand should be of elemental type of result type");
return success();
}
// Constant folding hook for SplatOp.
OpFoldResult SplatOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 1 && "splat takes one operand");
auto constOperand = operands.front();
if (!constOperand || !constOperand.isa<IntegerAttr, FloatAttr>())
return {};
auto shapedType = getType().cast<ShapedType>();
assert(shapedType.getElementType() == constOperand.getType() &&
"incorrect input attribute type for folding");
// SplatElementsAttr::get treats single value for second arg as being a splat.
return SplatElementsAttr::get(shapedType, {constOperand});
}
//===----------------------------------------------------------------------===//
// StoreOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(StoreOp op) {
if (op.getNumOperands() != 2 + op.getMemRefType().getRank())
return op.emitOpError("store index operand count not equal to memref rank");
return success();
}
LogicalResult StoreOp::fold(ArrayRef<Attribute> cstOperands,
SmallVectorImpl<OpFoldResult> &results) {
/// store(memrefcast) -> store
return foldMemRefCast(*this);
}
//===----------------------------------------------------------------------===//
// SubFOp
//===----------------------------------------------------------------------===//
OpFoldResult SubFOp::fold(ArrayRef<Attribute> operands) {
return constFoldBinaryOp<FloatAttr>(
operands, [](APFloat a, APFloat b) { return a - b; });
}
//===----------------------------------------------------------------------===//
// SubIOp
//===----------------------------------------------------------------------===//
OpFoldResult SubIOp::fold(ArrayRef<Attribute> operands) {
// subi(x,x) -> 0
if (getOperand(0) == getOperand(1))
return Builder(getContext()).getZeroAttr(getType());
// subi(x,0) -> x
if (matchPattern(rhs(), m_Zero()))
return lhs();
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a - b; });
}
//===----------------------------------------------------------------------===//
// UIToFPOp
//===----------------------------------------------------------------------===//
// uitofp is applicable from integer types to float types.
bool UIToFPOp::areCastCompatible(TypeRange inputs, TypeRange outputs) {
if (inputs.size() != 1 || outputs.size() != 1)
return false;
Type a = inputs.front(), b = outputs.front();
if (a.isSignlessInteger() && b.isa<FloatType>())
return true;
return areVectorCastSimpleCompatible(a, b, areCastCompatible);
}
//===----------------------------------------------------------------------===//
// SubViewOp
//===----------------------------------------------------------------------===//
namespace {
/// Helpers to write more idiomatic operations.
namespace saturated_arith {
struct Wrapper {
explicit Wrapper(int64_t v) : v(v) {}
operator int64_t() { return v; }
int64_t v;
};
Wrapper operator+(Wrapper a, int64_t b) {
if (ShapedType::isDynamicStrideOrOffset(a) ||
ShapedType::isDynamicStrideOrOffset(b))
return Wrapper(ShapedType::kDynamicStrideOrOffset);
return Wrapper(a.v + b);
}
Wrapper operator*(Wrapper a, int64_t b) {
if (ShapedType::isDynamicStrideOrOffset(a) ||
ShapedType::isDynamicStrideOrOffset(b))
return Wrapper(ShapedType::kDynamicStrideOrOffset);
return Wrapper(a.v * b);
}
} // end namespace saturated_arith
} // end namespace
/// A subview result type can be fully inferred from the source type and the
/// static representation of offsets, sizes and strides. Special sentinels
/// encode the dynamic case.
Type SubViewOp::inferResultType(MemRefType sourceMemRefType,
ArrayRef<int64_t> staticOffsets,
ArrayRef<int64_t> staticSizes,
ArrayRef<int64_t> staticStrides) {
unsigned rank = sourceMemRefType.getRank();
(void)rank;
assert(staticOffsets.size() == rank &&
"unexpected staticOffsets size mismatch");
assert(staticSizes.size() == rank && "unexpected staticSizes size mismatch");
assert(staticStrides.size() == rank &&
"unexpected staticStrides size mismatch");
// Extract source offset and strides.
int64_t sourceOffset;
SmallVector<int64_t, 4> sourceStrides;
auto res = getStridesAndOffset(sourceMemRefType, sourceStrides, sourceOffset);
assert(succeeded(res) && "SubViewOp expected strided memref type");
(void)res;
// Compute target offset whose value is:
// `sourceOffset + sum_i(staticOffset_i * sourceStrides_i)`.
int64_t targetOffset = sourceOffset;
for (auto it : llvm::zip(staticOffsets, sourceStrides)) {
auto staticOffset = std::get<0>(it), targetStride = std::get<1>(it);
using namespace saturated_arith;
targetOffset = Wrapper(targetOffset) + Wrapper(staticOffset) * targetStride;
}
// Compute target stride whose value is:
// `sourceStrides_i * staticStrides_i`.
SmallVector<int64_t, 4> targetStrides;
targetStrides.reserve(staticOffsets.size());
for (auto it : llvm::zip(sourceStrides, staticStrides)) {
auto sourceStride = std::get<0>(it), staticStride = std::get<1>(it);
using namespace saturated_arith;
targetStrides.push_back(Wrapper(sourceStride) * staticStride);
}
// The type is now known.
return MemRefType::get(
staticSizes, sourceMemRefType.getElementType(),
makeStridedLinearLayoutMap(targetStrides, targetOffset,
sourceMemRefType.getContext()),
sourceMemRefType.getMemorySpace());
}
/// Print a subview op of the form:
/// ```
/// `subview` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` strided-memref-type `to` strided-memref-type
/// ```
static void print(OpAsmPrinter &p, SubViewOp op) {
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' ';
p << op.source();
printOffsetsSizesAndStrides(p, op);
p << " : " << op.source().getType() << " to " << op.getType();
}
/// Parse a subview op of the form:
/// ```
/// `subview` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` strided-memref-type `to` strided-memref-type
/// ```
static ParseResult parseSubViewOp(OpAsmParser &parser, OperationState &result) {
OpAsmParser::OperandType srcInfo;
if (parser.parseOperand(srcInfo))
return failure();
Type srcType, dstType;
auto preResolutionFn = [&](OpAsmParser &parser, OperationState &result) {
return failure(parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.parseKeywordType("to", dstType) ||
parser.resolveOperand(srcInfo, srcType, result.operands));
};
if (failed(parseOffsetsSizesAndStrides(parser, result,
/*segmentSizes=*/{1}, // source memref
preResolutionFn)))
return failure();
return parser.addTypeToList(dstType, result.types);
}
// Build a SubViewOp with mixed static and dynamic entries and custom result
// type. If the type passed is nullptr, it is inferred.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
ShapedType::kDynamicStrideOrOffset);
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
ShapedType::kDynamicSize);
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
ShapedType::kDynamicStrideOrOffset);
auto sourceMemRefType = source.getType().cast<MemRefType>();
// Structuring implementation this way avoids duplication between builders.
if (!resultType) {
resultType = SubViewOp::inferResultType(sourceMemRefType, staticOffsets,
staticSizes, staticStrides)
.cast<MemRefType>();
}
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
result.addAttributes(attrs);
}
// Build a SubViewOp with mixed static and dynamic entries and inferred result
// type.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
build(b, result, MemRefType(), source, offsets, sizes, strides, attrs);
}
// Build a SubViewOp with static entries and inferred result type.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
ArrayRef<int64_t> offsets, ArrayRef<int64_t> sizes,
ArrayRef<int64_t> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
llvm::map_range(offsets, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
SmallVector<OpFoldResult> sizeValues =
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
build(b, result, source, offsetValues, sizeValues, strideValues, attrs);
}
// Build a SubViewOp with dynamic entries and custom result type. If the
// type passed is nullptr, it is inferred.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
ArrayRef<int64_t> offsets, ArrayRef<int64_t> sizes,
ArrayRef<int64_t> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
llvm::map_range(offsets, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
SmallVector<OpFoldResult> sizeValues =
llvm::to_vector<4>(llvm::map_range(sizes, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [&](int64_t v) -> OpFoldResult {
return b.getI64IntegerAttr(v);
}));
build(b, result, resultType, source, offsetValues, sizeValues, strideValues,
attrs);
}
// Build a SubViewOp with dynamic entries and custom result type. If the type
// passed is nullptr, it is inferred.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result,
MemRefType resultType, Value source,
ValueRange offsets, ValueRange sizes,
ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
}
// Build a SubViewOp with dynamic entries and inferred result type.
void mlir::SubViewOp::build(OpBuilder &b, OperationState &result, Value source,
ValueRange offsets, ValueRange sizes,
ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
build(b, result, MemRefType(), source, offsets, sizes, strides, attrs);
}
/// For ViewLikeOpInterface.
Value SubViewOp::getViewSource() { return source(); }
llvm::Optional<SmallVector<bool, 4>>
mlir::computeRankReductionMask(ArrayRef<int64_t> originalShape,
ArrayRef<int64_t> reducedShape) {
size_t originalRank = originalShape.size(), reducedRank = reducedShape.size();
SmallVector<bool, 4> mask(originalRank);
unsigned reducedIdx = 0;
for (unsigned originalIdx = 0; originalIdx < originalRank; ++originalIdx) {
// Skip matching dims greedily.
mask[originalIdx] =
(reducedIdx < reducedRank) &&
(originalShape[originalIdx] == reducedShape[reducedIdx]);
if (mask[originalIdx])
reducedIdx++;
// 1 is the only non-matching allowed.
else if (originalShape[originalIdx] != 1)
return {};
}
if (reducedIdx != reducedRank)
return {};
return mask;
}
enum SubViewVerificationResult {
Success,
RankTooLarge,
SizeMismatch,
StrideMismatch,
ElemTypeMismatch,
MemSpaceMismatch,
AffineMapMismatch
};
/// Checks if `original` Type type can be rank reduced to `reduced` type.
/// This function is slight variant of `is subsequence` algorithm where
/// not matching dimension must be 1.
static SubViewVerificationResult isRankReducedType(Type originalType,
Type reducedType) {
if (originalType == reducedType)
return SubViewVerificationResult::Success;
if (!originalType.isa<RankedTensorType>() && !originalType.isa<MemRefType>())
return SubViewVerificationResult::Success;
if (originalType.isa<RankedTensorType>() &&
!reducedType.isa<RankedTensorType>())
return SubViewVerificationResult::Success;
if (originalType.isa<MemRefType>() && !reducedType.isa<MemRefType>())
return SubViewVerificationResult::Success;
ShapedType originalShapedType = originalType.cast<ShapedType>();
ShapedType reducedShapedType = reducedType.cast<ShapedType>();
// Rank and size logic is valid for all ShapedTypes.
ArrayRef<int64_t> originalShape = originalShapedType.getShape();
ArrayRef<int64_t> reducedShape = reducedShapedType.getShape();
unsigned originalRank = originalShape.size(),
reducedRank = reducedShape.size();
if (reducedRank > originalRank)
return SubViewVerificationResult::RankTooLarge;
auto optionalMask = computeRankReductionMask(originalShape, reducedShape);
// Sizes cannot be matched in case empty vector is returned.
if (!optionalMask.hasValue())
return SubViewVerificationResult::SizeMismatch;
// We are done for the tensor case.
if (originalType.isa<RankedTensorType>())
return SubViewVerificationResult::Success;
// Strided layout logic is relevant for MemRefType only.
MemRefType original = originalType.cast<MemRefType>();
MemRefType reduced = reducedType.cast<MemRefType>();
MLIRContext *c = original.getContext();
int64_t originalOffset, reducedOffset;
SmallVector<int64_t, 4> originalStrides, reducedStrides, keepStrides;
SmallVector<bool, 4> keepMask = optionalMask.getValue();
getStridesAndOffset(original, originalStrides, originalOffset);
getStridesAndOffset(reduced, reducedStrides, reducedOffset);
// Filter strides based on the mask and check that they are the same
// as reduced ones.
unsigned reducedIdx = 0;
for (unsigned originalIdx = 0; originalIdx < originalRank; ++originalIdx) {
if (keepMask[originalIdx]) {
if (originalStrides[originalIdx] != reducedStrides[reducedIdx++])
return SubViewVerificationResult::StrideMismatch;
keepStrides.push_back(originalStrides[originalIdx]);
}
}
if (original.getElementType() != reduced.getElementType())
return SubViewVerificationResult::ElemTypeMismatch;
if (original.getMemorySpace() != reduced.getMemorySpace())
return SubViewVerificationResult::MemSpaceMismatch;
auto reducedMap = makeStridedLinearLayoutMap(keepStrides, originalOffset, c);
if (!reduced.getAffineMaps().empty() &&
reducedMap != reduced.getAffineMaps().front())
return SubViewVerificationResult::AffineMapMismatch;
return SubViewVerificationResult::Success;
}
template <typename OpTy>
static LogicalResult produceSubViewErrorMsg(SubViewVerificationResult result,
OpTy op, Type expectedType) {
auto memrefType = expectedType.cast<ShapedType>();
switch (result) {
case SubViewVerificationResult::Success:
return success();
case SubViewVerificationResult::RankTooLarge:
return op.emitError("expected result rank to be smaller or equal to ")
<< "the source rank.";
case SubViewVerificationResult::SizeMismatch:
return op.emitError("expected result type to be ")
<< expectedType
<< " or a rank-reduced version. (mismatch of result sizes)";
case SubViewVerificationResult::StrideMismatch:
return op.emitError("expected result type to be ")
<< expectedType
<< " or a rank-reduced version. (mismatch of result strides)";
case SubViewVerificationResult::ElemTypeMismatch:
return op.emitError("expected result element type to be ")
<< memrefType.getElementType();
case SubViewVerificationResult::MemSpaceMismatch:
return op.emitError("expected result and source memory spaces to match.");
case SubViewVerificationResult::AffineMapMismatch:
return op.emitError("expected result type to be ")
<< expectedType
<< " or a rank-reduced version. (mismatch of result affine map)";
}
llvm_unreachable("unexpected subview verification result");
}
/// Verifier for SubViewOp.
static LogicalResult verify(SubViewOp op) {
MemRefType baseType = op.getSourceType();
MemRefType subViewType = op.getType();
// The base memref and the view memref should be in the same memory space.
if (baseType.getMemorySpace() != subViewType.getMemorySpace())
return op.emitError("different memory spaces specified for base memref "
"type ")
<< baseType << " and subview memref type " << subViewType;
// Verify that the base memref type has a strided layout map.
if (!isStrided(baseType))
return op.emitError("base type ") << baseType << " is not strided";
// Verify result type against inferred type.
auto expectedType = SubViewOp::inferResultType(
baseType, extractFromI64ArrayAttr(op.static_offsets()),
extractFromI64ArrayAttr(op.static_sizes()),
extractFromI64ArrayAttr(op.static_strides()));
auto result = isRankReducedType(expectedType, subViewType);
return produceSubViewErrorMsg(result, op, expectedType);
}
raw_ostream &mlir::operator<<(raw_ostream &os, Range &range) {
return os << "range " << range.offset << ":" << range.size << ":"
<< range.stride;
}
/// Return the list of Range (i.e. offset, size, stride). Each Range
/// entry contains either the dynamic value or a ConstantIndexOp constructed
/// with `b` at location `loc`.
SmallVector<Range, 8> mlir::getOrCreateRanges(OffsetSizeAndStrideOpInterface op,
OpBuilder &b, Location loc) {
std::array<unsigned, 3> ranks = op.getArrayAttrRanks();
assert(ranks[0] == ranks[1] && "expected offset and sizes of equal ranks");
assert(ranks[1] == ranks[2] && "expected sizes and strides of equal ranks");
SmallVector<Range, 8> res;
unsigned rank = ranks[0];
res.reserve(rank);
for (unsigned idx = 0; idx < rank; ++idx) {
Value offset =
op.isDynamicOffset(idx)
? op.getDynamicOffset(idx)
: b.create<ConstantIndexOp>(loc, op.getStaticOffset(idx));
Value size = op.isDynamicSize(idx)
? op.getDynamicSize(idx)
: b.create<ConstantIndexOp>(loc, op.getStaticSize(idx));
Value stride =
op.isDynamicStride(idx)
? op.getDynamicStride(idx)
: b.create<ConstantIndexOp>(loc, op.getStaticStride(idx));
res.emplace_back(Range{offset, size, stride});
}
return res;
}
namespace {
/// Detects the `values` produced by a ConstantIndexOp and places the new
/// constant in place of the corresponding sentinel value.
void canonicalizeSubViewPart(SmallVectorImpl<OpFoldResult> &values,
llvm::function_ref<bool(int64_t)> isDynamic) {
for (OpFoldResult &ofr : values) {
if (ofr.is<Attribute>())
continue;
// Newly static, move from Value to constant.
if (auto cstOp = ofr.dyn_cast<Value>().getDefiningOp<ConstantIndexOp>())
ofr = OpBuilder(cstOp).getIndexAttr(cstOp.getValue());
}
}
static void replaceWithNewOp(PatternRewriter &rewriter, SubViewOp op,
SubViewOp newOp) {
rewriter.replaceOpWithNewOp<MemRefCastOp>(op, newOp, op.getType());
}
static void replaceWithNewOp(PatternRewriter &rewriter, SubTensorOp op,
SubTensorOp newOp) {
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, op.getType(), newOp);
}
/// Pattern to rewrite a subview op with constant arguments.
template <typename OpType>
class OpWithOffsetSizesAndStridesConstantArgumentFolder final
: public OpRewritePattern<OpType> {
public:
using OpRewritePattern<OpType>::OpRewritePattern;
LogicalResult matchAndRewrite(OpType op,
PatternRewriter &rewriter) const override {
// No constant operand, just return;
if (llvm::none_of(op.getOperands(), [](Value operand) {
return matchPattern(operand, m_ConstantIndex());
}))
return failure();
// At least one of offsets/sizes/strides is a new constant.
// Form the new list of operands and constant attributes from the existing.
SmallVector<OpFoldResult> mixedOffsets(op.getMixedOffsets());
SmallVector<OpFoldResult> mixedSizes(op.getMixedSizes());
SmallVector<OpFoldResult> mixedStrides(op.getMixedStrides());
canonicalizeSubViewPart(mixedOffsets, ShapedType::isDynamicStrideOrOffset);
canonicalizeSubViewPart(mixedSizes, ShapedType::isDynamic);
canonicalizeSubViewPart(mixedStrides, ShapedType::isDynamicStrideOrOffset);
// Create the new op in canonical form.
auto newOp = rewriter.create<OpType>(op.getLoc(), op.source(), mixedOffsets,
mixedSizes, mixedStrides);
replaceWithNewOp(rewriter, op, newOp);
return success();
}
};
} // end anonymous namespace
/// Determines whether MemRefCastOp casts to a more dynamic version of the
/// source memref. This is useful to to fold a memref_cast into a consuming op
/// and implement canonicalization patterns for ops in different dialects that
/// may consume the results of memref_cast operations. Such foldable memref_cast
/// operations are typically inserted as `view` and `subview` ops are
/// canonicalized, to preserve the type compatibility of their uses.
///
/// Returns true when all conditions are met:
/// 1. source and result are ranked memrefs with strided semantics and same
/// element type and rank.
/// 2. each of the source's size, offset or stride has more static information
/// than the corresponding result's size, offset or stride.
///
/// Example 1:
/// ```mlir
/// %1 = memref_cast %0 : memref<8x16xf32> to memref<?x?xf32>
/// %2 = consumer %1 ... : memref<?x?xf32> ...
/// ```
///
/// may fold into:
///
/// ```mlir
/// %2 = consumer %0 ... : memref<8x16xf32> ...
/// ```
///
/// Example 2:
/// ```
/// %1 = memref_cast %0 : memref<?x16xf32, affine_map<(i, j)->(16 * i + j)>>
/// to memref<?x?xf32>
/// consumer %1 : memref<?x?xf32> ...
/// ```
///
/// may fold into:
///
/// ```
/// consumer %0 ... : memref<?x16xf32, affine_map<(i, j)->(16 * i + j)>>
/// ```
bool mlir::canFoldIntoConsumerOp(MemRefCastOp castOp) {
MemRefType sourceType = castOp.source().getType().dyn_cast<MemRefType>();
MemRefType resultType = castOp.getType().dyn_cast<MemRefType>();
// Requires ranked MemRefType.
if (!sourceType || !resultType)
return false;
// Requires same elemental type.
if (sourceType.getElementType() != resultType.getElementType())
return false;
// Requires same rank.
if (sourceType.getRank() != resultType.getRank())
return false;
// Only fold casts between strided memref forms.
int64_t sourceOffset, resultOffset;
SmallVector<int64_t, 4> sourceStrides, resultStrides;
if (failed(getStridesAndOffset(sourceType, sourceStrides, sourceOffset)) ||
failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
return false;
// If cast is towards more static sizes along any dimension, don't fold.
for (auto it : llvm::zip(sourceType.getShape(), resultType.getShape())) {
auto ss = std::get<0>(it), st = std::get<1>(it);
if (ss != st)
if (MemRefType::isDynamic(ss) && !MemRefType::isDynamic(st))
return false;
}
// If cast is towards more static offset along any dimension, don't fold.
if (sourceOffset != resultOffset)
if (MemRefType::isDynamicStrideOrOffset(sourceOffset) &&
!MemRefType::isDynamicStrideOrOffset(resultOffset))
return false;
// If cast is towards more static strides along any dimension, don't fold.
for (auto it : llvm::zip(sourceStrides, resultStrides)) {
auto ss = std::get<0>(it), st = std::get<1>(it);
if (ss != st)
if (MemRefType::isDynamicStrideOrOffset(ss) &&
!MemRefType::isDynamicStrideOrOffset(st))
return false;
}
return true;
}
namespace {
/// Pattern to rewrite a subview op with MemRefCast arguments.
/// This essentially pushes memref_cast past its consuming subview when
/// `canFoldIntoConsumerOp` is true.
///
/// Example:
/// ```
/// %0 = memref_cast %V : memref<16x16xf32> to memref<?x?xf32>
/// %1 = subview %0[0, 0][3, 4][1, 1] :
/// memref<?x?xf32> to memref<3x4xf32, offset:?, strides:[?, 1]>
/// ```
/// is rewritten into:
/// ```
/// %0 = subview %V: memref<16x16xf32> to memref<3x4xf32, #[[map0]]>
/// %1 = memref_cast %0: memref<3x4xf32, offset:0, strides:[16, 1]> to
/// memref<3x4xf32, offset:?, strides:[?, 1]>
/// ```
class SubViewOpMemRefCastFolder final : public OpRewritePattern<SubViewOp> {
public:
using OpRewritePattern<SubViewOp>::OpRewritePattern;
LogicalResult matchAndRewrite(SubViewOp subViewOp,
PatternRewriter &rewriter) const override {
// Any constant operand, just return to let SubViewOpConstantFolder kick in.
if (llvm::any_of(subViewOp.getOperands(), [](Value operand) {
return matchPattern(operand, m_ConstantIndex());
}))
return failure();
auto castOp = subViewOp.source().getDefiningOp<MemRefCastOp>();
if (!castOp)
return failure();
if (!canFoldIntoConsumerOp(castOp))
return failure();
/// Deduce the resultType of the SubViewOp using `inferSubViewResultType` on
/// the cast source operand type and the SubViewOp static information. This
/// is the resulting type if the MemRefCastOp were folded.
Type resultType = SubViewOp::inferResultType(
castOp.source().getType().cast<MemRefType>(),
extractFromI64ArrayAttr(subViewOp.static_offsets()),
extractFromI64ArrayAttr(subViewOp.static_sizes()),
extractFromI64ArrayAttr(subViewOp.static_strides()));
Value newSubView = rewriter.create<SubViewOp>(
subViewOp.getLoc(), resultType, castOp.source(), subViewOp.offsets(),
subViewOp.sizes(), subViewOp.strides(), subViewOp.static_offsets(),
subViewOp.static_sizes(), subViewOp.static_strides());
rewriter.replaceOpWithNewOp<MemRefCastOp>(subViewOp, subViewOp.getType(),
newSubView);
return success();
}
};
} // namespace
void SubViewOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<OpWithOffsetSizesAndStridesConstantArgumentFolder<SubViewOp>,
SubViewOpMemRefCastFolder>(context);
}
OpFoldResult SubViewOp::fold(ArrayRef<Attribute> operands) {
if (getResult().getType().cast<ShapedType>().getRank() == 0 &&
source().getType().cast<ShapedType>().getRank() == 0)
return getViewSource();
return {};
}
//===----------------------------------------------------------------------===//
// SubTensorOp
//===----------------------------------------------------------------------===//
/// Print a subtensor op of the form:
/// ```
/// `subtensor` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` ranked-tensor-type `to` ranked-tensor-type
/// ```
static void print(OpAsmPrinter &p, SubTensorOp op) {
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' ';
p << op.source();
printOffsetsSizesAndStrides(p, op);
p << " : " << op.getSourceType() << " to " << op.getType();
}
/// Parse a subtensor op of the form:
/// ```
/// `subtensor` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` ranked-tensor-type `to` ranked-tensor-type
/// ```
static ParseResult parseSubTensorOp(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType srcInfo;
if (parser.parseOperand(srcInfo))
return failure();
Type srcType, dstType;
auto preResolutionFn = [&](OpAsmParser &parser, OperationState &result) {
return failure(parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.parseKeywordType("to", dstType) ||
parser.resolveOperand(srcInfo, srcType, result.operands));
};
if (failed(parseOffsetsSizesAndStrides(parser, result,
/*segmentSizes=*/{1}, // source tensor
preResolutionFn)))
return failure();
return parser.addTypeToList(dstType, result.types);
}
/// A subtensor result type can be fully inferred from the source type and the
/// static representation of offsets, sizes and strides. Special sentinels
/// encode the dynamic case.
Type SubTensorOp::inferResultType(RankedTensorType sourceRankedTensorType,
ArrayRef<int64_t> staticOffsets,
ArrayRef<int64_t> staticSizes,
ArrayRef<int64_t> staticStrides) {
unsigned rank = sourceRankedTensorType.getRank();
(void)rank;
assert(staticOffsets.size() == rank &&
"unexpected staticOffsets size mismatch");
assert(staticSizes.size() == rank && "unexpected staticSizes size mismatch");
assert(staticStrides.size() == rank &&
"unexpected staticStrides size mismatch");
return RankedTensorType::get(staticSizes,
sourceRankedTensorType.getElementType());
}
// Build a SubTensorOp with mixed static and dynamic entries and custom result
// type. If the type passed is nullptr, it is inferred.
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
RankedTensorType resultType, Value source,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
ShapedType::kDynamicStrideOrOffset);
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
ShapedType::kDynamicSize);
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
ShapedType::kDynamicStrideOrOffset);
auto sourceRankedTensorType = source.getType().cast<RankedTensorType>();
// Structuring implementation this way avoids duplication between builders.
if (!resultType) {
resultType =
SubTensorOp::inferResultType(sourceRankedTensorType, staticOffsets,
staticSizes, staticStrides)
.cast<RankedTensorType>();
}
build(b, result, resultType, source, dynamicOffsets, dynamicSizes,
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
result.addAttributes(attrs);
}
// Build a SubTensorOp with mixed static and dynamic entries and inferred result
// type.
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
Value source, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}
// Build a SubTensorOp with dynamic entries and custom result type. If the type
// passed is nullptr, it is inferred.
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
RankedTensorType resultType, Value source,
ValueRange offsets, ValueRange sizes,
ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
build(b, result, resultType, source, offsetValues, sizeValues, strideValues);
}
// Build a SubTensorOp with dynamic entries and inferred result type.
void mlir::SubTensorOp::build(OpBuilder &b, OperationState &result,
Value source, ValueRange offsets,
ValueRange sizes, ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
}
/// Verifier for SubTensorOp.
static LogicalResult verify(SubTensorOp op) {
// Verify result type against inferred type.
auto expectedType = SubTensorOp::inferResultType(
op.getSourceType(), extractFromI64ArrayAttr(op.static_offsets()),
extractFromI64ArrayAttr(op.static_sizes()),
extractFromI64ArrayAttr(op.static_strides()));
auto result = isRankReducedType(expectedType, op.getType());
return produceSubViewErrorMsg(result, op, expectedType);
}
void SubTensorOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results
.insert<OpWithOffsetSizesAndStridesConstantArgumentFolder<SubTensorOp>>(
context);
}
//===----------------------------------------------------------------------===//
// SubTensorInsertOp
//===----------------------------------------------------------------------===//
/// Print a subtensor_insert op of the form:
/// ```
/// `subtensor_insert` ssa-name `into` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` ranked-tensor-type `into` ranked-tensor-type
/// ```
static void print(OpAsmPrinter &p, SubTensorInsertOp op) {
int stdDotLen = StandardOpsDialect::getDialectNamespace().size() + 1;
p << op->getName().getStringRef().drop_front(stdDotLen) << ' ';
p << op.source() << " into " << op.dest();
printOffsetsSizesAndStrides(p, op);
p << " : " << op.getSourceType() << " into " << op.getType();
}
/// Parse a subtensor_insert op of the form:
/// ```
/// `subtensor_insert` ssa-name `into` ssa-name
/// `[` offset-list `]` `[` size-list `]` `[` stride-list `]`
/// `:` ranked-tensor-type `into` ranked-tensor-type
/// ```
static ParseResult parseSubTensorInsertOp(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType srcInfo, dstInfo;
if (parser.parseOperand(srcInfo) || parser.parseKeyword("into") ||
parser.parseOperand(dstInfo))
return failure();
Type srcType, dstType;
auto preResolutionFn = [&](OpAsmParser &parser, OperationState &result) {
return failure(parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.parseKeywordType("into", dstType) ||
parser.resolveOperand(srcInfo, srcType, result.operands) ||
parser.resolveOperand(dstInfo, dstType, result.operands));
};
if (failed(parseOffsetsSizesAndStrides(
parser, result,
/*segmentSizes=*/{1, 1}, // source tensor, destination tensor
preResolutionFn)))
return failure();
return parser.addTypeToList(dstType, result.types);
}
// Build a SubTensorInsertOp with mixed static and dynamic entries.
void mlir::SubTensorInsertOp::build(OpBuilder &b, OperationState &result,
Value source, Value dest,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
ShapedType::kDynamicStrideOrOffset);
dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
ShapedType::kDynamicSize);
dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
ShapedType::kDynamicStrideOrOffset);
build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
dynamicStrides, b.getI64ArrayAttr(staticOffsets),
b.getI64ArrayAttr(staticSizes), b.getI64ArrayAttr(staticStrides));
result.addAttributes(attrs);
}
// Build a SubTensorInsertOp with dynamic entries.
void mlir::SubTensorInsertOp::build(OpBuilder &b, OperationState &result,
Value source, Value dest,
ValueRange offsets, ValueRange sizes,
ValueRange strides,
ArrayRef<NamedAttribute> attrs) {
SmallVector<OpFoldResult> offsetValues = llvm::to_vector<4>(
llvm::map_range(offsets, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> sizeValues = llvm::to_vector<4>(
llvm::map_range(sizes, [](Value v) -> OpFoldResult { return v; }));
SmallVector<OpFoldResult> strideValues = llvm::to_vector<4>(
llvm::map_range(strides, [](Value v) -> OpFoldResult { return v; }));
build(b, result, source, dest, offsetValues, sizeValues, strideValues);
}
/// Verifier for SubViewOp.
static LogicalResult verify(SubTensorInsertOp op) {
if (op.getType() != op.dest().getType())
return op.emitError("expected result type to be ") << op.dest().getType();
return success();
}
//===----------------------------------------------------------------------===//
// TensorLoadOp
//===----------------------------------------------------------------------===//
OpFoldResult TensorLoadOp::fold(ArrayRef<Attribute>) {
if (auto tensorToMemref = memref().getDefiningOp<TensorToMemrefOp>())
return tensorToMemref.tensor();
return {};
}
//===----------------------------------------------------------------------===//
// TensorToMemrefOp
//===----------------------------------------------------------------------===//
OpFoldResult TensorToMemrefOp::fold(ArrayRef<Attribute>) {
if (auto tensorLoad = tensor().getDefiningOp<TensorLoadOp>())
if (tensorLoad.memref().getType() == getType())
return tensorLoad.memref();
return {};
}
//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//
/// Build a strided memref type by applying `permutationMap` tp `memRefType`.
static MemRefType inferTransposeResultType(MemRefType memRefType,
AffineMap permutationMap) {
auto rank = memRefType.getRank();
auto originalSizes = memRefType.getShape();
// Compute permuted sizes.
SmallVector<int64_t, 4> sizes(rank, 0);
for (auto en : llvm::enumerate(permutationMap.getResults()))
sizes[en.index()] =
originalSizes[en.value().cast<AffineDimExpr>().getPosition()];
// Compute permuted strides.
int64_t offset;
SmallVector<int64_t, 4> strides;
auto res = getStridesAndOffset(memRefType, strides, offset);
assert(succeeded(res) && strides.size() == static_cast<unsigned>(rank));
(void)res;
auto map =
makeStridedLinearLayoutMap(strides, offset, memRefType.getContext());
map = permutationMap ? map.compose(permutationMap) : map;
return MemRefType::Builder(memRefType).setShape(sizes).setAffineMaps(map);
}
void TransposeOp::build(OpBuilder &b, OperationState &result, Value in,
AffineMapAttr permutation,
ArrayRef<NamedAttribute> attrs) {
auto permutationMap = permutation.getValue();
assert(permutationMap);
auto memRefType = in.getType().cast<MemRefType>();
// Compute result type.
MemRefType resultType = inferTransposeResultType(memRefType, permutationMap);
build(b, result, resultType, in, attrs);
result.addAttribute(TransposeOp::getPermutationAttrName(), permutation);
}
// transpose $in $permutation attr-dict : type($in) `to` type(results)
static void print(OpAsmPrinter &p, TransposeOp op) {
p << "transpose " << op.in() << " " << op.permutation();
p.printOptionalAttrDict(op.getAttrs(),
{TransposeOp::getPermutationAttrName()});
p << " : " << op.in().getType() << " to " << op.getType();
}
static ParseResult parseTransposeOp(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType in;
AffineMap permutation;
MemRefType srcType, dstType;
if (parser.parseOperand(in) || parser.parseAffineMap(permutation) ||
parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.resolveOperand(in, srcType, result.operands) ||
parser.parseKeywordType("to", dstType) ||
parser.addTypeToList(dstType, result.types))
return failure();
result.addAttribute(TransposeOp::getPermutationAttrName(),
AffineMapAttr::get(permutation));
return success();
}
static LogicalResult verify(TransposeOp op) {
if (!op.permutation().isPermutation())
return op.emitOpError("expected a permutation map");
if (op.permutation().getNumDims() != op.getShapedType().getRank())
return op.emitOpError(
"expected a permutation map of same rank as the input");
auto srcType = op.in().getType().cast<MemRefType>();
auto dstType = op.getType().cast<MemRefType>();
auto transposedType = inferTransposeResultType(srcType, op.permutation());
if (dstType != transposedType)
return op.emitOpError("output type ")
<< dstType << " does not match transposed input type " << srcType
<< ", " << transposedType;
return success();
}
OpFoldResult TransposeOp::fold(ArrayRef<Attribute>) {
if (succeeded(foldMemRefCast(*this)))
return getResult();
return {};
}
//===----------------------------------------------------------------------===//
// TruncateIOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(TruncateIOp op) {
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
auto dstType = getElementTypeOrSelf(op.getType());
if (srcType.isa<IndexType>())
return op.emitError() << srcType << " is not a valid operand type";
if (dstType.isa<IndexType>())
return op.emitError() << dstType << " is not a valid result type";
if (srcType.cast<IntegerType>().getWidth() <=
dstType.cast<IntegerType>().getWidth())
return op.emitError("operand type ")
<< srcType << " must be wider than result type " << dstType;
return success();
}
//===----------------------------------------------------------------------===//
// UnsignedDivIOp
//===----------------------------------------------------------------------===//
OpFoldResult UnsignedDivIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "binary operation takes two operands");
// Don't fold if it would require a division by zero.
bool div0 = false;
auto result = constFoldBinaryOp<IntegerAttr>(operands, [&](APInt a, APInt b) {
if (div0 || !b) {
div0 = true;
return a;
}
return a.udiv(b);
});
// Fold out division by one. Assumes all tensors of all ones are splats.
if (auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>()) {
if (rhs.getValue() == 1)
return lhs();
} else if (auto rhs = operands[1].dyn_cast_or_null<SplatElementsAttr>()) {
if (rhs.getSplatValue<IntegerAttr>().getValue() == 1)
return lhs();
}
return div0 ? Attribute() : result;
}
//===----------------------------------------------------------------------===//
// UnsignedRemIOp
//===----------------------------------------------------------------------===//
OpFoldResult UnsignedRemIOp::fold(ArrayRef<Attribute> operands) {
assert(operands.size() == 2 && "remi_unsigned takes two operands");
auto rhs = operands.back().dyn_cast_or_null<IntegerAttr>();
if (!rhs)
return {};
auto rhsValue = rhs.getValue();
// x % 1 = 0
if (rhsValue.isOneValue())
return IntegerAttr::get(rhs.getType(), APInt(rhsValue.getBitWidth(), 0));
// Don't fold if it requires division by zero.
if (rhsValue.isNullValue())
return {};
auto lhs = operands.front().dyn_cast_or_null<IntegerAttr>();
if (!lhs)
return {};
return IntegerAttr::get(lhs.getType(), lhs.getValue().urem(rhsValue));
}
//===----------------------------------------------------------------------===//
// ViewOp
//===----------------------------------------------------------------------===//
static ParseResult parseViewOp(OpAsmParser &parser, OperationState &result) {
OpAsmParser::OperandType srcInfo;
SmallVector<OpAsmParser::OperandType, 1> offsetInfo;
SmallVector<OpAsmParser::OperandType, 4> sizesInfo;
auto indexType = parser.getBuilder().getIndexType();
Type srcType, dstType;
llvm::SMLoc offsetLoc;
if (parser.parseOperand(srcInfo) || parser.getCurrentLocation(&offsetLoc) ||
parser.parseOperandList(offsetInfo, OpAsmParser::Delimiter::Square))
return failure();
if (offsetInfo.size() != 1)
return parser.emitError(offsetLoc) << "expects 1 offset operand";
return failure(
parser.parseOperandList(sizesInfo, OpAsmParser::Delimiter::Square) ||
parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonType(srcType) ||
parser.resolveOperand(srcInfo, srcType, result.operands) ||
parser.resolveOperands(offsetInfo, indexType, result.operands) ||
parser.resolveOperands(sizesInfo, indexType, result.operands) ||
parser.parseKeywordType("to", dstType) ||
parser.addTypeToList(dstType, result.types));
}
static void print(OpAsmPrinter &p, ViewOp op) {
p << op.getOperationName() << ' ' << op.getOperand(0) << '[';
p.printOperand(op.byte_shift());
p << "][" << op.sizes() << ']';
p.printOptionalAttrDict(op.getAttrs());
p << " : " << op.getOperand(0).getType() << " to " << op.getType();
}
static LogicalResult verify(ViewOp op) {
auto baseType = op.getOperand(0).getType().cast<MemRefType>();
auto viewType = op.getType();
// The base memref should have identity layout map (or none).
if (baseType.getAffineMaps().size() > 1 ||
(baseType.getAffineMaps().size() == 1 &&
!baseType.getAffineMaps()[0].isIdentity()))
return op.emitError("unsupported map for base memref type ") << baseType;
// The result memref should have identity layout map (or none).
if (viewType.getAffineMaps().size() > 1 ||
(viewType.getAffineMaps().size() == 1 &&
!viewType.getAffineMaps()[0].isIdentity()))
return op.emitError("unsupported map for result memref type ") << viewType;
// The base memref and the view memref should be in the same memory space.
if (baseType.getMemorySpace() != viewType.getMemorySpace())
return op.emitError("different memory spaces specified for base memref "
"type ")
<< baseType << " and view memref type " << viewType;
// Verify that we have the correct number of sizes for the result type.
unsigned numDynamicDims = viewType.getNumDynamicDims();
if (op.sizes().size() != numDynamicDims)
return op.emitError("incorrect number of size operands for type ")
<< viewType;
return success();
}
Value ViewOp::getViewSource() { return source(); }
namespace {
struct ViewOpShapeFolder : public OpRewritePattern<ViewOp> {
using OpRewritePattern<ViewOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ViewOp viewOp,
PatternRewriter &rewriter) const override {
// Return if none of the operands are constants.
if (llvm::none_of(viewOp.getOperands(), [](Value operand) {
return matchPattern(operand, m_ConstantIndex());
}))
return failure();
// Get result memref type.
auto memrefType = viewOp.getType();
// Get offset from old memref view type 'memRefType'.
int64_t oldOffset;
SmallVector<int64_t, 4> oldStrides;
if (failed(getStridesAndOffset(memrefType, oldStrides, oldOffset)))
return failure();
assert(oldOffset == 0 && "Expected 0 offset");
SmallVector<Value, 4> newOperands;
// Offset cannot be folded into result type.
// Fold any dynamic dim operands which are produced by a constant.
SmallVector<int64_t, 4> newShapeConstants;
newShapeConstants.reserve(memrefType.getRank());
unsigned dynamicDimPos = 0;
unsigned rank = memrefType.getRank();
for (unsigned dim = 0, e = rank; dim < e; ++dim) {
int64_t dimSize = memrefType.getDimSize(dim);
// If this is already static dimension, keep it.
if (!ShapedType::isDynamic(dimSize)) {
newShapeConstants.push_back(dimSize);
continue;
}
auto *defOp = viewOp.sizes()[dynamicDimPos].getDefiningOp();
if (auto constantIndexOp = dyn_cast_or_null<ConstantIndexOp>(defOp)) {
// Dynamic shape dimension will be folded.
newShapeConstants.push_back(constantIndexOp.getValue());
} else {
// Dynamic shape dimension not folded; copy operand from old memref.
newShapeConstants.push_back(dimSize);
newOperands.push_back(viewOp.sizes()[dynamicDimPos]);
}
dynamicDimPos++;
}
// Create new memref type with constant folded dims.
MemRefType newMemRefType =
MemRefType::Builder(memrefType).setShape(newShapeConstants);
// Nothing new, don't fold.
if (newMemRefType == memrefType)
return failure();
// Create new ViewOp.
auto newViewOp = rewriter.create<ViewOp>(viewOp.getLoc(), newMemRefType,
viewOp.getOperand(0),
viewOp.byte_shift(), newOperands);
// Insert a cast so we have the same type as the old memref type.
rewriter.replaceOpWithNewOp<MemRefCastOp>(viewOp, newViewOp,
viewOp.getType());
return success();
}
};
struct ViewOpMemrefCastFolder : public OpRewritePattern<ViewOp> {
using OpRewritePattern<ViewOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ViewOp viewOp,
PatternRewriter &rewriter) const override {
Value memrefOperand = viewOp.getOperand(0);
MemRefCastOp memrefCastOp = memrefOperand.getDefiningOp<MemRefCastOp>();
if (!memrefCastOp)
return failure();
Value allocOperand = memrefCastOp.getOperand();
AllocOp allocOp = allocOperand.getDefiningOp<AllocOp>();
if (!allocOp)
return failure();
rewriter.replaceOpWithNewOp<ViewOp>(viewOp, viewOp.getType(), allocOperand,
viewOp.byte_shift(), viewOp.sizes());
return success();
}
};
} // end anonymous namespace
void ViewOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
MLIRContext *context) {
results.insert<ViewOpShapeFolder, ViewOpMemrefCastFolder>(context);
}
//===----------------------------------------------------------------------===//
// XOrOp
//===----------------------------------------------------------------------===//
OpFoldResult XOrOp::fold(ArrayRef<Attribute> operands) {
/// xor(x, 0) -> x
if (matchPattern(rhs(), m_Zero()))
return lhs();
/// xor(x,x) -> 0
if (lhs() == rhs())
return Builder(getContext()).getZeroAttr(getType());
return constFoldBinaryOp<IntegerAttr>(operands,
[](APInt a, APInt b) { return a ^ b; });
}
//===----------------------------------------------------------------------===//
// ZeroExtendIOp
//===----------------------------------------------------------------------===//
static LogicalResult verify(ZeroExtendIOp op) {
auto srcType = getElementTypeOrSelf(op.getOperand().getType());
auto dstType = getElementTypeOrSelf(op.getType());
if (srcType.isa<IndexType>())
return op.emitError() << srcType << " is not a valid operand type";
if (dstType.isa<IndexType>())
return op.emitError() << dstType << " is not a valid result type";
if (srcType.cast<IntegerType>().getWidth() >=
dstType.cast<IntegerType>().getWidth())
return op.emitError("result type ")
<< dstType << " must be wider than operand type " << srcType;
return success();
}
//===----------------------------------------------------------------------===//
// TableGen'd op method definitions
//===----------------------------------------------------------------------===//
#define GET_OP_CLASSES
#include "mlir/Dialect/StandardOps/IR/Ops.cpp.inc"