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llvm-project/mlir/lib/Conversion/VectorToGPU/VectorToGPU.cpp

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//===- VectorToGPU.cpp - Convert vector to GPU dialect ----------*- 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
//
//===----------------------------------------------------------------------===//
//
// This file implements lowering of vector operations to GPU dialect ops.
//
//===----------------------------------------------------------------------===//
#include <type_traits>
#include "mlir/Conversion/VectorToGPU/VectorToGPU.h"
#include "../PassDetail.h"
#include "mlir/Analysis/SliceAnalysis.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/GPU/GPUDialect.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
#include "mlir/IR/Builders.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/Passes.h"
using namespace mlir;
// Return true if the contract op can be convert to MMA matmul.
static bool contractSupportsMMAMatrixType(vector::ContractionOp contract) {
if (llvm::size(contract.masks()) != 0)
return false;
using MapList = ArrayRef<ArrayRef<AffineExpr>>;
auto infer = [](MapList m) { return AffineMap::inferFromExprList(m); };
AffineExpr m, n, k;
bindDims(contract.getContext(), m, n, k);
auto iteratorTypes = contract.iterator_types().getValue();
if (!(isParallelIterator(iteratorTypes[0]) &&
isParallelIterator(iteratorTypes[1]) &&
isReductionIterator(iteratorTypes[2])))
return false;
// The contract needs to represent a matmul to be able to convert to
// MMAMatrix matmul.
if (contract.getIndexingMaps() != infer({{m, k}, {k, n}, {m, n}}))
return false;
return true;
}
// Return the stide for the dimension 0 of |type| if it is a memref and has a
// constant stride.
static llvm::Optional<int64_t>
getMemrefConstantHorizontalStride(ShapedType type) {
auto memrefType = type.dyn_cast<MemRefType>();
if (!memrefType)
return false;
// If the memref is 0 or 1D the horizontal stride is 0.
if(memrefType.getRank() < 2)
return 0;
int64_t offset = 0;
SmallVector<int64_t, 2> strides;
if (failed(getStridesAndOffset(memrefType, strides, offset)))
return llvm::None;
int64_t stride = strides[strides.size() - 2];
if (stride == ShapedType::kDynamicStrideOrOffset)
return llvm::None;
return stride;
}
// Return true if the transfer op can be converted to a MMA matrix load.
static bool transferReadSupportsMMAMatrixType(vector::TransferReadOp readOp) {
if (readOp.mask() || readOp.hasOutOfBoundsDim() ||
readOp.getVectorType().getRank() != 2)
return false;
if (!getMemrefConstantHorizontalStride(readOp.getShapedType()))
return false;
AffineMap map = readOp.permutation_map();
OpBuilder b(readOp.getContext());
AffineExpr innerDim = b.getAffineDimExpr(map.getNumDims() - 1);
AffineExpr zero = b.getAffineConstantExpr(0);
auto broadcastInnerDim = AffineMap::get(map.getNumDims(), 0, {zero, innerDim},
readOp.getContext());
// TODO: Support transpose once it is added to GPU dialect ops.
// For now we only support (d0, d1) -> (d0, d1) and (d0, d1) -> (0, d1).
return !(!map.isMinorIdentity() && map != broadcastInnerDim);
}
// Return true if the transfer op can be converted to a MMA matrix store.
static bool
transferWriteSupportsMMAMatrixType(vector::TransferWriteOp writeOp) {
// TODO: support 0-d corner case.
if (writeOp.getTransferRank() == 0)
return false;
if (writeOp.mask() || writeOp.hasOutOfBoundsDim() ||
writeOp.getVectorType().getRank() != 2)
return false;
if (!getMemrefConstantHorizontalStride(writeOp.getShapedType()))
return false;
// TODO: Support transpose once it is added to GPU dialect ops.
if (!writeOp.permutation_map().isMinorIdentity())
return false;
return true;
}
/// Return true if the constant is a splat to a 2D vector so that it can be
/// converted to a MMA constant matrix op.
static bool constantSupportsMMAMatrixType(arith::ConstantOp constantOp) {
auto vecType = constantOp.getType().dyn_cast<VectorType>();
if (!vecType || vecType.getRank() != 2)
return false;
return constantOp.getValue().isa<SplatElementsAttr>();
}
/// Return true if this is a broadcast from scalar to a 2D vector.
static bool broadcastSupportsMMAMatrixType(vector::BroadcastOp broadcastOp) {
return broadcastOp.getVectorType().getRank() == 2 &&
broadcastOp.source().getType().isa<FloatType>();
}
/// Return the MMA elementwise enum associated with `op` if it is supported.
/// Return `llvm::None` otherwise.
static llvm::Optional<gpu::MMAElementwiseOp>
convertElementwiseOpToMMA(Operation *op) {
if (isa<arith::AddFOp>(op))
return gpu::MMAElementwiseOp::ADDF;
if (isa<arith::MulFOp>(op))
return gpu::MMAElementwiseOp::MULF;
if (isa<arith::MaxFOp>(op))
return gpu::MMAElementwiseOp::MAXF;
if (isa<arith::MinFOp>(op))
return gpu::MMAElementwiseOp::MINF;
if (isa<arith::DivFOp>(op))
return gpu::MMAElementwiseOp::DIVF;
return llvm::None;
}
/// Return true if the op is supported as elementwise op on MMAMatrix type.
static bool elementwiseSupportsMMAMatrixType(Operation *op) {
return convertElementwiseOpToMMA(op).hasValue();
}
static bool supportsMMaMatrixType(Operation *op) {
if (isa<scf::ForOp, scf::YieldOp>(op))
return true;
if (auto transferRead = dyn_cast<vector::TransferReadOp>(op))
return transferReadSupportsMMAMatrixType(transferRead);
if (auto transferWrite = dyn_cast<vector::TransferWriteOp>(op))
return transferWriteSupportsMMAMatrixType(transferWrite);
if (auto contract = dyn_cast<vector::ContractionOp>(op))
return contractSupportsMMAMatrixType(contract);
if (auto constant = dyn_cast<arith::ConstantOp>(op))
return constantSupportsMMAMatrixType(constant);
if (auto broadcast = dyn_cast<vector::BroadcastOp>(op))
return broadcastSupportsMMAMatrixType(broadcast);
return elementwiseSupportsMMAMatrixType(op);
}
/// Return an unsorted slice handling scf.for region differently than
/// `getSlice`. In scf.for we only want to include as part of the slice elements
/// that are part of the use/def chain.
static SetVector<Operation *> getSliceContract(Operation *op,
TransitiveFilter backwardFilter,
TransitiveFilter forwardFilter) {
SetVector<Operation *> slice;
slice.insert(op);
unsigned currentIndex = 0;
SetVector<Operation *> backwardSlice;
SetVector<Operation *> forwardSlice;
while (currentIndex != slice.size()) {
auto *currentOp = (slice)[currentIndex];
// Compute and insert the backwardSlice starting from currentOp.
backwardSlice.clear();
getBackwardSlice(currentOp, &backwardSlice, backwardFilter);
slice.insert(backwardSlice.begin(), backwardSlice.end());
// Compute and insert the forwardSlice starting from currentOp.
forwardSlice.clear();
// Special case for ForOp, we don't want to include the whole region but
// only the value using the region arguments.
// TODO: We should refine this to only care about the region arguments being
// converted to matrix type.
if (auto forOp = dyn_cast<scf::ForOp>(currentOp)) {
for (Value forOpResult : forOp.getResults())
getForwardSlice(forOpResult, &forwardSlice, forwardFilter);
for (BlockArgument &arg : forOp.getRegionIterArgs())
getForwardSlice(arg, &forwardSlice, forwardFilter);
} else {
getForwardSlice(currentOp, &forwardSlice, forwardFilter);
}
slice.insert(forwardSlice.begin(), forwardSlice.end());
++currentIndex;
}
return slice;
}
// Analyze slice of operations based on convert op to figure out if the whole
// slice can be converted to MMA operations.
static SetVector<Operation *> getOpToConvert(mlir::Operation *op) {
auto hasVectorDest = [](Operation *op) {
return llvm::any_of(op->getResultTypes(),
[](Type t) { return t.isa<VectorType>(); });
};
auto hasVectorSrc = [](Operation *op) {
return llvm::any_of(op->getOperandTypes(),
[](Type t) { return t.isa<VectorType>(); });
};
SetVector<Operation *> opToConvert;
op->walk([&](vector::ContractionOp contract) {
if (opToConvert.contains(contract.getOperation()))
return;
SetVector<Operation *> dependentOps =
getSliceContract(contract, hasVectorDest, hasVectorSrc);
// If any instruction cannot use MMA matrix type drop the whole
// chain. MMA matrix are stored in an opaque type so they cannot be used
// by all operations.
if (llvm::any_of(dependentOps,
[](Operation *op) { return !supportsMMaMatrixType(op); }))
return;
opToConvert.insert(dependentOps.begin(), dependentOps.end());
});
// Sort the operations so that we can convert them in topological order.
return topologicalSort(opToConvert);
}
namespace {
// Transform contract into (m, k)x(k, n)x(m, n) form so that it can be converted
// to MMA matmul.
struct PrepareContractToGPUMMA
: public OpRewritePattern<vector::ContractionOp> {
using OpRewritePattern<vector::ContractionOp>::OpRewritePattern;
LogicalResult matchAndRewrite(vector::ContractionOp op,
PatternRewriter &rewriter) const override {
Location loc = op.getLoc();
Value lhs = op.lhs(), rhs = op.rhs(), res = op.acc();
// Set up the parallel/reduction structure in right form.
using MapList = ArrayRef<ArrayRef<AffineExpr>>;
auto infer = [](MapList m) { return AffineMap::inferFromExprList(m); };
AffineExpr m, n, k;
bindDims(rewriter.getContext(), m, n, k);
static constexpr std::array<int64_t, 2> perm = {1, 0};
auto iteratorTypes = op.iterator_types().getValue();
SmallVector<AffineMap, 4> maps = op.getIndexingMaps();
if (!(isParallelIterator(iteratorTypes[0]) &&
isParallelIterator(iteratorTypes[1]) &&
isReductionIterator(iteratorTypes[2])))
return failure();
//
// Two outer parallel, one inner reduction (matmat flavor).
//
if (maps == infer({{m, k}, {k, n}, {m, n}})) {
// This is the classical row-major matmul, nothing to do.
return failure();
}
if (maps == infer({{m, k}, {n, k}, {m, n}})) {
rhs = rewriter.create<vector::TransposeOp>(loc, rhs, perm);
} else if (maps == infer({{k, m}, {k, n}, {m, n}})) {
lhs = rewriter.create<vector::TransposeOp>(loc, lhs, perm);
} else if (maps == infer({{k, m}, {n, k}, {m, n}})) {
rhs = rewriter.create<vector::TransposeOp>(loc, rhs, perm);
lhs = rewriter.create<vector::TransposeOp>(loc, lhs, perm);
} else if (maps == infer({{m, k}, {k, n}, {n, m}})) {
std::swap(rhs, lhs);
rhs = rewriter.create<vector::TransposeOp>(loc, rhs, perm);
lhs = rewriter.create<vector::TransposeOp>(loc, lhs, perm);
} else if (maps == infer({{m, k}, {n, k}, {n, m}})) {
std::swap(rhs, lhs);
rhs = rewriter.create<vector::TransposeOp>(loc, rhs, perm);
} else if (maps == infer({{k, m}, {k, n}, {n, m}})) {
std::swap(lhs, rhs);
lhs = rewriter.create<vector::TransposeOp>(loc, lhs, perm);
} else if (maps == infer({{k, m}, {n, k}, {n, m}})) {
std::swap(lhs, rhs);
} else {
return failure();
}
rewriter.replaceOpWithNewOp<vector::ContractionOp>(
op, lhs, rhs, res,
rewriter.getAffineMapArrayAttr(infer({{m, k}, {k, n}, {m, n}})),
op.iterator_types());
return success();
}
};
// Merge transpose op into the transfer read op. Transpose are not supported on
// MMA types but MMA load can transpose the matrix when loading.
struct CombineTransferReadOpTranspose final
: public OpRewritePattern<vector::TransposeOp> {
using OpRewritePattern<vector::TransposeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(vector::TransposeOp op,
PatternRewriter &rewriter) const override {
auto transferReadOp = op.vector().getDefiningOp<vector::TransferReadOp>();
if (!transferReadOp)
return failure();
// TODO: support 0-d corner case.
if (transferReadOp.getTransferRank() == 0)
return failure();
if (transferReadOp.mask() || transferReadOp.hasOutOfBoundsDim())
return failure();
SmallVector<int64_t, 2> perm;
op.getTransp(perm);
SmallVector<unsigned, 2> permU;
for (int64_t o : perm)
permU.push_back(unsigned(o));
AffineMap permutationMap =
AffineMap::getPermutationMap(permU, op.getContext());
AffineMap newMap = permutationMap.compose(transferReadOp.permutation_map());
rewriter.replaceOpWithNewOp<vector::TransferReadOp>(
op, op.getType(), transferReadOp.source(), transferReadOp.indices(),
AffineMapAttr::get(newMap), transferReadOp.padding(),
transferReadOp.mask(), transferReadOp.in_boundsAttr());
return success();
}
};
} // namespace
// MMA types have different layout based on how they are used in matmul ops.
// Figure the right layout to use by looking at op uses.
// TODO: Change the GPU dialect to abstract the layout at the this level and
// only care about it during lowering to NVVM.
template <typename OpTy>
static const char *inferFragType(OpTy op) {
for (Operation *users : op->getUsers()) {
auto contract = dyn_cast<vector::ContractionOp>(users);
if (!contract)
continue;
if (contract.lhs() == op.getResult())
return "AOp";
if (contract.rhs() == op.getResult())
return "BOp";
}
return "COp";
}
static void convertTransferReadOp(vector::TransferReadOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
assert(op.getTransferRank() > 0 && "unexpected 0-d transfer");
assert(transferReadSupportsMMAMatrixType(op));
Optional<int64_t> stride =
getMemrefConstantHorizontalStride(op.getShapedType());
AffineMap map = op.permutation_map();
// Handle broadcast by setting the stride to 0.
if (map.getResult(0).isa<AffineConstantExpr>()) {
assert(map.getResult(0).cast<AffineConstantExpr>().getValue() == 0);
stride = 0;
}
assert(stride);
const char *fragType = inferFragType(op);
gpu::MMAMatrixType type =
gpu::MMAMatrixType::get(op.getVectorType().getShape(),
op.getVectorType().getElementType(), fragType);
OpBuilder b(op);
Value load = b.create<gpu::SubgroupMmaLoadMatrixOp>(
op.getLoc(), type, op.source(), op.indices(), b.getIndexAttr(*stride));
valueMapping[op.getResult()] = load;
}
static void convertTransferWriteOp(vector::TransferWriteOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
assert(transferWriteSupportsMMAMatrixType(op));
Optional<int64_t> stride =
getMemrefConstantHorizontalStride(op.getShapedType());
assert(stride);
OpBuilder b(op);
Value matrix = valueMapping.find(op.vector())->second;
b.create<gpu::SubgroupMmaStoreMatrixOp>(
op.getLoc(), matrix, op.source(), op.indices(), b.getIndexAttr(*stride));
op.erase();
}
static void convertContractOp(vector::ContractionOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
OpBuilder b(op);
Value opA = valueMapping.find(op.lhs())->second;
Value opB = valueMapping.find(op.rhs())->second;
Value opC = valueMapping.find(op.acc())->second;
Value matmul = b.create<gpu::SubgroupMmaComputeOp>(op.getLoc(), opC.getType(),
opA, opB, opC);
valueMapping[op.getResult()] = matmul;
}
/// Convert a 2D splat ConstantOp to a SubgroupMmaConstantMatrix op.
static void convertConstantOp(arith::ConstantOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
assert(constantSupportsMMAMatrixType(op));
OpBuilder b(op);
Attribute splat =
op.getValue().cast<SplatElementsAttr>().getSplatValue<Attribute>();
auto scalarConstant =
b.create<arith::ConstantOp>(op.getLoc(), splat.getType(), splat);
const char *fragType = inferFragType(op);
auto vecType = op.getType().cast<VectorType>();
gpu::MMAMatrixType type = gpu::MMAMatrixType::get(
vecType.getShape(), vecType.getElementType(), llvm::StringRef(fragType));
auto matrix = b.create<gpu::SubgroupMmaConstantMatrixOp>(op.getLoc(), type,
scalarConstant);
valueMapping[op.getResult()] = matrix;
}
/// Convert a vector.broadcast from scalar to a SubgroupMmaConstantMatrix op.
static void convertBroadcastOp(vector::BroadcastOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
assert(broadcastSupportsMMAMatrixType(op));
OpBuilder b(op);
const char *fragType = inferFragType(op);
auto vecType = op.getVectorType();
gpu::MMAMatrixType type = gpu::MMAMatrixType::get(
vecType.getShape(), vecType.getElementType(), llvm::StringRef(fragType));
auto matrix = b.create<gpu::SubgroupMmaConstantMatrixOp>(op.getLoc(), type,
op.source());
valueMapping[op.getResult()] = matrix;
}
// Replace ForOp with a new ForOp with extra operands. The YieldOp is not
// updated and needs to be updated separatly for the loop to be correct.
static scf::ForOp replaceForOpWithNewSignature(OpBuilder &b, scf::ForOp loop,
ValueRange newIterOperands) {
// Create a new loop before the existing one, with the extra operands.
OpBuilder::InsertionGuard g(b);
b.setInsertionPoint(loop);
auto operands = llvm::to_vector<4>(loop.getIterOperands());
operands.append(newIterOperands.begin(), newIterOperands.end());
scf::ForOp newLoop =
b.create<scf::ForOp>(loop.getLoc(), loop.getLowerBound(),
loop.getUpperBound(), loop.getStep(), operands);
newLoop.getBody()->erase();
newLoop.getLoopBody().getBlocks().splice(
newLoop.getLoopBody().getBlocks().begin(),
loop.getLoopBody().getBlocks());
for (Value operand : newIterOperands)
newLoop.getBody()->addArgument(operand.getType(), operand.getLoc());
for (auto it : llvm::zip(loop.getResults(), newLoop.getResults().take_front(
loop.getNumResults())))
std::get<0>(it).replaceAllUsesWith(std::get<1>(it));
loop.erase();
return newLoop;
}
static void convertForOp(scf::ForOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
SmallVector<Value> newOperands;
SmallVector<std::pair<size_t, size_t>> argMapping;
for (const auto &operand : llvm::enumerate(op.getIterOperands())) {
auto it = valueMapping.find(operand.value());
if (it == valueMapping.end())
continue;
argMapping.push_back(std::make_pair(
operand.index(), op.getNumIterOperands() + newOperands.size()));
newOperands.push_back(it->second);
}
OpBuilder b(op);
scf::ForOp newForOp = replaceForOpWithNewSignature(b, op, newOperands);
Block &loopBody = *newForOp.getBody();
for (auto mapping : argMapping) {
valueMapping[newForOp.getResult(mapping.first)] =
newForOp.getResult(mapping.second);
valueMapping[loopBody.getArgument(mapping.first +
newForOp.getNumInductionVars())] =
loopBody.getArgument(mapping.second + newForOp.getNumInductionVars());
}
}
static void convertYieldOp(scf::YieldOp op,
llvm::DenseMap<Value, Value> &valueMapping) {
OpBuilder b(op);
auto loop = cast<scf::ForOp>(op->getParentOp());
auto yieldOperands = llvm::to_vector<4>(op.getOperands());
for (const auto &operand : llvm::enumerate(op.getOperands())) {
auto it = valueMapping.find(operand.value());
if (it == valueMapping.end())
continue;
// Replace the yield of old value with the for op argument to make it easier
// to remove the dead code.
yieldOperands[operand.index()] = loop.getIterOperands()[operand.index()];
yieldOperands.push_back(it->second);
}
b.create<scf::YieldOp>(op.getLoc(), yieldOperands);
op.erase();
}
/// Convert an elementwise op to the equivalent elementwise op on MMA matrix.
static void convertElementwiseOp(Operation *op, gpu::MMAElementwiseOp opType,
llvm::DenseMap<Value, Value> &valueMapping) {
OpBuilder b(op);
SmallVector<Value> matrixOperands;
for (Value operand : op->getOperands())
matrixOperands.push_back(valueMapping.find(operand)->second);
Value newOp = b.create<gpu::SubgroupMmaElementwiseOp>(
op->getLoc(), matrixOperands[0].getType(), matrixOperands, opType);
valueMapping[op->getResult(0)] = newOp;
}
namespace mlir {
void populatePrepareVectorToMMAPatterns(RewritePatternSet &patterns) {
patterns.add<PrepareContractToGPUMMA, CombineTransferReadOpTranspose>(
patterns.getContext());
}
void convertVectorToMMAOps(FuncOp funcOp) {
SetVector<Operation *> ops = getOpToConvert(funcOp);
llvm::DenseMap<Value, Value> valueMapping;
for (Operation *op : ops) {
if (auto transferRead = dyn_cast<vector::TransferReadOp>(op)) {
convertTransferReadOp(transferRead, valueMapping);
} else if (auto transferWrite = dyn_cast<vector::TransferWriteOp>(op)) {
convertTransferWriteOp(transferWrite, valueMapping);
} else if (auto contractOp = dyn_cast<vector::ContractionOp>(op)) {
convertContractOp(contractOp, valueMapping);
} else if (auto constantOp = dyn_cast<arith::ConstantOp>(op)) {
convertConstantOp(constantOp, valueMapping);
} else if (auto broadcastOp = dyn_cast<vector::BroadcastOp>(op)) {
convertBroadcastOp(broadcastOp, valueMapping);
} else if (auto forOp = dyn_cast<scf::ForOp>(op)) {
convertForOp(forOp, valueMapping);
} else if (auto yiledOp = dyn_cast<scf::YieldOp>(op)) {
convertYieldOp(yiledOp, valueMapping);
} else if (auto elementwiseType = convertElementwiseOpToMMA(op)) {
convertElementwiseOp(op, *elementwiseType, valueMapping);
}
}
}
} // namespace mlir
namespace {
struct ConvertVectorToGPUPass
: public ConvertVectorToGPUBase<ConvertVectorToGPUPass> {
void runOnOperation() override {
RewritePatternSet patterns(getOperation().getContext());
populatePrepareVectorToMMAPatterns(patterns);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
convertVectorToMMAOps(getOperation());
}
};
} // namespace
std::unique_ptr<Pass> mlir::createConvertVectorToGPUPass() {
return std::make_unique<ConvertVectorToGPUPass>();
}
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