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AffineOps.cpp
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AffineOps.cpp
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//===- AffineOps.cpp - MLIR Affine 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/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Affine/IR/AffineValueMap.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/Function.h"
#include "mlir/IR/IntegerSet.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/InliningUtils.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallBitVector.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/Debug.h"
using namespace mlir;
using llvm::dbgs;
#define DEBUG_TYPE "affine-analysis"
//===----------------------------------------------------------------------===//
// AffineDialect Interfaces
//===----------------------------------------------------------------------===//
namespace {
/// This class defines the interface for handling inlining with affine
/// operations.
struct AffineInlinerInterface : public DialectInlinerInterface {
using DialectInlinerInterface::DialectInlinerInterface;
//===--------------------------------------------------------------------===//
// Analysis Hooks
//===--------------------------------------------------------------------===//
/// Returns true if the given region 'src' can be inlined into the region
/// 'dest' that is attached to an operation registered to the current dialect.
bool isLegalToInline(Region *dest, Region *src,
BlockAndValueMapping &valueMapping) const final {
// Conservatively don't allow inlining into affine structures.
return false;
}
/// Returns true if the given operation 'op', that is registered to this
/// dialect, can be inlined into the given region, false otherwise.
bool isLegalToInline(Operation *op, Region *region,
BlockAndValueMapping &valueMapping) const final {
// Always allow inlining affine operations into the top-level region of a
// function. There are some edge cases when inlining *into* affine
// structures, but that is handled in the other 'isLegalToInline' hook
// above.
// TODO: We should be able to inline into other regions than functions.
return isa<FuncOp>(region->getParentOp());
}
/// Affine regions should be analyzed recursively.
bool shouldAnalyzeRecursively(Operation *op) const final { return true; }
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// AffineDialect
//===----------------------------------------------------------------------===//
void AffineDialect::initialize() {
addOperations<AffineDmaStartOp, AffineDmaWaitOp,
#define GET_OP_LIST
#include "mlir/Dialect/Affine/IR/AffineOps.cpp.inc"
>();
addInterfaces<AffineInlinerInterface>();
}
/// Materialize a single constant operation from a given attribute value with
/// the desired resultant type.
Operation *AffineDialect::materializeConstant(OpBuilder &builder,
Attribute value, Type type,
Location loc) {
return builder.create<ConstantOp>(loc, type, value);
}
/// A utility function to check if a value is defined at the top level of an
/// op with trait `AffineScope`. If the value is defined in an unlinked region,
/// conservatively assume it is not top-level. A value of index type defined at
/// the top level is always a valid symbol.
bool mlir::isTopLevelValue(Value value) {
if (auto arg = value.dyn_cast<BlockArgument>()) {
// The block owning the argument may be unlinked, e.g. when the surrounding
// region has not yet been attached to an Op, at which point the parent Op
// is null.
Operation *parentOp = arg.getOwner()->getParentOp();
return parentOp && parentOp->hasTrait<OpTrait::AffineScope>();
}
// The defining Op may live in an unlinked block so its parent Op may be null.
Operation *parentOp = value.getDefiningOp()->getParentOp();
return parentOp && parentOp->hasTrait<OpTrait::AffineScope>();
}
/// A utility function to check if a value is defined at the top level of
/// `region` or is an argument of `region`. A value of index type defined at the
/// top level of a `AffineScope` region is always a valid symbol for all
/// uses in that region.
static bool isTopLevelValue(Value value, Region *region) {
if (auto arg = value.dyn_cast<BlockArgument>())
return arg.getParentRegion() == region;
return value.getDefiningOp()->getParentRegion() == region;
}
/// Returns the closest region enclosing `op` that is held by an operation with
/// trait `AffineScope`; `nullptr` if there is no such region.
// TODO: getAffineScope should be publicly exposed for affine passes/utilities.
static Region *getAffineScope(Operation *op) {
auto *curOp = op;
while (auto *parentOp = curOp->getParentOp()) {
if (parentOp->hasTrait<OpTrait::AffineScope>())
return curOp->getParentRegion();
curOp = parentOp;
}
return nullptr;
}
// A Value can be used as a dimension id iff it meets one of the following
// conditions:
// *) It is valid as a symbol.
// *) It is an induction variable.
// *) It is the result of affine apply operation with dimension id arguments.
bool mlir::isValidDim(Value value) {
// The value must be an index type.
if (!value.getType().isIndex())
return false;
if (auto *defOp = value.getDefiningOp())
return isValidDim(value, getAffineScope(defOp));
// This value has to be a block argument for an op that has the
// `AffineScope` trait or for an affine.for or affine.parallel.
auto *parentOp = value.cast<BlockArgument>().getOwner()->getParentOp();
return parentOp && (parentOp->hasTrait<OpTrait::AffineScope>() ||
isa<AffineForOp, AffineParallelOp>(parentOp));
}
// Value can be used as a dimension id iff it meets one of the following
// conditions:
// *) It is valid as a symbol.
// *) It is an induction variable.
// *) It is the result of an affine apply operation with dimension id operands.
bool mlir::isValidDim(Value value, Region *region) {
// The value must be an index type.
if (!value.getType().isIndex())
return false;
// All valid symbols are okay.
if (isValidSymbol(value, region))
return true;
auto *op = value.getDefiningOp();
if (!op) {
// This value has to be a block argument for an affine.for or an
// affine.parallel.
auto *parentOp = value.cast<BlockArgument>().getOwner()->getParentOp();
return isa<AffineForOp, AffineParallelOp>(parentOp);
}
// Affine apply operation is ok if all of its operands are ok.
if (auto applyOp = dyn_cast<AffineApplyOp>(op))
return applyOp.isValidDim(region);
// The dim op is okay if its operand memref/tensor is defined at the top
// level.
if (auto dimOp = dyn_cast<DimOp>(op))
return isTopLevelValue(dimOp.memrefOrTensor());
return false;
}
/// Returns true if the 'index' dimension of the `memref` defined by
/// `memrefDefOp` is a statically shaped one or defined using a valid symbol
/// for `region`.
template <typename AnyMemRefDefOp>
static bool isMemRefSizeValidSymbol(AnyMemRefDefOp memrefDefOp, unsigned index,
Region *region) {
auto memRefType = memrefDefOp.getType();
// Statically shaped.
if (!memRefType.isDynamicDim(index))
return true;
// Get the position of the dimension among dynamic dimensions;
unsigned dynamicDimPos = memRefType.getDynamicDimIndex(index);
return isValidSymbol(*(memrefDefOp.getDynamicSizes().begin() + dynamicDimPos),
region);
}
/// Returns true if the result of the dim op is a valid symbol for `region`.
static bool isDimOpValidSymbol(DimOp dimOp, Region *region) {
// The dim op is okay if its operand memref/tensor is defined at the top
// level.
if (isTopLevelValue(dimOp.memrefOrTensor()))
return true;
// The dim op is also okay if its operand memref/tensor is a view/subview
// whose corresponding size is a valid symbol.
Optional<int64_t> index = dimOp.getConstantIndex();
assert(index.hasValue() &&
"expect only `dim` operations with a constant index");
int64_t i = index.getValue();
return TypeSwitch<Operation *, bool>(dimOp.memrefOrTensor().getDefiningOp())
.Case<ViewOp, SubViewOp, AllocOp>(
[&](auto op) { return isMemRefSizeValidSymbol(op, i, region); })
.Default([](Operation *) { return false; });
}
// A value can be used as a symbol (at all its use sites) iff it meets one of
// the following conditions:
// *) It is a constant.
// *) Its defining op or block arg appearance is immediately enclosed by an op
// with `AffineScope` trait.
// *) It is the result of an affine.apply operation with symbol operands.
// *) It is a result of the dim op on a memref whose corresponding size is a
// valid symbol.
bool mlir::isValidSymbol(Value value) {
// The value must be an index type.
if (!value.getType().isIndex())
return false;
// Check that the value is a top level value.
if (isTopLevelValue(value))
return true;
if (auto *defOp = value.getDefiningOp())
return isValidSymbol(value, getAffineScope(defOp));
return false;
}
/// A value can be used as a symbol for `region` iff it meets onf of the the
/// following conditions:
/// *) It is a constant.
/// *) It is the result of an affine apply operation with symbol arguments.
/// *) It is a result of the dim op on a memref whose corresponding size is
/// a valid symbol.
/// *) It is defined at the top level of 'region' or is its argument.
/// *) It dominates `region`'s parent op.
/// If `region` is null, conservatively assume the symbol definition scope does
/// not exist and only accept the values that would be symbols regardless of
/// the surrounding region structure, i.e. the first three cases above.
bool mlir::isValidSymbol(Value value, Region *region) {
// The value must be an index type.
if (!value.getType().isIndex())
return false;
// A top-level value is a valid symbol.
if (region && ::isTopLevelValue(value, region))
return true;
auto *defOp = value.getDefiningOp();
if (!defOp) {
// A block argument that is not a top-level value is a valid symbol if it
// dominates region's parent op.
if (region && !region->getParentOp()->isKnownIsolatedFromAbove())
if (auto *parentOpRegion = region->getParentOp()->getParentRegion())
return isValidSymbol(value, parentOpRegion);
return false;
}
// Constant operation is ok.
Attribute operandCst;
if (matchPattern(defOp, m_Constant(&operandCst)))
return true;
// Affine apply operation is ok if all of its operands are ok.
if (auto applyOp = dyn_cast<AffineApplyOp>(defOp))
return applyOp.isValidSymbol(region);
// Dim op results could be valid symbols at any level.
if (auto dimOp = dyn_cast<DimOp>(defOp))
return isDimOpValidSymbol(dimOp, region);
// Check for values dominating `region`'s parent op.
if (region && !region->getParentOp()->isKnownIsolatedFromAbove())
if (auto *parentRegion = region->getParentOp()->getParentRegion())
return isValidSymbol(value, parentRegion);
return false;
}
// Returns true if 'value' is a valid index to an affine operation (e.g.
// affine.load, affine.store, affine.dma_start, affine.dma_wait) where
// `region` provides the polyhedral symbol scope. Returns false otherwise.
static bool isValidAffineIndexOperand(Value value, Region *region) {
return isValidDim(value, region) || isValidSymbol(value, region);
}
/// Utility function to verify that a set of operands are valid dimension and
/// symbol identifiers. The operands should be laid out such that the dimension
/// operands are before the symbol operands. This function returns failure if
/// there was an invalid operand. An operation is provided to emit any necessary
/// errors.
template <typename OpTy>
static LogicalResult
verifyDimAndSymbolIdentifiers(OpTy &op, Operation::operand_range operands,
unsigned numDims) {
unsigned opIt = 0;
for (auto operand : operands) {
if (opIt++ < numDims) {
if (!isValidDim(operand, getAffineScope(op)))
return op.emitOpError("operand cannot be used as a dimension id");
} else if (!isValidSymbol(operand, getAffineScope(op))) {
return op.emitOpError("operand cannot be used as a symbol");
}
}
return success();
}
//===----------------------------------------------------------------------===//
// AffineApplyOp
//===----------------------------------------------------------------------===//
AffineValueMap AffineApplyOp::getAffineValueMap() {
return AffineValueMap(getAffineMap(), getOperands(), getResult());
}
static ParseResult parseAffineApplyOp(OpAsmParser &parser,
OperationState &result) {
auto &builder = parser.getBuilder();
auto indexTy = builder.getIndexType();
AffineMapAttr mapAttr;
unsigned numDims;
if (parser.parseAttribute(mapAttr, "map", result.attributes) ||
parseDimAndSymbolList(parser, result.operands, numDims) ||
parser.parseOptionalAttrDict(result.attributes))
return failure();
auto map = mapAttr.getValue();
if (map.getNumDims() != numDims ||
numDims + map.getNumSymbols() != result.operands.size()) {
return parser.emitError(parser.getNameLoc(),
"dimension or symbol index mismatch");
}
result.types.append(map.getNumResults(), indexTy);
return success();
}
static void print(OpAsmPrinter &p, AffineApplyOp op) {
p << AffineApplyOp::getOperationName() << " " << op.mapAttr();
printDimAndSymbolList(op.operand_begin(), op.operand_end(),
op.getAffineMap().getNumDims(), p);
p.printOptionalAttrDict(op.getAttrs(), /*elidedAttrs=*/{"map"});
}
static LogicalResult verify(AffineApplyOp op) {
// Check input and output dimensions match.
auto map = op.map();
// Verify that operand count matches affine map dimension and symbol count.
if (op.getNumOperands() != map.getNumDims() + map.getNumSymbols())
return op.emitOpError(
"operand count and affine map dimension and symbol count must match");
// Verify that the map only produces one result.
if (map.getNumResults() != 1)
return op.emitOpError("mapping must produce one value");
return success();
}
// The result of the affine apply operation can be used as a dimension id if all
// its operands are valid dimension ids.
bool AffineApplyOp::isValidDim() {
return llvm::all_of(getOperands(),
[](Value op) { return mlir::isValidDim(op); });
}
// The result of the affine apply operation can be used as a dimension id if all
// its operands are valid dimension ids with the parent operation of `region`
// defining the polyhedral scope for symbols.
bool AffineApplyOp::isValidDim(Region *region) {
return llvm::all_of(getOperands(),
[&](Value op) { return ::isValidDim(op, region); });
}
// The result of the affine apply operation can be used as a symbol if all its
// operands are symbols.
bool AffineApplyOp::isValidSymbol() {
return llvm::all_of(getOperands(),
[](Value op) { return mlir::isValidSymbol(op); });
}
// The result of the affine apply operation can be used as a symbol in `region`
// if all its operands are symbols in `region`.
bool AffineApplyOp::isValidSymbol(Region *region) {
return llvm::all_of(getOperands(), [&](Value operand) {
return mlir::isValidSymbol(operand, region);
});
}
OpFoldResult AffineApplyOp::fold(ArrayRef<Attribute> operands) {
auto map = getAffineMap();
// Fold dims and symbols to existing values.
auto expr = map.getResult(0);
if (auto dim = expr.dyn_cast<AffineDimExpr>())
return getOperand(dim.getPosition());
if (auto sym = expr.dyn_cast<AffineSymbolExpr>())
return getOperand(map.getNumDims() + sym.getPosition());
// Otherwise, default to folding the map.
SmallVector<Attribute, 1> result;
if (failed(map.constantFold(operands, result)))
return {};
return result[0];
}
AffineDimExpr AffineApplyNormalizer::renumberOneDim(Value v) {
DenseMap<Value, unsigned>::iterator iterPos;
bool inserted = false;
std::tie(iterPos, inserted) =
dimValueToPosition.insert(std::make_pair(v, dimValueToPosition.size()));
if (inserted) {
reorderedDims.push_back(v);
}
return getAffineDimExpr(iterPos->second, v.getContext())
.cast<AffineDimExpr>();
}
AffineMap AffineApplyNormalizer::renumber(const AffineApplyNormalizer &other) {
SmallVector<AffineExpr, 8> dimRemapping;
for (auto v : other.reorderedDims) {
auto kvp = other.dimValueToPosition.find(v);
if (dimRemapping.size() <= kvp->second)
dimRemapping.resize(kvp->second + 1);
dimRemapping[kvp->second] = renumberOneDim(kvp->first);
}
unsigned numSymbols = concatenatedSymbols.size();
unsigned numOtherSymbols = other.concatenatedSymbols.size();
SmallVector<AffineExpr, 8> symRemapping(numOtherSymbols);
for (unsigned idx = 0; idx < numOtherSymbols; ++idx) {
symRemapping[idx] =
getAffineSymbolExpr(idx + numSymbols, other.affineMap.getContext());
}
concatenatedSymbols.insert(concatenatedSymbols.end(),
other.concatenatedSymbols.begin(),
other.concatenatedSymbols.end());
auto map = other.affineMap;
return map.replaceDimsAndSymbols(dimRemapping, symRemapping,
reorderedDims.size(),
concatenatedSymbols.size());
}
// Gather the positions of the operands that are produced by an AffineApplyOp.
static llvm::SetVector<unsigned>
indicesFromAffineApplyOp(ArrayRef<Value> operands) {
llvm::SetVector<unsigned> res;
for (auto en : llvm::enumerate(operands))
if (isa_and_nonnull<AffineApplyOp>(en.value().getDefiningOp()))
res.insert(en.index());
return res;
}
// Support the special case of a symbol coming from an AffineApplyOp that needs
// to be composed into the current AffineApplyOp.
// This case is handled by rewriting all such symbols into dims for the purpose
// of allowing mathematical AffineMap composition.
// Returns an AffineMap where symbols that come from an AffineApplyOp have been
// rewritten as dims and are ordered after the original dims.
// TODO: This promotion makes AffineMap lose track of which
// symbols are represented as dims. This loss is static but can still be
// recovered dynamically (with `isValidSymbol`). Still this is annoying for the
// semi-affine map case. A dynamic canonicalization of all dims that are valid
// symbols (a.k.a `canonicalizePromotedSymbols`) into symbols helps and even
// results in better simplifications and foldings. But we should evaluate
// whether this behavior is what we really want after using more.
static AffineMap promoteComposedSymbolsAsDims(AffineMap map,
ArrayRef<Value> symbols) {
if (symbols.empty()) {
return map;
}
// Sanity check on symbols.
for (auto sym : symbols) {
assert(isValidSymbol(sym) && "Expected only valid symbols");
(void)sym;
}
// Extract the symbol positions that come from an AffineApplyOp and
// needs to be rewritten as dims.
auto symPositions = indicesFromAffineApplyOp(symbols);
if (symPositions.empty()) {
return map;
}
// Create the new map by replacing each symbol at pos by the next new dim.
unsigned numDims = map.getNumDims();
unsigned numSymbols = map.getNumSymbols();
unsigned numNewDims = 0;
unsigned numNewSymbols = 0;
SmallVector<AffineExpr, 8> symReplacements(numSymbols);
for (unsigned i = 0; i < numSymbols; ++i) {
symReplacements[i] =
symPositions.count(i) > 0
? getAffineDimExpr(numDims + numNewDims++, map.getContext())
: getAffineSymbolExpr(numNewSymbols++, map.getContext());
}
assert(numSymbols >= numNewDims);
AffineMap newMap = map.replaceDimsAndSymbols(
{}, symReplacements, numDims + numNewDims, numNewSymbols);
return newMap;
}
/// The AffineNormalizer composes AffineApplyOp recursively. Its purpose is to
/// keep a correspondence between the mathematical `map` and the `operands` of
/// a given AffineApplyOp. This correspondence is maintained by iterating over
/// the operands and forming an `auxiliaryMap` that can be composed
/// mathematically with `map`. To keep this correspondence in cases where
/// symbols are produced by affine.apply operations, we perform a local rewrite
/// of symbols as dims.
///
/// Rationale for locally rewriting symbols as dims:
/// ================================================
/// The mathematical composition of AffineMap must always concatenate symbols
/// because it does not have enough information to do otherwise. For example,
/// composing `(d0)[s0] -> (d0 + s0)` with itself must produce
/// `(d0)[s0, s1] -> (d0 + s0 + s1)`.
///
/// The result is only equivalent to `(d0)[s0] -> (d0 + 2 * s0)` when
/// applied to the same mlir::Value for both s0 and s1.
/// As a consequence mathematical composition of AffineMap always concatenates
/// symbols.
///
/// When AffineMaps are used in AffineApplyOp however, they may specify
/// composition via symbols, which is ambiguous mathematically. This corner case
/// is handled by locally rewriting such symbols that come from AffineApplyOp
/// into dims and composing through dims.
/// TODO: Composition via symbols comes at a significant code
/// complexity. Alternatively we should investigate whether we want to
/// explicitly disallow symbols coming from affine.apply and instead force the
/// user to compose symbols beforehand. The annoyances may be small (i.e. 1 or 2
/// extra API calls for such uses, which haven't popped up until now) and the
/// benefit potentially big: simpler and more maintainable code for a
/// non-trivial, recursive, procedure.
AffineApplyNormalizer::AffineApplyNormalizer(AffineMap map,
ArrayRef<Value> operands)
: AffineApplyNormalizer() {
static_assert(kMaxAffineApplyDepth > 0, "kMaxAffineApplyDepth must be > 0");
assert(map.getNumInputs() == operands.size() &&
"number of operands does not match the number of map inputs");
LLVM_DEBUG(map.print(dbgs() << "\nInput map: "));
// Promote symbols that come from an AffineApplyOp to dims by rewriting the
// map to always refer to:
// (dims, symbols coming from AffineApplyOp, other symbols).
// The order of operands can remain unchanged.
// This is a simplification that relies on 2 ordering properties:
// 1. rewritten symbols always appear after the original dims in the map;
// 2. operands are traversed in order and either dispatched to:
// a. auxiliaryExprs (dims and symbols rewritten as dims);
// b. concatenatedSymbols (all other symbols)
// This allows operand order to remain unchanged.
unsigned numDimsBeforeRewrite = map.getNumDims();
map = promoteComposedSymbolsAsDims(map,
operands.take_back(map.getNumSymbols()));
LLVM_DEBUG(map.print(dbgs() << "\nRewritten map: "));
SmallVector<AffineExpr, 8> auxiliaryExprs;
bool furtherCompose = (affineApplyDepth() <= kMaxAffineApplyDepth);
// We fully spell out the 2 cases below. In this particular instance a little
// code duplication greatly improves readability.
// Note that the first branch would disappear if we only supported full
// composition (i.e. infinite kMaxAffineApplyDepth).
if (!furtherCompose) {
// 1. Only dispatch dims or symbols.
for (auto en : llvm::enumerate(operands)) {
auto t = en.value();
assert(t.getType().isIndex());
bool isDim = (en.index() < map.getNumDims());
if (isDim) {
// a. The mathematical composition of AffineMap composes dims.
auxiliaryExprs.push_back(renumberOneDim(t));
} else {
// b. The mathematical composition of AffineMap concatenates symbols.
// We do the same for symbol operands.
concatenatedSymbols.push_back(t);
}
}
} else {
assert(numDimsBeforeRewrite <= operands.size());
// 2. Compose AffineApplyOps and dispatch dims or symbols.
for (unsigned i = 0, e = operands.size(); i < e; ++i) {
auto t = operands[i];
auto affineApply = t.getDefiningOp<AffineApplyOp>();
if (affineApply) {
// a. Compose affine.apply operations.
LLVM_DEBUG(affineApply.getOperation()->print(
dbgs() << "\nCompose AffineApplyOp recursively: "));
AffineMap affineApplyMap = affineApply.getAffineMap();
SmallVector<Value, 8> affineApplyOperands(
affineApply.getOperands().begin(), affineApply.getOperands().end());
AffineApplyNormalizer normalizer(affineApplyMap, affineApplyOperands);
LLVM_DEBUG(normalizer.affineMap.print(
dbgs() << "\nRenumber into current normalizer: "));
auto renumberedMap = renumber(normalizer);
LLVM_DEBUG(
renumberedMap.print(dbgs() << "\nRecursive composition yields: "));
auxiliaryExprs.push_back(renumberedMap.getResult(0));
} else {
if (i < numDimsBeforeRewrite) {
// b. The mathematical composition of AffineMap composes dims.
auxiliaryExprs.push_back(renumberOneDim(t));
} else {
// c. The mathematical composition of AffineMap concatenates symbols.
// Note that the map composition will put symbols already present
// in the map before any symbols coming from the auxiliary map, so
// we insert them before any symbols that are due to renumbering,
// and after the proper symbols we have seen already.
concatenatedSymbols.insert(
std::next(concatenatedSymbols.begin(), numProperSymbols++), t);
}
}
}
}
// Early exit if `map` is already composed.
if (auxiliaryExprs.empty()) {
affineMap = map;
return;
}
assert(concatenatedSymbols.size() >= map.getNumSymbols() &&
"Unexpected number of concatenated symbols");
auto numDims = dimValueToPosition.size();
auto numSymbols = concatenatedSymbols.size() - map.getNumSymbols();
auto auxiliaryMap =
AffineMap::get(numDims, numSymbols, auxiliaryExprs, map.getContext());
LLVM_DEBUG(map.print(dbgs() << "\nCompose map: "));
LLVM_DEBUG(auxiliaryMap.print(dbgs() << "\nWith map: "));
LLVM_DEBUG(map.compose(auxiliaryMap).print(dbgs() << "\nResult: "));
// TODO: Disabling simplification results in major speed gains.
// Another option is to cache the results as it is expected a lot of redundant
// work is performed in practice.
affineMap = simplifyAffineMap(map.compose(auxiliaryMap));
LLVM_DEBUG(affineMap.print(dbgs() << "\nSimplified result: "));
LLVM_DEBUG(dbgs() << "\n");
}
void AffineApplyNormalizer::normalize(AffineMap *otherMap,
SmallVectorImpl<Value> *otherOperands) {
AffineApplyNormalizer other(*otherMap, *otherOperands);
*otherMap = renumber(other);
otherOperands->reserve(reorderedDims.size() + concatenatedSymbols.size());
otherOperands->assign(reorderedDims.begin(), reorderedDims.end());
otherOperands->append(concatenatedSymbols.begin(), concatenatedSymbols.end());
}
/// Implements `map` and `operands` composition and simplification to support
/// `makeComposedAffineApply`. This can be called to achieve the same effects
/// on `map` and `operands` without creating an AffineApplyOp that needs to be
/// immediately deleted.
static void composeAffineMapAndOperands(AffineMap *map,
SmallVectorImpl<Value> *operands) {
AffineApplyNormalizer normalizer(*map, *operands);
auto normalizedMap = normalizer.getAffineMap();
auto normalizedOperands = normalizer.getOperands();
canonicalizeMapAndOperands(&normalizedMap, &normalizedOperands);
*map = normalizedMap;
*operands = normalizedOperands;
assert(*map);
}
void mlir::fullyComposeAffineMapAndOperands(AffineMap *map,
SmallVectorImpl<Value> *operands) {
while (llvm::any_of(*operands, [](Value v) {
return isa_and_nonnull<AffineApplyOp>(v.getDefiningOp());
})) {
composeAffineMapAndOperands(map, operands);
}
}
AffineApplyOp mlir::makeComposedAffineApply(OpBuilder &b, Location loc,
AffineMap map,
ArrayRef<Value> operands) {
AffineMap normalizedMap = map;
SmallVector<Value, 8> normalizedOperands(operands.begin(), operands.end());
composeAffineMapAndOperands(&normalizedMap, &normalizedOperands);
assert(normalizedMap);
return b.create<AffineApplyOp>(loc, normalizedMap, normalizedOperands);
}
// A symbol may appear as a dim in affine.apply operations. This function
// canonicalizes dims that are valid symbols into actual symbols.
template <class MapOrSet>
static void canonicalizePromotedSymbols(MapOrSet *mapOrSet,
SmallVectorImpl<Value> *operands) {
if (!mapOrSet || operands->empty())
return;
assert(mapOrSet->getNumInputs() == operands->size() &&
"map/set inputs must match number of operands");
auto *context = mapOrSet->getContext();
SmallVector<Value, 8> resultOperands;
resultOperands.reserve(operands->size());
SmallVector<Value, 8> remappedSymbols;
remappedSymbols.reserve(operands->size());
unsigned nextDim = 0;
unsigned nextSym = 0;
unsigned oldNumSyms = mapOrSet->getNumSymbols();
SmallVector<AffineExpr, 8> dimRemapping(mapOrSet->getNumDims());
for (unsigned i = 0, e = mapOrSet->getNumInputs(); i != e; ++i) {
if (i < mapOrSet->getNumDims()) {
if (isValidSymbol((*operands)[i])) {
// This is a valid symbol that appears as a dim, canonicalize it.
dimRemapping[i] = getAffineSymbolExpr(oldNumSyms + nextSym++, context);
remappedSymbols.push_back((*operands)[i]);
} else {
dimRemapping[i] = getAffineDimExpr(nextDim++, context);
resultOperands.push_back((*operands)[i]);
}
} else {
resultOperands.push_back((*operands)[i]);
}
}
resultOperands.append(remappedSymbols.begin(), remappedSymbols.end());
*operands = resultOperands;
*mapOrSet = mapOrSet->replaceDimsAndSymbols(dimRemapping, {}, nextDim,
oldNumSyms + nextSym);
assert(mapOrSet->getNumInputs() == operands->size() &&
"map/set inputs must match number of operands");
}
// Works for either an affine map or an integer set.
template <class MapOrSet>
static void canonicalizeMapOrSetAndOperands(MapOrSet *mapOrSet,
SmallVectorImpl<Value> *operands) {
static_assert(llvm::is_one_of<MapOrSet, AffineMap, IntegerSet>::value,
"Argument must be either of AffineMap or IntegerSet type");
if (!mapOrSet || operands->empty())
return;
assert(mapOrSet->getNumInputs() == operands->size() &&
"map/set inputs must match number of operands");
canonicalizePromotedSymbols<MapOrSet>(mapOrSet, operands);
// Check to see what dims are used.
llvm::SmallBitVector usedDims(mapOrSet->getNumDims());
llvm::SmallBitVector usedSyms(mapOrSet->getNumSymbols());
mapOrSet->walkExprs([&](AffineExpr expr) {
if (auto dimExpr = expr.dyn_cast<AffineDimExpr>())
usedDims[dimExpr.getPosition()] = true;
else if (auto symExpr = expr.dyn_cast<AffineSymbolExpr>())
usedSyms[symExpr.getPosition()] = true;
});
auto *context = mapOrSet->getContext();
SmallVector<Value, 8> resultOperands;
resultOperands.reserve(operands->size());
llvm::SmallDenseMap<Value, AffineExpr, 8> seenDims;
SmallVector<AffineExpr, 8> dimRemapping(mapOrSet->getNumDims());
unsigned nextDim = 0;
for (unsigned i = 0, e = mapOrSet->getNumDims(); i != e; ++i) {
if (usedDims[i]) {
// Remap dim positions for duplicate operands.
auto it = seenDims.find((*operands)[i]);
if (it == seenDims.end()) {
dimRemapping[i] = getAffineDimExpr(nextDim++, context);
resultOperands.push_back((*operands)[i]);
seenDims.insert(std::make_pair((*operands)[i], dimRemapping[i]));
} else {
dimRemapping[i] = it->second;
}
}
}
llvm::SmallDenseMap<Value, AffineExpr, 8> seenSymbols;
SmallVector<AffineExpr, 8> symRemapping(mapOrSet->getNumSymbols());
unsigned nextSym = 0;
for (unsigned i = 0, e = mapOrSet->getNumSymbols(); i != e; ++i) {
if (!usedSyms[i])
continue;
// Handle constant operands (only needed for symbolic operands since
// constant operands in dimensional positions would have already been
// promoted to symbolic positions above).
IntegerAttr operandCst;
if (matchPattern((*operands)[i + mapOrSet->getNumDims()],
m_Constant(&operandCst))) {
symRemapping[i] =
getAffineConstantExpr(operandCst.getValue().getSExtValue(), context);
continue;
}
// Remap symbol positions for duplicate operands.
auto it = seenSymbols.find((*operands)[i + mapOrSet->getNumDims()]);
if (it == seenSymbols.end()) {
symRemapping[i] = getAffineSymbolExpr(nextSym++, context);
resultOperands.push_back((*operands)[i + mapOrSet->getNumDims()]);
seenSymbols.insert(std::make_pair((*operands)[i + mapOrSet->getNumDims()],
symRemapping[i]));
} else {
symRemapping[i] = it->second;
}
}
*mapOrSet = mapOrSet->replaceDimsAndSymbols(dimRemapping, symRemapping,
nextDim, nextSym);
*operands = resultOperands;
}
void mlir::canonicalizeMapAndOperands(AffineMap *map,
SmallVectorImpl<Value> *operands) {
canonicalizeMapOrSetAndOperands<AffineMap>(map, operands);
}
void mlir::canonicalizeSetAndOperands(IntegerSet *set,
SmallVectorImpl<Value> *operands) {
canonicalizeMapOrSetAndOperands<IntegerSet>(set, operands);
}
namespace {
/// Simplify AffineApply, AffineLoad, and AffineStore operations by composing
/// maps that supply results into them.
///
template <typename AffineOpTy>
struct SimplifyAffineOp : public OpRewritePattern<AffineOpTy> {
using OpRewritePattern<AffineOpTy>::OpRewritePattern;
/// Replace the affine op with another instance of it with the supplied
/// map and mapOperands.
void replaceAffineOp(PatternRewriter &rewriter, AffineOpTy affineOp,
AffineMap map, ArrayRef<Value> mapOperands) const;
LogicalResult matchAndRewrite(AffineOpTy affineOp,
PatternRewriter &rewriter) const override {
static_assert(llvm::is_one_of<AffineOpTy, AffineLoadOp, AffinePrefetchOp,
AffineStoreOp, AffineApplyOp, AffineMinOp,
AffineMaxOp>::value,
"affine load/store/apply/prefetch/min/max op expected");
auto map = affineOp.getAffineMap();
AffineMap oldMap = map;
auto oldOperands = affineOp.getMapOperands();
SmallVector<Value, 8> resultOperands(oldOperands);
composeAffineMapAndOperands(&map, &resultOperands);
if (map == oldMap && std::equal(oldOperands.begin(), oldOperands.end(),
resultOperands.begin()))
return failure();
replaceAffineOp(rewriter, affineOp, map, resultOperands);
return success();
}
};
// Specialize the template to account for the different build signatures for
// affine load, store, and apply ops.
template <>
void SimplifyAffineOp<AffineLoadOp>::replaceAffineOp(
PatternRewriter &rewriter, AffineLoadOp load, AffineMap map,
ArrayRef<Value> mapOperands) const {
rewriter.replaceOpWithNewOp<AffineLoadOp>(load, load.getMemRef(), map,
mapOperands);
}
template <>
void SimplifyAffineOp<AffinePrefetchOp>::replaceAffineOp(
PatternRewriter &rewriter, AffinePrefetchOp prefetch, AffineMap map,
ArrayRef<Value> mapOperands) const {
rewriter.replaceOpWithNewOp<AffinePrefetchOp>(
prefetch, prefetch.memref(), map, mapOperands,
prefetch.localityHint().getZExtValue(), prefetch.isWrite(),
prefetch.isDataCache());
}
template <>
void SimplifyAffineOp<AffineStoreOp>::replaceAffineOp(
PatternRewriter &rewriter, AffineStoreOp store, AffineMap map,
ArrayRef<Value> mapOperands) const {
rewriter.replaceOpWithNewOp<AffineStoreOp>(
store, store.getValueToStore(), store.getMemRef(), map, mapOperands);
}
// Generic version for ops that don't have extra operands.
template <typename AffineOpTy>
void SimplifyAffineOp<AffineOpTy>::replaceAffineOp(
PatternRewriter &rewriter, AffineOpTy op, AffineMap map,
ArrayRef<Value> mapOperands) const {
rewriter.replaceOpWithNewOp<AffineOpTy>(op, map, mapOperands);
}
} // end anonymous namespace.
void AffineApplyOp::getCanonicalizationPatterns(
OwningRewritePatternList &results, MLIRContext *context) {
results.insert<SimplifyAffineOp<AffineApplyOp>>(context);
}
//===----------------------------------------------------------------------===//
// 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);
}
//===----------------------------------------------------------------------===//
// AffineDmaStartOp
//===----------------------------------------------------------------------===//
// TODO: Check that map operands are loop IVs or symbols.
void AffineDmaStartOp::build(OpBuilder &builder, OperationState &result,
Value srcMemRef, AffineMap srcMap,
ValueRange srcIndices, Value destMemRef,
AffineMap dstMap, ValueRange destIndices,
Value tagMemRef, AffineMap tagMap,
ValueRange tagIndices, Value numElements,
Value stride, Value elementsPerStride) {
result.addOperands(srcMemRef);
result.addAttribute(getSrcMapAttrName(), AffineMapAttr::get(srcMap));
result.addOperands(srcIndices);
result.addOperands(destMemRef);
result.addAttribute(getDstMapAttrName(), AffineMapAttr::get(dstMap));
result.addOperands(destIndices);
result.addOperands(tagMemRef);
result.addAttribute(getTagMapAttrName(), AffineMapAttr::get(tagMap));
result.addOperands(tagIndices);
result.addOperands(numElements);
if (stride) {
result.addOperands({stride, elementsPerStride});
}
}
void AffineDmaStartOp::print(OpAsmPrinter &p) {
p << "affine.dma_start " << getSrcMemRef() << '[';
p.printAffineMapOfSSAIds(getSrcMapAttr(), getSrcIndices());
p << "], " << getDstMemRef() << '[';
p.printAffineMapOfSSAIds(getDstMapAttr(), getDstIndices());
p << "], " << getTagMemRef() << '[';
p.printAffineMapOfSSAIds(getTagMapAttr(), getTagIndices());
p << "], " << getNumElements();
if (isStrided()) {
p << ", " << getStride();
p << ", " << getNumElementsPerStride();
}
p << " : " << getSrcMemRefType() << ", " << getDstMemRefType() << ", "
<< getTagMemRefType();
}
// Parse AffineDmaStartOp.
// Ex:
// affine.dma_start %src[%i, %j], %dst[%k, %l], %tag[%index], %size,
// %stride, %num_elt_per_stride
// : memref<3076 x f32, 0>, memref<1024 x f32, 2>, memref<1 x i32>
//
ParseResult AffineDmaStartOp::parse(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::OperandType srcMemRefInfo;
AffineMapAttr srcMapAttr;
SmallVector<OpAsmParser::OperandType, 4> srcMapOperands;
OpAsmParser::OperandType dstMemRefInfo;
AffineMapAttr dstMapAttr;
SmallVector<OpAsmParser::OperandType, 4> dstMapOperands;
OpAsmParser::OperandType tagMemRefInfo;
AffineMapAttr tagMapAttr;
SmallVector<OpAsmParser::OperandType, 4> tagMapOperands;
OpAsmParser::OperandType numElementsInfo;
SmallVector<OpAsmParser::OperandType, 2> strideInfo;
SmallVector<Type, 3> types;
auto indexType = parser.getBuilder().getIndexType();
// Parse and resolve the following list of operands:
// *) dst memref followed by its affine maps operands (in square brackets).
// *) src memref followed by its affine map operands (in square brackets).
// *) tag memref followed by its affine map operands (in square brackets).
// *) number of elements transferred by DMA operation.
if (parser.parseOperand(srcMemRefInfo) ||
parser.parseAffineMapOfSSAIds(srcMapOperands, srcMapAttr,
getSrcMapAttrName(), result.attributes) ||
parser.parseComma() || parser.parseOperand(dstMemRefInfo) ||