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llvm-mirror/lib/Transforms/Scalar/LowerMatrixIntrinsics.cpp
Adam Nemet d9613eb43c [Matrix] Fix shape for factored transpose 
The shape of the input is C x R.

Differential Revision: https://reviews.llvm.org/D106722
2021-07-27 11:36:13 -07:00

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//===- LowerMatrixIntrinsics.cpp - Lower matrix intrinsics -----*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Lower matrix intrinsics to vector operations.
//
// TODO:
// * Improve fusion:
// * Support more cases, e.g. multiply-add, multiply-sub, operands/results
// transposed.
// * Improve cost-modeling, e.g. choose different number of rows/columns
// columns for tiles, consider cost of copies on alias.
//
//===----------------------------------------------------------------------===//
#include "llvm/Transforms/Scalar/LowerMatrixIntrinsics.h"
#include "llvm/ADT/GraphTraits.h"
#include "llvm/ADT/PostOrderIterator.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/DomTreeUpdater.h"
#include "llvm/Analysis/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Analysis/ValueTracking.h"
#include "llvm/Analysis/VectorUtils.h"
#include "llvm/IR/CFG.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/DebugInfoMetadata.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/IRBuilder.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/MatrixBuilder.h"
#include "llvm/IR/PatternMatch.h"
#include "llvm/InitializePasses.h"
#include "llvm/Pass.h"
#include "llvm/Support/Alignment.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/LoopUtils.h"
#include "llvm/Transforms/Utils/MatrixUtils.h"
using namespace llvm;
using namespace PatternMatch;
#define DEBUG_TYPE "lower-matrix-intrinsics"
static cl::opt<bool>
FuseMatrix("fuse-matrix", cl::init(true), cl::Hidden,
cl::desc("Enable/disable fusing matrix instructions."));
// TODO: Allow and use non-square tiles.
static cl::opt<unsigned> TileSize(
"fuse-matrix-tile-size", cl::init(4), cl::Hidden,
cl::desc(
"Tile size for matrix instruction fusion using square-shaped tiles."));
static cl::opt<bool> TileUseLoops("fuse-matrix-use-loops", cl::init(false),
cl::Hidden,
cl::desc("Generate loop nest for tiling."));
static cl::opt<bool> ForceFusion(
"force-fuse-matrix", cl::init(false), cl::Hidden,
cl::desc("Force matrix instruction fusion even if not profitable."));
static cl::opt<bool> AllowContractEnabled(
"matrix-allow-contract", cl::init(false), cl::Hidden,
cl::desc("Allow the use of FMAs if available and profitable. This may "
"result in different results, due to less rounding error."));
enum class MatrixLayoutTy { ColumnMajor, RowMajor };
static cl::opt<MatrixLayoutTy> MatrixLayout(
"matrix-default-layout", cl::init(MatrixLayoutTy::ColumnMajor),
cl::desc("Sets the default matrix layout"),
cl::values(clEnumValN(MatrixLayoutTy::ColumnMajor, "column-major",
"Use column-major layout"),
clEnumValN(MatrixLayoutTy::RowMajor, "row-major",
"Use row-major layout")));
/// Helper function to either return Scope, if it is a subprogram or the
/// attached subprogram for a local scope.
static DISubprogram *getSubprogram(DIScope *Scope) {
if (auto *Subprogram = dyn_cast<DISubprogram>(Scope))
return Subprogram;
return cast<DILocalScope>(Scope)->getSubprogram();
}
namespace {
// Given an element pointer \p BasePtr to the start of a (sub) matrix, compute
// the start address of vector \p VecIdx with type (\p EltType x \p NumElements)
// assuming \p Stride elements between start two consecutive vectors.
// \p Stride must be >= \p NumElements.
// For column-major matrixes, the function computes the address of a column
// vectors and \p NumElements must be set to the number of elements in a column
// (= number of rows of the matrix). For row-major matrixes, the function
// computes the address of a row vector and \p NumElements must be set to the
// number of elements in a column (= number of columns of the matrix).
//
// Consider a 4x4 matrix in column-mjaor layout like below
//
// 0 1 2 3
// 0 v_0_0 v_0_1 v_0_2 v_0_3
// 1 v_1_0 v_1_1 v_1_2 v_1_3
// 2 v_2_0 v_2_1 v_2_2 v_2_3
// 3 v_3_0 v_3_1 v_3_2 v_3_3
// To compute the column addresses for a 2x3 sub-matrix at row 1 and column 1,
// we need a pointer to the first element of the submatrix as base pointer.
// Then we can use computeVectorAddr to compute the addresses for the columns
// of the sub-matrix.
//
// Column 0: computeVectorAddr(Base, 0 (column), 4 (stride), 2 (num rows), ..)
// -> just returns Base
// Column 1: computeVectorAddr(Base, 1 (column), 4 (stride), 2 (num rows), ..)
// -> returns Base + (1 * 4)
// Column 2: computeVectorAddr(Base, 2 (column), 4 (stride), 2 (num rows), ..)
// -> returns Base + (2 * 4)
//
// The graphic below illustrates the number of elements in a column (marked
// with |) and the number of skipped elements (marked with }).
//
// v_0_0 v_0_1 {v_0_2 {v_0_3
// Base Col 1 Col 2
// | | |
// v_1_0 |v_1_1 |v_1_2 |v_1_3
// v_2_0 |v_2_1 |v_2_2 |v_2_3
// v_3_0 {v_3_1 {v_3_2 v_3_3
//
Value *computeVectorAddr(Value *BasePtr, Value *VecIdx, Value *Stride,
unsigned NumElements, Type *EltType,
IRBuilder<> &Builder) {
assert((!isa<ConstantInt>(Stride) ||
cast<ConstantInt>(Stride)->getZExtValue() >= NumElements) &&
"Stride must be >= the number of elements in the result vector.");
unsigned AS = cast<PointerType>(BasePtr->getType())->getAddressSpace();
// Compute the start of the vector with index VecIdx as VecIdx * Stride.
Value *VecStart = Builder.CreateMul(VecIdx, Stride, "vec.start");
// Get pointer to the start of the selected vector. Skip GEP creation,
// if we select vector 0.
if (isa<ConstantInt>(VecStart) && cast<ConstantInt>(VecStart)->isZero())
VecStart = BasePtr;
else
VecStart = Builder.CreateGEP(EltType, BasePtr, VecStart, "vec.gep");
// Cast elementwise vector start pointer to a pointer to a vector
// (EltType x NumElements)*.
auto *VecType = FixedVectorType::get(EltType, NumElements);
Type *VecPtrType = PointerType::get(VecType, AS);
return Builder.CreatePointerCast(VecStart, VecPtrType, "vec.cast");
}
/// LowerMatrixIntrinsics contains the methods used to lower matrix intrinsics.
///
/// Currently, the lowering for each matrix intrinsic is done as follows:
/// 1. Propagate the shape information from intrinsics to connected
/// instructions.
/// 2. Lower instructions with shape information (assuming column-major layout).
/// The lowering works similarly using row-major layout.
/// 2.1. Get column vectors for each argument. If we already lowered the
/// definition of an argument, use the produced column vectors directly.
/// If not, split the operand vector containing an embedded matrix into
/// a set of column vectors,
/// 2.2. Lower the instruction in terms of column major operations, which
/// yields a set of column vectors containing result matrix. Note that we
/// lower all instructions that have shape information. Besides the
/// intrinsics, this includes stores for example.
/// 2.3. Update uses of the lowered instruction. If we have shape information
/// for a user, there is nothing to do, as we will look up the result
/// column matrix when lowering the user. For other uses, we embed the
/// result matrix in a flat vector and update the use.
/// 2.4. Cache the result column matrix for the instruction we lowered
/// 3. After we lowered all instructions in a function, remove the now
/// obsolete instructions.
///
class LowerMatrixIntrinsics {
Function &Func;
const DataLayout &DL;
const TargetTransformInfo &TTI;
AliasAnalysis *AA;
DominatorTree *DT;
LoopInfo *LI;
OptimizationRemarkEmitter *ORE;
/// Contains estimates of the number of operations (loads, stores, compute) required to lower a matrix operation.
struct OpInfoTy {
/// Number of stores emitted to generate this matrix.
unsigned NumStores = 0;
/// Number of loads emitted to generate this matrix.
unsigned NumLoads = 0;
/// Number of compute operations emitted to generate this matrix.
unsigned NumComputeOps = 0;
/// Most of the time transposes can be fused with matrix multiplies or can
/// be folded away via algebraic simplifications. This is the number of
/// transposes that we failed to make "free" via such optimizations.
unsigned NumExposedTransposes = 0;
OpInfoTy &operator+=(const OpInfoTy &RHS) {
NumStores += RHS.NumStores;
NumLoads += RHS.NumLoads;
NumComputeOps += RHS.NumComputeOps;
NumExposedTransposes += RHS.NumExposedTransposes;
return *this;
}
};
/// Wrapper class representing a matrix as a set of vectors, either in row or
/// column major layout. All vectors must have the same vector type.
class MatrixTy {
SmallVector<Value *, 16> Vectors;
OpInfoTy OpInfo;
bool IsColumnMajor = true;
public:
MatrixTy()
: Vectors(),
IsColumnMajor(MatrixLayout == MatrixLayoutTy::ColumnMajor) {}
MatrixTy(ArrayRef<Value *> Vectors)
: Vectors(Vectors.begin(), Vectors.end()),
IsColumnMajor(MatrixLayout == MatrixLayoutTy::ColumnMajor) {}
MatrixTy(unsigned NumRows, unsigned NumColumns, Type *EltTy)
: IsColumnMajor(MatrixLayout == MatrixLayoutTy::ColumnMajor) {
unsigned D = isColumnMajor() ? NumColumns : NumRows;
for (unsigned J = 0; J < D; ++J)
addVector(UndefValue::get(FixedVectorType::get(
EltTy, isColumnMajor() ? NumRows : NumColumns)));
}
Value *getVector(unsigned i) const { return Vectors[i]; }
Value *getColumn(unsigned i) const {
assert(isColumnMajor() && "only supported for column-major matrixes");
return Vectors[i];
}
Value *getRow(unsigned i) const {
assert(!isColumnMajor() && "only supported for row-major matrixes");
return Vectors[i];
}
void setVector(unsigned i, Value *V) { Vectors[i] = V; }
Type *getElementType() const { return getVectorTy()->getElementType(); }
unsigned getNumVectors() const {
if (isColumnMajor())
return getNumColumns();
return getNumRows();
}
unsigned getNumColumns() const {
if (isColumnMajor())
return Vectors.size();
else {
assert(Vectors.size() > 0 && "Cannot call getNumRows without columns");
return cast<FixedVectorType>(Vectors[0]->getType())->getNumElements();
}
}
unsigned getNumRows() const {
if (isColumnMajor()) {
assert(Vectors.size() > 0 && "Cannot call getNumRows without columns");
return cast<FixedVectorType>(Vectors[0]->getType())->getNumElements();
} else
return Vectors.size();
}
void addVector(Value *V) { Vectors.push_back(V); }
VectorType *getColumnTy() {
assert(isColumnMajor() && "only supported for column-major matrixes");
return getVectorTy();
}
VectorType *getVectorTy() const {
return cast<VectorType>(Vectors[0]->getType());
}
iterator_range<SmallVector<Value *, 8>::iterator> columns() {
assert(isColumnMajor() &&
"columns() only supported for column-major matrixes");
return make_range(Vectors.begin(), Vectors.end());
}
iterator_range<SmallVector<Value *, 8>::iterator> vectors() {
return make_range(Vectors.begin(), Vectors.end());
}
/// Embed the vectors of the matrix into a flat vector by concatenating
/// them.
Value *embedInVector(IRBuilder<> &Builder) const {
return Vectors.size() == 1 ? Vectors[0]
: concatenateVectors(Builder, Vectors);
}
MatrixTy &addNumLoads(unsigned N) {
OpInfo.NumLoads += N;
return *this;
}
void setNumLoads(unsigned N) { OpInfo.NumLoads = N; }
MatrixTy &addNumStores(unsigned N) {
OpInfo.NumStores += N;
return *this;
}
MatrixTy &addNumExposedTransposes(unsigned N) {
OpInfo.NumExposedTransposes += N;
return *this;
}
MatrixTy &addNumComputeOps(unsigned N) {
OpInfo.NumComputeOps += N;
return *this;
}
unsigned getNumStores() const { return OpInfo.NumStores; }
unsigned getNumLoads() const { return OpInfo.NumLoads; }
unsigned getNumComputeOps() const { return OpInfo.NumComputeOps; }
const OpInfoTy &getOpInfo() const { return OpInfo; }
bool isColumnMajor() const { return IsColumnMajor; }
unsigned getStride() const {
if (isColumnMajor())
return getNumRows();
return getNumColumns();
}
/// Extract a vector of \p NumElts starting at index (\p I, \p J). If the
/// matrix is column-major, the result vector is extracted from a column
/// vector, otherwise from a row vector.
Value *extractVector(unsigned I, unsigned J, unsigned NumElts,
IRBuilder<> &Builder) const {
Value *Vec = isColumnMajor() ? getColumn(J) : getRow(I);
return Builder.CreateShuffleVector(
Vec, createSequentialMask(isColumnMajor() ? I : J, NumElts, 0),
"block");
}
};
struct ShapeInfo {
unsigned NumRows;
unsigned NumColumns;
bool IsColumnMajor;
ShapeInfo(unsigned NumRows = 0, unsigned NumColumns = 0)
: NumRows(NumRows), NumColumns(NumColumns),
IsColumnMajor(MatrixLayout == MatrixLayoutTy::ColumnMajor) {}
ShapeInfo(Value *NumRows, Value *NumColumns)
: ShapeInfo(cast<ConstantInt>(NumRows)->getZExtValue(),
cast<ConstantInt>(NumColumns)->getZExtValue()) {}
bool operator==(const ShapeInfo &other) {
return NumRows == other.NumRows && NumColumns == other.NumColumns;
}
bool operator!=(const ShapeInfo &other) { return !(*this == other); }
/// Returns true if shape-information is defined, meaning both dimensions
/// are != 0.
operator bool() const {
assert(NumRows == 0 || NumColumns != 0);
return NumRows != 0;
}
unsigned getStride() const {
if (IsColumnMajor)
return NumRows;
return NumColumns;
}
unsigned getNumVectors() const {
if (IsColumnMajor)
return NumColumns;
return NumRows;
}
};
/// Maps instructions to their shape information. The shape information
/// describes the shape to be used while lowering. This matches the shape of
/// the result value of the instruction, with the only exceptions being store
/// instructions and the matrix_column_major_store intrinsics. For those, the
/// shape information indicates that those instructions should be lowered
/// using shape information as well. A ValueMap is used so that when
/// sub-passes like optimizeTransposes performs RAUW the map stays
/// up-to-date.
ValueMap<Value *, ShapeInfo> ShapeMap;
/// List of instructions to remove. While lowering, we are not replacing all
/// users of a lowered instruction, if shape information is available and
/// those need to be removed after we finished lowering.
SmallVector<Instruction *, 16> ToRemove;
/// Map from instructions to their produced column matrix.
MapVector<Value *, MatrixTy> Inst2ColumnMatrix;
private:
static FastMathFlags getFastMathFlags(Instruction *Inst) {
FastMathFlags FMF;
if (isa<FPMathOperator>(*Inst))
FMF = Inst->getFastMathFlags();
FMF.setAllowContract(AllowContractEnabled || FMF.allowContract());
return FMF;
}
public:
LowerMatrixIntrinsics(Function &F, TargetTransformInfo &TTI,
AliasAnalysis *AA, DominatorTree *DT, LoopInfo *LI,
OptimizationRemarkEmitter *ORE)
: Func(F), DL(F.getParent()->getDataLayout()), TTI(TTI), AA(AA), DT(DT),
LI(LI), ORE(ORE) {}
unsigned getNumOps(Type *VT) {
assert(isa<VectorType>(VT) && "Expected vector type");
return getNumOps(VT->getScalarType(),
cast<FixedVectorType>(VT)->getNumElements());
}
/// Is this the minimal version executed in the backend pipelines.
bool isMinimal() const {
return !DT;
}
/// Return the estimated number of vector ops required for an operation on
/// \p VT * N.
unsigned getNumOps(Type *ST, unsigned N) {
return std::ceil((ST->getPrimitiveSizeInBits() * N).getFixedSize() /
double(TTI.getRegisterBitWidth(
TargetTransformInfo::RGK_FixedWidthVector)
.getFixedSize()));
}
/// Return the set of vectors that a matrix value is lowered to.
///
/// If we lowered \p MatrixVal, just return the cache result matrix. Otherwise
/// split the flat vector \p MatrixVal containing a matrix with shape \p SI
/// into vectors.
MatrixTy getMatrix(Value *MatrixVal, const ShapeInfo &SI,
IRBuilder<> &Builder) {
VectorType *VType = dyn_cast<VectorType>(MatrixVal->getType());
assert(VType && "MatrixVal must be a vector type");
assert(cast<FixedVectorType>(VType)->getNumElements() ==
SI.NumRows * SI.NumColumns &&
"The vector size must match the number of matrix elements");
// Check if we lowered MatrixVal using shape information. In that case,
// return the existing matrix, if it matches the requested shape
// information. If there is a mis-match, embed the result in a flat
// vector and split it later.
auto Found = Inst2ColumnMatrix.find(MatrixVal);
if (Found != Inst2ColumnMatrix.end()) {
MatrixTy &M = Found->second;
// Return the found matrix, if its shape matches the requested shape
// information
if (SI.NumRows == M.getNumRows() && SI.NumColumns == M.getNumColumns())
return M;
MatrixVal = M.embedInVector(Builder);
}
// Otherwise split MatrixVal.
SmallVector<Value *, 16> SplitVecs;
for (unsigned MaskStart = 0;
MaskStart < cast<FixedVectorType>(VType)->getNumElements();
MaskStart += SI.getStride()) {
Value *V = Builder.CreateShuffleVector(
MatrixVal, createSequentialMask(MaskStart, SI.getStride(), 0),
"split");
SplitVecs.push_back(V);
}
return {SplitVecs};
}
/// If \p V already has a known shape return false. Otherwise set the shape
/// for instructions that support it.
bool setShapeInfo(Value *V, ShapeInfo Shape) {
assert(Shape && "Shape not set");
if (isa<UndefValue>(V) || !supportsShapeInfo(V))
return false;
auto SIter = ShapeMap.find(V);
if (SIter != ShapeMap.end()) {
LLVM_DEBUG(dbgs() << " not overriding existing shape: "
<< SIter->second.NumRows << " "
<< SIter->second.NumColumns << " for " << *V << "\n");
return false;
}
ShapeMap.insert({V, Shape});
LLVM_DEBUG(dbgs() << " " << Shape.NumRows << " x " << Shape.NumColumns
<< " for " << *V << "\n");
return true;
}
bool isUniformShape(Value *V) {
Instruction *I = dyn_cast<Instruction>(V);
if (!I)
return true;
switch (I->getOpcode()) {
case Instruction::FAdd:
case Instruction::FSub:
case Instruction::FMul: // Scalar multiply.
case Instruction::FNeg:
case Instruction::Add:
case Instruction::Mul:
case Instruction::Sub:
return true;
default:
return false;
}
}
/// Returns true if shape information can be used for \p V. The supported
/// instructions must match the instructions that can be lowered by this pass.
bool supportsShapeInfo(Value *V) {
Instruction *Inst = dyn_cast<Instruction>(V);
if (!Inst)
return false;
IntrinsicInst *II = dyn_cast<IntrinsicInst>(Inst);
if (II)
switch (II->getIntrinsicID()) {
case Intrinsic::matrix_multiply:
case Intrinsic::matrix_transpose:
case Intrinsic::matrix_column_major_load:
case Intrinsic::matrix_column_major_store:
return true;
default:
return false;
}
return isUniformShape(V) || isa<StoreInst>(V) || isa<LoadInst>(V);
}
/// Propagate the shape information of instructions to their users.
/// The work list contains instructions for which we can compute the shape,
/// either based on the information provided by matrix intrinsics or known
/// shapes of operands.
SmallVector<Instruction *, 32>
propagateShapeForward(SmallVectorImpl<Instruction *> &WorkList) {
SmallVector<Instruction *, 32> NewWorkList;
// Pop an element for which we guaranteed to have at least one of the
// operand shapes. Add the shape for this and then add users to the work
// list.
LLVM_DEBUG(dbgs() << "Forward-propagate shapes:\n");
while (!WorkList.empty()) {
Instruction *Inst = WorkList.pop_back_val();
// New entry, set the value and insert operands
bool Propagate = false;
Value *MatrixA;
Value *MatrixB;
Value *M;
Value *N;
Value *K;
if (match(Inst, m_Intrinsic<Intrinsic::matrix_multiply>(
m_Value(MatrixA), m_Value(MatrixB), m_Value(M),
m_Value(N), m_Value(K)))) {
Propagate = setShapeInfo(Inst, {M, K});
} else if (match(Inst, m_Intrinsic<Intrinsic::matrix_transpose>(
m_Value(MatrixA), m_Value(M), m_Value(N)))) {
// Flip dimensions.
Propagate = setShapeInfo(Inst, {N, M});
} else if (match(Inst, m_Intrinsic<Intrinsic::matrix_column_major_store>(
m_Value(MatrixA), m_Value(), m_Value(),
m_Value(), m_Value(M), m_Value(N)))) {
Propagate = setShapeInfo(Inst, {N, M});
} else if (match(Inst, m_Intrinsic<Intrinsic::matrix_column_major_load>(
m_Value(), m_Value(), m_Value(), m_Value(M),
m_Value(N)))) {
Propagate = setShapeInfo(Inst, {M, N});
} else if (match(Inst, m_Store(m_Value(MatrixA), m_Value()))) {
auto OpShape = ShapeMap.find(MatrixA);
if (OpShape != ShapeMap.end())
setShapeInfo(Inst, OpShape->second);
continue;
} else if (isUniformShape(Inst)) {
// Find the first operand that has a known shape and use that.
for (auto &Op : Inst->operands()) {
auto OpShape = ShapeMap.find(Op.get());
if (OpShape != ShapeMap.end()) {
Propagate |= setShapeInfo(Inst, OpShape->second);
break;
}
}
}
if (Propagate) {
NewWorkList.push_back(Inst);
for (auto *User : Inst->users())
if (ShapeMap.count(User) == 0)
WorkList.push_back(cast<Instruction>(User));
}
}
return NewWorkList;
}
/// Propagate the shape to operands of instructions with shape information.
/// \p Worklist contains the instruction for which we already know the shape.
SmallVector<Instruction *, 32>
propagateShapeBackward(SmallVectorImpl<Instruction *> &WorkList) {
SmallVector<Instruction *, 32> NewWorkList;
auto pushInstruction = [](Value *V,
SmallVectorImpl<Instruction *> &WorkList) {
Instruction *I = dyn_cast<Instruction>(V);
if (I)
WorkList.push_back(I);
};
// Pop an element with known shape. Traverse the operands, if their shape
// derives from the result shape and is unknown, add it and add them to the
// worklist.
LLVM_DEBUG(dbgs() << "Backward-propagate shapes:\n");
while (!WorkList.empty()) {
Value *V = WorkList.pop_back_val();
size_t BeforeProcessingV = WorkList.size();
if (!isa<Instruction>(V))
continue;
Value *MatrixA;
Value *MatrixB;
Value *M;
Value *N;
Value *K;
if (match(V, m_Intrinsic<Intrinsic::matrix_multiply>(
m_Value(MatrixA), m_Value(MatrixB), m_Value(M),
m_Value(N), m_Value(K)))) {
if (setShapeInfo(MatrixA, {M, N}))
pushInstruction(MatrixA, WorkList);
if (setShapeInfo(MatrixB, {N, K}))
pushInstruction(MatrixB, WorkList);
} else if (match(V, m_Intrinsic<Intrinsic::matrix_transpose>(
m_Value(MatrixA), m_Value(M), m_Value(N)))) {
// Flip dimensions.
if (setShapeInfo(MatrixA, {M, N}))
pushInstruction(MatrixA, WorkList);
} else if (match(V, m_Intrinsic<Intrinsic::matrix_column_major_store>(
m_Value(MatrixA), m_Value(), m_Value(), m_Value(),
m_Value(M), m_Value(N)))) {
if (setShapeInfo(MatrixA, {M, N})) {
pushInstruction(MatrixA, WorkList);
}
} else if (isa<LoadInst>(V) ||
match(V, m_Intrinsic<Intrinsic::matrix_column_major_load>())) {
// Nothing to do, no matrix input.
} else if (isa<StoreInst>(V)) {
// Nothing to do. We forward-propagated to this so we would just
// backward propagate to an instruction with an already known shape.
} else if (isUniformShape(V)) {
// Propagate to all operands.
ShapeInfo Shape = ShapeMap[V];
for (Use &U : cast<Instruction>(V)->operands()) {
if (setShapeInfo(U.get(), Shape))
pushInstruction(U.get(), WorkList);
}
}
// After we discovered new shape info for new instructions in the
// worklist, we use their users as seeds for the next round of forward
// propagation.
for (size_t I = BeforeProcessingV; I != WorkList.size(); I++)
for (User *U : WorkList[I]->users())
if (isa<Instruction>(U) && V != U)
NewWorkList.push_back(cast<Instruction>(U));
}
return NewWorkList;
}
/// Try moving transposes in order to fold them away or into multiplies.
void optimizeTransposes() {
auto ReplaceAllUsesWith = [this](Instruction &Old, Value *New) {
// We need to remove Old from the ShapeMap otherwise RAUW will replace it
// with New. We should only add New it it supportsShapeInfo so we insert
// it conditionally instead.
auto S = ShapeMap.find(&Old);
if (S != ShapeMap.end()) {
ShapeMap.erase(S);
if (supportsShapeInfo(New))
ShapeMap.insert({New, S->second});
}
Old.replaceAllUsesWith(New);
};
// First sink all transposes inside matmuls, hoping that we end up with NN,
// NT or TN variants.
for (BasicBlock &BB : reverse(Func)) {
for (auto II = BB.rbegin(); II != BB.rend();) {
Instruction &I = *II;
// We may remove II. By default continue on the next/prev instruction.
++II;
// If we were to erase II, move again.
auto EraseFromParent = [&II](Value *V) {
auto *Inst = cast<Instruction>(V);
if (Inst->use_empty()) {
if (Inst == &*II) {
++II;
}
Inst->eraseFromParent();
}
};
// If we're creating a new instruction, continue from there.
Instruction *NewInst = nullptr;
IRBuilder<> IB(&I);
MatrixBuilder<IRBuilder<>> Builder(IB);
Value *TA, *TAMA, *TAMB;
ConstantInt *R, *K, *C;
if (match(&I, m_Intrinsic<Intrinsic::matrix_transpose>(m_Value(TA)))) {
// Transpose of a transpose is a nop
Value *TATA;
if (match(TA,
m_Intrinsic<Intrinsic::matrix_transpose>(m_Value(TATA)))) {
ReplaceAllUsesWith(I, TATA);
EraseFromParent(&I);
EraseFromParent(TA);
}
// (A * B)^t -> B^t * A^t
// RxK KxC CxK KxR
else if (match(TA, m_Intrinsic<Intrinsic::matrix_multiply>(
m_Value(TAMA), m_Value(TAMB), m_ConstantInt(R),
m_ConstantInt(K), m_ConstantInt(C)))) {
Value *T0 = Builder.CreateMatrixTranspose(TAMB, K->getZExtValue(),
C->getZExtValue(),
TAMB->getName() + "_t");
// We are being run after shape prop, add shape for newly created
// instructions so that we lower them later.
setShapeInfo(T0, {C, K});
Value *T1 = Builder.CreateMatrixTranspose(TAMA, R->getZExtValue(),
K->getZExtValue(),
TAMA->getName() + "_t");
setShapeInfo(T1, {K, R});
NewInst = Builder.CreateMatrixMultiply(T0, T1, C->getZExtValue(),
K->getZExtValue(),
R->getZExtValue(), "mmul");
ReplaceAllUsesWith(I, NewInst);
EraseFromParent(&I);
EraseFromParent(TA);
}
}
// If we replaced I with a new instruction, continue from there.
if (NewInst)
II = std::next(BasicBlock::reverse_iterator(NewInst));
}
}
// If we have a TT matmul, lift the transpose. We may be able to fold into
// consuming multiply.
for (BasicBlock &BB : Func) {
for (BasicBlock::iterator II = BB.begin(); II != BB.end();) {
Instruction *I = &*II;
// We may remove I.
++II;
Value *A, *B, *AT, *BT;
ConstantInt *R, *K, *C;
// A^t * B ^t -> (B * A)^t
if (match(&*I, m_Intrinsic<Intrinsic::matrix_multiply>(
m_Value(A), m_Value(B), m_ConstantInt(R),
m_ConstantInt(K), m_ConstantInt(C))) &&
match(A, m_Intrinsic<Intrinsic::matrix_transpose>(m_Value(AT))) &&
match(B, m_Intrinsic<Intrinsic::matrix_transpose>(m_Value((BT))))) {
IRBuilder<> IB(&*I);
MatrixBuilder<IRBuilder<>> Builder(IB);
Value *M = Builder.CreateMatrixMultiply(
BT, AT, C->getZExtValue(), K->getZExtValue(), R->getZExtValue());
setShapeInfo(M, {C, R});
Instruction *NewInst = Builder.CreateMatrixTranspose(
M, C->getZExtValue(), R->getZExtValue());
ReplaceAllUsesWith(*I, NewInst);
if (I->use_empty())
I->eraseFromParent();
if (A->use_empty())
cast<Instruction>(A)->eraseFromParent();
if (A != B && B->use_empty())
cast<Instruction>(B)->eraseFromParent();
}
}
}
}
bool Visit() {
SmallVector<Instruction *, 32> WorkList;
// Initially only the shape of matrix intrinsics is known.
// Initialize the work list with ops carrying shape information.
for (BasicBlock &BB : Func)
for (Instruction &Inst : BB) {
IntrinsicInst *II = dyn_cast<IntrinsicInst>(&Inst);
if (!II)
continue;
switch (II->getIntrinsicID()) {
case Intrinsic::matrix_multiply:
case Intrinsic::matrix_transpose:
case Intrinsic::matrix_column_major_load:
case Intrinsic::matrix_column_major_store:
WorkList.push_back(&Inst);
break;
default:
break;
}
}
// Avoid unnecessary work if there are no matrix intrinsics in the function.
if (WorkList.empty())
return false;
// Propagate shapes until nothing changes any longer.
while (!WorkList.empty()) {
WorkList = propagateShapeForward(WorkList);
WorkList = propagateShapeBackward(WorkList);
}
if (!isMinimal()) {
optimizeTransposes();
LLVM_DEBUG({
dbgs() << "Dump after matrix transpose optimization:\n";
Func.dump();
});
}
bool Changed = false;
SmallVector<CallInst *, 16> MaybeFusableInsts;
SmallVector<Instruction *, 16> MatrixInsts;
// First, collect all instructions with shape information and candidates for
// fusion (currently only matrix multiplies).
ReversePostOrderTraversal<Function *> RPOT(&Func);
for (auto *BB : RPOT)
for (Instruction &I : *BB) {
if (ShapeMap.find(&I) == ShapeMap.end())
continue;
if (match(&I, m_Intrinsic<Intrinsic::matrix_multiply>()))
MaybeFusableInsts.push_back(cast<CallInst>(&I));
MatrixInsts.push_back(&I);
}
// Second, try to fuse candidates.
SmallPtrSet<Instruction *, 16> FusedInsts;
for (CallInst *CI : MaybeFusableInsts)
LowerMatrixMultiplyFused(CI, FusedInsts);
Changed = !FusedInsts.empty();
// Third, lower remaining instructions with shape information.
for (Instruction *Inst : MatrixInsts) {
if (FusedInsts.count(Inst))
continue;
IRBuilder<> Builder(Inst);
if (CallInst *CInst = dyn_cast<CallInst>(Inst))
Changed |= VisitCallInst(CInst);
Value *Op1;
Value *Op2;
if (auto *BinOp = dyn_cast<BinaryOperator>(Inst))
Changed |= VisitBinaryOperator(BinOp);
if (auto *UnOp = dyn_cast<UnaryOperator>(Inst))
Changed |= VisitUnaryOperator(UnOp);
if (match(Inst, m_Load(m_Value(Op1))))
Changed |= VisitLoad(cast<LoadInst>(Inst), Op1, Builder);
else if (match(Inst, m_Store(m_Value(Op1), m_Value(Op2))))
Changed |= VisitStore(cast<StoreInst>(Inst), Op1, Op2, Builder);
}
if (ORE) {
RemarkGenerator RemarkGen(Inst2ColumnMatrix, *ORE, Func);
RemarkGen.emitRemarks();
}
// Delete the instructions backwards, as it has a reduced likelihood of
// having to update as many def-use and use-def chains.
//
// Because we add to ToRemove during fusion we can't guarantee that defs
// are before uses. Change uses to undef temporarily as these should get
// removed as well.
//
// For verification, we keep track of where we changed uses to undefs in
// UndefedInsts and then check that we in fact remove them.
SmallSet<Instruction *, 16> UndefedInsts;
for (auto *Inst : reverse(ToRemove)) {
for (auto I = Inst->use_begin(), E = Inst->use_end(); I != E;) {
Use &U = *I++;
if (auto *Undefed = dyn_cast<Instruction>(U.getUser()))
UndefedInsts.insert(Undefed);
U.set(UndefValue::get(Inst->getType()));
}
Inst->eraseFromParent();
UndefedInsts.erase(Inst);
}
if (!UndefedInsts.empty()) {
// If we didn't remove all undefed instructions, it's a hard error.
dbgs() << "Undefed but present instructions:\n";
for (auto *I : UndefedInsts)
dbgs() << *I << "\n";
llvm_unreachable("Undefed but instruction not removed");
}
return Changed;
}
/// Turns \p BasePtr into an elementwise pointer to \p EltType.
Value *createElementPtr(Value *BasePtr, Type *EltType, IRBuilder<> &Builder) {
unsigned AS = cast<PointerType>(BasePtr->getType())->getAddressSpace();
Type *EltPtrType = PointerType::get(EltType, AS);
return Builder.CreatePointerCast(BasePtr, EltPtrType);
}
/// Replace intrinsic calls
bool VisitCallInst(CallInst *Inst) {
if (!Inst->getCalledFunction() || !Inst->getCalledFunction()->isIntrinsic())
return false;
switch (Inst->getCalledFunction()->getIntrinsicID()) {
case Intrinsic::matrix_multiply:
LowerMultiply(Inst);
break;
case Intrinsic::matrix_transpose:
LowerTranspose(Inst);
break;
case Intrinsic::matrix_column_major_load:
LowerColumnMajorLoad(Inst);
break;
case Intrinsic::matrix_column_major_store:
LowerColumnMajorStore(Inst);
break;
default:
return false;
}
return true;
}
/// Compute the alignment for a column/row \p Idx with \p Stride between them.
/// The address at \p Idx == 0 has alignment \p A. If \p Stride is a
/// ConstantInt, reduce the initial alignment based on the byte offset. For
/// non-ConstantInt strides, return the common alignment of the initial
/// alignment and the element size in bytes.
Align getAlignForIndex(unsigned Idx, Value *Stride, Type *ElementTy,
MaybeAlign A) const {
Align InitialAlign = DL.getValueOrABITypeAlignment(A, ElementTy);
if (Idx == 0)
return InitialAlign;
TypeSize ElementSizeInBits = DL.getTypeSizeInBits(ElementTy);
if (auto *ConstStride = dyn_cast<ConstantInt>(Stride)) {
uint64_t StrideInBytes =
ConstStride->getZExtValue() * ElementSizeInBits / 8;
return commonAlignment(InitialAlign, Idx * StrideInBytes);
}
return commonAlignment(InitialAlign, ElementSizeInBits / 8);
}
/// Load a matrix with \p Shape starting at \p Ptr and using \p Stride between
/// vectors.
MatrixTy loadMatrix(Type *Ty, Value *Ptr, MaybeAlign MAlign, Value *Stride,
bool IsVolatile, ShapeInfo Shape, IRBuilder<> &Builder) {
auto *VType = cast<VectorType>(Ty);
Type *EltTy = VType->getElementType();
Type *VecTy = FixedVectorType::get(EltTy, Shape.getStride());
Value *EltPtr = createElementPtr(Ptr, EltTy, Builder);
MatrixTy Result;
for (unsigned I = 0, E = Shape.getNumVectors(); I < E; ++I) {
Value *GEP = computeVectorAddr(EltPtr, Builder.getInt64(I), Stride,
Shape.getStride(), EltTy, Builder);
Value *Vector = Builder.CreateAlignedLoad(
VecTy, GEP, getAlignForIndex(I, Stride, EltTy, MAlign),
IsVolatile, "col.load");
Result.addVector(Vector);
}
return Result.addNumLoads(getNumOps(Result.getVectorTy()) *
Result.getNumVectors());
}
/// Loads a sub-matrix with shape \p ResultShape from a \p R x \p C matrix,
/// starting at \p MatrixPtr[I][J].
MatrixTy loadMatrix(Value *MatrixPtr, MaybeAlign Align, bool IsVolatile,
ShapeInfo MatrixShape, Value *I, Value *J,
ShapeInfo ResultShape, Type *EltTy,
IRBuilder<> &Builder) {
Value *Offset = Builder.CreateAdd(
Builder.CreateMul(J, Builder.getInt64(MatrixShape.getStride())), I);
unsigned AS = cast<PointerType>(MatrixPtr->getType())->getAddressSpace();
Value *EltPtr =
Builder.CreatePointerCast(MatrixPtr, PointerType::get(EltTy, AS));
Value *TileStart = Builder.CreateGEP(EltTy, EltPtr, Offset);
auto *TileTy = FixedVectorType::get(EltTy, ResultShape.NumRows *
ResultShape.NumColumns);
Type *TilePtrTy = PointerType::get(TileTy, AS);
Value *TilePtr =
Builder.CreatePointerCast(TileStart, TilePtrTy, "col.cast");
return loadMatrix(TileTy, TilePtr, Align,
Builder.getInt64(MatrixShape.getStride()), IsVolatile,
ResultShape, Builder);
}
/// Lower a load instruction with shape information.
void LowerLoad(Instruction *Inst, Value *Ptr, MaybeAlign Align, Value *Stride,
bool IsVolatile, ShapeInfo Shape) {
IRBuilder<> Builder(Inst);
finalizeLowering(Inst,
loadMatrix(Inst->getType(), Ptr, Align, Stride, IsVolatile,
Shape, Builder),
Builder);
}
/// Lowers llvm.matrix.column.major.load.
///
/// The intrinsic loads a matrix from memory using a stride between columns.
void LowerColumnMajorLoad(CallInst *Inst) {
assert(MatrixLayout == MatrixLayoutTy::ColumnMajor &&
"Intrinsic only supports column-major layout!");
Value *Ptr = Inst->getArgOperand(0);
Value *Stride = Inst->getArgOperand(1);
LowerLoad(Inst, Ptr, Inst->getParamAlign(0), Stride,
cast<ConstantInt>(Inst->getArgOperand(2))->isOne(),
{Inst->getArgOperand(3), Inst->getArgOperand(4)});
}
/// Stores a sub-matrix \p StoreVal into the \p R x \p C matrix starting at \p
/// MatrixPtr[I][J].
void storeMatrix(const MatrixTy &StoreVal, Value *MatrixPtr,
MaybeAlign MAlign, bool IsVolatile, ShapeInfo MatrixShape,
Value *I, Value *J, Type *EltTy, IRBuilder<> &Builder) {
Value *Offset = Builder.CreateAdd(
Builder.CreateMul(J, Builder.getInt64(MatrixShape.getStride())), I);
unsigned AS = cast<PointerType>(MatrixPtr->getType())->getAddressSpace();
Value *EltPtr =
Builder.CreatePointerCast(MatrixPtr, PointerType::get(EltTy, AS));
Value *TileStart = Builder.CreateGEP(EltTy, EltPtr, Offset);
auto *TileTy = FixedVectorType::get(EltTy, StoreVal.getNumRows() *
StoreVal.getNumColumns());
Type *TilePtrTy = PointerType::get(TileTy, AS);
Value *TilePtr =
Builder.CreatePointerCast(TileStart, TilePtrTy, "col.cast");
storeMatrix(TileTy, StoreVal, TilePtr, MAlign,
Builder.getInt64(MatrixShape.getStride()), IsVolatile, Builder);
}
/// Store matrix \p StoreVal starting at \p Ptr and using \p Stride between
/// vectors.
MatrixTy storeMatrix(Type *Ty, MatrixTy StoreVal, Value *Ptr,
MaybeAlign MAlign, Value *Stride, bool IsVolatile,
IRBuilder<> &Builder) {
auto VType = cast<VectorType>(Ty);
Value *EltPtr = createElementPtr(Ptr, VType->getElementType(), Builder);
for (auto Vec : enumerate(StoreVal.vectors())) {
Value *GEP = computeVectorAddr(EltPtr, Builder.getInt64(Vec.index()),
Stride, StoreVal.getStride(),
VType->getElementType(), Builder);
Builder.CreateAlignedStore(Vec.value(), GEP,
getAlignForIndex(Vec.index(), Stride,
VType->getElementType(),
MAlign),
IsVolatile);
}
return MatrixTy().addNumStores(getNumOps(StoreVal.getVectorTy()) *
StoreVal.getNumVectors());
}
/// Lower a store instruction with shape information.
void LowerStore(Instruction *Inst, Value *Matrix, Value *Ptr, MaybeAlign A,
Value *Stride, bool IsVolatile, ShapeInfo Shape) {
IRBuilder<> Builder(Inst);
auto StoreVal = getMatrix(Matrix, Shape, Builder);
finalizeLowering(Inst,
storeMatrix(Matrix->getType(), StoreVal, Ptr, A, Stride,
IsVolatile, Builder),
Builder);
}
/// Lowers llvm.matrix.column.major.store.
///
/// The intrinsic store a matrix back memory using a stride between columns.
void LowerColumnMajorStore(CallInst *Inst) {
assert(MatrixLayout == MatrixLayoutTy::ColumnMajor &&
"Intrinsic only supports column-major layout!");
Value *Matrix = Inst->getArgOperand(0);
Value *Ptr = Inst->getArgOperand(1);
Value *Stride = Inst->getArgOperand(2);
LowerStore(Inst, Matrix, Ptr, Inst->getParamAlign(1), Stride,
cast<ConstantInt>(Inst->getArgOperand(3))->isOne(),
{Inst->getArgOperand(4), Inst->getArgOperand(5)});
}
// Set elements I..I+NumElts-1 to Block
Value *insertVector(Value *Col, unsigned I, Value *Block,
IRBuilder<> &Builder) {
// First, bring Block to the same size as Col
unsigned BlockNumElts =
cast<FixedVectorType>(Block->getType())->getNumElements();
unsigned NumElts = cast<FixedVectorType>(Col->getType())->getNumElements();
assert(NumElts >= BlockNumElts && "Too few elements for current block");
Block = Builder.CreateShuffleVector(
Block, createSequentialMask(0, BlockNumElts, NumElts - BlockNumElts));
// If Col is 7 long and I is 2 and BlockNumElts is 2 the mask is: 0, 1, 7,
// 8, 4, 5, 6
SmallVector<int, 16> Mask;
unsigned i;
for (i = 0; i < I; i++)
Mask.push_back(i);
unsigned VecNumElts =
cast<FixedVectorType>(Col->getType())->getNumElements();
for (; i < I + BlockNumElts; i++)
Mask.push_back(i - I + VecNumElts);
for (; i < VecNumElts; i++)
Mask.push_back(i);
return Builder.CreateShuffleVector(Col, Block, Mask);
}
Value *createMulAdd(Value *Sum, Value *A, Value *B, bool UseFPOp,
IRBuilder<> &Builder, bool AllowContraction,
unsigned &NumComputeOps) {
NumComputeOps += getNumOps(A->getType());
if (!Sum)
return UseFPOp ? Builder.CreateFMul(A, B) : Builder.CreateMul(A, B);
if (UseFPOp) {
if (AllowContraction) {
// Use fmuladd for floating point operations and let the backend decide
// if that's profitable.
Function *FMulAdd = Intrinsic::getDeclaration(
Func.getParent(), Intrinsic::fmuladd, A->getType());
return Builder.CreateCall(FMulAdd, {A, B, Sum});
}
NumComputeOps += getNumOps(A->getType());
Value *Mul = Builder.CreateFMul(A, B);
return Builder.CreateFAdd(Sum, Mul);
}
NumComputeOps += getNumOps(A->getType());
Value *Mul = Builder.CreateMul(A, B);
return Builder.CreateAdd(Sum, Mul);
}
/// Cache \p Matrix as result of \p Inst and update the uses of \p Inst. For
/// users with shape information, there's nothing to do: they will use the
/// cached value when they are lowered. For other users, \p Matrix is
/// flattened and the uses are updated to use it. Also marks \p Inst for
/// deletion.
void finalizeLowering(Instruction *Inst, MatrixTy Matrix,
IRBuilder<> &Builder) {
auto inserted = Inst2ColumnMatrix.insert(std::make_pair(Inst, Matrix));
(void)inserted;
assert(inserted.second && "multiple matrix lowering mapping");
ToRemove.push_back(Inst);
Value *Flattened = nullptr;
for (Use &U : llvm::make_early_inc_range(Inst->uses())) {
if (ShapeMap.find(U.getUser()) == ShapeMap.end()) {
if (!Flattened)
Flattened = Matrix.embedInVector(Builder);
U.set(Flattened);
}
}
}
/// Compute \p Result += \p A * \p B for input matrices with left-associating
/// addition.
///
/// We can fold a transpose into the operand that is used to extract scalars.
/// This is the first operands with row-major and the second with
/// column-major. If \p IsScalarMatrixTransposed we assume the appropriate
/// operand is transposed.
void emitMatrixMultiply(MatrixTy &Result, const MatrixTy &A,
const MatrixTy &B, IRBuilder<> &Builder, bool IsTiled,
bool IsScalarMatrixTransposed, FastMathFlags FMF) {
const unsigned VF = std::max<unsigned>(
TTI.getRegisterBitWidth(TargetTransformInfo::RGK_FixedWidthVector)
.getFixedSize() /
Result.getElementType()->getPrimitiveSizeInBits().getFixedSize(),
1U);
unsigned R = Result.getNumRows();
unsigned C = Result.getNumColumns();
unsigned M = A.getNumColumns();
bool IsFP = Result.getElementType()->isFloatingPointTy();
assert(A.isColumnMajor() == B.isColumnMajor() &&
Result.isColumnMajor() == A.isColumnMajor() &&
"operands must agree on matrix layout");
unsigned NumComputeOps = 0;
Builder.setFastMathFlags(FMF);
if (A.isColumnMajor()) {
// Multiply columns from the first operand with scalars from the second
// operand. Then move along the K axes and accumulate the columns. With
// this the adds can be vectorized without reassociation.
for (unsigned J = 0; J < C; ++J) {
unsigned BlockSize = VF;
// If Result is zero, we don't need to accumulate in the K==0 iteration.
bool isSumZero = isa<ConstantAggregateZero>(Result.getColumn(J));
for (unsigned I = 0; I < R; I += BlockSize) {
// Gradually lower the vectorization factor to cover the remainder.
while (I + BlockSize > R)
BlockSize /= 2;
Value *Sum = IsTiled ? Result.extractVector(I, J, BlockSize, Builder)
: nullptr;
for (unsigned K = 0; K < M; ++K) {
Value *L = A.extractVector(I, K, BlockSize, Builder);
Value *RH = Builder.CreateExtractElement(
B.getColumn(IsScalarMatrixTransposed ? K : J),
IsScalarMatrixTransposed ? J : K);
Value *Splat = Builder.CreateVectorSplat(BlockSize, RH, "splat");
Sum =
createMulAdd(isSumZero && K == 0 ? nullptr : Sum, L, Splat,
IsFP, Builder, FMF.allowContract(), NumComputeOps);
}
Result.setVector(J,
insertVector(Result.getVector(J), I, Sum, Builder));
}
}
} else {
// Multiply rows from the second operand with scalars from the first
// operand. Then move along the K axes and accumulate the rows. With this
// the adds can be vectorized without reassociation.
for (unsigned I = 0; I < R; ++I) {
unsigned BlockSize = VF;
bool isSumZero = isa<ConstantAggregateZero>(Result.getRow(I));
for (unsigned J = 0; J < C; J += BlockSize) {
// Gradually lower the vectorization factor to cover the remainder.
while (J + BlockSize > C)
BlockSize /= 2;
Value *Sum = nullptr;
for (unsigned K = 0; K < M; ++K) {
Value *R = B.extractVector(K, J, BlockSize, Builder);
Value *LH = Builder.CreateExtractElement(
A.getVector(IsScalarMatrixTransposed ? K : I),
IsScalarMatrixTransposed ? I : K);
Value *Splat = Builder.CreateVectorSplat(BlockSize, LH, "splat");
Sum =
createMulAdd(isSumZero && K == 0 ? nullptr : Sum, Splat, R,
IsFP, Builder, FMF.allowContract(), NumComputeOps);
}
Result.setVector(I,
insertVector(Result.getVector(I), J, Sum, Builder));
}
}
}
Result.addNumComputeOps(NumComputeOps);
}
/// Ensure that the memory in \p Load does not alias \p Store by potentially
/// copying it to a new location. This new or otherwise the original location
/// is returned.
Value *getNonAliasingPointer(LoadInst *Load, StoreInst *Store,
CallInst *MatMul) {
MemoryLocation StoreLoc = MemoryLocation::get(Store);
MemoryLocation LoadLoc = MemoryLocation::get(Load);
// If we can statically determine noalias we're good.
if (AA->isNoAlias(LoadLoc, StoreLoc))
return Load->getPointerOperand();
// Create code to check if the memory locations of the Load and Store
// overlap and if they do, copy Load's operand to a new buffer.
// First, create new blocks for 2n part of the check and the copy.
BasicBlock *Check0 = MatMul->getParent();
// FIXME: Use lazy DTU and update SplitBlock to accept a DTU instead of a
// DT. Manually collect dominator tree updates, to avoid unnecessary work,
// as we adjust Check0 and Check1's branches.
SmallVector<DominatorTree::UpdateType, 4> DTUpdates;
for (BasicBlock *Succ : successors(Check0))
DTUpdates.push_back({DT->Delete, Check0, Succ});
BasicBlock *Check1 =
SplitBlock(MatMul->getParent(), MatMul, (DomTreeUpdater *)nullptr, LI,
nullptr, "alias_cont");
BasicBlock *Copy =
SplitBlock(MatMul->getParent(), MatMul, (DomTreeUpdater *)nullptr, LI,
nullptr, "copy");
BasicBlock *Fusion =
SplitBlock(MatMul->getParent(), MatMul, (DomTreeUpdater *)nullptr, LI,
nullptr, "no_alias");
// Check if the loaded memory location begins before the end of the store
// location. If the condition holds, they might overlap, otherwise they are
// guaranteed to not overlap.
IRBuilder<> Builder(MatMul);
Check0->getTerminator()->eraseFromParent();
Builder.SetInsertPoint(Check0);
Type *IntPtrTy = Builder.getIntPtrTy(Load->getModule()->getDataLayout());
Value *StoreBegin = Builder.CreatePtrToInt(
const_cast<Value *>(StoreLoc.Ptr), IntPtrTy, "store.begin");
Value *StoreEnd = Builder.CreateAdd(
StoreBegin, ConstantInt::get(IntPtrTy, StoreLoc.Size.getValue()),
"store.end", true, true);
Value *LoadBegin = Builder.CreatePtrToInt(const_cast<Value *>(LoadLoc.Ptr),
IntPtrTy, "load.begin");
Builder.CreateCondBr(Builder.CreateICmpULT(LoadBegin, StoreEnd), Check1,
Fusion);
// Check if the store begins before the end of the load location. If the
// condition holds, they alias, otherwise they are guaranteed to not
// overlap.
Check1->getTerminator()->eraseFromParent();
Builder.SetInsertPoint(Check1, Check1->begin());
Value *LoadEnd = Builder.CreateAdd(
LoadBegin, ConstantInt::get(IntPtrTy, LoadLoc.Size.getValue()),
"load.end", true, true);
Builder.CreateCondBr(Builder.CreateICmpULT(StoreBegin, LoadEnd), Copy,
Fusion);
// Copy load operand to new alloca.
Builder.SetInsertPoint(Copy, Copy->begin());
AllocaInst *NewLd =
Builder.CreateAlloca(Load->getType(), Load->getPointerAddressSpace());
Builder.CreateMemCpy(NewLd, NewLd->getAlign(),
Load->getPointerOperand(), Load->getAlign(),
LoadLoc.Size.getValue());
Builder.SetInsertPoint(Fusion, Fusion->begin());
PHINode *PHI = Builder.CreatePHI(Load->getPointerOperandType(), 3);
PHI->addIncoming(Load->getPointerOperand(), Check0);
PHI->addIncoming(Load->getPointerOperand(), Check1);
PHI->addIncoming(NewLd, Copy);
// Adjust DT.
DTUpdates.push_back({DT->Insert, Check0, Check1});
DTUpdates.push_back({DT->Insert, Check0, Fusion});
DTUpdates.push_back({DT->Insert, Check1, Copy});
DTUpdates.push_back({DT->Insert, Check1, Fusion});
DT->applyUpdates(DTUpdates);
return PHI;
}
bool isFusionProfitable(CallInst *MatMul) {
if (ForceFusion)
return true;
ShapeInfo LShape(MatMul->getArgOperand(2), MatMul->getArgOperand(3));
ShapeInfo RShape(MatMul->getArgOperand(3), MatMul->getArgOperand(4));
const unsigned R = LShape.NumRows;
const unsigned C = RShape.NumColumns;
const unsigned M = LShape.NumColumns;
auto *EltType = cast<VectorType>(MatMul->getType())->getElementType();
const unsigned VF = std::max<unsigned>(
TTI.getRegisterBitWidth(TargetTransformInfo::RGK_FixedWidthVector)
.getFixedSize() /
EltType->getPrimitiveSizeInBits().getFixedSize(),
1U);
// Cost model for tiling
//
// For tiling to be beneficial, we need reuse either along the R or
// the C axis. We vectorize along the R axis so that means at least
// 3 elements.
// TODO: Also consider cost of copying if operands alias.
if (R <= VF && C == 1)
return false;
// Then we need enough elements to exceed the number of vector
// registers we have. Note that this is an oversimplification since
// fusing also takes some extra loads which may exceed the number of
// reloads necessary.
unsigned Op0Regs = (R + VF - 1) / VF * M;
unsigned Op1Regs = (M + VF - 1) / VF * C;
return Op0Regs + Op1Regs > TTI.getNumberOfRegisters(true);
}
MatrixTy getZeroMatrix(Type *EltType, unsigned R, unsigned C) {
MatrixTy Res;
auto *ColumType = FixedVectorType::get(EltType, R);
for (unsigned I = 0; I < C; ++I)
Res.addVector(ConstantAggregateZero::get(ColumType));
return Res;
}
void createTiledLoops(CallInst *MatMul, Value *LPtr, ShapeInfo LShape,
Value *RPtr, ShapeInfo RShape, StoreInst *Store) {
auto *EltType = cast<VectorType>(MatMul->getType())->getElementType();
// Create the main tiling loop nest.
TileInfo TI(LShape.NumRows, RShape.NumColumns, LShape.NumColumns, TileSize);
DomTreeUpdater DTU(DT, DomTreeUpdater::UpdateStrategy::Lazy);
Instruction *InsertI = cast<Instruction>(MatMul);
BasicBlock *Start = InsertI->getParent();
BasicBlock *End =
SplitBlock(InsertI->getParent(), InsertI, DT, LI, nullptr, "continue");
IRBuilder<> Builder(MatMul);
BasicBlock *InnerBody = TI.CreateTiledLoops(Start, End, Builder, DTU, *LI);
Type *TileVecTy =
FixedVectorType::get(MatMul->getType()->getScalarType(), TileSize);
MatrixTy TileResult;
// Insert in the inner loop header.
Builder.SetInsertPoint(TI.InnerLoopHeader->getTerminator());
// Create PHI nodes for the result columns to accumulate across iterations.
SmallVector<PHINode *, 4> ColumnPhis;
for (unsigned I = 0; I < TileSize; I++) {
auto *Phi = Builder.CreatePHI(TileVecTy, 2, "result.vec." + Twine(I));
Phi->addIncoming(ConstantAggregateZero::get(TileVecTy),
TI.RowLoopHeader->getSingleSuccessor());
TileResult.addVector(Phi);
ColumnPhis.push_back(Phi);
}
// Insert in the inner loop body, which computes
// Res += Load(CurrentRow, K) * Load(K, CurrentColumn)
Builder.SetInsertPoint(InnerBody->getTerminator());
// Load tiles of the operands.
MatrixTy A = loadMatrix(LPtr, {}, false, LShape, TI.CurrentRow, TI.CurrentK,
{TileSize, TileSize}, EltType, Builder);
MatrixTy B = loadMatrix(RPtr, {}, false, RShape, TI.CurrentK, TI.CurrentCol,
{TileSize, TileSize}, EltType, Builder);
emitMatrixMultiply(TileResult, A, B, Builder, true, false,
getFastMathFlags(MatMul));
// Store result after the inner loop is done.
Builder.SetInsertPoint(TI.RowLoopLatch->getTerminator());
storeMatrix(TileResult, Store->getPointerOperand(), Store->getAlign(),
Store->isVolatile(), {LShape.NumRows, RShape.NumColumns},
TI.CurrentRow, TI.CurrentCol, EltType, Builder);
for (unsigned I = 0; I < TileResult.getNumVectors(); I++)
ColumnPhis[I]->addIncoming(TileResult.getVector(I), TI.InnerLoopLatch);
// Force unrolling of a few iterations of the inner loop, to make sure there
// is enough work per iteration.
// FIXME: The unroller should make this decision directly instead, but
// currently the cost-model is not up to the task.
unsigned InnerLoopUnrollCount = std::min(10u, LShape.NumColumns / TileSize);
addStringMetadataToLoop(LI->getLoopFor(TI.InnerLoopHeader),
"llvm.loop.unroll.count", InnerLoopUnrollCount);
}
void emitSIMDTiling(CallInst *MatMul, LoadInst *LoadOp0, LoadInst *LoadOp1,
StoreInst *Store,
SmallPtrSetImpl<Instruction *> &FusedInsts) {
assert(MatrixLayout == MatrixLayoutTy::ColumnMajor &&
"Tiling only supported for column-major matrixes at the moment!");
if (!isFusionProfitable(MatMul))
return;
ShapeInfo LShape(MatMul->getArgOperand(2), MatMul->getArgOperand(3));
ShapeInfo RShape(MatMul->getArgOperand(3), MatMul->getArgOperand(4));
const unsigned R = LShape.NumRows;
const unsigned C = RShape.NumColumns;
const unsigned M = LShape.NumColumns;
auto *EltType = cast<VectorType>(MatMul->getType())->getElementType();
Value *APtr = getNonAliasingPointer(LoadOp0, Store, MatMul);
Value *BPtr = getNonAliasingPointer(LoadOp1, Store, MatMul);
Value *CPtr = Store->getPointerOperand();
if (TileUseLoops && (R % TileSize == 0 && C % TileSize == 0))
createTiledLoops(MatMul, APtr, LShape, BPtr, RShape, Store);
else {
IRBuilder<> Builder(Store);
for (unsigned J = 0; J < C; J += TileSize)
for (unsigned I = 0; I < R; I += TileSize) {
const unsigned TileR = std::min(R - I, unsigned(TileSize));
const unsigned TileC = std::min(C - J, unsigned(TileSize));
MatrixTy Res = getZeroMatrix(EltType, TileR, TileC);
for (unsigned K = 0; K < M; K += TileSize) {
const unsigned TileM = std::min(M - K, unsigned(TileSize));
MatrixTy A =
loadMatrix(APtr, LoadOp0->getAlign(), LoadOp0->isVolatile(),
LShape, Builder.getInt64(I), Builder.getInt64(K),
{TileR, TileM}, EltType, Builder);
MatrixTy B =
loadMatrix(BPtr, LoadOp1->getAlign(), LoadOp1->isVolatile(),
RShape, Builder.getInt64(K), Builder.getInt64(J),
{TileM, TileC}, EltType, Builder);
emitMatrixMultiply(Res, A, B, Builder, true, false,
getFastMathFlags(MatMul));
}
storeMatrix(Res, CPtr, Store->getAlign(), Store->isVolatile(), {R, M},
Builder.getInt64(I), Builder.getInt64(J), EltType,
Builder);
}
}
// Mark eliminated instructions as fused and remove them.
FusedInsts.insert(Store);
FusedInsts.insert(MatMul);
Store->eraseFromParent();
MatMul->eraseFromParent();
if (LoadOp0->hasNUses(0)) {
FusedInsts.insert(LoadOp0);
LoadOp0->eraseFromParent();
}
if (LoadOp1 != LoadOp0 && LoadOp1->hasNUses(0)) {
FusedInsts.insert(LoadOp1);
LoadOp1->eraseFromParent();
}
}
/// Try to lower matrix multiply chains by fusing operations.
///
/// Call finalizeLowering on lowered instructions. Instructions that are
/// completely eliminated by fusion are added to \p FusedInsts.
void LowerMatrixMultiplyFused(CallInst *MatMul,
SmallPtrSetImpl<Instruction *> &FusedInsts) {
if (!FuseMatrix || !DT)
return;
assert(AA && LI && "Analyses should be available");
Value *A = MatMul->getArgOperand(0);
Value *B = MatMul->getArgOperand(1);
// We can fold the transpose into the operand that is used to fetch scalars.
Value *T;
if (MatrixLayout == MatrixLayoutTy::ColumnMajor
? match(B, m_Intrinsic<Intrinsic::matrix_transpose>(m_Value(T)))
: match(A, m_Intrinsic<Intrinsic::matrix_transpose>(m_Value(T)))) {
IRBuilder<> Builder(MatMul);
auto *EltType = cast<VectorType>(MatMul->getType())->getElementType();
ShapeInfo LShape(MatMul->getArgOperand(2), MatMul->getArgOperand(3));
ShapeInfo RShape(MatMul->getArgOperand(3), MatMul->getArgOperand(4));
const unsigned R = LShape.NumRows;
const unsigned M = LShape.NumColumns;
const unsigned C = RShape.NumColumns;
MatrixTy MA;
MatrixTy MB;
Value *Transpose;
if (MatrixLayout == MatrixLayoutTy::ColumnMajor) {
MA = getMatrix(A, ShapeInfo(R, M), Builder);
MB = getMatrix(T, ShapeInfo(C, M), Builder);
Transpose = B;
} else {
MA = getMatrix(T, ShapeInfo(R, M), Builder);
MB = getMatrix(B, ShapeInfo(C, M), Builder);
Transpose = A;
}
// Initialize the output
MatrixTy Result(R, C, EltType);
emitMatrixMultiply(Result, MA, MB, Builder, false, true,
getFastMathFlags(MatMul));
FusedInsts.insert(MatMul);
if (Transpose->hasOneUse()) {
FusedInsts.insert(cast<Instruction>(Transpose));
ToRemove.push_back(cast<Instruction>(Transpose));
// TODO: add a fake entry for the folded instruction so that this is
// included in the expression in the remark.
Inst2ColumnMatrix[Transpose] = MatrixTy(M, C, EltType);
}
finalizeLowering(MatMul, Result, Builder);
return;
}
if (!MatMul->hasOneUse() || MatrixLayout != MatrixLayoutTy::ColumnMajor)
return;
// Lower {ld, ld} -> matmul -> st chains. No need to call finalizeLowering
// since the single store user will be lowered as part of this.
auto *LoadOp0 = dyn_cast<LoadInst>(A);
auto *LoadOp1 = dyn_cast<LoadInst>(B);
auto *Store = dyn_cast<StoreInst>(*MatMul->user_begin());
if (LoadOp0 && LoadOp1 && Store) {
// The store address must dominate the MatMul instruction, otherwise
// we create invalid IR.
SetVector<Value *> WorkList;
WorkList.insert(Store->getOperand(1));
SmallVector<Instruction *> ToHoist;
for (unsigned I = 0; I != WorkList.size(); ++I) {
Value *Current = WorkList[I];
auto *CurrI = dyn_cast<Instruction>(Current);
if (!CurrI)
continue;
if (isa<PHINode>(CurrI))
return;
if (DT->dominates(CurrI, MatMul))
continue;
if (CurrI->mayHaveSideEffects() || CurrI->mayReadFromMemory())
return;
ToHoist.push_back(CurrI);
WorkList.insert(CurrI->op_begin(), CurrI->op_end());
}
sort(ToHoist, [this](Instruction *A, Instruction *B) {
return DT->dominates(A, B);
});
for (Instruction *I : ToHoist)
I->moveBefore(MatMul);
emitSIMDTiling(MatMul, LoadOp0, LoadOp1, Store, FusedInsts);
return;
}
}
/// Lowers llvm.matrix.multiply.
void LowerMultiply(CallInst *MatMul) {
IRBuilder<> Builder(MatMul);
auto *EltType = cast<VectorType>(MatMul->getType())->getElementType();
ShapeInfo LShape(MatMul->getArgOperand(2), MatMul->getArgOperand(3));
ShapeInfo RShape(MatMul->getArgOperand(3), MatMul->getArgOperand(4));
const MatrixTy &Lhs = getMatrix(MatMul->getArgOperand(0), LShape, Builder);
const MatrixTy &Rhs = getMatrix(MatMul->getArgOperand(1), RShape, Builder);
assert(Lhs.getElementType() == Rhs.getElementType() &&
"Matrix multiply argument element types do not match.");
const unsigned R = LShape.NumRows;
const unsigned C = RShape.NumColumns;
assert(LShape.NumColumns == RShape.NumRows);
// Initialize the output
MatrixTy Result(R, C, EltType);
assert(Lhs.getElementType() == Result.getElementType() &&
"Matrix multiply result element type does not match arguments.");
emitMatrixMultiply(Result, Lhs, Rhs, Builder, false, false,
getFastMathFlags(MatMul));
finalizeLowering(MatMul, Result, Builder);
}
/// Lowers llvm.matrix.transpose.
void LowerTranspose(CallInst *Inst) {
MatrixTy Result;
IRBuilder<> Builder(Inst);
Value *InputVal = Inst->getArgOperand(0);
VectorType *VectorTy = cast<VectorType>(InputVal->getType());
ShapeInfo ArgShape(Inst->getArgOperand(1), Inst->getArgOperand(2));
MatrixTy InputMatrix = getMatrix(InputVal, ArgShape, Builder);
const unsigned NewNumVecs =
InputMatrix.isColumnMajor() ? ArgShape.NumRows : ArgShape.NumColumns;
const unsigned NewNumElts =
InputMatrix.isColumnMajor() ? ArgShape.NumColumns : ArgShape.NumRows;
for (unsigned I = 0; I < NewNumVecs; ++I) {
// Build a single result vector. First initialize it.
Value *ResultVector = UndefValue::get(
FixedVectorType::get(VectorTy->getElementType(), NewNumElts));
// Go through the old elements and insert it into the resulting vector.
for (auto J : enumerate(InputMatrix.vectors())) {
Value *Elt = Builder.CreateExtractElement(J.value(), I);
// Row and column indices are transposed.
ResultVector =
Builder.CreateInsertElement(ResultVector, Elt, J.index());
}
Result.addVector(ResultVector);
}
// TODO: Improve estimate of operations needed for transposes. Currently we
// just count the insertelement/extractelement instructions, but do not
// account for later simplifications/combines.
finalizeLowering(
Inst,
Result.addNumComputeOps(2 * ArgShape.NumRows * ArgShape.NumColumns)
.addNumExposedTransposes(1),
Builder);
}
/// Lower load instructions, if shape information is available.
bool VisitLoad(LoadInst *Inst, Value *Ptr, IRBuilder<> &Builder) {
auto I = ShapeMap.find(Inst);
if (I == ShapeMap.end())
return false;
LowerLoad(Inst, Ptr, Inst->getAlign(),
Builder.getInt64(I->second.getStride()), Inst->isVolatile(),
I->second);
return true;
}
bool VisitStore(StoreInst *Inst, Value *StoredVal, Value *Ptr,
IRBuilder<> &Builder) {
auto I = ShapeMap.find(StoredVal);
if (I == ShapeMap.end())
return false;
LowerStore(Inst, StoredVal, Ptr, Inst->getAlign(),
Builder.getInt64(I->second.getStride()), Inst->isVolatile(),
I->second);
return true;
}
/// Lower binary operators, if shape information is available.
bool VisitBinaryOperator(BinaryOperator *Inst) {
auto I = ShapeMap.find(Inst);
if (I == ShapeMap.end())
return false;
Value *Lhs = Inst->getOperand(0);
Value *Rhs = Inst->getOperand(1);
IRBuilder<> Builder(Inst);
ShapeInfo &Shape = I->second;
MatrixTy Result;
MatrixTy A = getMatrix(Lhs, Shape, Builder);
MatrixTy B = getMatrix(Rhs, Shape, Builder);
assert(A.isColumnMajor() == B.isColumnMajor() &&
Result.isColumnMajor() == A.isColumnMajor() &&
"operands must agree on matrix layout");
Builder.setFastMathFlags(getFastMathFlags(Inst));
// Helper to perform binary op on vectors.
auto BuildVectorOp = [&Builder, Inst](Value *LHS, Value *RHS) {
switch (Inst->getOpcode()) {
case Instruction::Add:
return Builder.CreateAdd(LHS, RHS);
case Instruction::Mul:
return Builder.CreateMul(LHS, RHS);
case Instruction::Sub:
return Builder.CreateSub(LHS, RHS);
case Instruction::FAdd:
return Builder.CreateFAdd(LHS, RHS);
case Instruction::FMul:
return Builder.CreateFMul(LHS, RHS);
case Instruction::FSub:
return Builder.CreateFSub(LHS, RHS);
default:
llvm_unreachable("Unsupported binary operator for matrix");
}
};
for (unsigned I = 0; I < Shape.getNumVectors(); ++I)
Result.addVector(BuildVectorOp(A.getVector(I), B.getVector(I)));
finalizeLowering(Inst,
Result.addNumComputeOps(getNumOps(Result.getVectorTy()) *
Result.getNumVectors()),
Builder);
return true;
}
/// Lower unary operators, if shape information is available.
bool VisitUnaryOperator(UnaryOperator *Inst) {
auto I = ShapeMap.find(Inst);
if (I == ShapeMap.end())
return false;
Value *Op = Inst->getOperand(0);
IRBuilder<> Builder(Inst);
ShapeInfo &Shape = I->second;
MatrixTy Result;
MatrixTy M = getMatrix(Op, Shape, Builder);
Builder.setFastMathFlags(getFastMathFlags(Inst));
// Helper to perform unary op on vectors.
auto BuildVectorOp = [&Builder, Inst](Value *Op) {
switch (Inst->getOpcode()) {
case Instruction::FNeg:
return Builder.CreateFNeg(Op);
default:
llvm_unreachable("Unsupported unary operator for matrix");
}
};
for (unsigned I = 0; I < Shape.getNumVectors(); ++I)
Result.addVector(BuildVectorOp(M.getVector(I)));
finalizeLowering(Inst,
Result.addNumComputeOps(getNumOps(Result.getVectorTy()) *
Result.getNumVectors()),
Builder);
return true;
}
/// Helper to linearize a matrix expression tree into a string. Currently
/// matrix expressions are linarized by starting at an expression leaf and
/// linearizing bottom up.
struct ExprLinearizer {
unsigned LengthToBreak = 100;
std::string Str;
raw_string_ostream Stream;
unsigned LineLength = 0;
const DataLayout &DL;
/// Mapping from instructions to matrixes. It is used to identify
/// matrix instructions.
const MapVector<Value *, MatrixTy> &Inst2Matrix;
/// Mapping from values to the leaves of all expressions that the value is
/// part of.
const DenseMap<Value *, SmallPtrSet<Value *, 2>> &Shared;
/// Set of matrix expressions in the scope of a given DISubprogram.
const SmallSetVector<Value *, 32> &ExprsInSubprogram;
/// Leaf node of the expression to linearize.
Value *Leaf;
/// Used to keep track of sub-expressions that get reused while linearizing
/// the expression. Re-used sub-expressions are marked as (reused).
SmallPtrSet<Value *, 8> ReusedExprs;
ExprLinearizer(const DataLayout &DL,
const MapVector<Value *, MatrixTy> &Inst2Matrix,
const DenseMap<Value *, SmallPtrSet<Value *, 2>> &Shared,
const SmallSetVector<Value *, 32> &ExprsInSubprogram,
Value *Leaf)
: Str(), Stream(Str), DL(DL), Inst2Matrix(Inst2Matrix), Shared(Shared),
ExprsInSubprogram(ExprsInSubprogram), Leaf(Leaf) {}
void indent(unsigned N) {
LineLength += N;
for (unsigned i = 0; i < N; i++)
Stream << " ";
}
void lineBreak() {
Stream << "\n";
LineLength = 0;
}
void maybeIndent(unsigned Indent) {
if (LineLength >= LengthToBreak)
lineBreak();
if (LineLength == 0)
indent(Indent);
}
void write(StringRef S) {
LineLength += S.size();
Stream << S;
}
Value *getUnderlyingObjectThroughLoads(Value *V) {
if (Value *Ptr = getPointerOperand(V))
return getUnderlyingObjectThroughLoads(Ptr);
else if (V->getType()->isPointerTy())
return getUnderlyingObject(V);
return V;
}
/// Returns true if \p V is a matrix value in the given subprogram.
bool isMatrix(Value *V) const { return ExprsInSubprogram.count(V); }
/// If \p V is a matrix value, print its shape as as NumRows x NumColumns to
/// \p SS.
void prettyPrintMatrixType(Value *V, raw_string_ostream &SS) {
auto M = Inst2Matrix.find(V);
if (M == Inst2Matrix.end())
SS << "unknown";
else {
SS << M->second.getNumRows();
SS << "x";
SS << M->second.getNumColumns();
}
}
/// Write the called function name. Handles calls to llvm.matrix.*
/// specially: we write the name, followed by the dimensions of the input
/// matrixes, followed by the scalar type name.
void writeFnName(CallInst *CI) {
if (!CI->getCalledFunction())
write("<no called fn>");
else {
StringRef Name = CI->getCalledFunction()->getName();
if (!Name.startswith("llvm.matrix")) {
write(Name);
return;
}
IntrinsicInst *II = dyn_cast<IntrinsicInst>(CI);
write(Intrinsic::getBaseName(II->getIntrinsicID())
.drop_front(StringRef("llvm.matrix.").size()));
write(".");
std::string Tmp;
raw_string_ostream SS(Tmp);
switch (II->getIntrinsicID()) {
case Intrinsic::matrix_multiply:
prettyPrintMatrixType(II->getOperand(0), SS);
SS << ".";
prettyPrintMatrixType(II->getOperand(1), SS);
SS << "." << *II->getType()->getScalarType();
break;
case Intrinsic::matrix_transpose:
prettyPrintMatrixType(II->getOperand(0), SS);
SS << "." << *II->getType()->getScalarType();
break;
case Intrinsic::matrix_column_major_load:
prettyPrintMatrixType(II, SS);
SS << "." << *II->getType()->getScalarType();
break;
case Intrinsic::matrix_column_major_store:
prettyPrintMatrixType(II->getOperand(0), SS);
SS << "." << *II->getOperand(0)->getType()->getScalarType();
break;
default:
llvm_unreachable("Unhandled case");
}
SS.flush();
write(Tmp);
}
}
unsigned getNumShapeArgs(CallInst *CI) const {
if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(CI)) {
switch (II->getIntrinsicID()) {
case Intrinsic::matrix_multiply:
return 3;
case Intrinsic::matrix_transpose:
return 2;
case Intrinsic::matrix_column_major_load:
case Intrinsic::matrix_column_major_store:
return 3;
default:
return 0;
}
}
return 0;
}
/// Special printing for values: for pointers, we print if they refer to an
/// (function) external address or a stack address, for other values we
/// either print the constant or "scalar"/"matrix" for other values.
void write(Value *V) {
V = getUnderlyingObjectThroughLoads(V);
if (V->getType()->isPointerTy()) {
if (isa<AllocaInst>(V)) {
Stream << "stack addr";
LineLength += StringRef("stack addr").size();
} else {
Stream << "addr";
LineLength += StringRef("addr").size();
}
if (!V->getName().empty()) {
Stream << " %" << V->getName() << "";
LineLength += V->getName().size() + 2;
}
return;
}
std::string Tmp;
raw_string_ostream TmpStream(Tmp);
if (auto *CI = dyn_cast<ConstantInt>(V))
TmpStream << CI->getValue();
else if (isa<Constant>(V))
TmpStream << "constant";
else {
if (isMatrix(V))
TmpStream << "matrix";
else
TmpStream << "scalar";
}
TmpStream.flush();
Tmp = std::string(StringRef(Tmp).trim());
LineLength += Tmp.size();
Stream << Tmp;
}
/// Linearize expression \p Expr starting at an indentation of \p Indent.
/// Expressions that are re-used multiple times are prefixed with (reused)
/// at the re-used root instruction.
void linearizeExpr(Value *Expr, unsigned Indent, bool ParentReused,
bool ParentShared) {
auto *I = cast<Instruction>(Expr);
maybeIndent(Indent);
SmallVector<Value *, 8> Ops;
// Is Expr shared with other expression leaves?
bool ExprShared = false;
// Deal with shared subtrees. Mark them as shared, if required.
if (!ParentShared) {
auto SI = Shared.find(Expr);
assert(SI != Shared.end() && SI->second.count(Leaf));
for (Value *S : SI->second) {
if (S == Leaf)
continue;
DebugLoc DL = cast<Instruction>(S)->getDebugLoc();
write("shared with remark at line " + std::to_string(DL.getLine()) +
" column " + std::to_string(DL.getCol()) + " (");
}
ExprShared = SI->second.size() > 1;
}
bool Reused = !ReusedExprs.insert(Expr).second;
if (Reused && !ParentReused)
write("(reused) ");
if (auto *CI = dyn_cast<CallInst>(I)) {
writeFnName(CI);
Ops.append(CI->arg_begin(), CI->arg_end() - getNumShapeArgs(CI));
} else if (isa<BitCastInst>(Expr)) {
// Special case bitcasts, which are used to materialize matrixes from
// non-matrix ops.
write("matrix");
return;
} else {
Ops.append(I->value_op_begin(), I->value_op_end());
write(std::string(I->getOpcodeName()));
}
write(std::string("("));
unsigned NumOpsToBreak = 1;
if (match(Expr, m_Intrinsic<Intrinsic::matrix_column_major_load>()))
NumOpsToBreak = 2;
for (Value *Op : Ops) {
if (Ops.size() > NumOpsToBreak)
lineBreak();
maybeIndent(Indent + 1);
if (isMatrix(Op))
linearizeExpr(Op, Indent + 1, Reused, ExprShared);
else
write(Op);
if (Op != Ops.back())
write(", ");
}
write(")");
}
const std::string &getResult() {
Stream.flush();
return Str;
}
};
/// Generate remarks for matrix operations in a function. To generate remarks
/// for matrix expressions, the following approach is used:
/// 1. Use the inlined-at debug information to group matrix operations to the
/// DISubprograms they are contained in.
/// 2. Collect leaves of matrix expressions (done in
/// RemarkGenerator::getExpressionLeaves) for each subprogram - expression
// mapping. Leaves are lowered matrix instructions without other matrix
// users (like stores) in the current subprogram.
/// 3. For each leaf, create a remark containing a linearizied version of the
/// matrix expression. The expression is linearized by a recursive
/// bottom-up traversal of the matrix operands, starting at a leaf. Note
/// that multiple leaves can share sub-expressions. Shared subexpressions
/// are explicitly marked as shared().
struct RemarkGenerator {
const MapVector<Value *, MatrixTy> &Inst2Matrix;
OptimizationRemarkEmitter &ORE;
Function &Func;
const DataLayout &DL;
RemarkGenerator(const MapVector<Value *, MatrixTy> &Inst2Matrix,
OptimizationRemarkEmitter &ORE, Function &Func)
: Inst2Matrix(Inst2Matrix), ORE(ORE), Func(Func),
DL(Func.getParent()->getDataLayout()) {}
/// Return all leaves of the expressions in \p ExprsInSubprogram. Those are
/// instructions in Inst2Matrix returning void or without any users in
/// \p ExprsInSubprogram. Currently that should only include stores.
SmallVector<Value *, 4>
getExpressionLeaves(const SmallSetVector<Value *, 32> &ExprsInSubprogram) {
SmallVector<Value *, 4> Leaves;
for (auto *Expr : ExprsInSubprogram)
if (Expr->getType()->isVoidTy() ||
!any_of(Expr->users(), [&ExprsInSubprogram](User *U) {
return ExprsInSubprogram.count(U);
}))
Leaves.push_back(Expr);
return Leaves;
}
/// Recursively traverse expression \p V starting at \p Leaf and add \p Leaf
/// to all visited expressions in \p Shared. Limit the matrix operations to
/// the ones in \p ExprsInSubprogram.
void collectSharedInfo(Value *Leaf, Value *V,
const SmallSetVector<Value *, 32> &ExprsInSubprogram,
DenseMap<Value *, SmallPtrSet<Value *, 2>> &Shared) {
if (!ExprsInSubprogram.count(V))
return;
auto I = Shared.insert({V, {}});
I.first->second.insert(Leaf);
for (Value *Op : cast<Instruction>(V)->operand_values())
collectSharedInfo(Leaf, Op, ExprsInSubprogram, Shared);
}
/// Calculate the number of exclusive and shared op counts for expression
/// starting at \p V. Expressions used multiple times are counted once.
/// Limit the matrix operations to the ones in \p ExprsInSubprogram.
std::pair<OpInfoTy, OpInfoTy>
sumOpInfos(Value *Root, SmallPtrSetImpl<Value *> &ReusedExprs,
const SmallSetVector<Value *, 32> &ExprsInSubprogram,
DenseMap<Value *, SmallPtrSet<Value *, 2>> &Shared) const {
if (!ExprsInSubprogram.count(Root))
return {};
// Already counted this expression. Stop.
if (!ReusedExprs.insert(Root).second)
return {};
OpInfoTy SharedCount;
OpInfoTy Count;
auto I = Shared.find(Root);
auto CM = Inst2Matrix.find(Root);
if (I->second.size() == 1)
Count = CM->second.getOpInfo();
else
SharedCount = CM->second.getOpInfo();
for (Value *Op : cast<Instruction>(Root)->operand_values()) {
auto C = sumOpInfos(Op, ReusedExprs, ExprsInSubprogram, Shared);
Count += C.first;
SharedCount += C.second;
}
return {Count, SharedCount};
}
void emitRemarks() {
if (!ORE.allowExtraAnalysis(DEBUG_TYPE))
return;
// Map matrix operations to their containting subprograms, by traversing
// the inlinedAt chain. If the function does not have a DISubprogram, we
// only map them to the containing function.
MapVector<DISubprogram *, SmallVector<Value *, 8>> Subprog2Exprs;
for (auto &KV : Inst2Matrix) {
if (Func.getSubprogram()) {
auto *I = cast<Instruction>(KV.first);
DILocation *Context = I->getDebugLoc();
while (Context) {
auto I =
Subprog2Exprs.insert({getSubprogram(Context->getScope()), {}});
I.first->second.push_back(KV.first);
Context = DebugLoc(Context).getInlinedAt();
}
} else {
auto I = Subprog2Exprs.insert({nullptr, {}});
I.first->second.push_back(KV.first);
}
}
for (auto &KV : Subprog2Exprs) {
SmallSetVector<Value *, 32> ExprsInSubprogram(KV.second.begin(),
KV.second.end());
auto Leaves = getExpressionLeaves(ExprsInSubprogram);
DenseMap<Value *, SmallPtrSet<Value *, 2>> Shared;
for (Value *Leaf : Leaves)
collectSharedInfo(Leaf, Leaf, ExprsInSubprogram, Shared);
// Generate remarks for each leaf.
for (auto *L : Leaves) {
DebugLoc Loc = cast<Instruction>(L)->getDebugLoc();
DILocation *Context = cast<Instruction>(L)->getDebugLoc();
while (Context) {
if (getSubprogram(Context->getScope()) == KV.first) {
Loc = Context;
break;
}
Context = DebugLoc(Context).getInlinedAt();
}
SmallPtrSet<Value *, 8> ReusedExprs;
OpInfoTy Counts, SharedCounts;
std::tie(Counts, SharedCounts) =
sumOpInfos(L, ReusedExprs, ExprsInSubprogram, Shared);
OptimizationRemark Rem(DEBUG_TYPE, "matrix-lowered", Loc,
cast<Instruction>(L)->getParent());
Rem << "Lowered with ";
Rem << ore::NV("NumStores", Counts.NumStores) << " stores, "
<< ore::NV("NumLoads", Counts.NumLoads) << " loads, "
<< ore::NV("NumComputeOps", Counts.NumComputeOps)
<< " compute ops, "
<< ore::NV("NumExposedTransposes", Counts.NumExposedTransposes)
<< " exposed transposes";
if (SharedCounts.NumStores > 0 || SharedCounts.NumLoads > 0 ||
SharedCounts.NumComputeOps > 0) {
Rem << ",\nadditionally "
<< ore::NV("NumStores", SharedCounts.NumStores) << " stores, "
<< ore::NV("NumLoads", SharedCounts.NumLoads) << " loads, "
<< ore::NV("NumFPOps", SharedCounts.NumComputeOps)
<< " compute ops"
<< " are shared with other expressions";
}
Rem << ("\n" + linearize(L, Shared, ExprsInSubprogram, DL));
ORE.emit(Rem);
}
}
}
std::string
linearize(Value *L,
const DenseMap<Value *, SmallPtrSet<Value *, 2>> &Shared,
const SmallSetVector<Value *, 32> &ExprsInSubprogram,
const DataLayout &DL) {
ExprLinearizer Lin(DL, Inst2Matrix, Shared, ExprsInSubprogram, L);
Lin.linearizeExpr(L, 0, false, false);
return Lin.getResult();
}
};
};
} // namespace
PreservedAnalyses LowerMatrixIntrinsicsPass::run(Function &F,
FunctionAnalysisManager &AM) {
auto &TTI = AM.getResult<TargetIRAnalysis>(F);
OptimizationRemarkEmitter *ORE = nullptr;
AAResults *AA = nullptr;
DominatorTree *DT = nullptr;
LoopInfo *LI = nullptr;
if (!Minimal) {
ORE = &AM.getResult<OptimizationRemarkEmitterAnalysis>(F);
AA = &AM.getResult<AAManager>(F);
DT = &AM.getResult<DominatorTreeAnalysis>(F);
LI = &AM.getResult<LoopAnalysis>(F);
}
LowerMatrixIntrinsics LMT(F, TTI, AA, DT, LI, ORE);
if (LMT.Visit()) {
PreservedAnalyses PA;
if (!Minimal) {
PA.preserve<LoopAnalysis>();
PA.preserve<DominatorTreeAnalysis>();
}
return PA;
}
return PreservedAnalyses::all();
}
namespace {
class LowerMatrixIntrinsicsLegacyPass : public FunctionPass {
public:
static char ID;
LowerMatrixIntrinsicsLegacyPass() : FunctionPass(ID) {
initializeLowerMatrixIntrinsicsLegacyPassPass(
*PassRegistry::getPassRegistry());
}
bool runOnFunction(Function &F) override {
auto &TTI = getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
auto &ORE = getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
auto &AA = getAnalysis<AAResultsWrapperPass>().getAAResults();
auto &DT = getAnalysis<DominatorTreeWrapperPass>().getDomTree();
auto &LI = getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
LowerMatrixIntrinsics LMT(F, TTI, &AA, &DT, &LI, &ORE);
bool C = LMT.Visit();
return C;
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
AU.addRequired<AAResultsWrapperPass>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addPreserved<DominatorTreeWrapperPass>();
AU.addRequired<LoopInfoWrapperPass>();
AU.addPreserved<LoopInfoWrapperPass>();
}
};
} // namespace
static const char pass_name[] = "Lower the matrix intrinsics";
char LowerMatrixIntrinsicsLegacyPass::ID = 0;
INITIALIZE_PASS_BEGIN(LowerMatrixIntrinsicsLegacyPass, DEBUG_TYPE, pass_name,
false, false)
INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_END(LowerMatrixIntrinsicsLegacyPass, DEBUG_TYPE, pass_name,
false, false)
Pass *llvm::createLowerMatrixIntrinsicsPass() {
return new LowerMatrixIntrinsicsLegacyPass();
}
namespace {
/// A lightweight version of the matrix lowering pass that only requires TTI.
/// Advanced features that require DT, AA or ORE like tiling are disabled. This
/// is used to lower matrix intrinsics if the main lowering pass is not run, for
/// example with -O0.
class LowerMatrixIntrinsicsMinimalLegacyPass : public FunctionPass {
public:
static char ID;
LowerMatrixIntrinsicsMinimalLegacyPass() : FunctionPass(ID) {
initializeLowerMatrixIntrinsicsMinimalLegacyPassPass(
*PassRegistry::getPassRegistry());
}
bool runOnFunction(Function &F) override {
auto &TTI = getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
LowerMatrixIntrinsics LMT(F, TTI, nullptr, nullptr, nullptr, nullptr);
bool C = LMT.Visit();
return C;
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.setPreservesCFG();
}
};
} // namespace
static const char pass_name_minimal[] = "Lower the matrix intrinsics (minimal)";
char LowerMatrixIntrinsicsMinimalLegacyPass::ID = 0;
INITIALIZE_PASS_BEGIN(LowerMatrixIntrinsicsMinimalLegacyPass,
"lower-matrix-intrinsics-minimal", pass_name_minimal,
false, false)
INITIALIZE_PASS_END(LowerMatrixIntrinsicsMinimalLegacyPass,
"lower-matrix-intrinsics-minimal", pass_name_minimal, false,
false)
Pass *llvm::createLowerMatrixIntrinsicsMinimalPass() {
return new LowerMatrixIntrinsicsMinimalLegacyPass();
}
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