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llvm / llvm-project / 395f8695da4c0e22d562dc7255be19491d5169b5 / . / mlir / lib / Dialect / Linalg / IR / LinalgOps.cpp
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//===- LinalgOps.cpp - Implementation of the linalg operations ------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements the Linalg operations.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/AsmParser/AsmParser.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Utils/Utils.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/Dialect/Utils/StaticValueUtils.h"
#include "mlir/IR/AffineExprVisitor.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/BuiltinTypeInterfaces.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpImplementation.h"
#include "mlir/IR/OperationSupport.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Interfaces/InferTypeOpInterface.h"
#include "mlir/Interfaces/SideEffectInterfaces.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SetOperations.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringSet.h"
#include "llvm/ADT/TypeSwitch.h"
#include "llvm/Support/FormatVariadic.h"
#include "llvm/Support/InterleavedRange.h"
#include "llvm/Support/LogicalResult.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"
#include <cassert>
#include <optional>
using namespace mlir;
using namespace mlir::linalg;
/// Return a `memref.dim` or `tensor.dim` for the shape of `v` at `dim`.
static OpFoldResult getDimValue(OpBuilder &builder, Location loc, Value v,
int64_t dim) {
auto type = cast<ShapedType>(v.getType());
if (!type.isDynamicDim(dim))
return builder.getIndexAttr(type.getDimSize(dim));
return getAsOpFoldResult(
TypeSwitch<Type, Value>(v.getType())
.Case<RankedTensorType>([&](RankedTensorType t) -> Value {
return builder.create<tensor::DimOp>(loc, v, dim);
})
.Case<MemRefType>([&](MemRefType t) -> Value {
return builder.create<memref::DimOp>(loc, v, dim);
}));
}
/// Returns a memref.subview or a tensor.extract_slice based on the type of the
/// `source`.
static Operation *getSlice(OpBuilder &b, Location loc, Value source,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes,
ArrayRef<OpFoldResult> strides) {
return TypeSwitch<Type, Operation *>(source.getType())
.Case<RankedTensorType>([&](RankedTensorType t) -> Operation * {
return b.create<tensor::ExtractSliceOp>(loc, source, offsets, sizes,
strides);
})
.Case<MemRefType>([&](MemRefType type) -> Operation * {
return b.create<memref::SubViewOp>(loc, source, offsets, sizes,
strides);
})
.Default([&](Type t) -> Operation * { return nullptr; });
}
//===----------------------------------------------------------------------===//
// Helper functions
//===----------------------------------------------------------------------===//
Value linalg::createOrFoldDimOp(OpBuilder &b, Location loc, Value source,
int64_t dim) {
if (llvm::isa<UnrankedMemRefType, MemRefType>(source.getType()))
return b.createOrFold<memref::DimOp>(loc, source, dim);
if (llvm::isa<UnrankedTensorType, RankedTensorType>(source.getType()))
return b.createOrFold<tensor::DimOp>(loc, source, dim);
llvm_unreachable("Expected MemRefType or TensorType");
}
OpFoldResult linalg::createFoldedDimOp(OpBuilder &b, Location loc, Value source,
int64_t dim) {
auto shapedType = llvm::cast<ShapedType>(source.getType());
if (!shapedType.hasRank() || shapedType.isDynamicDim(dim))
return createOrFoldDimOp(b, loc, source, dim);
return b.getIndexAttr(shapedType.getDimSize(dim));
}
//===----------------------------------------------------------------------===//
// Support for named Linalg ops defined in ods-gen.
//===----------------------------------------------------------------------===//
using RegionBuilderFn = llvm::function_ref<void(ImplicitLocOpBuilder &, Block &,
ArrayRef<NamedAttribute>)>;
/// Fills the region of a structured operation using the provided
/// `regionBuilder`. The method is used by both named structured ops created by
/// ods-gen and by manually defined C++ ops. It is called by both builders and
/// parsers and creates a block with arguments corresponding to the elemental
/// types of `inputTypes` and `outputTypes`.
static void fillStructuredOpRegion(OpBuilder &opBuilder, Region &region,
TypeRange inputTypes, TypeRange outputTypes,
ArrayRef<NamedAttribute> attrs,
RegionBuilderFn regionBuilder) {
SmallVector<Type, 8> argTypes;
SmallVector<Location, 8> argLocs;
for (auto containers : {inputTypes, outputTypes}) {
for (auto t : containers) {
argTypes.push_back(
isa<MemRefType, RankedTensorType>(t) ? getElementTypeOrSelf(t) : t);
// TODO: Pass in a proper location here.
argLocs.push_back(opBuilder.getUnknownLoc());
}
}
// RAII.
OpBuilder::InsertionGuard guard(opBuilder);
Block *body =
opBuilder.createBlock(&region, /*insertPt=*/{}, argTypes, argLocs);
opBuilder.setInsertionPointToStart(body);
ImplicitLocOpBuilder b(opBuilder.getUnknownLoc(), opBuilder);
regionBuilder(b, *body, attrs);
// indexing_maps is an auto-generated method.
// iterator_types is an auto-generated method.
}
/// Creates a structured operation given `inputs`, `outputs`, and `attributes`.
/// The result types are derived automatically if `resultTensorTypes` is none.
/// The body of the operation is filled using `regionBuilder`. All ods-gen
/// created structured operations use the method to implement their builders.
static void buildStructuredOp(OpBuilder &b, OperationState &state,
std::optional<TypeRange> resultTensorTypes,
ValueRange inputs, ValueRange outputs,
ArrayRef<NamedAttribute> attributes,
RegionBuilderFn regionBuilder) {
// Derive the result types if needed.
SmallVector<Type> derivedResultTypes =
resultTensorTypes.value_or(TypeRange());
if (!resultTensorTypes)
copy_if(outputs.getTypes(), std::back_inserter(derivedResultTypes),
llvm::IsaPred<RankedTensorType>);
state.addOperands(inputs);
state.addOperands(outputs);
state.addTypes(derivedResultTypes);
state.addAttributes(attributes);
state.addAttribute(
"operandSegmentSizes",
b.getDenseI32ArrayAttr({static_cast<int32_t>(inputs.size()),
static_cast<int32_t>(outputs.size())}));
// Create and fill the region of the structured operation.
Region &region = *state.addRegion();
fillStructuredOpRegion(b, region, TypeRange(inputs), TypeRange(outputs),
state.attributes.getAttrs(), regionBuilder);
}
static void buildMatmulOp(OpBuilder &b, OperationState &state,
std::optional<TypeRange> resultTensorTypes,
ValueRange inputs, ValueRange outputs,
ArrayRef<NamedAttribute> attributes,
RegionBuilderFn regionBuilder,
ArrayRef<AffineMap> indexingMaps) {
// Initialize indexingMaps attribute, for MatmulOp.
SmallVector<Attribute, 3> indexingMapsAttrVal;
indexingMapsAttrVal = llvm::map_to_vector(
MatmulOp::getDefaultIndexingMaps(b.getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
state.addAttribute("indexing_maps", b.getArrayAttr(indexingMapsAttrVal));
return buildStructuredOp(b, state, resultTensorTypes, inputs, outputs,
attributes, regionBuilder);
}
static void buildBatchMatmulOp(OpBuilder &b, OperationState &state,
std::optional<TypeRange> resultTensorTypes,
ValueRange inputs, ValueRange outputs,
ArrayRef<NamedAttribute> attributes,
RegionBuilderFn regionBuilder,
ArrayRef<AffineMap> indexingMaps) {
// Initialize indexingMaps attribute, for BatchMatmulOp.
SmallVector<Attribute, 4> indexingMapsAttrVal;
indexingMapsAttrVal =
llvm::map_to_vector(indexingMaps, [](AffineMap map) -> Attribute {
return AffineMapAttr::get(map);
});
state.addAttribute("indexing_maps", b.getArrayAttr(indexingMapsAttrVal));
return buildStructuredOp(b, state, resultTensorTypes, inputs, outputs,
attributes, regionBuilder);
}
static void buildBatchReduceMatmulOp(OpBuilder &b, OperationState &state,
std::optional<TypeRange> resultTensorTypes,
ValueRange inputs, ValueRange outputs,
ArrayRef<NamedAttribute> attributes,
RegionBuilderFn regionBuilder,
ArrayRef<AffineMap> indexingMaps) {
// Initialize indexingMaps attribute, for BatchReduceMatmulOp.
SmallVector<Attribute, 4> indexingMapsAttrVal;
indexingMapsAttrVal =
llvm::map_to_vector(indexingMaps, [](AffineMap map) -> Attribute {
return AffineMapAttr::get(map);
});
state.addAttribute("indexing_maps", b.getArrayAttr(indexingMapsAttrVal));
return buildStructuredOp(b, state, resultTensorTypes, inputs, outputs,
attributes, regionBuilder);
}
/// Common parsing used for both named structured ops created by ods-gen and by
/// manually defined C++ ops. Does not handle regions.
static ParseResult
parseCommonStructuredOpParts(OpAsmParser &parser, OperationState &result,
SmallVectorImpl<Type> &inputTypes,
SmallVectorImpl<Type> &outputTypes,
bool addOperandSegmentSizes = true) {
SMLoc attrsLoc, inputsOperandsLoc, outputsOperandsLoc;
SmallVector<OpAsmParser::UnresolvedOperand, 4> inputsOperands,
outputsOperands;
if (succeeded(parser.parseOptionalLess())) {
if (parser.parseAttribute(result.propertiesAttr) || parser.parseGreater())
return failure();
}
attrsLoc = parser.getCurrentLocation();
if (parser.parseOptionalAttrDict(result.attributes))
return failure();
if (succeeded(parser.parseOptionalKeyword("ins"))) {
if (parser.parseLParen())
return failure();
inputsOperandsLoc = parser.getCurrentLocation();
if (parser.parseOperandList(inputsOperands) ||
parser.parseColonTypeList(inputTypes) || parser.parseRParen())
return failure();
}
if (succeeded(parser.parseOptionalKeyword("outs"))) {
outputsOperandsLoc = parser.getCurrentLocation();
if (parser.parseLParen() || parser.parseOperandList(outputsOperands) ||
parser.parseColonTypeList(outputTypes) || parser.parseRParen())
return failure();
}
if (parser.resolveOperands(inputsOperands, inputTypes, inputsOperandsLoc,
result.operands) ||
parser.resolveOperands(outputsOperands, outputTypes, outputsOperandsLoc,
result.operands))
return failure();
if (addOperandSegmentSizes) {
// This is a bit complex because we're trying to be backward compatible with
// operation syntax that mix the inherent attributes and the discardable
// ones in the same dictionary. If the properties are used, we append the
// operandSegmentSizes there directly. Otherwise we append it to the
// discardable attributes dictionary where it is handled by the generic
// Operation::create(...) method.
if (result.propertiesAttr) {
NamedAttrList attrs = llvm::cast<DictionaryAttr>(result.propertiesAttr);
attrs.append("operandSegmentSizes",
parser.getBuilder().getDenseI32ArrayAttr(
{static_cast<int32_t>(inputsOperands.size()),
static_cast<int32_t>(outputsOperands.size())}));
result.propertiesAttr = attrs.getDictionary(parser.getContext());
} else {
result.addAttribute("operandSegmentSizes",
parser.getBuilder().getDenseI32ArrayAttr(
{static_cast<int32_t>(inputsOperands.size()),
static_cast<int32_t>(outputsOperands.size())}));
}
}
if (!result.propertiesAttr) {
std::optional<RegisteredOperationName> info =
result.name.getRegisteredInfo();
if (info) {
if (failed(info->verifyInherentAttrs(result.attributes, [&]() {
return parser.emitError(attrsLoc)
<< "'" << result.name.getStringRef() << "' op ";
})))
return failure();
}
}
return success();
}
static void printCommonStructuredOpParts(OpAsmPrinter &p, ValueRange inputs,
ValueRange outputs) {
if (!inputs.empty())
p << " ins(" << inputs << " : " << inputs.getTypes() << ")";
if (!outputs.empty())
p << " outs(" << outputs << " : " << outputs.getTypes() << ")";
}
//===----------------------------------------------------------------------===//
// Specific parsing and printing for named structured ops created by ods-gen.
//===----------------------------------------------------------------------===//
static ParseResult parseNamedStructuredOpRegion(
OpAsmParser &parser, Region &region, unsigned numRegionArgs,
TypeRange inputTypes, TypeRange outputTypes, ArrayRef<NamedAttribute> attrs,
RegionBuilderFn regionBuilder) {
if (numRegionArgs != inputTypes.size() + outputTypes.size()) {
return parser.emitError(
parser.getCurrentLocation(),
llvm::formatv("[parseNamedStructuredOpRegion] ods-gen generated "
"region expects {0} args, got {1}",
numRegionArgs, inputTypes.size() + outputTypes.size()));
}
OpBuilder opBuilder(parser.getContext());
fillStructuredOpRegion(opBuilder, region, inputTypes, outputTypes, attrs,
regionBuilder);
return success();
}
static ParseResult
parseNamedStructuredOpResults(OpAsmParser &parser,
SmallVectorImpl<Type> &resultTypes) {
if (parser.parseOptionalArrowTypeList(resultTypes))
return failure();
return success();
}
static ParseResult parseNamedStructuredOp(OpAsmParser &parser,
OperationState &result,
unsigned numRegionArgs,
RegionBuilderFn regionBuilder) {
// TODO: Enable when ods-gen supports captures.
SmallVector<Type, 1> inputTypes, outputTypes;
if (parseCommonStructuredOpParts(parser, result, inputTypes, outputTypes))
return failure();
// Parse optional attributes.
if (parser.parseOptionalAttrDict(result.attributes))
return failure();
// TODO: consider merging results parsing into region parsing.
// Need to wait for declarative assembly resolution to decide.
SmallVector<Type, 1> outputTensorsTypes;
if (parseNamedStructuredOpResults(parser, outputTensorsTypes))
return failure();
result.addTypes(outputTensorsTypes);
std::unique_ptr<Region> region = std::make_unique<Region>();
if (parseNamedStructuredOpRegion(parser, *region, numRegionArgs, inputTypes,
outputTypes, result.attributes.getAttrs(),
regionBuilder))
return failure();
result.addRegion(std::move(region));
return success();
}
static void printNamedStructuredOpResults(OpAsmPrinter &p,
TypeRange resultTypes) {
if (resultTypes.empty())
return;
p.printOptionalArrowTypeList(resultTypes);
}
static void printNamedStructuredOp(OpAsmPrinter &p, Operation *op,
ValueRange inputs, ValueRange outputs,
ArrayRef<StringRef> elidedAttrs = {}) {
p.printOptionalAttrDict(op->getAttrs(), elidedAttrs);
// Printing is shared with generic ops, except for the region and
// attributes.
printCommonStructuredOpParts(p, inputs, outputs);
// Results printing.
printNamedStructuredOpResults(p, op->getResultTypes());
// Region is elided.
}
//===----------------------------------------------------------------------===//
// Region builder helper.
// TODO: Move this to a utility library.
// The public methods on this class are referenced directly from generated code.
// Helper build the unary, binary, and type conversion functions defined by the
// DSL. See LinalgNamedStructuredOps.yamlgen.cpp.inc for the code that uses this
// class.
//
// Implementations of the math functions must be polymorphic over numeric types,
// internally performing necessary casts. If the function application makes no
// sense, then the only recourse is to assert and return nullptr. This can be
// extended later if it becomes possible to fail construction of the region. The
// invariant should be enforced at a higher level.
//
// TODO: These helpers are currently type polymorphic over the class of integer
// and floating point types, but they will not internally cast within bit
// widths of a class (mixed precision such as i8->i32) or across classes
// (i.e. mixed float and integer). Many such combinations are ambiguous or need
// to be handled with care and work is being considered to extend the op
// language to make such cases explicit. In the mean-time, violating this will
// fail verification, which is deemed acceptable.
//===----------------------------------------------------------------------===//
namespace {
class RegionBuilderHelper {
public:
RegionBuilderHelper(OpBuilder &builder, Block &block)
: builder(builder), block(block) {}
// Build the unary functions defined by OpDSL.
Value buildUnaryFn(UnaryFn unaryFn, Value arg) {
if (!isFloatingPoint(arg))
llvm_unreachable("unsupported non numeric type");
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
switch (unaryFn) {
case UnaryFn::exp:
return builder.create<math::ExpOp>(arg.getLoc(), arg);
case UnaryFn::log:
return builder.create<math::LogOp>(arg.getLoc(), arg);
case UnaryFn::abs:
return builder.create<math::AbsFOp>(arg.getLoc(), arg);
case UnaryFn::ceil:
return builder.create<math::CeilOp>(arg.getLoc(), arg);
case UnaryFn::floor:
return builder.create<math::FloorOp>(arg.getLoc(), arg);
case UnaryFn::negf:
return builder.create<arith::NegFOp>(arg.getLoc(), arg);
case UnaryFn::reciprocal: {
Attribute oneAttr = builder.getOneAttr(arg.getType());
auto one = builder.create<arith::ConstantOp>(arg.getLoc(),
::cast<TypedAttr>(oneAttr));
return builder.create<arith::DivFOp>(arg.getLoc(), one, arg);
}
case UnaryFn::round:
return builder.create<math::RoundOp>(arg.getLoc(), arg);
case UnaryFn::sqrt:
return builder.create<math::SqrtOp>(arg.getLoc(), arg);
case UnaryFn::rsqrt:
return builder.create<math::RsqrtOp>(arg.getLoc(), arg);
case UnaryFn::square:
return builder.create<arith::MulFOp>(arg.getLoc(), arg, arg);
case UnaryFn::tanh:
return builder.create<math::TanhOp>(arg.getLoc(), arg);
case UnaryFn::erf:
return builder.create<math::ErfOp>(arg.getLoc(), arg);
}
llvm_unreachable("unsupported unary function");
}
// Build the binary functions defined by OpDSL.
Value buildBinaryFn(BinaryFn binaryFn, Value arg0, Value arg1) {
bool allComplex = isComplex(arg0) && isComplex(arg1);
bool allFloatingPoint = isFloatingPoint(arg0) && isFloatingPoint(arg1);
bool allInteger = isInteger(arg0) && isInteger(arg1);
bool allBool = allInteger && arg0.getType().getIntOrFloatBitWidth() == 1 &&
arg1.getType().getIntOrFloatBitWidth() == 1;
if (!allComplex && !allFloatingPoint && !allInteger)
llvm_unreachable("unsupported non numeric type");
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
switch (binaryFn) {
case BinaryFn::add:
if (allComplex)
return builder.create<complex::AddOp>(arg0.getLoc(), arg0, arg1);
if (allFloatingPoint)
return builder.create<arith::AddFOp>(arg0.getLoc(), arg0, arg1);
if (allBool)
return builder.create<arith::OrIOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::AddIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::sub:
if (allComplex)
return builder.create<complex::SubOp>(arg0.getLoc(), arg0, arg1);
if (allFloatingPoint)
return builder.create<arith::SubFOp>(arg0.getLoc(), arg0, arg1);
if (allBool)
llvm_unreachable("unsupported operation: sub with bools");
return builder.create<arith::SubIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::mul:
if (allComplex)
return builder.create<complex::MulOp>(arg0.getLoc(), arg0, arg1);
if (allFloatingPoint)
return builder.create<arith::MulFOp>(arg0.getLoc(), arg0, arg1);
if (allBool)
return builder.create<arith::AndIOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MulIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::div:
if (allComplex)
return builder.create<complex::DivOp>(arg0.getLoc(), arg0, arg1);
if (allFloatingPoint)
return builder.create<arith::DivFOp>(arg0.getLoc(), arg0, arg1);
if (allBool)
llvm_unreachable("unsupported operation: div with bools");
return builder.create<arith::DivSIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::div_unsigned:
if (!allInteger || allBool)
llvm_unreachable("unsupported operation: unsigned div not on uint");
return builder.create<arith::DivUIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::max_signed:
assert(!allComplex);
if (allFloatingPoint)
return builder.create<arith::MaximumFOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MaxSIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::min_signed:
assert(!allComplex);
if (allFloatingPoint)
return builder.create<arith::MinimumFOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MinSIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::max_unsigned:
assert(!allComplex);
if (allFloatingPoint)
return builder.create<arith::MaximumFOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MaxUIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::min_unsigned:
assert(!allComplex);
if (allFloatingPoint)
return builder.create<arith::MinimumFOp>(arg0.getLoc(), arg0, arg1);
return builder.create<arith::MinUIOp>(arg0.getLoc(), arg0, arg1);
case BinaryFn::powf:
assert(allFloatingPoint);
return builder.create<math::PowFOp>(arg0.getLoc(), arg0, arg1);
}
llvm_unreachable("unsupported binary function");
}
// Build the ternary functions defined by OpDSL.
Value buildTernaryFn(TernaryFn ternaryFn, Value arg0, Value arg1,
Value arg2) {
bool headBool =
isInteger(arg0) && arg0.getType().getIntOrFloatBitWidth() == 1;
bool tailFloatingPoint =
isFloatingPoint(arg0) && isFloatingPoint(arg1) && isFloatingPoint(arg2);
bool tailInteger = isInteger(arg0) && isInteger(arg1) && isInteger(arg2);
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
switch (ternaryFn) {
case TernaryFn::select:
if (!headBool && !(tailFloatingPoint || tailInteger))
llvm_unreachable("unsupported non numeric type");
return builder.create<arith::SelectOp>(arg0.getLoc(), arg0, arg1, arg2);
}
llvm_unreachable("unsupported ternary function");
}
// Build the type functions defined by OpDSL.
Value buildTypeFn(TypeFn typeFn, Type toType, Value operand) {
switch (typeFn) {
case TypeFn::cast_signed:
return cast(toType, operand, false);
case TypeFn::cast_unsigned:
return cast(toType, operand, true);
}
llvm_unreachable("unsupported type conversion function");
}
void yieldOutputs(ValueRange values) {
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
Location loc = builder.getUnknownLoc();
builder.create<YieldOp>(loc, values);
}
Value constant(const std::string &value) {
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
Location loc = builder.getUnknownLoc();
Attribute valueAttr = parseAttribute(value, builder.getContext());
return builder.create<arith::ConstantOp>(loc, ::cast<TypedAttr>(valueAttr));
}
Value index(int64_t dim) {
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
return builder.create<IndexOp>(builder.getUnknownLoc(), dim);
}
Type getIntegerType(unsigned width) {
return IntegerType::get(builder.getContext(), width);
}
Type getFloat32Type() { return Float32Type::get(builder.getContext()); }
Type getFloat64Type() { return Float64Type::get(builder.getContext()); }
private:
// Generates operations to cast the given operand to a specified type.
// If the cast cannot be performed, a warning will be issued and the
// operand returned as-is (which will presumably yield a verification
// issue downstream).
Value cast(Type toType, Value operand, bool isUnsignedCast) {
OpBuilder::InsertionGuard g(builder);
builder.setInsertionPointToEnd(&block);
auto loc = operand.getLoc();
return convertScalarToDtype(builder, loc, operand, toType, isUnsignedCast);
}
bool isComplex(Value value) {
return llvm::isa<ComplexType>(value.getType());
}
bool isFloatingPoint(Value value) {
return llvm::isa<FloatType>(value.getType());
}
bool isInteger(Value value) {
return llvm::isa<IntegerType>(value.getType());
}
OpBuilder &builder;
Block &block;
};
} // namespace
//===----------------------------------------------------------------------===//
// CopyOp
//===----------------------------------------------------------------------===//
namespace {
struct EraseSelfCopy : OpRewritePattern<CopyOp> {
using OpRewritePattern<CopyOp>::OpRewritePattern;
LogicalResult matchAndRewrite(CopyOp copyOp,
PatternRewriter &rewriter) const override {
if (copyOp.getInputs() != copyOp.getOutputs())
return rewriter.notifyMatchFailure(copyOp, "not a self copy");
if (copyOp.hasPureBufferSemantics())
rewriter.eraseOp(copyOp);
else
rewriter.replaceOp(copyOp, copyOp.getInputs());
return success();
}
};
} // namespace
void CopyOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<EraseSelfCopy>(context);
}
//===----------------------------------------------------------------------===//
// FillOp
//===----------------------------------------------------------------------===//
namespace {
/// Fold linalg.fill -> tensor.expand/collapse_shape chain.
///
/// For such op chains, we can create new linalg.fill ops with the result
/// type of the tensor.expand/collapse_shape op.
template <typename TensorReshapeOp>
struct FoldFillWithTensorReshape : OpRewritePattern<TensorReshapeOp> {
using OpRewritePattern<TensorReshapeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(TensorReshapeOp reshapeOp,
PatternRewriter &rewriter) const override {
auto oldFill = reshapeOp.getSrc().template getDefiningOp<FillOp>();
if (!oldFill)
return failure();
Location loc = oldFill.getLoc();
TensorReshapeOp newInit;
if constexpr (std::is_same<TensorReshapeOp, tensor::ExpandShapeOp>::value) {
newInit = rewriter.create<TensorReshapeOp>(
loc, reshapeOp.getResultType(), oldFill.output(),
reshapeOp.getReassociation(), reshapeOp.getOutputShape(),
reshapeOp.getStaticOutputShape());
} else {
newInit = rewriter.create<TensorReshapeOp>(loc, reshapeOp.getResultType(),
oldFill.output(),
reshapeOp.getReassociation());
}
rewriter.replaceOpWithNewOp<FillOp>(reshapeOp, ValueRange{oldFill.value()},
ValueRange{newInit});
return success();
}
};
/// Fold tensor.pad(linalg.fill) into linalg.fill if the padding value and the
/// filling value are the same.
struct FoldFillWithPad final : public OpRewritePattern<tensor::PadOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::PadOp padOp,
PatternRewriter &rewriter) const override {
auto fillOp = padOp.getSource().getDefiningOp<linalg::FillOp>();
if (!fillOp)
return failure();
// We can only fold if the padding value is the same as the original
// filling value.
Value padValue = padOp.getConstantPaddingValue();
if (!padValue || fillOp.value() != padValue)
return failure();
ReifiedRankedShapedTypeDims reifiedShape;
if (failed(reifyResultShapes(rewriter, padOp, reifiedShape)))
return rewriter.notifyMatchFailure(
padOp, "failed to reify tensor.pad op result shape");
auto emptyTensor = rewriter.create<tensor::EmptyOp>(
padOp.getLoc(), reifiedShape.front(),
padOp.getResultType().getElementType());
Value replacement =
rewriter
.create<FillOp>(fillOp.getLoc(), ValueRange{padValue},
ValueRange{emptyTensor})
.getResult(0);
if (replacement.getType() != padOp.getResultType()) {
replacement = rewriter.create<tensor::CastOp>(
fillOp.getLoc(), padOp.getResultType(), replacement);
}
rewriter.replaceOp(padOp, replacement);
return success();
}
};
/// Fold tensor.insert_slice(tensor.pad(<input>), linalg.fill) into
/// tensor.insert_slice(<input>, linalg.fill) if the padding value and the
/// filling value are the same.
struct FoldInsertPadIntoFill : public OpRewritePattern<tensor::InsertSliceOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::InsertSliceOp insertOp,
PatternRewriter &rewriter) const override {
auto srcPadOp = insertOp.getSource().getDefiningOp<tensor::PadOp>();
if (!srcPadOp)
return failure();
if (insertOp.getType().getRank() != insertOp.getSourceType().getRank())
return failure();
// Walk back the tensor.insert_slice chain and find the first destination
// value at the start of the chain.
Value firstDest = insertOp.getDest();
while (auto prevOp = firstDest.getDefiningOp<tensor::InsertSliceOp>()) {
if (prevOp.getType().getRank() != prevOp.getSourceType().getRank())
return failure();
// Make sure the range of values accessed are disjoint. Without this, we
// cannot fold tensor.pad away.
bool disjoint = false;
for (int i = 0, e = prevOp.getType().getRank(); i < e; ++i) {
// If the dimension has dynamic offset/size, we cannot guarantee
// disjoint. So just skip it.
if (insertOp.isDynamicOffset(i) || insertOp.isDynamicSize(i) ||
insertOp.isDynamicStride(i) || prevOp.isDynamicOffset(i) ||
prevOp.isDynamicSize(i) || prevOp.isDynamicStride(i))
continue;
// Get the range start and end, inclusively for both.
int64_t prevStart = prevOp.getStaticOffset(i);
int64_t prevEnd = prevStart + (prevOp.getStaticSize(i) - 1) *
prevOp.getStaticStride(i);
int64_t nextStart = insertOp.getStaticOffset(i);
int64_t nextEnd = nextStart + (insertOp.getStaticSize(i) - 1) *
insertOp.getStaticStride(i);
if (prevEnd < nextStart || nextEnd < prevStart) {
disjoint = true;
break;
}
}
if (!disjoint)
break;
firstDest = prevOp.getDest();
}
// Check whether the first destination is a fill op. For overlapped cases,
// this also cannot be true.
auto dstFillOp = firstDest.getDefiningOp<linalg::FillOp>();
if (!dstFillOp)
return failure();
// We can only fold if the padding value is the same as the original
// filling value.
Value padValue = srcPadOp.getConstantPaddingValue();
if (!padValue || dstFillOp.value() != padValue)
return failure();
SmallVector<OpFoldResult> lowPads = srcPadOp.getMixedLowPad();
SmallVector<OpFoldResult> oldOffsets = insertOp.getMixedOffsets();
Location loc = insertOp.getLoc();
MLIRContext *context = getContext();
AffineExpr sym0, sym1;
bindSymbols(context, sym0, sym1);
auto addMap = AffineMap::get(0, 2, {sym0 + sym1}, context);
// Calculate the new offsets for the insert. It should be the old offsets
// plus low padding sizes.
SmallVector<OpFoldResult, 4> newOffsets;
for (const auto &p : llvm::zip(lowPads, oldOffsets)) {
newOffsets.push_back(affine::makeComposedFoldedAffineApply(
rewriter, loc, addMap, {std::get<0>(p), std::get<1>(p)}));
}
RankedTensorType srcPadType = srcPadOp.getSourceType();
SmallVector<OpFoldResult, 4> newSizes;
for (int i = 0, e = srcPadType.getRank(); i < e; ++i) {
if (srcPadType.isDynamicDim(i)) {
newSizes.push_back(
rewriter.create<tensor::DimOp>(loc, srcPadOp.getSource(), i)
.getResult());
} else {
newSizes.push_back(rewriter.getIndexAttr(srcPadType.getDimSize(i)));
}
}
rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
insertOp, srcPadOp.getSource(), insertOp.getDest(), newOffsets,
newSizes, insertOp.getMixedStrides());
return success();
}
};
/// Fold tensor.extract(linalg.fill(<input>)) into <input>
struct FoldFillWithTensorExtract : public OpRewritePattern<tensor::ExtractOp> {
public:
using OpRewritePattern<tensor::ExtractOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::ExtractOp extractOp,
PatternRewriter &rewriter) const override {
// See if tensor input of tensor.extract op is the result of a linalg.fill
// op.
auto fillOp = extractOp.getTensor().getDefiningOp<linalg::FillOp>();
if (!fillOp)
return failure();
// Get scalar input operand of linalg.fill op.
Value extractedScalar = fillOp.getInputs()[0];
// Replace tensor.extract op with scalar value used to fill the tensor.
rewriter.replaceOp(extractOp, extractedScalar);
return success();
}
};
/// Folds pack(fill) into a single fill op if
/// 1. The pack op does not have padding value, or
/// 2. The filled value and padding value are the same.
static FailureOr<FillOp> foldFillPackIntoFillOp(RewriterBase &rewriter,
linalg::PackOp packOp) {
auto fillOp = packOp.getSource().getDefiningOp<FillOp>();
if (!fillOp)
return failure();
if (auto paddingValue = packOp.getPaddingValue())
if (!isEqualConstantIntOrValue(paddingValue, fillOp.value()))
return failure();
Value packOpDest = packOp.getDest();
if (!packOpDest.hasOneUse())
return failure();
return rewriter.create<linalg::FillOp>(packOp.getLoc(), fillOp.getInputs(),
packOp.getDest());
}
/// Wrapper pattern that applies foldFillPackIntoFillOp method.
struct FoldFillWithPack : public OpRewritePattern<linalg::PackOp> {
public:
FoldFillWithPack(MLIRContext *context)
: OpRewritePattern<linalg::PackOp>(context) {}
LogicalResult matchAndRewrite(linalg::PackOp packOp,
PatternRewriter &rewriter) const override {
auto fillOp = foldFillPackIntoFillOp(rewriter, packOp);
if (failed(fillOp))
return failure();
rewriter.replaceOp(packOp, fillOp.value().result());
return success();
}
};
/// Fold fill with copy.
struct FoldFillWithCopy : OpRewritePattern<linalg::CopyOp> {
using OpRewritePattern<linalg::CopyOp>::OpRewritePattern;
LogicalResult matchAndRewrite(linalg::CopyOp copyOp,
PatternRewriter &rewriter) const override {
if (auto fillOp = copyOp.getInputs().front().getDefiningOp<FillOp>()) {
rewriter.replaceOpWithNewOp<FillOp>(copyOp, copyOp.getResultTypes(),
fillOp.getInputs(),
copyOp.getOutputs());
return success();
}
if (auto fillOp = copyOp.getOutputs().front().getDefiningOp<FillOp>()) {
rewriter.replaceOpWithNewOp<linalg::CopyOp>(copyOp, copyOp.getInputs(),
fillOp.getOutputs());
return success();
}
return failure();
}
};
/// Fold fill with transpose.
struct FoldFillWithTranspose : OpRewritePattern<linalg::TransposeOp> {
using OpRewritePattern<linalg::TransposeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(linalg::TransposeOp transposeOp,
PatternRewriter &rewriter) const override {
if (auto fillOp = transposeOp.getInput().getDefiningOp<FillOp>()) {
rewriter.replaceOpWithNewOp<FillOp>(
transposeOp, transposeOp.getResultTypes(), fillOp.getInputs(),
transposeOp.getDpsInitOperand(0)->get());
return success();
}
return failure();
}
};
/// Fold a concat with all elements being fills of the same value
/// into a fill of the concat result shape.
struct FoldConcatsOfFill : public OpRewritePattern<tensor::ConcatOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::ConcatOp concatOp,
PatternRewriter &rewriter) const override {
auto concatOperands = concatOp.getInputs();
if (concatOperands.empty()) {
return failure();
}
auto firstFillOp = concatOperands.front().getDefiningOp<linalg::FillOp>();
if (!firstFillOp) {
return failure();
}
// Prefetch the fill value.
OpFoldResult firstFillVal =
getAsOpFoldResult(firstFillOp.getDpsInputOperand(0)->get());
// Collect all the outs values for the fill operations.
SmallVector<Value> allOuts;
allOuts.push_back(firstFillOp.getDpsInitOperand(0)->get());
auto isDefinedByCompatibleFillOp = [&](Value v) -> bool {
auto fillOp = v.getDefiningOp<linalg::FillOp>();
if (!fillOp) {
return false;
}
OpFoldResult fillVal =
getAsOpFoldResult(fillOp.getDpsInputOperand(0)->get());
if (fillVal != firstFillVal)
return false;
allOuts.push_back(fillOp.getDpsInitOperand(0)->get());
return true;
};
if (!llvm::all_of(concatOperands.drop_front(),
isDefinedByCompatibleFillOp)) {
return rewriter.notifyMatchFailure(
concatOp, "not all operands are defined by a compatible fill op");
}
Value outsConcat = rewriter.create<tensor::ConcatOp>(
concatOp.getLoc(), concatOp.getDim(), allOuts);
rewriter.replaceOpWithNewOp<linalg::FillOp>(
concatOp, firstFillOp.getDpsInputOperand(0)->get(), outsConcat);
return success();
}
};
} // namespace
void FillOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<FoldConcatsOfFill, FoldFillWithCopy, FoldFillWithTensorExtract,
FoldFillWithPack, FoldFillWithPad,
FoldFillWithTensorReshape<tensor::CollapseShapeOp>,
FoldFillWithTensorReshape<tensor::ExpandShapeOp>,
FoldInsertPadIntoFill, FoldFillWithTranspose>(context);
}
//===----------------------------------------------------------------------===//
// GenericOp
//===----------------------------------------------------------------------===//
static void buildGenericRegion(
OpBuilder &builder, Location loc, Region &region, ValueRange inputs,
ValueRange outputs,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild) {
SmallVector<Type, 4> blockArgTypes;
SmallVector<Location, 4> blockArgLocs;
for (ValueRange container : {inputs, outputs}) {
for (Value v : container) {
Type t = v.getType();
blockArgTypes.push_back(
isa<MemRefType, RankedTensorType>(t) ? getElementTypeOrSelf(t) : t);
blockArgLocs.push_back(v.getLoc());
}
}
OpBuilder::InsertionGuard guard(builder);
Block *bodyBlock =
builder.createBlock(&region, region.end(), blockArgTypes, blockArgLocs);
bodyBuild(builder, loc, bodyBlock->getArguments());
}
void GenericOp::getAsmBlockArgumentNames(Region &region,
OpAsmSetValueNameFn setNameFn) {
for (Value v : getRegionInputArgs())
setNameFn(v, "in");
for (Value v : getRegionOutputArgs())
setNameFn(v, "out");
}
void GenericOp::build(
OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
ValueRange inputs, ValueRange outputs, ArrayAttr indexingMaps,
ArrayAttr iteratorTypes, StringAttr doc, StringAttr libraryCall,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, resultTensorTypes, inputs, outputs, indexingMaps,
iteratorTypes, doc, libraryCall);
result.addAttributes(attributes);
if (bodyBuild)
buildGenericRegion(builder, result.location, *result.regions.front(),
inputs, outputs, bodyBuild);
}
void GenericOp::build(
OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
ValueRange inputs, ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
ArrayRef<utils::IteratorType> iteratorTypes, StringRef doc,
StringRef libraryCall,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, resultTensorTypes, inputs, outputs,
builder.getAffineMapArrayAttr(indexingMaps),
builder.getArrayAttr(llvm::to_vector(llvm::map_range(
iteratorTypes,
[&](utils::IteratorType iter) -> mlir::Attribute {
return IteratorTypeAttr::get(builder.getContext(), iter);
}))),
doc.empty() ? StringAttr() : builder.getStringAttr(doc),
libraryCall.empty() ? StringAttr() : builder.getStringAttr(libraryCall),
bodyBuild, attributes);
}
void GenericOp::build(
OpBuilder &builder, OperationState &result, ValueRange inputs,
ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
ArrayRef<utils::IteratorType> iteratorTypes, StringRef doc,
StringRef libraryCall,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, TypeRange{}, inputs, outputs, indexingMaps,
iteratorTypes, doc, libraryCall, bodyBuild, attributes);
}
void GenericOp::build(
OpBuilder &builder, OperationState &result, ValueRange inputs,
ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
ArrayRef<utils::IteratorType> iteratorTypes,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, inputs, outputs, indexingMaps, iteratorTypes,
/*doc=*/"",
/*libraryCall=*/"", bodyBuild, attributes);
}
void GenericOp::build(
OpBuilder &builder, OperationState &result, TypeRange resultTensorTypes,
ValueRange inputs, ValueRange outputs, ArrayRef<AffineMap> indexingMaps,
ArrayRef<utils::IteratorType> iteratorTypes,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, resultTensorTypes, inputs, outputs, indexingMaps,
iteratorTypes,
/*doc=*/"",
/*libraryCall=*/"", bodyBuild, attributes);
}
void GenericOp::print(OpAsmPrinter &p) {
p << " ";
// Print extra attributes.
auto genericAttrNames = linalgTraitAttrNames();
llvm::StringSet<> genericAttrNamesSet;
genericAttrNamesSet.insert_range(genericAttrNames);
SmallVector<NamedAttribute, 8> genericAttrs;
for (auto attr : (*this)->getAttrs()) {
if (attr.getName() == getIteratorTypesAttrName()) {
auto iteratorTypes =
llvm::cast<ArrayAttr>(attr.getValue())
.getAsValueRange<IteratorTypeAttr, utils::IteratorType>();
// Convert IteratorType enums into the string representation. This is
// needed, because tests still use the old format when 'iterator_types'
// attribute is represented as an array of strings.
// TODO: Remove this conversion once tests are fixed.
SmallVector<Attribute> iteratorTypeNames =
llvm::to_vector(llvm::map_range(
iteratorTypes, [&](utils::IteratorType t) -> Attribute {
return StringAttr::get(getContext(), stringifyIteratorType(t));
}));
genericAttrs.emplace_back(
getIteratorTypesAttrName(),
ArrayAttr::get(getContext(), iteratorTypeNames));
} else if (genericAttrNamesSet.count(attr.getName().strref()) > 0) {
genericAttrs.push_back(attr);
}
}
if (!genericAttrs.empty()) {
auto genericDictAttr = DictionaryAttr::get(getContext(), genericAttrs);
p << genericDictAttr;
}
// Printing is shared with named ops, except for the region and attributes
printCommonStructuredOpParts(p, getDpsInputs(), getDpsInits());
genericAttrNames.push_back("operandSegmentSizes");
genericAttrNamesSet.insert(genericAttrNames.back());
bool hasExtraAttrs = false;
for (NamedAttribute n : (*this)->getAttrs()) {
if ((hasExtraAttrs = !genericAttrNamesSet.contains(n.getName().strref())))
break;
}
if (hasExtraAttrs) {
p << " attrs = ";
p.printOptionalAttrDict((*this)->getAttrs(),
/*elidedAttrs=*/genericAttrNames);
}
// Print region.
if (!getRegion().empty()) {
p << ' ';
p.printRegion(getRegion());
}
// Print results.
printNamedStructuredOpResults(p, getResultTensors().getTypes());
}
ParseResult GenericOp::parse(OpAsmParser &parser, OperationState &result) {
DictionaryAttr dictAttr;
// Parse the core linalg traits that must check into a dictAttr.
// The name is unimportant as we will overwrite result.attributes.
// The core linalg traits must contain the information necessary to pass the
// verifier.
llvm::SMLoc attributeLocation = parser.getCurrentLocation();
if (parser.parseAttribute(dictAttr, "_", result.attributes))
return failure();
result.attributes.assign(dictAttr.getValue().begin(),
dictAttr.getValue().end());
// Convert array of string into an array of IteratorType enums. This is
// needed, because tests still use the old format when 'iterator_types'
// attribute is represented as an array of strings.
// TODO: Remove this conversion once tests are fixed.
auto iteratorTypes = dyn_cast_or_null<ArrayAttr>(
result.attributes.get(getIteratorTypesAttrName(result.name)));
if (!iteratorTypes) {
return parser.emitError(attributeLocation)
<< "expected " << getIteratorTypesAttrName(result.name)
<< " array attribute";
}
SmallVector<Attribute> iteratorTypeAttrs;
for (StringRef s : iteratorTypes.getAsValueRange<StringAttr>()) {
auto maybeIteratorType = utils::symbolizeIteratorType(s);
if (!maybeIteratorType.has_value())
return parser.emitError(parser.getCurrentLocation())
<< "unexpected iterator_type (" << s << ")";
iteratorTypeAttrs.push_back(
IteratorTypeAttr::get(parser.getContext(), maybeIteratorType.value()));
}
result.attributes.set(getIteratorTypesAttrName(result.name),
parser.getBuilder().getArrayAttr(iteratorTypeAttrs));
// Parsing is shared with named ops, except for the region.
SmallVector<Type, 1> inputTypes, outputTypes;
if (parseCommonStructuredOpParts(parser, result, inputTypes, outputTypes))
return failure();
// Optional attributes may be added.
if (succeeded(parser.parseOptionalKeyword("attrs")))
if (failed(parser.parseEqual()) ||
failed(parser.parseOptionalAttrDict(result.attributes)))
return failure();
std::unique_ptr<Region> region = std::make_unique<Region>();
if (parser.parseRegion(*region, {}))
return failure();
result.addRegion(std::move(region));
// Generic ops may specify that a subset of its outputs are tensors. Such
// outputs are specified in the result type.
// TODO: may need to move output parsing before region parsing.
// Need to wait for declarative assembly resolution to decide.
SmallVector<Type, 1> outputTensorsTypes;
if (parseNamedStructuredOpResults(parser, outputTensorsTypes))
return failure();
result.addTypes(outputTensorsTypes);
return success();
}
static void getGenericEffectsImpl(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects,
LinalgOp linalgOp) {
for (auto [index, operand] : llvm::enumerate(linalgOp.getDpsInputs())) {
if (!llvm::isa<MemRefType>(operand.getType()))
continue;
effects.emplace_back(
MemoryEffects::Read::get(), &linalgOp->getOpOperand(index), /*stage=*/0,
/*effectOnFullRegion=*/true, SideEffects::DefaultResource::get());
}
for (OpOperand &operand : linalgOp.getDpsInitsMutable()) {
if (!llvm::isa<MemRefType>(operand.get().getType()))
continue;
if (linalgOp.payloadUsesValueFromOperand(&operand)) {
effects.emplace_back(MemoryEffects::Read::get(), &operand, /*stage=*/0,
/*effectOnFullRegion=*/true,
SideEffects::DefaultResource::get());
}
effects.emplace_back(MemoryEffects::Write::get(), &operand, /*stage=*/0,
/*effectOnFullRegion=*/true,
SideEffects::DefaultResource::get());
}
}
void GenericOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
static Speculation::Speculatability
getGenericSpeculatabilityImpl(LinalgOp linalgOp) {
// Operands with value semantics are speculatable, while operands with memory
// semantics are not.
if (!linalgOp.hasPureTensorSemantics())
return Speculation::NotSpeculatable;
// The body of the op can still have speculation in its region.
return Speculation::RecursivelySpeculatable;
}
Speculation::Speculatability GenericOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
LogicalResult GenericOp::verify() { return success(); }
namespace {
/// Remove any linalg operation (on tensors) that are just copying
/// the values from inputs to the results. Requirements are
/// 1) All iterator types are parallel
/// 2) The body contains just a yield operation with the yielded values being
/// the arguments corresponding to the operands.
template <typename OpTy>
struct EraseIdentityLinalgOp : public OpRewritePattern<OpTy> {
using OpRewritePattern<OpTy>::OpRewritePattern;
LogicalResult matchAndRewrite(OpTy linalgOp,
PatternRewriter &rewriter) const override {
// All indexing maps must be equal. It follows that they are permutations.
if (!llvm::all_equal(linalgOp.getIndexingMapsArray()))
return failure();
// Check that the body of the linalg operation is just a linalg.yield
// operation.
Block &body = linalgOp->getRegion(0).front();
if (!llvm::hasSingleElement(body))
return failure();
auto yieldOp = dyn_cast<linalg::YieldOp>(body.getTerminator());
if (!yieldOp)
return failure();
// In the buffer case, we need to check exact buffer equality.
if (linalgOp.hasPureBufferSemantics()) {
if (linalgOp.getNumDpsInputs() == 1 && linalgOp.getNumDpsInits() == 1 &&
linalgOp.getDpsInputOperand(0)->get() ==
linalgOp.getDpsInitOperand(0)->get()) {
rewriter.eraseOp(linalgOp);
return success();
}
return failure();
}
// Mixed semantics is not supported yet.
if (!linalgOp.hasPureTensorSemantics())
return failure();
// Get the argument number of the returned values. That is the operand
// number to use for replacing uses of this operation.
SmallVector<Value> returnedArgs;
for (const auto &yieldVal : llvm::enumerate(yieldOp.getValues())) {
auto yieldArg = llvm::dyn_cast<BlockArgument>(yieldVal.value());
if (!yieldArg || yieldArg.getOwner() != &body)
return failure();
unsigned argumentNumber = yieldArg.getArgNumber();
Value returnedArg = linalgOp->getOperand(argumentNumber);
Type resultType = linalgOp->getResult(yieldVal.index()).getType();
// The input can have a different type than the result, e.g. a dynamic
// input dimension can be turned into a static output dimension.
Type returnType = returnedArg.getType();
if (returnType != resultType) {
// Distinguish between sparse conversion or dense tensor casting.
// TODO: unify the two ops?
if (sparse_tensor::getSparseTensorEncoding(returnType) ||
sparse_tensor::getSparseTensorEncoding(resultType))
returnedArg = rewriter.create<sparse_tensor::ConvertOp>(
linalgOp.getLoc(), resultType, returnedArg);
else {
if (!tensor::CastOp::areCastCompatible(returnedArg.getType(),
resultType))
return failure();
returnedArg = rewriter.create<tensor::CastOp>(
linalgOp.getLoc(), resultType, returnedArg);
}
}
returnedArgs.push_back(returnedArg);
}
if (returnedArgs.size() != linalgOp->getNumResults())
return failure();
rewriter.replaceOp(linalgOp, returnedArgs);
return success();
}
};
} // namespace
void GenericOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<EraseIdentityLinalgOp<GenericOp>>(context);
}
LogicalResult GenericOp::fold(FoldAdaptor, SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
//===----------------------------------------------------------------------===//
// MapOp
//===----------------------------------------------------------------------===//
static ParseResult parseDstStyleOp(
OpAsmParser &parser, OperationState &result,
function_ref<ParseResult(OpAsmParser &, NamedAttrList &)> parseAttrsFn =
nullptr) {
// Parse `ins` and `outs`.
SmallVector<Type, 4> inputTypes, outputTypes;
if (parseCommonStructuredOpParts(parser, result, inputTypes, outputTypes,
/*addOperandSegmentSizes=*/false))
return failure();
// Add result types.
for (Type outputType : outputTypes) {
if (llvm::isa<RankedTensorType>(outputType))
result.addTypes(outputType);
}
// Parse required attributes.
if (parseAttrsFn && failed(parseAttrsFn(parser, result.attributes)))
return failure();
// Parse optional attributes.
if (parser.parseOptionalAttrDict(result.attributes))
return failure();
return success();
}
void MapOp::getAsmBlockArgumentNames(Region &region,
OpAsmSetValueNameFn setNameFn) {
for (Value v : getRegionInputArgs())
setNameFn(v, "in");
}
void MapOp::getAsmResultNames(function_ref<void(Value, StringRef)> setNameFn) {
if (!getResults().empty())
setNameFn(getResults().front(), "mapped");
}
void MapOp::build(
OpBuilder &builder, OperationState &result, ValueRange inputs, Value init,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, TypeRange{}, inputs, init);
result.addAttributes(attributes);
// Add output types for `RankedTensorType` output arguments.
Type initType = init.getType();
if (llvm::isa<RankedTensorType>(initType))
result.addTypes(initType);
if (bodyBuild)
buildGenericRegion(builder, result.location, *result.regions.front(),
inputs, /*outputs=*/{}, bodyBuild);
}
static void addBodyWithPayloadOp(OpAsmParser &parser, OperationState &result,
const OperationName &payloadOpName,
const NamedAttrList &payloadOpAttrs,
ArrayRef<Value> operands,
bool initFirst = false) {
OpBuilder b(parser.getContext());
Region *body = result.addRegion();
Block &block = body->emplaceBlock();
b.setInsertionPointToStart(&block);
for (auto &operand : operands) {
block.addArgument(
llvm::cast<ShapedType>(operand.getType()).getElementType(),
b.getUnknownLoc());
}
SmallVector<Value> payloadOpOperands;
// If initFirst flag is enabled, we consider init as the first position of
// payload operands.
if (initFirst) {
payloadOpOperands.push_back(block.getArguments().back());
for (const auto &arg : block.getArguments().drop_back())
payloadOpOperands.push_back(arg);
} else {
payloadOpOperands = {block.getArguments().begin(),
block.getArguments().end()};
}
Operation *payloadOp = b.create(
result.location, b.getStringAttr(payloadOpName.getStringRef()),
payloadOpOperands,
TypeRange{llvm::cast<ShapedType>(result.operands.back().getType())
.getElementType()},
payloadOpAttrs);
b.create<YieldOp>(result.location, payloadOp->getResults());
}
ParseResult MapOp::parse(OpAsmParser &parser, OperationState &result) {
std::optional<OperationName> payloadOpName;
NamedAttrList payloadOpAttrs;
if (succeeded(parser.parseOptionalLBrace())) {
FailureOr<OperationName> operationName = parser.parseCustomOperationName();
if (failed(operationName))
return failure();
if (parser.parseOptionalAttrDict(payloadOpAttrs))
return failure();
payloadOpName = operationName.value();
if (parser.parseRBrace())
return failure();
}
if (parseDstStyleOp(parser, result))
return failure();
if (payloadOpName.has_value()) {
if (!result.operands.empty())
addBodyWithPayloadOp(parser, result, payloadOpName.value(),
payloadOpAttrs,
ArrayRef(result.operands).drop_back());
else
result.addRegion();
} else {
SmallVector<OpAsmParser::Argument> regionArgs;
if (parser.parseArgumentList(regionArgs, OpAsmParser::Delimiter::Paren,
/*allowType=*/true, /*allowAttrs=*/true)) {
return failure();
}
Region *body = result.addRegion();
if (parser.parseRegion(*body, regionArgs))
return failure();
}
return success();
}
// Retrieve the operation from the body, if it is the only one (except
// yield) and if it gets the same amount of arguments as the body does.
// If initFirst flag is enabled, we check that init takes the first position in
// operands of payload.
static Operation *findPayloadOp(Block *body, bool initFirst = false) {
if (body->getOperations().size() != 2)
return nullptr;
Operation &payload = body->getOperations().front();
assert(isa<YieldOp>(body->getOperations().back()));
if (payload.getNumOperands() == 0 ||
payload.getNumOperands() != body->getNumArguments())
return nullptr;
if (initFirst) {
// check init
if (payload.getOperands().back() != body->getArgument(0))
return nullptr;
// check rest
for (const auto &[operand, bbArg] :
llvm::zip(payload.getOperands(), body->getArguments().drop_front())) {
if (bbArg != operand)
return nullptr;
}
} else {
for (const auto &[operand, bbArg] :
llvm::zip(payload.getOperands(), body->getArguments())) {
if (bbArg != operand)
return nullptr;
}
}
return &payload;
}
void printShortForm(OpAsmPrinter &p, Operation *payloadOp) {
SmallVector<StringRef> elidedAttrs;
std::string attrToElide;
p << " { " << payloadOp->getName().getStringRef();
for (const auto &attr : payloadOp->getAttrs()) {
auto fastAttr =
llvm::dyn_cast<mlir::arith::FastMathFlagsAttr>(attr.getValue());
if (fastAttr && fastAttr.getValue() == mlir::arith::FastMathFlags::none) {
attrToElide = attr.getName().str();
elidedAttrs.push_back(attrToElide);
break;
}
}
p.printOptionalAttrDict(payloadOp->getAttrs(), elidedAttrs);
p << " }";
}
void MapOp::print(OpAsmPrinter &p) {
Block *mapper = getBody();
Operation *payloadOp = findPayloadOp(mapper);
if (payloadOp) {
printShortForm(p, payloadOp);
}
printCommonStructuredOpParts(p, getDpsInputs(), getDpsInits());
p.printOptionalAttrDict((*this)->getAttrs());
if (!payloadOp) {
// Print region if the payload op was not detected.
p.increaseIndent();
p.printNewline();
p << "(";
llvm::interleaveComma(mapper->getArguments(), p,
[&](auto arg) { p.printRegionArgument(arg); });
p << ") ";
p.printRegion(getMapper(), /*printEntryBlockArgs=*/false);
p.decreaseIndent();
}
}
LogicalResult MapOp::verify() {
auto *bodyBlock = getBody();
auto blockArgs = bodyBlock->getArguments();
// Checks if the number of `inputs` match the arity of the `mapper` region.
if (getInputs().size() != blockArgs.size())
return emitOpError() << "expects number of operands to match the arity of "
"mapper, but got: "
<< getInputs().size() << " and " << blockArgs.size();
// The parameters of mapper should all match the element type of inputs.
for (const auto &[bbArgType, inputArg] :
llvm::zip(bodyBlock->getArgumentTypes(), getInputs())) {
auto inputElemType =
llvm::cast<ShapedType>(inputArg.getType()).getElementType();
if (bbArgType != inputElemType) {
return emitOpError() << "expected element type of input " << inputElemType
<< " to match bbArg type " << bbArgType;
}
}
// The shape of each input must match the shape of the output.
auto outputShape = getInit().getType().getShape();
for (Type inputArgType : TypeRange{getInputs()}) {
auto inputElemShape = llvm::cast<ShapedType>(inputArgType).getShape();
if (inputElemShape != outputShape) {
return emitOpError() << "expected shape of input (" << inputElemShape
<< ") to match shape of output (" << outputShape
<< ")";
}
}
return success();
}
SmallVector<utils::IteratorType> MapOp::getIteratorTypesArray() {
int64_t rank = getInit().getType().getRank();
return SmallVector<utils::IteratorType>(rank, utils::IteratorType::parallel);
}
ArrayAttr MapOp::getIndexingMaps() {
Builder builder(getContext());
int64_t rank = getInit().getType().getRank();
int64_t numIndexingMaps = getOperands().size();
return builder.getAffineMapArrayAttr(SmallVector<AffineMap>(
numIndexingMaps, builder.getMultiDimIdentityMap(rank)));
}
void MapOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability MapOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
//===----------------------------------------------------------------------===//
// ReduceOp
//===----------------------------------------------------------------------===//
void ReduceOp::getAsmBlockArgumentNames(Region &region,
OpAsmSetValueNameFn setNameFn) {
for (Value v : getRegionInputArgs())
setNameFn(v, "in");
for (Value v : getRegionOutputArgs())
setNameFn(v, "init");
}
void ReduceOp::getAsmResultNames(
function_ref<void(Value, StringRef)> setNameFn) {
if (!getResults().empty())
setNameFn(getResults().front(), "reduced");
}
void ReduceOp::build(
OpBuilder &builder, OperationState &result, ValueRange inputs,
ValueRange inits, ArrayRef<int64_t> dimensions,
function_ref<void(OpBuilder &, Location, ValueRange)> bodyBuild,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, TypeRange{}, inputs, inits, dimensions);
result.addAttributes(attributes);
// Add output types for `RankedTensorType` output arguments.
for (Value init : inits) {
Type initType = init.getType();
if (llvm::isa<RankedTensorType>(initType))
result.addTypes(initType);
}
if (bodyBuild)
buildGenericRegion(builder, result.location, *result.regions.front(),
inputs, inits, bodyBuild);
}
SmallVector<utils::IteratorType> ReduceOp::getIteratorTypesArray() {
int64_t inputRank =
llvm::cast<ShapedType>(getInputs()[0].getType()).getRank();
SmallVector<utils::IteratorType> iteratorTypes(inputRank,
utils::IteratorType::parallel);
for (int64_t reductionDim : getDimensions())
iteratorTypes[reductionDim] = utils::IteratorType::reduction;
return iteratorTypes;
}
ArrayAttr ReduceOp::getIndexingMaps() {
int64_t inputRank =
llvm::cast<ShapedType>(getInputs()[0].getType()).getRank();
SmallVector<AffineMap> affineMaps(
getNumDpsInputs(),
AffineMap::getMultiDimIdentityMap(inputRank, getContext()));
AffineMap resultMap =
AffineMap::getMultiDimIdentityMap(inputRank, getContext())
.dropResults(getDimensions());
for (int64_t i = 0, e = getNumDpsInits(); i < e; ++i)
affineMaps.push_back(resultMap);
return Builder(getContext()).getAffineMapArrayAttr(affineMaps);
}
void ReduceOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability ReduceOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
static ParseResult parseDenseI64ArrayAttr(OpAsmParser &parser,
NamedAttrList &attributes,
StringRef attributeName) {
if (parser.parseKeyword(attributeName) || parser.parseEqual())
return failure();
attributes.set(attributeName, DenseI64ArrayAttr::parse(parser, Type{}));
return success();
}
ParseResult ReduceOp::parse(OpAsmParser &parser, OperationState &result) {
std::optional<OperationName> payloadOpName;
NamedAttrList payloadOpAttrs;
if (succeeded(parser.parseOptionalLBrace())) {
FailureOr<OperationName> operationName = parser.parseCustomOperationName();
if (failed(operationName))
return failure();
if (parser.parseOptionalAttrDict(payloadOpAttrs))
return failure();
payloadOpName = operationName.value();
if (parser.parseRBrace())
return failure();
}
if (parseDstStyleOp(
parser, result, [&](OpAsmParser &parser, NamedAttrList &attributes) {
return parseDenseI64ArrayAttr(parser, attributes, "dimensions");
}))
return failure();
if (payloadOpName.has_value()) {
addBodyWithPayloadOp(parser, result, payloadOpName.value(), payloadOpAttrs,
ArrayRef(result.operands), /*initFirst=*/true);
} else {
SmallVector<OpAsmParser::Argument> regionArgs;
if (parser.parseArgumentList(regionArgs, OpAsmParser::Delimiter::Paren,
/*allowType=*/true, /*allowAttrs=*/true)) {
return failure();
}
Region *body = result.addRegion();
if (parser.parseRegion(*body, regionArgs))
return failure();
}
return success();
}
static void printDenseI64ArrayAttr(OpAsmPrinter &p, StringRef attributeName,
ArrayRef<int64_t> attributeValue) {
p << ' ' << attributeName << " = [" << attributeValue << "] ";
}
void ReduceOp::print(OpAsmPrinter &p) {
Block *mapper = getBody();
Operation *payloadOp = findPayloadOp(mapper, /*initFirst=*/true);
if (payloadOp) {
printShortForm(p, payloadOp);
}
printCommonStructuredOpParts(p, getDpsInputs(), getDpsInits());
printDenseI64ArrayAttr(p, getDimensionsAttrName(), getDimensions());
p.printOptionalAttrDict((*this)->getAttrs(), {getDimensionsAttrName()});
if (!payloadOp) {
// Print region if the payload op was not detected.
p.increaseIndent();
p.printNewline();
p << "(";
llvm::interleaveComma(mapper->getArguments(), p,
[&](auto arg) { p.printRegionArgument(arg); });
p << ") ";
p.printRegion(getCombiner(), /*printEntryBlockArgs=*/false);
p.decreaseIndent();
}
}
LogicalResult ReduceOp::verify() {
ArrayRef<int64_t> dimensionsRef = getDimensions();
for (int64_t i = 1; i < getNumDpsInputs(); ++i) {
if (llvm::cast<ShapedType>(getInputs()[i].getType()).getShape() !=
llvm::cast<ShapedType>(getInputs()[0].getType()).getShape()) {
return emitOpError() << "expects all inputs to have the same shapes. "
"Shape at input-index "
<< i
<< " is not equal to the shape at input-index 0.";
}
}
for (int64_t i = 1; i < getNumDpsInits(); ++i) {
if (llvm::cast<ShapedType>(getInits()[i].getType()).getShape() !=
llvm::cast<ShapedType>(getInits()[0].getType()).getShape()) {
return emitOpError() << "expects all outputs to have the same shapes. "
"Shape at output-index "
<< i
<< " is not equal to the shape at output-index 0.";
}
}
auto inputType = llvm::cast<ShapedType>(getInputs()[0].getType());
auto initType = llvm::cast<ShapedType>(getInits()[0].getType());
DenseSet<int64_t> dimensionsToReduce;
for (int64_t dimension : dimensionsRef) {
if (dimension < 0 || dimension >= inputType.getRank()) {
return emitOpError()
<< "dimensions for reduction should be in the range [0, "
<< inputType.getRank() - 1 << "].";
}
dimensionsToReduce.insert(dimension);
}
auto inputDims = inputType.getShape();
auto initDims = initType.getShape();
// Input dimensions that will be left after the reduction.
SmallVector<int64_t> reducedInputDims;
for (const auto &en : llvm::enumerate(inputDims)) {
if (!dimensionsToReduce.count(en.index()))
reducedInputDims.push_back(en.value());
}
if (reducedInputDims.size() != static_cast<size_t>(initType.getRank())) {
return emitOpError() << "number of dimensions after reduction "
<< reducedInputDims.size()
<< " doesn't match the init rank "
<< initType.getRank();
}
if (reducedInputDims != initDims)
return emitOpError() << "init dimensions [" << initDims
<< "] doesn't match input dimensions after reduction ["
<< reducedInputDims << "]";
Block *block = getBody();
if (block->getNumArguments() != this->getNumOperands())
return emitOpError()
<< "mismatching number of operands and block arguments";
// Check that the first block arguments match the element type of the inputs.
for (auto [input, bbArg] : llvm::zip(getInputs(), block->getArguments())) {
Type inputElementType =
llvm::cast<ShapedType>(input.getType()).getElementType();
if (inputElementType != bbArg.getType())
return emitOpError()
<< "input element type " << inputElementType
<< " does not match corresponding block argument type "
<< bbArg.getType();
}
// Check that the last block arguments match the element type of the outputs.
for (auto [output, bbArg] : llvm::zip(
getDpsInits(), block->getArguments().take_back(getNumDpsInits()))) {
auto outputElementType =
llvm::cast<ShapedType>(output.getType()).getElementType();
if (outputElementType != bbArg.getType())
return emitOpError()
<< "output element type " << outputElementType
<< " does not match corresponding block argument type "
<< bbArg.getType();
}
return success();
}
//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//
static void buildIdentityRegion(OpBuilder &builder, Location loc,
Region &region, ValueRange inputs,
ValueRange outputs) {
buildGenericRegion(builder, loc, region, inputs, outputs,
[](OpBuilder &b, Location loc, ValueRange args) {
if (!args.empty())
b.create<linalg::YieldOp>(loc, args[0]);
});
}
void TransposeOp::build(::mlir::OpBuilder &builder,
::mlir::OperationState &result, Value input, Value init,
DenseI64ArrayAttr permutation,
ArrayRef<NamedAttribute> attributes) {
result.addOperands(input);
result.addOperands(init);
result.addAttribute(getPermutationAttrName(result.name), permutation);
result.addAttributes(attributes);
// Add output types for `RankedTensorType` output arguments.
Type initType = init.getType();
if (llvm::isa<RankedTensorType>(initType))
result.addTypes(initType);
buildIdentityRegion(builder, result.location, *result.addRegion(), input,
init);
}
void TransposeOp::build(::mlir::OpBuilder &builder,
::mlir::OperationState &result, Value input, Value init,
ArrayRef<int64_t> permutation,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, input, init, builder.getDenseI64ArrayAttr(permutation),
attributes);
}
ParseResult TransposeOp::parse(OpAsmParser &parser, OperationState &result) {
if (failed(parseDstStyleOp(
parser, result, [&](OpAsmParser &parser, NamedAttrList &attributes) {
return parseDenseI64ArrayAttr(parser, attributes, "permutation");
})))
return failure();
OpBuilder builder(parser.getContext());
buildIdentityRegion(builder, result.location, *result.addRegion(),
/*inputs=*/result.operands,
/*outputs=*/{});
return success();
}
void TransposeOp::getAsmResultNames(
function_ref<void(Value, StringRef)> setNameFn) {
if (!getResults().empty())
setNameFn(getResults().front(), "transposed");
}
void TransposeOp::print(OpAsmPrinter &p) {
printCommonStructuredOpParts(p, getDpsInputs(), getDpsInits());
printDenseI64ArrayAttr(p, getPermutationAttrName(), getPermutation());
p.printOptionalAttrDict((*this)->getAttrs(), {getPermutationAttrName()});
}
LogicalResult TransposeOp::verify() {
ArrayRef<int64_t> permutationRef = getPermutation();
if (!isPermutationVector(permutationRef))
return emitOpError("permutation is not valid");
auto inputType = getInput().getType();
auto initType = getInit().getType();
int64_t rank = inputType.getRank();
if (rank != initType.getRank())
return emitOpError() << "input rank " << rank
<< " does not match init rank " << initType.getRank();
if (rank != static_cast<int64_t>(permutationRef.size()))
return emitOpError() << "size of permutation " << permutationRef.size()
<< " does not match the argument rank " << rank;
auto inputDims = inputType.getShape();
auto initDims = initType.getShape();
for (int64_t i = 0; i < rank; ++i) {
int64_t inputDim = inputDims[permutationRef[i]];
int64_t initDim = initDims[i];
if (inputDim != initDim) {
return emitOpError() << "dim(result, " << i << ") = " << initDim
<< " doesn't match dim(input, permutation[" << i
<< "]) = " << inputDim;
}
}
return success();
}
SmallVector<utils::IteratorType> TransposeOp::getIteratorTypesArray() {
int64_t rank = getInit().getType().getRank();
return SmallVector<utils::IteratorType>(rank, utils::IteratorType::parallel);
}
ArrayAttr TransposeOp::getIndexingMaps() {
Builder builder(getContext());
int64_t rank = getInit().getType().getRank();
return builder.getAffineMapArrayAttr(
{inversePermutation(AffineMap::getPermutationMap(
llvm::to_vector_of<unsigned>(getPermutation()), getContext())),
builder.getMultiDimIdentityMap(rank)});
}
void TransposeOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability TransposeOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
LogicalResult TransposeOp::fold(FoldAdaptor adaptor,
SmallVectorImpl<OpFoldResult> &result) {
// Only the tensor type is supported.
if (!isa<TensorType>(getInput().getType()))
return failure();
// Single dimension transpose.
if (getPermutation().size() == 0) {
result.push_back(getInput());
return success();
}
// Identity permutation.
if (isIdentityPermutation(getPermutation())) {
result.push_back(getInput());
return success();
}
return failure();
}
/// Fold transpose with transpose.
struct FoldTransposeWithTranspose : OpRewritePattern<linalg::TransposeOp> {
using OpRewritePattern<linalg::TransposeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(linalg::TransposeOp transposeOp,
PatternRewriter &rewriter) const override {
auto defTransposeOp = transposeOp.getInput().getDefiningOp<TransposeOp>();
if (!defTransposeOp)
return failure();
ArrayRef<int64_t> defPerms = defTransposeOp.getPermutation();
ArrayRef<int64_t> perms = transposeOp.getPermutation();
SmallVector<int64_t> foldedPerms;
foldedPerms.reserve(perms.size());
for (int64_t perm : perms)
foldedPerms.push_back(defPerms[perm]);
rewriter.replaceOpWithNewOp<TransposeOp>(
transposeOp, defTransposeOp.getInput(), transposeOp.getInit(),
foldedPerms);
return success();
}
};
/// This pattern canonicalize transpose by swapping the order of
/// broadcast and transpose:
/// transpose(broadcast(input)) -> broadcast(transpose(input))
struct SwapTransposeWithBroadcast : OpRewritePattern<linalg::TransposeOp> {
using OpRewritePattern<linalg::TransposeOp>::OpRewritePattern;
LogicalResult matchAndRewrite(linalg::TransposeOp transposeOp,
PatternRewriter &rewriter) const override {
Value input = transposeOp.getInput();
BroadcastOp broadcastOp = input.getDefiningOp<BroadcastOp>();
if (!input.hasOneUse() || !broadcastOp)
return failure();
ArrayRef<int64_t> dimensions = broadcastOp.getDimensions();
ArrayRef<int64_t> perms = transposeOp.getPermutation();
// Get new perms and new dimensions.
SmallVector<int64_t> resultPerms = dropDims(perms, dimensions);
SmallVector<int64_t> invertPerm = invertPermutationVector(perms);
SmallVector<int64_t> resultDimensions;
unsigned dimensionSize = dimensions.size();
for (unsigned i = 0; i < dimensionSize; ++i)
resultDimensions.push_back(invertPerm[dimensions[i]]);
// Create transpose result.
Value broadcastInput = broadcastOp.getInput();
Location loc = transposeOp.getLoc();
MLIRContext *ctx = transposeOp.getContext();
SmallVector<OpFoldResult> dims;
auto broadcastInputTy =
mlir::cast<RankedTensorType>(broadcastInput.getType());
unsigned inputRank = broadcastInputTy.getRank();
for (unsigned i = 0; i < inputRank; ++i) {
if (broadcastInputTy.isDynamicDim(i)) {
dims.push_back(rewriter.create<tensor::DimOp>(loc, broadcastInput, i)
->getResult(0));
} else {
dims.push_back(IntegerAttr::get(IndexType::get(ctx),
broadcastInputTy.getDimSize(i)));
}
}
SmallVector<OpFoldResult> transposeResultShapes =
applyPermutation(dims, resultPerms);
Value transposeInit = rewriter.create<tensor::EmptyOp>(
transposeOp.getLoc(), transposeResultShapes,
broadcastInputTy.getElementType());
// Create broadcast(transpose(input)).
Value transposeResult =
rewriter
.create<TransposeOp>(loc, broadcastOp.getInput(), transposeInit,
resultPerms)
->getResult(0);
rewriter.replaceOpWithNewOp<BroadcastOp>(
transposeOp, transposeResult, transposeOp.getInit(), resultDimensions);
return success();
}
};
void TransposeOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<FoldTransposeWithTranspose, SwapTransposeWithBroadcast>(context);
}
//===----------------------------------------------------------------------===//
// BroadcastOp
//===----------------------------------------------------------------------===//
void BroadcastOp::build(::mlir::OpBuilder &builder,
::mlir::OperationState &result, Value input, Value init,
DenseI64ArrayAttr dimensions,
ArrayRef<NamedAttribute> attributes) {
result.addOperands(input);
result.addOperands(init);
result.addAttribute(getDimensionsAttrName(result.name), dimensions);
result.addAttributes(attributes);
// Add output types for `RankedTensorType` output arguments.
Type initType = init.getType();
if (llvm::isa<RankedTensorType>(initType))
result.addTypes(initType);
buildIdentityRegion(builder, result.location, *result.addRegion(), input,
init);
}
void BroadcastOp::build(::mlir::OpBuilder &builder,
::mlir::OperationState &result, Value input, Value init,
ArrayRef<int64_t> dimensions,
ArrayRef<NamedAttribute> attributes) {
build(builder, result, input, init, builder.getDenseI64ArrayAttr(dimensions),
attributes);
}
ParseResult BroadcastOp::parse(OpAsmParser &parser, OperationState &result) {
if (failed(parseDstStyleOp(
parser, result, [&](OpAsmParser &parser, NamedAttrList &attributes) {
return parseDenseI64ArrayAttr(parser, attributes, "dimensions");
})))
return failure();
OpBuilder builder(parser.getContext());
buildIdentityRegion(builder, result.location, *result.addRegion(),
/*inputs=*/result.operands,
/*outputs=*/{});
return success();
}
void BroadcastOp::getAsmResultNames(
function_ref<void(Value, StringRef)> setNameFn) {
if (!getResults().empty())
setNameFn(getResults().front(), "broadcasted");
}
void BroadcastOp::print(OpAsmPrinter &p) {
printCommonStructuredOpParts(p, getDpsInputs(), getDpsInits());
printDenseI64ArrayAttr(p, getDimensionsAttrName(), getDimensions());
p.printOptionalAttrDict((*this)->getAttrs(), {getDimensionsAttrName()});
}
LogicalResult BroadcastOp::verify() {
ArrayRef<int64_t> dimensionsRef = getDimensions();
auto inputType = getInput().getType();
auto initType = getInit().getType();
int64_t inputRank = inputType.getRank();
int64_t initRank = initType.getRank();
auto inputShape = inputType.getShape();
auto initShape = initType.getShape();
if ((size_t)inputRank + dimensionsRef.size() != (size_t)initRank)
return emitOpError() << "input rank plus added dimensions does not "
"match init rank. input rank: "
<< inputRank
<< ", dimensions size: " << dimensionsRef.size()
<< ", init rank: " << initRank;
for (const auto &[idx, dim] : llvm::enumerate(dimensionsRef)) {
if (dim < 0 || dim >= initRank)
return emitOpError() << "dimension " << idx
<< " is out of range. expected range: [0, "
<< initRank - 1 << "], got: " << dim;
}
// Mapping from input dims to init dims.
SmallVector<int64_t> dimMap;
for (auto dim : llvm::seq<int64_t>(0, initRank)) {
if (!llvm::is_contained(dimensionsRef, dim))
dimMap.push_back(dim);
}
for (const auto &[inputDimIdx, initDimIdx] : llvm::enumerate(dimMap)) {
// This dimensions is mapped from the input. Init and input dims should
// match.
if (inputShape[inputDimIdx] != initShape[initDimIdx])
return emitOpError() << "input dim " << inputDimIdx
<< " should match init dim " << initDimIdx
<< ". input: " << inputShape[inputDimIdx]
<< ", init: " << initShape[initDimIdx];
}
return success();
}
SmallVector<utils::IteratorType> BroadcastOp::getIteratorTypesArray() {
int64_t rank = getInit().getType().getRank();
return SmallVector<utils::IteratorType>(rank, utils::IteratorType::parallel);
}
ArrayAttr BroadcastOp::getIndexingMaps() {
Builder builder(getContext());
int64_t rank = getInit().getType().getRank();
return builder.getAffineMapArrayAttr(
{builder.getMultiDimIdentityMap(rank).dropResults(getDimensions()),
builder.getMultiDimIdentityMap(rank)});
}
void BroadcastOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability BroadcastOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<EraseIdentityLinalgOp<BroadcastOp>>(context);
}
//===----------------------------------------------------------------------===//
// YieldOp
//===----------------------------------------------------------------------===//
void linalg::YieldOp::print(OpAsmPrinter &p) {
if (getNumOperands() > 0)
p << ' ' << getOperands();
p.printOptionalAttrDict((*this)->getAttrs());
if (getNumOperands() > 0)
p << " : " << getOperandTypes();
}
ParseResult YieldOp::parse(OpAsmParser &parser, OperationState &result) {
SmallVector<OpAsmParser::UnresolvedOperand, 2> opInfo;
SmallVector<Type, 2> types;
SMLoc loc = parser.getCurrentLocation();
return failure(parser.parseOperandList(opInfo) ||
parser.parseOptionalAttrDict(result.attributes) ||
(!opInfo.empty() && parser.parseColonTypeList(types)) ||
parser.resolveOperands(opInfo, types, loc, result.operands));
}
// Check the operand number and types must match the element types of the
// LinalgOp interface's shaped operands.
static LogicalResult verifyYield(linalg::YieldOp op, LinalgOp linalgOp) {
if (op.getNumOperands() != linalgOp.getNumDpsInits())
return op.emitOpError("expected number of yield values (")
<< op.getNumOperands()
<< ") to match the number of inits / outs operands of the enclosing "
<< "LinalgOp (" << linalgOp.getNumDpsInits() << ")";
for (OpOperand &opOperand : op->getOpOperands()) {
OpOperand *outputOperand =
linalgOp.getDpsInitOperand(opOperand.getOperandNumber());
Type elementType = outputOperand->get().getType();
if (isa<MemRefType, RankedTensorType>(elementType))
elementType = getElementTypeOrSelf(outputOperand->get().getType());
if (opOperand.get().getType() != elementType)
return op.emitOpError("type of yield operand ")
<< (opOperand.getOperandNumber() + 1) << " ("
<< opOperand.get().getType() << ") doesn't match "
<< "the element type of the enclosing linalg.generic op ("
<< elementType << ")";
}
return success();
}
LogicalResult linalg::YieldOp::verify() {
auto *parentOp = (*this)->getParentOp();
if (parentOp->getNumRegions() != 1 || parentOp->getRegion(0).empty())
return emitOpError("expected single non-empty parent region");
if (auto linalgOp = dyn_cast<LinalgOp>(parentOp))
return verifyYield(*this, linalgOp);
return emitOpError("expected parent op with LinalgOp interface");
}
//===----------------------------------------------------------------------===//
// IndexOp
//===----------------------------------------------------------------------===//
LogicalResult IndexOp::verify() {
auto linalgOp = dyn_cast<LinalgOp>((*this)->getParentOp());
if (!linalgOp)
return emitOpError("expected parent op with LinalgOp interface");
if (linalgOp.getNumLoops() <= getDim())
return emitOpError("expected dim (")
<< getDim() << ") to be lower than the number of loops ("
<< linalgOp.getNumLoops() << ") of the enclosing LinalgOp";
return success();
}
OpFoldResult IndexOp::fold(FoldAdaptor adaptor) {
auto linalgOp = dyn_cast_or_null<LinalgOp>((*this)->getParentOp());
// Bail out if `linalg.index` does not have a proper parent yet at this
// point, e.g., when calling `createOrFold` during IR construction in
// `genericOp::build`.
if (!linalgOp)
return OpFoldResult{};
// Index of unit dims is always 0.
SmallVector<int64_t, 4> loopBounds = linalgOp.getStaticLoopRanges();
uint64_t dim = getDim();
assert(dim < loopBounds.size() && "Dim is out of bounds");
if (loopBounds[dim] == 1)
return IntegerAttr::get(IndexType::get(getContext()), 0);
return OpFoldResult{};
}
/////// Operations corresponding to library calls defined with Tablegen ////////
#include "mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yamlgen.cpp.inc"
#define GET_OP_CLASSES
#include "mlir/Dialect/Linalg/IR/LinalgOps.cpp.inc"
#define GET_OP_CLASSES
#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
#define GET_OP_CLASSES
#include "mlir/Dialect/Linalg/IR/LinalgRelayoutOps.cpp.inc"
AffineMap mlir::linalg::extractOrIdentityMap(std::optional<AffineMap> maybeMap,
unsigned rank,
MLIRContext *context) {
if (maybeMap)
return *maybeMap;
if (rank == 0)
return AffineMap::get(context);
return AffineMap::getMultiDimIdentityMap(rank, context);
}
SmallVector<AffineExpr, 4>
mlir::linalg::makeAffineDimExprs(unsigned num, unsigned &startIdx,
MLIRContext *context) {
SmallVector<AffineExpr, 4> res;
res.reserve(num);
for (unsigned i = 0; i < num; ++i)
res.push_back(getAffineDimExpr(startIdx++, context));
return res;
}
SmallVector<AffineExpr, 4> mlir::linalg::concat(ArrayRef<AffineExpr> a,
ArrayRef<AffineExpr> b) {
auto rangeA = llvm::make_range(a.begin(), a.end());
auto rangeB = llvm::make_range(b.begin(), b.end());
auto concatRanges = llvm::concat<const AffineExpr>(rangeA, rangeB);
return llvm::to_vector<4>(concatRanges);
}
static LogicalResult appendMangledType(llvm::raw_string_ostream &ss, Type t) {
if (auto memref = llvm::dyn_cast<MemRefType>(t)) {
ss << "view";
for (auto size : memref.getShape())
if (size < 0)
ss << "sx";
else
ss << size << "x";
if (failed(appendMangledType(ss, memref.getElementType())))
return failure();
if (auto as = memref.getMemorySpace()) {
if (auto attr = llvm::dyn_cast<IntegerAttr>(as))
ss << "as" << attr.getInt();
else
return failure();
}
return success();
}
if (auto vec = llvm::dyn_cast<VectorType>(t)) {
ss << "vector";
llvm::interleave(
vec.getShape(), [&](int64_t i) { ss << i; }, [&]() { ss << "x"; });
if (failed(appendMangledType(ss, vec.getElementType())))
return failure();
return success();
}
if (t.isSignlessIntOrIndexOrFloat()) {
ss << t;
return success();
}
return failure();
}
std::string mlir::linalg::generateLibraryCallName(Operation *op) {
assert(isa<LinalgOp>(op));
std::string name(op->getName().getStringRef().str());
std::string fun = "";
for (NamedAttribute kv : op->getAttrs()) {
if (UnaryFnAttr ufa = llvm::dyn_cast<UnaryFnAttr>(kv.getValue())) {
fun = stringifyEnum(ufa.getValue()).str() + "_";
} else if (BinaryFnAttr bfa = llvm::dyn_cast<BinaryFnAttr>(kv.getValue())) {
fun = stringifyEnum(bfa.getValue()).str() + "_";
}
}
name.reserve(128);
llvm::replace(name, '.', '_');
llvm::raw_string_ostream ss(name);
ss << "_" << fun;
for (Type t : op->getOperandTypes()) {
if (failed(appendMangledType(ss, t)))
return std::string();
ss << "_";
}
name.pop_back();
return name;
}
//===----------------------------------------------------------------------===//
// Canonicalizers and Folders.
//===----------------------------------------------------------------------===//
namespace {
struct EraseDeadLinalgOp : public OpInterfaceRewritePattern<LinalgOp> {
using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;
LogicalResult matchAndRewrite(LinalgOp op,
PatternRewriter &rewriter) const override {
for (OpOperand &opOperand : op->getOpOperands()) {
// Linalg "inputs" may be either tensor or memref type.
// tensor<0xelt_type> is a convention that may not always mean
// "0 iterations". Only erase in cases we see memref<...x0x...>.
auto mt = llvm::dyn_cast<MemRefType>(opOperand.get().getType());
if (!mt)
continue;
if (llvm::is_contained(op.getShape(&opOperand), 0)) {
rewriter.eraseOp(op);
return success();
}
}
return failure();
}
};
/// Fold LinalgOps with `tensor.cast` consumer if the `tensor.cast` has
/// result that is more static than the linalg op.
struct FoldTensorCastConsumerOp : public OpRewritePattern<tensor::CastOp> {
using OpRewritePattern<tensor::CastOp>::OpRewritePattern;
LogicalResult matchAndRewrite(tensor::CastOp castOp,
PatternRewriter &rewriter) const override {
if (!tensor::canFoldIntoProducerOp(castOp))
return failure();
auto linalgOp = castOp.getSource().getDefiningOp<LinalgOp>();
if (!linalgOp)
return failure();
// Cast can be in conditionally reachable region, if which case folding will
// generate invalid code. Only conservatively fold ops in same block for
// now.
if (castOp->getBlock() != linalgOp->getBlock())
return failure();
OpBuilder::InsertionGuard guard(rewriter);
rewriter.setInsertionPoint(linalgOp);
Location loc = linalgOp.getLoc();
OpResult resultValue = llvm::cast<OpResult>(castOp.getSource());
unsigned resultNumber = resultValue.getResultNumber();
auto resultType =
llvm::cast<RankedTensorType>(castOp->getResult(0).getType());
// Replace the `outs` for the result with a `tensor.cast`. This cast is now
// going from a more dynamic shape to a less dynamic shape. If the producer
// for this cast, i.e. producer of the out operand, is also an operation
// that folds with tensor.cast consumer (like this pattern), the cast will
// continue to propagate as far up the stack as it can go.
OpOperand *outOperand = linalgOp.getDpsInitOperand(resultNumber);
Value newOperand =
rewriter.create<tensor::CastOp>(loc, resultType, outOperand->get());
SmallVector<Value> newOperands = linalgOp.getDpsInputs();
SmallVector<Value> outputOperands(linalgOp.getDpsInits().begin(),
linalgOp.getDpsInits().end());
outputOperands[resultNumber] = newOperand;
newOperands.append(outputOperands.begin(), outputOperands.end());
SmallVector<Type> resultTypes(linalgOp->result_type_begin(),
linalgOp->result_type_end());
resultTypes[resultNumber] = resultType;
Operation *newOp = clone(rewriter, linalgOp, resultTypes, newOperands);
// Create a tensor.cast operation back to the original type.
Value castBack = rewriter.create<tensor::CastOp>(
loc, resultValue.getType(), newOp->getResult(resultNumber));
SmallVector<Value> results(newOp->result_begin(), newOp->result_end());
results[resultNumber] = castBack;
rewriter.replaceOp(linalgOp, results);
rewriter.replaceOp(castOp, newOp->getResult(resultNumber));
return success();
}
};
/// For each of the operand in `operands` this function maps the static sizes of
/// dimensions to their affine dim expressions.
static void populateMap(LinalgOp linalgOp, MutableArrayRef<OpOperand> operands,
llvm::DenseMap<AffineExpr, int64_t> &affineExprToSize) {
for (OpOperand &opOperand : operands) {
if (linalgOp.isScalar(&opOperand))
continue;
Value src = opOperand.get();
auto sourceType = llvm::cast<RankedTensorType>(src.getType());
auto sourceMap = linalgOp.getMatchingIndexingMap(&opOperand);
// Get the `sourceShape` of the `sourceType`. If the operand is a result of
// `tensor.cast` operation and source of the cast operation has a static
// shape, then assign it to the `sourceShape`.
auto *parentOp = src.getDefiningOp();
ArrayRef<int64_t> sourceShape = sourceType.getShape();
if (parentOp) {
if (auto castOp = dyn_cast<tensor::CastOp>(parentOp)) {
Value castSource = castOp.getSource();
auto castSourceType =
llvm::dyn_cast<RankedTensorType>(castSource.getType());
if (castSourceType && castSourceType.hasStaticShape())
sourceShape = castSourceType.getShape();
}
}
// If the source shape's dimension has a static shape, map the affine dim
// expression to the known static size.
for (unsigned i = 0; i < sourceShape.size(); i++) {
if (sourceType.isDynamicDim(i))
continue;
if (auto affineDimExpr = dyn_cast<AffineDimExpr>(sourceMap.getResult(i)))
affineExprToSize.try_emplace(affineDimExpr, sourceShape[i]);
}
}
}
/// Creates new operand w.r.t 'opOperand' of `linalgOp` with static sizes
/// mapped in `affineExprToSize`. New operands are created in `newOperands` and
/// their result types is stored in `resultTypes`. If `opOperand` requires no
/// change then `changeNeeded` is false and same operand is added in the
/// `newOperands` list.
static void createNewOperandWithStaticSizes(
Location loc, PatternRewriter &rewriter, OpOperand *opOperand,
llvm::DenseMap<AffineExpr, int64_t> &affineExprToSize, LinalgOp linalgOp,
SmallVector<Value> &newOperands, SmallVector<Type> &resultTypes,
bool &changeNeeded) {
Value src = opOperand->get();
newOperands.push_back(src);
if (linalgOp.isScalar(opOperand))
return;
auto sourceType = llvm::cast<RankedTensorType>(src.getType());
Type resultType = sourceType;
if (sourceType.hasStaticShape() && linalgOp.isDpsInit(opOperand)) {
resultTypes.push_back(resultType);
return;
}
ArrayRef<int64_t> sourceShape = sourceType.getShape();
AffineMap sourceMap = linalgOp.getMatchingIndexingMap(opOperand);
SmallVector<int64_t> newShape;
// If operand is updated with new shape, `newOperandNeeded` will be
// true.
bool newOperandNeeded = false;
for (unsigned i = 0; i < sourceShape.size(); i++) {
int64_t dimShape = sourceShape[i];
AffineExpr dimExpr = sourceMap.getResult(i);
if (!affineExprToSize.contains(dimExpr) || !sourceType.isDynamicDim(i)) {
newShape.push_back(dimShape);
continue;
}
// Dimension has a dynamic shape and corresponding affine dim
// expression is present in the map. So assign the size for the
// given affine dim expression to the dimension.
newShape.push_back(affineExprToSize[dimExpr]);
newOperandNeeded = true;
}
resultType = RankedTensorType::get(newShape, sourceType.getElementType(),
sourceType.getEncoding());
if (newOperandNeeded) {
changeNeeded = true;
// Get the new operand value given its size and element type by
// casting it.
Value newOperand = rewriter.create<tensor::CastOp>(loc, resultType, src);
unsigned index = opOperand->getOperandNumber();
newOperands[index] = newOperand;
}
if (linalgOp.isDpsInit(opOperand))
resultTypes.push_back(resultType);
}
/// Static shapes for the operands can be inferred if any one of the operands
/// have a static shape. This can be done by referring to the affine dim
/// expressions for the operand.
struct InferStaticShapeOfOperands : public OpInterfaceRewritePattern<LinalgOp> {
using OpInterfaceRewritePattern<LinalgOp>::OpInterfaceRewritePattern;
LogicalResult matchAndRewrite(LinalgOp linalgOp,
PatternRewriter &rewriter) const override {
if (!linalgOp.hasPureTensorSemantics())
return failure();
// Maps must be projected permutations.
if (llvm::any_of(linalgOp.getIndexingMapsArray(), [](AffineMap map) {
return !map.isProjectedPermutation();
}))
return failure();
// Maps affine dim expressions to the static size of that dimension.
llvm::DenseMap<AffineExpr, int64_t> affineExprToSize;
Location loc = linalgOp.getLoc();
// For each of the affine dim expression, check if the size is known. If
// known add that in the map.
populateMap(linalgOp, linalgOp->getOpOperands(), affineExprToSize);
SmallVector<Value> newOperands;
SmallVector<Type> resultTypes;
// `changeNeeded` is `false` if the operands of `linalgOp` require no
// change in their types.
bool changeNeeded = false;
newOperands.reserve(linalgOp->getNumOperands());
resultTypes.reserve(linalgOp.getNumDpsInits());
// Iterate over all the operands and update the static sizes.
for (OpOperand &opOperand : linalgOp->getOpOperands()) {
createNewOperandWithStaticSizes(loc, rewriter, &opOperand,
affineExprToSize, linalgOp, newOperands,
resultTypes, changeNeeded);
}
// If the generic op has all the required static information, no
// canonicalization needed.
if (!changeNeeded)
return failure();
// Clone op.
Operation *newOp = clone(rewriter, linalgOp, resultTypes, newOperands);
SmallVector<Value> replacements;
replacements.reserve(newOp->getNumResults());
for (auto it : llvm::zip(linalgOp->getResults(), newOp->getResults())) {
Value newResult = std::get<1>(it);
Value oldResult = std::get<0>(it);
Type newType = newResult.getType();
Type oldType = oldResult.getType();
replacements.push_back(
(newType != oldType)
? rewriter.create<tensor::CastOp>(loc, oldType, newResult)
: newResult);
}
rewriter.replaceOp(linalgOp, replacements);
return success();
}
};
} // namespace
// All named ops canonicalizers and folders are auto-generated in the
// .cpp.inc.
//===----------------------------------------------------------------------===//
// SoftmaxOp
//===----------------------------------------------------------------------===//
LogicalResult SoftmaxOp::verify() {
ShapedType inputType = getInputOperandType();
ShapedType outputType = getOutputOperandType();
ArrayRef<int64_t> inputShape = inputType.getShape();
ArrayRef<int64_t> outputShape = outputType.getShape();
if (failed(verifyCompatibleShape(inputShape, outputShape)))
return emitOpError("incompatible output shape");
int64_t inputRank = getInputOperandRank();
int64_t dimension = getDimension();
if ((dimension < 0) || (dimension >= inputRank))
return emitOpError("incorrect dimension specified");
return success();
}
SmallVector<Range> SoftmaxOp::getIterationDomain(OpBuilder &builder) {
int64_t operandRank = getInputOperandRank();
SmallVector<Range> loopBounds(operandRank);
Location loc = getLoc();
Value zero = builder.create<arith::ConstantIndexOp>(loc, 0);
Value one = builder.create<arith::ConstantIndexOp>(loc, 1);
Value source = getInput();
for (auto dim : llvm::seq<int64_t>(0, operandRank)) {
loopBounds[dim].offset = zero;
loopBounds[dim].size = getDimValue(builder, loc, source, dim);
loopBounds[dim].stride = one;
}
return loopBounds;
}
SmallVector<utils::IteratorType> SoftmaxOp::getLoopIteratorTypes() {
SmallVector<utils::IteratorType> iteratorTypes(getInputOperandRank(),
utils::IteratorType::parallel);
iteratorTypes[getDimension()] = utils::IteratorType::reduction;
return iteratorTypes;
}
FailureOr<TilingResult>
SoftmaxOp::getTiledImplementation(OpBuilder &builder,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes) {
int64_t rank = getInputOperandRank();
auto oneAttr = builder.getI64IntegerAttr(1);
SmallVector<OpFoldResult> strides(rank, oneAttr);
SmallVector<Value> tiledOperands;
Operation *inputSlice =
getSlice(builder, getLoc(), getInput(), offsets, sizes, strides);
if (!inputSlice) {
return emitOpError("failed to compute input slice");
}
tiledOperands.emplace_back(inputSlice->getResult(0));
Operation *outputSlice =
getSlice(builder, getLoc(), getOutput(), offsets, sizes, strides);
if (!outputSlice) {
return emitOpError("failed to compute output slice");
}
tiledOperands.emplace_back(outputSlice->getResult(0));
SmallVector<Type, 4> resultTypes;
if (hasPureTensorSemantics())
resultTypes.push_back(tiledOperands[1].getType());
Operation *tiledOp =
mlir::clone(builder, getOperation(), resultTypes, tiledOperands);
return TilingResult{
{tiledOp},
SmallVector<Value>(tiledOp->getResults()),
llvm::to_vector(ArrayRef<Operation *>{inputSlice, outputSlice})};
}
LogicalResult SoftmaxOp::getResultTilePosition(
OpBuilder &builder, unsigned resultNumber, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes, SmallVector<OpFoldResult> &resultOffsets,
SmallVector<OpFoldResult> &resultSizes) {
if (resultNumber == 0) {
resultOffsets.assign(offsets.begin(), offsets.end());
resultSizes.assign(sizes.begin(), sizes.end());
return success();
}
return failure();
}
// cast(dynamic) -> static.
LogicalResult SoftmaxOp::fold(FoldAdaptor, SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
LogicalResult
SoftmaxOp::reifyResultShapes(OpBuilder &b,
ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
SmallVector<OpFoldResult> shapes;
Location loc = getOperation()->getLoc();
IRRewriter rewriter(b);
auto inputShapedType = llvm::cast<ShapedType>(getInputOperandType());
auto outputShapedType = llvm::cast<ShapedType>(getOutputOperandType());
for (int64_t dim : llvm::seq<int64_t>(0, getOutputOperandRank())) {
if (!outputShapedType.isDynamicDim(dim)) {
// Static dim: Return IntegerAttr.
shapes.push_back(b.getIndexAttr(inputShapedType.getDimSize(dim)));
} else {
// Dynamic dim: Return Value.
OpFoldResult ofr = createOrFoldDimOp(b, loc, getInput(), dim);
shapes.push_back(getValueOrCreateConstantIndexOp(b, loc, ofr));
}
}
reifiedReturnShapes.emplace_back(std::move(shapes));
return success();
}
void SoftmaxOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
for (auto [index, operand] : llvm::enumerate(getDpsInputs())) {
if (!llvm::isa<MemRefType>(operand.getType()))
continue;
effects.emplace_back(MemoryEffects::Read::get(),
&getOperation()->getOpOperand(index), /*stage=*/0,
/*effectOnFullRegion=*/true,
SideEffects::DefaultResource::get());
}
for (OpOperand &operand : getDpsInitsMutable()) {
if (!llvm::isa<MemRefType>(operand.get().getType()))
continue;
effects.emplace_back(MemoryEffects::Read::get(), &operand, /*stage=*/0,
/*effectOnFullRegion=*/true,
SideEffects::DefaultResource::get());
effects.emplace_back(MemoryEffects::Write::get(), &operand, /*stage=*/0,
/*effectOnFullRegion=*/true,
SideEffects::DefaultResource::get());
}
}
// Helper functions for softmax decomposition.
// @{
// Helper function to produce the iterator types (reduction or parallel) and
// affine maps for the iterators used in the decomposition of softmax.
// This method creates:
// If allParallel == true:
// - iterator type: {parallel, ..., parallel}
// - affine maps:
// -- identity with inputRank dimensions.
// -- (d0, ..., dN) -> (d0, ..., d_dim-1, d_dim+1, ..., dN),
// where N == inputRank.
//
// If allParallel == false:
// - iterator type at dim(i) == parallel for i != \p dim and
// dim(dim) == reduction.
// - affine map:
// -- identity with inputRank dimensions.
// -- (d0, ..., dN) -> (d0, ..., d_dim-1, d_dim+1, ..., dN),
// where N == inputRank.
static std::tuple<SmallVector<utils::IteratorType>, SmallVector<AffineMap>>
computeIteratorTypesAndIndexingMaps(OpBuilder &builder, int64_t inputRank,
int64_t dim, bool allParallel = false) {
SmallVector<utils::IteratorType> iteratorTypes(inputRank,
utils::IteratorType::parallel);
if (!allParallel)
iteratorTypes[dim] = utils::IteratorType::reduction;
MLIRContext *ctxt = builder.getContext();
auto identityMap = AffineMap::getMultiDimIdentityMap(inputRank, ctxt);
SmallVector<AffineExpr, 2> affineExprs;
for (int i = 0; i < inputRank; i++) {
if (i != dim)
affineExprs.push_back(mlir::getAffineDimExpr(i, ctxt));
}
auto reductionMap =
AffineMap::get(inputRank, /*symbols=*/0, affineExprs, ctxt);
SmallVector<AffineMap> indexingMaps{identityMap, reductionMap};
return std::make_tuple(iteratorTypes, indexingMaps);
}
// Helper function to produce a linalg.generic that computes a reduction on
// dimension \p dim with the operation type \p T.
template <typename T>
static Value reduce(OpBuilder &builder, Location loc, Value input, Value output,
int64_t dim) {
auto inputType = cast<ShapedType>(input.getType());
ArrayRef<int64_t> inputShape = inputType.getShape();
int64_t inputRank = inputShape.size();
auto [iteratorTypes, indexingMaps] =
computeIteratorTypesAndIndexingMaps(builder, inputRank, dim);
assert(indexingMaps.size() == 2 &&
"We should have two maps: 1 for the input, 1 for the output");
assert(indexingMaps[0].isIdentity() && "input map should be identity");
auto genericOp = builder.create<linalg::GenericOp>(
loc, output.getType(), input, output, indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value result = b.create<T>(loc, args[0], args[1]);
b.create<linalg::YieldOp>(loc, result);
});
return genericOp.getResult(0);
}
/// Produce a linalg generic that computes the second step of the softmax
/// decomposition: res = exp(input - max), where \p max is the max of \p input
/// on dimension \p dim.
static Value buildSubAndExpOp(OpBuilder &builder, Location loc, Value input,
Value max, Value output, int64_t dim) {
auto inputType = cast<ShapedType>(input.getType());
ArrayRef<int64_t> inputShape = inputType.getShape();
int64_t inputRank = inputShape.size();
auto [iteratorTypes, indexingMaps] = computeIteratorTypesAndIndexingMaps(
builder, inputRank, dim, /*allParallel=*/true);
assert(indexingMaps.size() == 2 && "We should have one map for each input");
assert(indexingMaps[0].isIdentity() && "input map should be identity");
// Add the affine map for the output argument.
indexingMaps.push_back(indexingMaps[0]);
auto genericOp = builder.create<linalg::GenericOp>(
loc, input.getType(), ValueRange{input, max}, output, indexingMaps,
iteratorTypes, [&](OpBuilder &b, Location loc, ValueRange args) {
Value diff = b.create<arith::SubFOp>(loc, args[0], args[1]);
Value result = b.create<math::ExpOp>(loc, diff);
b.create<linalg::YieldOp>(loc, result);
});
return genericOp.getResult(0);
}
/// Produce a linalg generic that computes the final step of the softmax
/// decomposition.
/// \returns linalg.generic ins(\p numerator, \p denominator) outs(\p output) {
/// yield n / d
/// }
static Value buildDivOp(OpBuilder &builder, Location loc, Value numerator,
Value denominator, Value output, int64_t dim) {
auto inputType = cast<ShapedType>(numerator.getType());
ArrayRef<int64_t> inputShape = inputType.getShape();
int64_t inputRank = inputShape.size();
auto [iteratorTypes, indexingMaps] = computeIteratorTypesAndIndexingMaps(
builder, inputRank, dim, /*allParallel=*/true);
assert(indexingMaps.size() == 2 &&
"We should have one map for each input (2)");
assert(indexingMaps[0].isIdentity() && "Numerator map should be identity");
// Add the affine map for the output tensor.
indexingMaps.push_back(indexingMaps[0]);
auto genericOp = builder.create<linalg::GenericOp>(
loc, numerator.getType(), ValueRange{numerator, denominator}, output,
indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
Value result = b.create<arith::DivFOp>(loc, args[0], args[1]);
b.create<linalg::YieldOp>(loc, result);
});
return genericOp.getResult(0);
}
// @} End helper functions for softmax decomposition.
/// Given an N-dimensional tensor x, this method converts
/// softmax(x) to the following sequence of operations:
///
/// 1. Compute the max of x along dimension d. This results
/// in a N-1 dimensional tensor m.
/// m = max(x, dim = d)
///
/// 2. Subtract a broadcasted m from x and exponentiate. This results in
/// a N dimensional tensor z.
/// z = exp(x - m)
///
/// 3. Compute the sum of z along dimension d. This results in
/// a N-1 dimensional tensor l.
/// l = sum(z, dim = d)
///
/// 4. Divide z and l. This gives the N-dimensional softmax.
/// softmax = z / l
///
FailureOr<SmallVector<Value>> SoftmaxOp::decomposeOperation(OpBuilder &b) {
OpBuilder::InsertionGuard guard(b);
b.setInsertionPoint(*this);
Location loc = getLoc();
Value input = getInput();
ShapedType inputType = getInputOperandType();
Type elementType = inputType.getElementType();
int64_t reductionDim = getDimension();
SmallVector<OpFoldResult> dims = tensor::getMixedSizes(b, loc, input);
Value output = getOutput();
dims.erase(dims.begin() + reductionDim);
// Step 1: Compute max along dim.
Value outputReduce = b.create<tensor::EmptyOp>(loc, dims, elementType);
Value neutralForMaxF = arith::getIdentityValue(arith::AtomicRMWKind::maxnumf,
elementType, b, loc,
/*useOnlyFiniteValue=*/true);
Value neutralForMaxFInit =
b.create<linalg::FillOp>(loc, Value{neutralForMaxF}, outputReduce)
.result();
Value max =
reduce<arith::MaxNumFOp>(b, loc, input, neutralForMaxFInit, reductionDim);
// Step 2: Subtract max from input and exponentiate.
Value numerator = buildSubAndExpOp(b, loc, input, max, output, reductionDim);
// Step 3: Compute sum along dim.
Value zero = arith::getIdentityValue(arith::AtomicRMWKind::addf, elementType,
b, loc, /*useOnlyFiniteValue=*/true);
Value zeroInit =
b.create<linalg::FillOp>(loc, Value{zero}, outputReduce).result();
Value denominator =
reduce<arith::AddFOp>(b, loc, numerator, zeroInit, reductionDim);
// Step 4: Compute softmax.
Value result =
buildDivOp(b, loc, numerator, denominator, output, reductionDim);
return SmallVector<Value>{result};
}
//===----------------------------------------------------------------------===//
// WinogradFilterTransformOp
//===----------------------------------------------------------------------===//
LogicalResult WinogradFilterTransformOp::verify() {
auto filterType = cast<ShapedType>(getFilter().getType());
ArrayRef<int64_t> filterShape = filterType.getShape();
int64_t filterH = filterShape[getFilterHDim()];
int64_t filterW = filterShape[getFilterWDim()];
int64_t r = getR();
int64_t m = getM();
if (filterH != r && filterH != 1)
return emitOpError("expect filter height either equals to r or 1");
if (filterW != r && filterW != 1)
return emitOpError("expect filter width either equals to r or 1");
if (filterH == 1 && filterW == 1)
return emitOpError("expect either filter height or width equals to r");
SmallVector<int64_t> expectedOutputShape;
expectedOutputShape.push_back(filterH == r ? m + r - 1 : 1);
expectedOutputShape.push_back(filterW == r ? m + r - 1 : 1);
expectedOutputShape.push_back(filterShape[getFilterCDim()]);
expectedOutputShape.push_back(filterShape[getFilterFDim()]);
auto outputType = cast<ShapedType>(getOutput().getType());
ArrayRef<int64_t> outputShape = outputType.getShape();
if (failed(verifyCompatibleShape(expectedOutputShape, outputShape))) {
return emitOpError("the output shape is not expected");
}
return success();
}
SmallVector<Range>
WinogradFilterTransformOp::getIterationDomain(OpBuilder &builder) {
Location loc = getLoc();
IntegerAttr zeroAttr = builder.getIndexAttr(0);
IntegerAttr oneAttr = builder.getIndexAttr(1);
Value filter = getFilter();
int64_t filterRank = getFilterOperandRank();
SmallVector<Range> loopBounds(filterRank);
for (unsigned dim = 0; dim < filterRank; ++dim) {
loopBounds[dim].offset = zeroAttr;
loopBounds[dim].size = getDimValue(builder, loc, filter, dim);
loopBounds[dim].stride = oneAttr;
}
return loopBounds;
}
SmallVector<utils::IteratorType>
WinogradFilterTransformOp::getLoopIteratorTypes() {
int64_t filterRank = getFilterOperandRank();
SmallVector<utils::IteratorType> iteratorTypes(filterRank,
utils::IteratorType::parallel);
return iteratorTypes;
}
LogicalResult WinogradFilterTransformOp::getResultTilePosition(
OpBuilder &builder, unsigned resultNumber, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes, SmallVector<OpFoldResult> &resultOffsets,
SmallVector<OpFoldResult> &resultSizes) {
IntegerAttr zeroAttr = builder.getI64IntegerAttr(0);
ShapedType filterType = getFilterOperandType();
ArrayRef<int64_t> filterShape = filterType.getShape();
int64_t filterH = filterShape[getFilterHDim()];
int64_t filterW = filterShape[getFilterWDim()];
int64_t m = getM();
int64_t r = getR();
int64_t alpha = m + r - 1;
int64_t alphaH = filterH != 1 ? alpha : 1;
int64_t alphaW = filterW != 1 ? alpha : 1;
IntegerAttr alphaHAttr = builder.getI64IntegerAttr(alphaH);
IntegerAttr alphaWAttr = builder.getI64IntegerAttr(alphaW);
resultOffsets.append(
{zeroAttr, zeroAttr, offsets[getFilterCDim()], offsets[getFilterFDim()]});
resultSizes.append(
{alphaHAttr, alphaWAttr, sizes[getFilterCDim()], sizes[getFilterFDim()]});
return success();
}
/// Implement tiling for winograd_filter_transform
/// The input of winograd_filter_transform is (F, KH, KW, C).
/// The output of winograd_filter_transform is (alphaH, alphaW, C, F)
/// Users can specify the tile sizes of F and C.
/// `offsets` are the values for the offsets of F, KH, KW, C for one tile.
/// `sizes` are the values for the sizes of F, KH, KW, C for one tile.
FailureOr<TilingResult> WinogradFilterTransformOp::getTiledImplementation(
OpBuilder &builder, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes) {
IntegerAttr oneAttr = builder.getI64IntegerAttr(1);
IntegerAttr zeroAttr = builder.getI64IntegerAttr(0);
ShapedType filterType = getFilterOperandType();
ArrayRef<int64_t> filterShape = filterType.getShape();
int64_t filterH = filterShape[getFilterHDim()];
int64_t filterW = filterShape[getFilterWDim()];
IntegerAttr filterHAttr = builder.getI64IntegerAttr(filterH);
IntegerAttr filterWAttr = builder.getI64IntegerAttr(filterW);
SmallVector<Value> tiledOperands;
SmallVector<OpFoldResult> sliceOffsets, sliceSizes;
sliceOffsets.append(
{offsets[getFilterFDim()], zeroAttr, zeroAttr, offsets[getFilterCDim()]});
sliceSizes.append({sizes[getFilterFDim()], filterHAttr, filterWAttr,
sizes[getFilterCDim()]});
int64_t filterRank = getFilterOperandRank();
SmallVector<OpFoldResult> filterStrides(filterRank, oneAttr);
Location loc = getLoc();
auto filterSlice = builder.create<tensor::ExtractSliceOp>(
loc, getFilter(), sliceOffsets, sliceSizes, filterStrides);
tiledOperands.emplace_back(filterSlice);
SmallVector<OpFoldResult> resultOffsets, resultSizes;
if (failed(getResultTilePosition(builder, 1, offsets, sizes, resultOffsets,
resultSizes)))
return failure();
int64_t outputRank = getOutputOperandRank();
SmallVector<OpFoldResult> outputStrides(outputRank, oneAttr);
auto outputSlice = builder.create<tensor::ExtractSliceOp>(
loc, getOutput(), resultOffsets, resultSizes, outputStrides);
tiledOperands.emplace_back(outputSlice);
SmallVector<Type> resultTypes;
resultTypes.push_back(tiledOperands[1].getType());
Operation *tiledOp =
mlir::clone(builder, getOperation(), resultTypes, tiledOperands);
return TilingResult{
{tiledOp},
SmallVector<Value>(tiledOp->getResults()),
llvm::to_vector(ArrayRef<Operation *>{filterSlice, outputSlice})};
}
//===----------------------------------------------------------------------===//
// WinogradInputTransformOp
//===----------------------------------------------------------------------===//
LogicalResult WinogradInputTransformOp::verify() {
auto inputType = cast<ShapedType>(getInput().getType());
ArrayRef<int64_t> inputShape = inputType.getShape();
int64_t inputH = inputShape[getInputHDim()];
int64_t inputW = inputShape[getInputWDim()];
int m = getM();
int r = getR();
int64_t tileSize = m + r - 1;
auto outputType = cast<ShapedType>(getOutput().getType());
ArrayRef<int64_t> outputShape = outputType.getShape();
bool leftTransform = outputShape[getOutputAlphaHDim()] != 1;
bool rightTransform = outputShape[getOutputAlphaWDim()] != 1;
SmallVector<int64_t> expectedOutputShape(6, inputH);
if (ShapedType::isDynamic(inputH)) {
expectedOutputShape[getOutputAlphaHDim()] = tileSize;
expectedOutputShape[getOutputTileHDim()] = ShapedType::kDynamic;
} else {
expectedOutputShape[getOutputAlphaHDim()] = leftTransform ? tileSize : 1;
expectedOutputShape[getOutputTileHDim()] =
leftTransform ? (inputH - (r - 1)) / m : inputH;
}
if (ShapedType::isDynamic(inputW)) {
expectedOutputShape[getOutputAlphaWDim()] = tileSize;
expectedOutputShape[getOutputTileWDim()] = ShapedType::kDynamic;
} else {
expectedOutputShape[getOutputAlphaWDim()] = rightTransform ? tileSize : 1;
expectedOutputShape[getOutputTileWDim()] =
rightTransform ? (inputW - (r - 1)) / m : inputW;
}
expectedOutputShape[getOutputNDim()] = inputShape[getInputNDim()];
expectedOutputShape[getOutputCDim()] = inputShape[getInputCDim()];
if (failed(verifyCompatibleShape(expectedOutputShape, outputShape))) {
return emitOpError("the output shape is not expected");
}
return success();
}
SmallVector<Range>
WinogradInputTransformOp::getIterationDomain(OpBuilder &builder) {
Location loc = getLoc();
IntegerAttr zeroAttr = builder.getIndexAttr(0);
IntegerAttr oneAttr = builder.getIndexAttr(1);
Value output = getOutput();
int64_t outputRank = getOutputOperandRank();
SmallVector<Range> loopBounds(outputRank);
for (unsigned dim = 0; dim < outputRank; ++dim) {
loopBounds[dim].offset = zeroAttr;
// alphaH, alphaW, tileH, tileW, N, C
loopBounds[dim].size = getDimValue(builder, loc, output, dim);
loopBounds[dim].stride = oneAttr;
}
return loopBounds;
}
SmallVector<utils::IteratorType>
WinogradInputTransformOp::getLoopIteratorTypes() {
int64_t outputRank = getOutputOperandRank();
SmallVector<utils::IteratorType> iteratorTypes(outputRank,
utils::IteratorType::parallel);
return iteratorTypes;
}
LogicalResult WinogradInputTransformOp::getResultTilePosition(
OpBuilder &builder, unsigned resultNumber, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes, SmallVector<OpFoldResult> &resultOffsets,
SmallVector<OpFoldResult> &resultSizes) {
IntegerAttr zeroAttr = builder.getI64IntegerAttr(0);
ShapedType outputType = getOutputOperandType();
ArrayRef<int64_t> outputShape = outputType.getShape();
int64_t outputAlphaH = outputShape[getOutputAlphaHDim()];
int64_t outputAlphaW = outputShape[getOutputAlphaWDim()];
int64_t m = getM();
int64_t r = getR();
int64_t alpha = m + r - 1;
int64_t alphaH = outputAlphaH != 1 ? alpha : 1;
int64_t alphaW = outputAlphaW != 1 ? alpha : 1;
IntegerAttr alphaHAttr = builder.getI64IntegerAttr(alphaH);
IntegerAttr alphaWAttr = builder.getI64IntegerAttr(alphaW);
resultOffsets.append({zeroAttr, zeroAttr, offsets[getOutputTileHDim()],
offsets[getOutputTileWDim()], offsets[getOutputNDim()],
offsets[getOutputCDim()]});
resultSizes.append({alphaHAttr, alphaWAttr, sizes[getOutputTileHDim()],
sizes[getOutputTileWDim()], sizes[getOutputNDim()],
sizes[getOutputCDim()]});
return success();
}
/// Implement tiling for winograd_input_transform
/// The input of winograd_input_transform is (N, H, W, C).
/// The output of winograd_input_transform is (alphaH, alphaW, tileH, tileW, N,
/// C) Users can specify the tile sizes of tileH, tileW, N, and C. `offsets` are
/// the values for the offsets of tileH, tileW, N, C for one tile. `sizes` are
/// the values for the sizes of tileH, tileW, N, C for one tile.
FailureOr<TilingResult>
WinogradInputTransformOp::getTiledImplementation(OpBuilder &builder,
ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes) {
IntegerAttr oneAttr = builder.getI64IntegerAttr(1);
int64_t m = getM();
int64_t r = getR();
ShapedType outputType = getOutputOperandType();
ArrayRef<int64_t> outputShape = outputType.getShape();
int64_t alphaH = outputShape[getOutputAlphaHDim()];
int64_t alphaW = outputShape[getOutputAlphaWDim()];
Location loc = getLoc();
MLIRContext *context = builder.getContext();
auto identityAffineMap =
AffineMap::get(1, 0, {builder.getAffineDimExpr(0)}, context);
auto offsetAffineMap =
AffineMap::get(1, 0, {builder.getAffineDimExpr(0) * m}, context);
Value mappedOffsetH = affine::makeComposedAffineApply(
builder, loc, (alphaH != 1 ? offsetAffineMap : identityAffineMap),
offsets[getOutputTileHDim()]);
Value mappedOffsetW = affine::makeComposedAffineApply(
builder, loc, (alphaW != 1 ? offsetAffineMap : identityAffineMap),
offsets[getOutputTileWDim()]);
auto sizeAffineMap = AffineMap::get(
1, 0, {builder.getAffineDimExpr(0) * m + (r - 1)}, context);
Value mappedSizeH = affine::makeComposedAffineApply(
builder, loc, sizeAffineMap, sizes[getOutputTileHDim()]);
Value mappedSizeW = affine::makeComposedAffineApply(
builder, loc, sizeAffineMap, sizes[getOutputTileWDim()]);
SmallVector<Value> tiledOperands;
SmallVector<OpFoldResult> sliceOffsets, sliceSizes;
OpFoldResult offsetH = OpFoldResult(mappedOffsetH);
OpFoldResult offsetW = OpFoldResult(mappedOffsetW);
sliceOffsets.append(
{offsets[getOutputNDim()], offsetH, offsetW, offsets[getOutputCDim()]});
OpFoldResult sizeH =
alphaH != 1 ? OpFoldResult(mappedSizeH) : OpFoldResult(oneAttr);
OpFoldResult sizeW =
alphaW != 1 ? OpFoldResult(mappedSizeW) : OpFoldResult(oneAttr);
sliceSizes.append(
{sizes[getOutputNDim()], sizeH, sizeW, sizes[getOutputCDim()]});
int64_t inputRank = getInputOperandRank();
SmallVector<OpFoldResult> inputStrides(inputRank, oneAttr);
auto inputSlice = builder.create<tensor::ExtractSliceOp>(
loc, getInput(), sliceOffsets, sliceSizes, inputStrides);
tiledOperands.emplace_back(inputSlice);
SmallVector<OpFoldResult> resultOffsets, resultSizes;
if (failed(getResultTilePosition(builder, 1, offsets, sizes, resultOffsets,
resultSizes)))
return failure();
int64_t outputRank = getOutputOperandRank();
SmallVector<OpFoldResult> outputStrides(outputRank, oneAttr);
auto outputSlice = builder.create<tensor::ExtractSliceOp>(
loc, getOutput(), resultOffsets, resultSizes, outputStrides);
tiledOperands.emplace_back(outputSlice);
SmallVector<Type> resultTypes;
resultTypes.push_back(tiledOperands[1].getType());
Operation *tiledOp =
mlir::clone(builder, getOperation(), resultTypes, tiledOperands);
return TilingResult{
{tiledOp},
SmallVector<Value>(tiledOp->getResults()),
llvm::to_vector(ArrayRef<Operation *>{inputSlice, outputSlice})};
}
//===----------------------------------------------------------------------===//
// WinogradOutputTransformOp
//===----------------------------------------------------------------------===//
LogicalResult WinogradOutputTransformOp::verify() {
auto valueType = cast<ShapedType>(getValue().getType());
ArrayRef<int64_t> valueShape = valueType.getShape();
int64_t valueH = valueShape[getValueAlphaHDim()];
int64_t valueW = valueShape[getValueAlphaWDim()];
int64_t valueTileH = valueShape[getValueTileHDim()];
int64_t valueTileW = valueShape[getValueTileWDim()];
int m = getM();
int r = getR();
bool leftTransform = valueH != 1;
bool rightTransform = valueW != 1;
int64_t outputRank = getOutputOperandRank();
SmallVector<int64_t> expectedOutputShape(outputRank, valueH);
if (ShapedType::isDynamic(valueH) || ShapedType::isDynamic(valueTileH)) {
expectedOutputShape[getOutputHDim()] = ShapedType::kDynamic;
} else {
if (valueH != (leftTransform ? m + r - 1 : 1))
return emitOpError("expect input height equals to input tile size");
expectedOutputShape[getOutputHDim()] = (leftTransform ? m : 1) * valueTileH;
}
if (ShapedType::isDynamic(valueW) || ShapedType::isDynamic(valueTileW)) {
expectedOutputShape[getOutputWDim()] = ShapedType::kDynamic;
} else {
if (valueW != (rightTransform ? m + r - 1 : 1))
return emitOpError("expect input width equals to input tile size");
expectedOutputShape[getOutputWDim()] =
(rightTransform ? m : 1) * valueTileW;
}
expectedOutputShape[getOutputNDim()] = valueShape[getValueNDim()];
expectedOutputShape[getOutputFDim()] = valueShape[getValueFDim()];
auto outputType = cast<ShapedType>(getOutput().getType());
ArrayRef<int64_t> outputShape = outputType.getShape();
if (failed(verifyCompatibleShape(expectedOutputShape, outputShape))) {
return emitOpError("the output shape is not expected");
}
return success();
}
SmallVector<Range>
WinogradOutputTransformOp::getIterationDomain(OpBuilder &builder) {
Location loc = getLoc();
IntegerAttr zeroAttr = builder.getIndexAttr(0);
IntegerAttr oneAttr = builder.getIndexAttr(1);
Value value = getValue();
int64_t valueRank = getValueOperandRank();
SmallVector<Range> loopBounds(valueRank);
for (unsigned dim = 0; dim < valueRank; ++dim) {
loopBounds[dim].offset = zeroAttr;
// alphaH, alphaW, tileH, tileW, N, F
loopBounds[dim].size = getDimValue(builder, loc, value, dim);
loopBounds[dim].stride = oneAttr;
}
return loopBounds;
}
SmallVector<utils::IteratorType>
WinogradOutputTransformOp::getLoopIteratorTypes() {
int64_t valueRank = getValueOperandRank();
SmallVector<utils::IteratorType> iteratorTypes(valueRank,
utils::IteratorType::parallel);
return iteratorTypes;
}
LogicalResult WinogradOutputTransformOp::getResultTilePosition(
OpBuilder &builder, unsigned resultNumber, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes, SmallVector<OpFoldResult> &resultOffsets,
SmallVector<OpFoldResult> &resultSizes) {
int64_t m = getM();
Location loc = getLoc();
MLIRContext *context = builder.getContext();
auto identityAffineMap =
AffineMap::get(1, 0, {builder.getAffineDimExpr(0)}, context);
auto affineMap =
AffineMap::get(1, 0, {builder.getAffineDimExpr(0) * m}, context);
ShapedType valueType = getValueOperandType();
ArrayRef<int64_t> valueShape = valueType.getShape();
int64_t valueH = valueShape[0];
int64_t valueW = valueShape[1];
Value mappedOffsetH = affine::makeComposedAffineApply(
builder, loc, (valueH != 1 ? affineMap : identityAffineMap),
offsets[getValueTileHDim()]);
Value mappedOffsetW = affine::makeComposedAffineApply(
builder, loc, (valueW != 1 ? affineMap : identityAffineMap),
offsets[getValueTileWDim()]);
Value mappedSizeH = affine::makeComposedAffineApply(
builder, loc, affineMap, sizes[getValueTileHDim()]);
Value mappedSizeW = affine::makeComposedAffineApply(
builder, loc, affineMap, sizes[getValueTileWDim()]);
IntegerAttr oneAttr = builder.getI64IntegerAttr(1);
OpFoldResult offsetH = OpFoldResult(mappedOffsetH);
OpFoldResult offsetW = OpFoldResult(mappedOffsetW);
OpFoldResult sizeH =
valueH != 1 ? OpFoldResult(mappedSizeH) : OpFoldResult(oneAttr);
OpFoldResult sizeW =
valueW != 1 ? OpFoldResult(mappedSizeW) : OpFoldResult(oneAttr);
resultOffsets.append(
{offsets[getValueNDim()], offsetH, offsetW, offsets[getValueFDim()]});
resultSizes.append(
{sizes[getValueNDim()], sizeH, sizeW, sizes[getValueFDim()]});
return success();
}
/// Implement tiling for winograd_output_transform
/// The input of winograd_output_transform is (alphaH, alphaW, tileH, tileW, N,
/// F). The output of winograd_output_transform is (N, H, W, F) Users can
/// specify the tile sizes of tileH, tileW, N, and F. `offsets` are the values
/// for the offsets of tileH, tileW, N, F for one tile. `sizes` are the values
/// for the sizes of tileH, tileW, N, F for one tile.
FailureOr<TilingResult> WinogradOutputTransformOp::getTiledImplementation(
OpBuilder &builder, ArrayRef<OpFoldResult> offsets,
ArrayRef<OpFoldResult> sizes) {
IntegerAttr oneAttr = builder.getI64IntegerAttr(1);
IntegerAttr zeroAttr = builder.getI64IntegerAttr(0);
Location loc = getLoc();
SmallVector<Value> tiledOperands;
SmallVector<OpFoldResult> sliceOffsets, sliceSizes;
ShapedType valueType = getValueOperandType();
ArrayRef<int64_t> valueShape = valueType.getShape();
int64_t alphaH = valueShape[getValueAlphaHDim()];
int64_t alphaW = valueShape[getValueAlphaWDim()];
IntegerAttr alphaHAttr = builder.getI64IntegerAttr(alphaH);
IntegerAttr alphaWAttr = builder.getI64IntegerAttr(alphaW);
sliceOffsets.append({zeroAttr, zeroAttr, offsets[getValueTileHDim()],
offsets[getValueTileWDim()], offsets[getValueNDim()],
offsets[getValueFDim()]});
sliceSizes.append({alphaHAttr, alphaWAttr, sizes[getValueTileHDim()],
sizes[getValueTileWDim()], sizes[getValueNDim()],
sizes[getValueFDim()]});
int64_t valueRank = getValueOperandRank();
SmallVector<OpFoldResult> sliceStrides(valueRank, oneAttr);
auto valueSlice = builder.create<tensor::ExtractSliceOp>(
loc, getValue(), sliceOffsets, sliceSizes, sliceStrides);
tiledOperands.emplace_back(valueSlice);
SmallVector<OpFoldResult> resultOffsets, resultSizes;
if (failed(getResultTilePosition(builder, 1, offsets, sizes, resultOffsets,
resultSizes)))
return failure();
int64_t outputRank = getOutputOperandRank();
SmallVector<OpFoldResult> strides(outputRank, oneAttr);
auto outputSlice = builder.create<tensor::ExtractSliceOp>(
loc, getOutput(), resultOffsets, resultSizes, strides);
tiledOperands.emplace_back(outputSlice);
SmallVector<Type> resultTypes;
resultTypes.push_back(tiledOperands[1].getType());
Operation *tiledOp =
mlir::clone(builder, getOperation(), resultTypes, tiledOperands);
return TilingResult{
{tiledOp},
SmallVector<Value>(tiledOp->getResults()),
llvm::to_vector(ArrayRef<Operation *>{valueSlice, outputSlice})};
}
//===----------------------------------------------------------------------===//
// LinalgDialect
// TODO: Merge with the LinalgDialect block at the bottom
//===----------------------------------------------------------------------===//
// Returns true if the result expression of `subMap` are a subset of `fullMap`.
static bool areResultExprsSubsetOf(AffineMap subMap, AffineMap fullMap) {
auto explicitRange = subMap.getResults();
auto defaultRange = fullMap.getResults();
DenseSet<AffineExpr> explicitSet(explicitRange.begin(), explicitRange.end());
DenseSet<AffineExpr> defaultSet(defaultRange.begin(), defaultRange.end());
llvm::set_union(explicitSet, defaultSet);
return explicitSet == defaultSet;
}
/// Check if the user defined map is valid broadcast map. Here broadcast
/// indexing maps are defined in context of corresponding default indexing maps
/// for the given Op. This way the check becomes very simple i.e just check the
/// number of result dims.
/// Returns true if the explictMap is broadcasted with respect to the
/// defaultMap.
static bool isBroadcasted(AffineMap explictMap, AffineMap defaultMap) {
return explictMap.getNumResults() < defaultMap.getNumResults();
}
/// Verifies the broadcast and transpose semantic sepecified by the explicit
/// indexing map for the MatmulOp \p op for each operand specified by \p
/// opIndex.
static LogicalResult verifyExtendedMatmulSemantic(MatmulOp matmulOp,
unsigned opIndex) {
SmallVector<AffineMap, 3> opIndexingMaps = matmulOp.getIndexingMapsArray();
SmallVector<AffineMap, 3> defaultIndexingMaps =
matmulOp.getDefaultIndexingMaps(matmulOp->getContext());
auto opIndexingMap = opIndexingMaps[opIndex];
auto defaultIndexingMap = defaultIndexingMaps[opIndex];
// Check general validity of indexing map results.
if (!areResultExprsSubsetOf(opIndexingMap, defaultIndexingMap))
return matmulOp->emitOpError()
<< "Unexpected dim expression in map result.";
if (isBroadcasted(opIndexingMap, defaultIndexingMap)) {
if (!matmulOp.isValidLhsRhsBroadcastMap(opIndexingMap)) {
return matmulOp->emitOpError()
<< "Invalid broadcast requested, should be (d2).";
}
return success();
}
return success();
}
// Check general validity of input indexing map of
// BatchMatmulOp/BatchReduceMatmulOp.
template <typename OpTy>
static LogicalResult verifyInputMaps(OpTy batchVariantMatmulOp,
AffineMap opIndexingMap,
AffineMap defaultIndexingMap, bool isLHS) {
assert((isa<BatchMatmulOp>(batchVariantMatmulOp) ||
isa<BatchReduceMatmulOp>(batchVariantMatmulOp)) &&
"Expected BatchMatmulOp or BatchReduceMatmulOp");
// Check the result dims are valid.
if (!areResultExprsSubsetOf(opIndexingMap, defaultIndexingMap))
return batchVariantMatmulOp->emitOpError()
<< "Unexpected result dim expression (outside the set of default "
"result dims).";
// Check for valid number of result dims of input maps.
if (opIndexingMap.getNumResults() > 3)
return batchVariantMatmulOp->emitOpError()
<< "no. of result dim expressions exceeds 3.";
auto hasValidBatchDim = [](AffineMap map) {
AffineExpr batchDim = map.getResult(0);
return batchDim.isFunctionOfDim(0);
};
// Check if the requested broadcast is valid.
if (isBroadcasted(opIndexingMap, defaultIndexingMap)) {
if (!batchVariantMatmulOp.isValidLhsRhsBroadcastMap(opIndexingMap, isLHS))
return batchVariantMatmulOp->emitOpError()
<< "Invalid broadcast requested.";
} else if (!hasValidBatchDim(opIndexingMap)) {
return batchVariantMatmulOp->emitOpError()
<< "Invalid batch dimension expression.";
}
return success();
}
/// This function checks if the given AffineMap for the output of a
/// BatchMatmulOp/BatchReduceMatmulOp has exactly the desired number of result
/// dimensions and if the output map result dimensions are valid.
template <typename OpTy>
static LogicalResult verifyOutputMap(OpTy batchVariantMatmulOp,
AffineMap opIndexingMap) {
assert((isa<BatchMatmulOp>(batchVariantMatmulOp) ||
isa<BatchReduceMatmulOp>(batchVariantMatmulOp)) &&
"Expected BatchMatmulOp or BatchReduceMatmulOp");
if (isa<BatchMatmulOp>(batchVariantMatmulOp) &&
opIndexingMap.getNumResults() != 3) {
return batchVariantMatmulOp->emitOpError()
<< "expects 3 dims, but got (" << opIndexingMap.getNumResults()
<< ").";
}
if (isa<BatchReduceMatmulOp>(batchVariantMatmulOp) &&
opIndexingMap.getNumResults() != 2) {
return batchVariantMatmulOp->emitOpError()
<< "expects 2 dims, but got (" << opIndexingMap.getNumResults()
<< ").";
}
auto areValidOutputResultDim = [&](AffineMap outputMap) {
return isa<BatchMatmulOp>(batchVariantMatmulOp)
? outputMap.getResult(0).isFunctionOfDim(0) &&
outputMap.getResult(1).isFunctionOfDim(1) &&
outputMap.getResult(2).isFunctionOfDim(2)
: outputMap.getResult(0).isFunctionOfDim(1) &&
outputMap.getResult(1).isFunctionOfDim(2);
};
if (!areValidOutputResultDim(opIndexingMap)) {
return batchVariantMatmulOp->emitOpError()
<< "Invalid output map result dimension.";
}
return success();
}
/// Verifies the broadcast and transpose semantic specified by the explicit
/// indexing map for the BatchMatmulOp/BatchReduceMatmulOp op for each operand
/// specified by opIndex.
template <typename OpTy>
static LogicalResult
verifyExtendedBatchVariantMatmulSemantic(OpTy batchVariantMatmulOp,
unsigned opIndex) {
SmallVector<AffineMap, 3> opIndexingMaps =
batchVariantMatmulOp.getIndexingMapsArray();
SmallVector<AffineMap, 3> defaultIndexingMaps =
batchVariantMatmulOp.getDefaultIndexingMaps(
batchVariantMatmulOp->getContext());
if (opIndexingMaps.size() != 3)
return batchVariantMatmulOp->emitOpError()
<< "Indexing_map attribute must have 3 affine maps.";
auto opIndexingMap = opIndexingMaps[opIndex];
auto defaultIndexingMap = defaultIndexingMaps[opIndex];
if (opIndex == 2 &&
failed(verifyOutputMap(batchVariantMatmulOp, opIndexingMap)))
return failure();
if (opIndex != 2 &&
failed(verifyInputMaps(batchVariantMatmulOp, opIndexingMap,
defaultIndexingMap, opIndex == 0)))
return failure();
return success();
}
namespace mlir {
namespace linalg {
//===----------------------------------------------------------------------===//
// MatMulOp
//===----------------------------------------------------------------------===//
/// Returns a list of AffineMap with the typical matmul indexing charactristic.
SmallVector<AffineMap> MatmulOp::getDefaultIndexingMaps(MLIRContext *context) {
AffineExpr d0, d1, d2;
SmallVector<AffineMap> indexingMaps;
bindDims(context, d0, d1, d2);
indexingMaps.push_back(AffineMap::get(3, 0, {d0, d2}, context));
indexingMaps.push_back(AffineMap::get(3, 0, {d2, d1}, context));
indexingMaps.push_back(AffineMap::get(3, 0, {d0, d1}, context));
return indexingMaps;
}
SmallVector<utils::IteratorType> MatmulOp::getIteratorTypesArray() {
return SmallVector<utils::IteratorType>{utils::IteratorType::parallel,
utils::IteratorType::parallel,
utils::IteratorType::reduction};
}
unsigned MatmulOp::getNumRegionArgs() { return 3; }
std::string MatmulOp::getLibraryCallName() {
return generateLibraryCallName(getOperation());
}
bool MatmulOp::hasDynamicIndexingMaps() { return true; }
/// Check if the op has broadcast and/or transpose semantic. Returns true if
/// the user defined indexing maps are not equal to default map.
bool MatmulOp::hasUserDefinedMaps() {
SmallVector<AffineMap, 3> defaultMaps =
getDefaultIndexingMaps(this->getContext());
SmallVector<AffineMap, 3> explicitMaps = getIndexingMapsArray();
return defaultMaps != explicitMaps;
}
/// Implements the block region builder for the MatmulOp. This is called by
/// 'fillStructuredOpRegion'.
void MatmulOp::regionBuilder(ImplicitLocOpBuilder &b, Block &block,
ArrayRef<NamedAttribute> attrs) {
assert(3 > 0 && block.getNumArguments() == 3 &&
"MatmulOp regionBuilder expects 3 (>=0) args");
RegionBuilderHelper helper(b, block);
SmallVector<Value> yields;
TypeFn castVal = TypeFn::cast_signed;
const auto *castIter = llvm::find_if(attrs, [&](const NamedAttribute &attr) {
return attr.getName() == "cast";
});
if (castIter != attrs.end()) {
if (auto attr = llvm::dyn_cast<TypeFnAttr>(castIter->getValue()))
castVal = attr.getValue();
}
Value value1 = helper.buildTypeFn(castVal, block.getArgument(2).getType(),
block.getArgument(0));
Value value2 = helper.buildTypeFn(castVal, block.getArgument(2).getType(),
block.getArgument(1));
Value value3 = helper.buildBinaryFn(BinaryFn::mul, value1, value2);
Value value4 =
helper.buildBinaryFn(BinaryFn::add, block.getArgument(2), value3);
yields.push_back(value4);
helper.yieldOutputs(yields);
}
/// Returns true if the given bcastMap map is a valid broadcast map. A valid
/// broadcast map must include K dimension.
/// TODO: Strict inclusion of K dimension in the broadcast map is not
/// necessary for both input matrices simultaneously. We can relax this
/// condition to have K dimension for one input matrix map and infer the K
/// dimension for other input matrix map from the one already having K
/// dimension.
bool MatmulOp::isValidLhsRhsBroadcastMap(AffineMap bcastMap) {
assert(bcastMap.getNumResults() == 1 && "Expected single result dim expr.");
AffineExpr expr = bcastMap.getResult(0);
// Invalid map if the common dimension of matmul not found.
return expr.isFunctionOfDim(bcastMap.getNumDims() - 1);
}
FailureOr<ArrayAttr> parseIndexingMapsAttr(OpAsmParser &parser) {
if (parser.parseOptionalKeyword("indexing_maps"))
return ArrayAttr{
nullptr}; // Success in case indexing_maps was not provided.
ArrayAttr arrayAttr;
if (parser.parseEqual() || parser.parseAttribute(arrayAttr))
return failure();
if (llvm::any_of(arrayAttr,
[](auto elt) { return !dyn_cast<AffineMapAttr>(elt); }))
return parser.emitError(parser.getCurrentLocation())
<< "element of indexing_maps array is not an affine_map";
return arrayAttr;
}
ParseResult MatmulOp::parse(OpAsmParser &parser, OperationState &result) {
FailureOr<ArrayAttr> indexingMapsAttr = parseIndexingMapsAttr(parser);
if (failed(indexingMapsAttr))
return failure();
if (*indexingMapsAttr == nullptr) {
auto indexingMapAttrs = llvm::map_to_vector(
MatmulOp::getDefaultIndexingMaps(parser.getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
indexingMapsAttr = parser.getBuilder().getArrayAttr(indexingMapAttrs);
}
result.addAttribute("indexing_maps", *indexingMapsAttr);
return parseNamedStructuredOp(parser, result, MatmulOp::getNumRegionArgs(),
MatmulOp::getRegionBuilder());
}
void MatmulOp::print(OpAsmPrinter &p) {
SmallVector<Attribute, 3> indexingMaps = llvm::map_to_vector<3>(
MatmulOp::getDefaultIndexingMaps(getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
if (!llvm::equal(getIndexingMaps(), indexingMaps))
p << " indexing_maps = " << llvm::interleaved_array(getIndexingMaps());
std::array<StringRef, 3> elidedAttrs = {
"operandSegmentSizes", "linalg.memoized_indexing_maps", "indexing_maps"};
printNamedStructuredOp(p, getOperation(), getInputs(), getOutputs(),
elidedAttrs);
}
/// Verify the user defined indexing maps.
LogicalResult MatmulOp::verify() {
// Verification of pure matmul is handled by verifyStructuredOpInterface().
if (!hasUserDefinedMaps())
return success();
for (unsigned opIndex = 0; opIndex < 2; opIndex++) {
if (failed(verifyExtendedMatmulSemantic(*this, opIndex)))
return failure();
}
return success();
}
LogicalResult MatmulOp::fold(FoldAdaptor, SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
void MatmulOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
if (hasPureTensorSemantics())
return;
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability MatmulOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
//===----------------------------------------------------------------------===//
// ContractOp
//===----------------------------------------------------------------------===//
SmallVector<utils::IteratorType> ContractOp::getIteratorTypesArray() {
AffineMap outAffineMap = getIndexingMapsArray().pop_back_val();
// On well-formed IR, indexing_maps is non-empty, contained affine_maps'
// domains are all the same, and each implements a projected permutation.
// Each iteration space dim must occur for at least one operand and either
// takes part in a contraction/reduction or else has parallel iteration type.
// We have that a dim is a contraction/reduction dim if and only if the dim
// occurs for the output operand. We use this fact for fast inference:
// NB: In case we allow dims to occur solely for one input, the above still
// holds: per the einsum semantics, these are reduction dims as well.
SmallVector<bool> dimsInOutput(outAffineMap.getNumDims(), false);
for (auto result : outAffineMap.getResults()) {
auto dimExpr = dyn_cast<AffineDimExpr>(result);
assert(dimExpr && "affine_map is a projected permutation");
dimsInOutput[dimExpr.getPosition()] = true;
}
SmallVector<utils::IteratorType> iteratorTypes;
for (auto dimOccursInOutput : dimsInOutput)
iteratorTypes.push_back(dimOccursInOutput ? utils::IteratorType::parallel
: utils::IteratorType::reduction);
return iteratorTypes;
}
unsigned ContractOp::getNumRegionArgs() { return 3; }
/// Implement block region builder, which is called by 'fillStructuredOpRegion'.
void ContractOp::regionBuilder(ImplicitLocOpBuilder &b, Block &block,
ArrayRef<NamedAttribute> attrs) {
assert(block.getNumArguments() == 3 &&
"ContractOp regionBuilder expects 3 args");
RegionBuilderHelper helper(b, block);
TypeFn castSignedness = TypeFn::cast_signed;
auto castIter = llvm::find_if(attrs, [&](const NamedAttribute &attr) {
return attr.getName() == "cast";
});
if (castIter != attrs.end()) {
if (auto attr = llvm::dyn_cast<TypeFnAttr>(castIter->getValue()))
castSignedness = attr.getValue();
}
// TODO: Support fields with operators besides mult & add.
Type outType = block.getArgument(2).getType();
Value lhsAtOutType =
helper.buildTypeFn(castSignedness, outType, block.getArgument(0));
Value rhsAtOutType =
helper.buildTypeFn(castSignedness, outType, block.getArgument(1));
Value productAtOutType =
helper.buildBinaryFn(BinaryFn::mul, lhsAtOutType, rhsAtOutType);
Value result = helper.buildBinaryFn(BinaryFn::add, block.getArgument(2),
productAtOutType);
helper.yieldOutputs({result});
}
ParseResult ContractOp::parse(OpAsmParser &parser, OperationState &result) {
FailureOr<ArrayAttr> indexingMapsAttr = parseIndexingMapsAttr(parser);
if (failed(indexingMapsAttr) || *indexingMapsAttr == nullptr)
return parser.emitError(parser.getCurrentLocation(),
"expected 'indexing_maps' attribute");
result.addAttribute("indexing_maps", *indexingMapsAttr);
return parseNamedStructuredOp(parser, result, getNumRegionArgs(),
regionBuilder);
}
void ContractOp::print(OpAsmPrinter &p) {
p << " indexing_maps = " << llvm::interleaved_array(getIndexingMaps());
printNamedStructuredOp(
p, getOperation(), getInputs(), getOutputs(),
/*elidedAttrs=*/{"indexing_maps", "operandSegmentSizes"});
}
LogicalResult ContractOp::verify() {
int iterationSpaceDims = -1;
// Map iter space dims to #occurrences in inputs' and output's affine_maps:
// e.g., inOccurrences[0] will hold #times that dim (with index) 0 is used to
// access an input operand (so occurrence count can be at most 2) and
// outOccurrences[1] will indicate whether dim 1 occurred in the output, etc.
SmallVector<size_t> inOccurrences;
SmallVector<size_t> outOccurrences;
// A helper so that for each operand's affine_map and type we check that ...
auto checkAffineMapAndType = [&](AffineMap affineMap, Type operandType,
bool isInput) -> LogicalResult {
// ... the affine_map is a projected permutation;
if (!affineMap.isProjectedPermutation())
return emitError("provided affine_map is not a projected permutation");
// ... the rank of the affine_map's results and corresponding type match;
if (auto shapedType = dyn_cast<ShapedType>(operandType)) {
if (affineMap.getNumResults() != shapedType.getRank())
return emitError("ranks of shaped operand and results of corresponding "
"affine_map differ");
} else if (affineMap.getNumResults() != 0) {
return emitError("affine_map specifies shaped access while operand has "
"non-shaped type");
}
// ... the rank of the affine_map's domain is the same as those seen prior;
if (iterationSpaceDims == -1) {
iterationSpaceDims = affineMap.getNumDims();
inOccurrences = SmallVector<size_t>(iterationSpaceDims, 0);
outOccurrences = SmallVector<size_t>(iterationSpaceDims, 0);
} else if (iterationSpaceDims != (int)affineMap.getNumDims()) {
return emitError("iteration spaces of provided affine_maps differ");
}
// ... update counts of dims used to access either an input or the output.
for (AffineExpr affineExpr : affineMap.getResults()) {
auto affineDimExpr = dyn_cast<AffineDimExpr>(affineExpr);
if (!affineDimExpr)
llvm_unreachable("affine_map is a projected permutation");
if (isInput)
inOccurrences[affineDimExpr.getPosition()] += 1;
else
outOccurrences[affineDimExpr.getPosition()] += 1;
}
return success();
};
for (auto &&[affineMap, operandType, isInput] :
llvm::zip(getIndexingMapsArray(), getOperandTypes(),
SmallVector<bool>{true, true, false})) {
if (failed(checkAffineMapAndType(affineMap, operandType, isInput)))
return failure(); // NB: checkAffineMapAndType will emit relevant error.
}
bool hasContractingDim = false;
for (size_t dimIndex = 0; dimIndex < (size_t)iterationSpaceDims; dimIndex++) {
size_t inOccCount = inOccurrences[dimIndex];
size_t outOccCount = outOccurrences[dimIndex];
// We have a contracting dim if and only if ...
hasContractingDim |= inOccCount == 2 && outOccCount == 0;
if (inOccCount == 0 && outOccCount == 0)
return emitError() << "iteration space dim at index " << dimIndex
<< " not used to access any operand";
// NB: We disallow a dim which occurs for only one input operand and not
// for the output. In terms of einsum semantics such dims have a
// sensible meaning - namely an additional reduction per each such dim.
// By contrast, the ContractionOpInterface does not know about this
// iter type - cf. inferContractionDims' supported dim kinds. Similarly,
// while vector.contract's verifier accepts dims of this kind many of
// its lowerings give up on encountering these dims.
// TODO: Remove following once we have comprehensive support for input-only
// reduction dims, at both the linalg- and vector-dialect levels.
if (inOccCount == 1 && outOccCount != 1)
return emitError()
<< "iteration space dim at index " << dimIndex
<< " is neither a contracting dim nor of parallel iteration type";
}
if (!hasContractingDim)
return emitError("'indexing_maps' do not specify a contracting dimension");
return success();
}
LogicalResult ContractOp::fold(FoldAdaptor, SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
void ContractOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
if (hasPureTensorSemantics())
return;
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability ContractOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
//===----------------------------------------------------------------------===//
// Implementation of BatchMatmulOp
//===----------------------------------------------------------------------===//
SmallVector<AffineMap>
BatchMatmulOp::getDefaultIndexingMaps(MLIRContext *context) {
AffineExpr d0, d1, d2, d3;
SmallVector<AffineMap> indexingMaps;
bindDims(context, d0, d1, d2, d3);
indexingMaps.push_back(AffineMap::get(4, 0, {d0, d1, d3}, context));
indexingMaps.push_back(AffineMap::get(4, 0, {d0, d3, d2}, context));
indexingMaps.push_back(AffineMap::get(4, 0, {d0, d1, d2}, context));
return indexingMaps;
}
SmallVector<utils::IteratorType> BatchMatmulOp::getIteratorTypesArray() {
return SmallVector<utils::IteratorType>{
utils::IteratorType::parallel, utils::IteratorType::parallel,
utils::IteratorType::parallel, utils::IteratorType::reduction};
}
unsigned BatchMatmulOp::getNumRegionArgs() { return 3; }
std::string BatchMatmulOp::getLibraryCallName() {
return generateLibraryCallName(getOperation());
}
/// Check if the op has broadcast and/or transpose semantic. Returns true if
/// the user defined indexing maps are not equal to default map.
bool BatchMatmulOp::hasUserDefinedMaps() {
SmallVector<AffineMap, 3> defaultMaps =
getDefaultIndexingMaps(this->getContext());
SmallVector<AffineMap, 3> explicitMaps = getIndexingMapsArray();
return defaultMaps != explicitMaps;
}
/// Returns true if the given bcastMap map is a valid broadcast map. A valid
/// broadcast map must include K dimension.
/// TODO: Strict inclusion of K dimension in the broadcast map is not
/// necessary for both input matrices simultaneously. We can relax this
/// condition to have K dimension for one input matrix map and infer the K
/// dimension for other input matrix map from the one already having K
/// dimension.
bool BatchMatmulOp::isValidLhsRhsBroadcastMap(AffineMap bcastMap, bool isLHS) {
assert(bcastMap.getNumResults() < 3 &&
"Expected less than 3 result dim expr.");
bool isValid = false;
enum Indices { batchPos, mPos, nPos, kPos };
if (bcastMap.getNumResults() == 1) {
AffineExpr expr = bcastMap.getResult(0);
isValid = expr.isFunctionOfDim(kPos);
} else if (bcastMap.getNumResults() == 2) {
AffineExpr expr0 = bcastMap.getResult(0);
AffineExpr expr1 = bcastMap.getResult(1);
isValid =
isLHS ? ((expr0.isFunctionOfDim(batchPos) ||
expr0.isFunctionOfDim(mPos)) &&
expr1.isFunctionOfDim(kPos))
: ((expr0.isFunctionOfDim(batchPos) &&
expr1.isFunctionOfDim(kPos)) ||
(expr0.isFunctionOfDim(kPos) && expr1.isFunctionOfDim(nPos)));
}
return isValid;
}
void BatchMatmulOp::regionBuilder(ImplicitLocOpBuilder &b, Block &block,
ArrayRef<NamedAttribute> attrs) {
assert(block.getNumArguments() == 3 &&
"BatchMatmulOp regionBuilder expects 3 (>=0) args");
RegionBuilderHelper helper(b, block);
SmallVector<Value> yields;
TypeFn castVal = TypeFn::cast_signed;
auto castIter = llvm::find_if(attrs, [&](const NamedAttribute &attr) {
return attr.getName() == "cast";
});
if (castIter != attrs.end()) {
if (auto attr = llvm::dyn_cast<TypeFnAttr>(castIter->getValue()))
castVal = attr.getValue();
}
auto toType = block.getArgument(2).getType();
Value castValA = helper.buildTypeFn(castVal, toType, block.getArgument(0));
Value castValB = helper.buildTypeFn(castVal, toType, block.getArgument(1));
Value mulVal = helper.buildBinaryFn(BinaryFn::mul, castValA, castValB);
Value addVal =
helper.buildBinaryFn(BinaryFn::add, block.getArgument(2), mulVal);
yields.push_back(addVal);
helper.yieldOutputs(yields);
}
ParseResult BatchMatmulOp::parse(OpAsmParser &parser, OperationState &result) {
SmallVector<Attribute, 3> indexingMapsAttr;
Attribute mapAttr;
if (succeeded(parser.parseOptionalKeyword("indexing_maps"))) {
if (parser.parseEqual())
return failure();
if (parser.parseLSquare())
return failure();
do {
if (parser.parseAttribute(mapAttr))
return failure();
if (!isa<AffineMapAttr>(mapAttr)) {
return parser.emitError(parser.getCurrentLocation(),
"expected affine map attribute");
}
indexingMapsAttr.push_back(mapAttr);
if (parser.parseOptionalComma())
break;
} while (true);
if (parser.parseRSquare())
return failure();
}
// Initialize indexingMaps, if not supplied explicitly.
if (indexingMapsAttr.empty()) {
indexingMapsAttr = llvm::map_to_vector(
BatchMatmulOp::getDefaultIndexingMaps(parser.getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
}
result.addAttribute("indexing_maps",
parser.getBuilder().getArrayAttr(indexingMapsAttr));
return ::parseNamedStructuredOp(parser, result,
BatchMatmulOp::getNumRegionArgs(),
BatchMatmulOp::getRegionBuilder());
}
void BatchMatmulOp::print(OpAsmPrinter &p) {
SmallVector<Attribute, 3> indexingMaps = llvm::map_to_vector<3>(
BatchMatmulOp::getDefaultIndexingMaps(getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
if (!llvm::equal(getIndexingMaps(), indexingMaps))
p << " indexing_maps = " << llvm::interleaved_array(getIndexingMaps());
std::array<StringRef, 3> elidedAttrs = {
"operandSegmentSizes", "linalg.memoized_indexing_maps", "indexing_maps"};
::printNamedStructuredOp(p, getOperation(), getInputs(), getOutputs(),
elidedAttrs);
}
/// Verify the user defined indexing maps.
LogicalResult BatchMatmulOp::verify() {
// Verification of pure batch_matmul is handled by
// verifyStructuredOpInterface().
if (!hasUserDefinedMaps())
return success();
for (unsigned opIndex = 0; opIndex < 3; opIndex++) {
if (failed(verifyExtendedBatchVariantMatmulSemantic(*this, opIndex)))
return failure();
}
return success();
}
LogicalResult BatchMatmulOp::fold(FoldAdaptor,
SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
void BatchMatmulOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
if (hasPureTensorSemantics())
return;
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability BatchMatmulOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
//===----------------------------------------------------------------------===//
// ElementwiseOp
//===----------------------------------------------------------------------===//
//
namespace {
struct ArityGroupAndKind {
// The enum class {Unary, Binary, Ternary, ..}
ElementwiseArityGroup arityGroup;
// The kind (e.g. `exp` or `add`) belonging to the arity group.
union Kind {
UnaryFn unaryFn;
BinaryFn binaryFn;
TernaryFn ternaryFn;
} kind;
};
unsigned getArityGroupAsUInt(ElementwiseArityGroup arityGroup) {
return static_cast<unsigned>(arityGroup);
}
} // namespace
static ArityGroupAndKind getArityGroupAndKind(ElementwiseKind kind) {
constexpr int lastUnary = static_cast<int>(ElementwiseCaseLimits::LastUnary);
constexpr int lastBinary =
static_cast<int>(ElementwiseCaseLimits::LastBinary);
constexpr int lastTernary =
static_cast<int>(ElementwiseCaseLimits::LastTernary);
int val = static_cast<int>(kind);
ArityGroupAndKind result;
if (val < lastUnary) {
result.arityGroup = ElementwiseArityGroup::Unary;
result.kind.unaryFn = static_cast<UnaryFn>(val);
return result;
}
if (val < lastBinary) {
result.arityGroup = ElementwiseArityGroup::Binary;
result.kind.binaryFn = static_cast<BinaryFn>(val - lastUnary);
return result;
}
if (val >= lastTernary) {
llvm_unreachable("unhandled ElementwiseFn");
}
result.arityGroup = ElementwiseArityGroup::Ternary;
result.kind.ternaryFn = static_cast<TernaryFn>(val - lastBinary);
return result;
}
SmallVector<utils::IteratorType> ElementwiseOp::getIteratorTypesArray() {
auto rank = getResultRank();
return SmallVector<utils::IteratorType>(rank, utils::IteratorType::parallel);
}
SmallVector<AffineMap>
ElementwiseOp::getDefaultIndexingMaps(unsigned numMaps, unsigned numDims,
MLIRContext *context) {
auto map = AffineMap::getMultiDimIdentityMap(numDims, context);
return SmallVector<AffineMap>(numMaps, map);
}
ParseResult ElementwiseOp::parse(OpAsmParser &parser, OperationState &result) {
// Expect e.g. `kind = #linalg.elemwise_kind<add>`
Attribute attr;
mlir::linalg::ElementwiseKind elemwiseKindVal;
if (parser.parseKeyword("kind") || parser.parseEqual())
return failure();
if (succeeded(parser.parseAttribute(attr))) {
auto elemwiseKindAttr = dyn_cast<ElementwiseKindAttr>(attr);
if (!elemwiseKindAttr)
return parser.emitError(parser.getCurrentLocation(),
"expected ElementwiseKind attribute");
elemwiseKindVal = elemwiseKindAttr.getValue();
} else {
return parser.emitError(parser.getCurrentLocation(),
"expected operation 'kind' attribute");
}
result.addAttribute(
"kind", ElementwiseKindAttr::get(parser.getContext(), elemwiseKindVal));
// Parse optional `indexing_maps`
SmallVector<Attribute, 3> indexingMapsAttr;
Attribute mapAttr;
if (succeeded(parser.parseOptionalKeyword("indexing_maps"))) {
if (parser.parseEqual())
return failure();
if (parser.parseLSquare())
return failure();
do {
if (parser.parseAttribute(mapAttr))
return failure();
if (!isa<AffineMapAttr>(mapAttr))
return parser.emitError(parser.getCurrentLocation(),
"expected affine map attribute");
indexingMapsAttr.push_back(mapAttr);
if (parser.parseOptionalComma())
break;
} while (true);
if (parser.parseRSquare())
return failure();
}
// At this stage of parsing the only way to infer number of region
// args is through op kind, as input output tensors are not parsed yet.
auto arityGroupAndKind = getArityGroupAndKind(elemwiseKindVal);
int numRegionArgs =
getArityGroupAsUInt(arityGroupAndKind.arityGroup) + 1 /*output*/;
if (parseNamedStructuredOp(parser, result, numRegionArgs,
ElementwiseOp::getRegionBuilder())) {
return parser.emitError(parser.getCurrentLocation(),
"unable to parse elemwise op");
}
// Initialize indexingMaps, if not supplied explicitly.
if (indexingMapsAttr.empty()) {
// We need to infer the numDims of the indexing maps from the output
// type which is already parsed by now.
auto resultType = result.operands[result.operands.size() - 1].getType();
auto shapedType = llvm::dyn_cast<ShapedType>(resultType);
if (!shapedType)
return parser.emitError(parser.getCurrentLocation(),
"return type needs to be shaped type");
auto numDims = shapedType.getRank();
indexingMapsAttr = llvm::map_to_vector(
ElementwiseOp::getDefaultIndexingMaps(numRegionArgs, numDims,
parser.getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
}
result.addAttribute("indexing_maps",
parser.getBuilder().getArrayAttr(indexingMapsAttr));
return success();
}
void ElementwiseOp::print(OpAsmPrinter &p) {
p << " kind=";
p.printAttribute(getKindAttr());
SmallVector<StringRef, 3> elidedAttrs = {"operandSegmentSizes", "kind",
"indexing_maps"};
unsigned arity =
getArityGroupAsUInt(getArityGroupAndKind(getKind()).arityGroup);
unsigned numDims = getResultRank();
SmallVector<Attribute, 3> indexingMaps = llvm::map_to_vector<3>(
ElementwiseOp::getDefaultIndexingMaps(arity + 1 /*output*/, numDims,
getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
if (!llvm::equal(getIndexingMaps(), indexingMaps))
p << " indexing_maps = " << llvm::interleaved_array(getIndexingMaps());
printNamedStructuredOp(p, getOperation(), getInputs(), getOutputs(),
elidedAttrs);
}
LogicalResult ElementwiseOp::verify() {
// All necessary checks are done either by
// - EnumAttr (e.g. unknown operation kind)
// - verifyStructuredOpInterface (incorrect map, sizes).
return success();
}
/// Implements the block region builder for the ElementwiseOp. This is called by
/// 'fillStructuredOpRegion'.
void ElementwiseOp::regionBuilder(ImplicitLocOpBuilder &b, Block &block,
ArrayRef<NamedAttribute> attrs) {
ElementwiseKind elemwiseKind;
for (auto attr : attrs) {
if (attr.getName() == b.getStringAttr("kind")) {
auto kindAttr = dyn_cast<ElementwiseKindAttr>(attr.getValue());
assert(kindAttr && "op kind attribute incorrectly set");
elemwiseKind = kindAttr.getValue();
break;
}
}
ArityGroupAndKind groupAndKind = getArityGroupAndKind(elemwiseKind);
auto arityGroup = groupAndKind.arityGroup;
auto kind = groupAndKind.kind;
assert(block.getNumArguments() ==
getArityGroupAsUInt(arityGroup) + 1 /*output*/
&& "Elementwise regionBuilder number of block args mismatch");
RegionBuilderHelper helper(b, block);
SmallVector<Value> yields;
Value result;
if (arityGroup == ElementwiseArityGroup::Unary) {
result = helper.buildUnaryFn(kind.unaryFn, block.getArgument(0));
} else if (arityGroup == ElementwiseArityGroup::Binary) {
result = helper.buildBinaryFn(kind.binaryFn, block.getArgument(0),
block.getArgument(1));
} else if (arityGroup == ElementwiseArityGroup::Ternary) {
result = helper.buildTernaryFn(kind.ternaryFn, block.getArgument(0),
block.getArgument(1), block.getArgument(2));
} else {
assert(false && "found unhandled category in elemwise");
}
yields.push_back(result);
helper.yieldOutputs(yields);
}
LogicalResult ElementwiseOp::fold(FoldAdaptor,
SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
void ElementwiseOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
if (hasPureTensorSemantics())
return;
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability ElementwiseOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
//===----------------------------------------------------------------------===//
// PackOp/UnPackOp Common
//===----------------------------------------------------------------------===//
// Given the (potentially) updated packed type, `newPackedTy`, generates an
// updated mixed-tile-sizes attribute. A tile size is updated only
// when:
// * a dim from newPackedTy is static, and
// * the corresponding size from mixedTiles is still dynamic.
// Otherwise, the original tile size is preserved.
// Note - packed-type-dim and mixed-tile-size should always match!
static SmallVector<OpFoldResult>
getNewMixedTileSizes(PatternRewriter &rewriter, Type newPackedTy,
SmallVector<OpFoldResult> mixedTiles) {
SmallVector<OpFoldResult> newMixedTileSizes;
for (auto it : llvm::zip(cast<ShapedType>(newPackedTy)
.getShape()
.take_back(mixedTiles.size()),
mixedTiles)) {
int64_t shape = std::get<0>(it);
if (shape == ShapedType::kDynamic) {
newMixedTileSizes.push_back(std::get<1>(it));
continue;
}
// If the current result dim is static, update the dynamic mixed-size
// (provided the original value is dynamic).
OpFoldResult tile = std::get<1>(it);
if (Attribute attr = llvm::dyn_cast_if_present<Attribute>(tile)) {
// Already a constant
newMixedTileSizes.push_back(tile);
} else {
assert(getConstantIntValue(tile).value() == shape &&
"tile size and dim size don't match!");
newMixedTileSizes.push_back(
(rewriter.getIntegerAttr(rewriter.getIndexType(), shape)));
}
}
return newMixedTileSizes;
}
template <typename OpTy>
static LogicalResult
reifyResultShapesImpl(OpTy op, OpBuilder &builder,
ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
int64_t destRank = op.getDestRank();
reifiedReturnShapes.resize(1, SmallVector<OpFoldResult>(destRank));
reifiedReturnShapes[0] =
tensor::getMixedSizes(builder, op.getLoc(), op.getDest());
return success();
}
template <typename OpTy>
static DenseMap<int64_t, OpFoldResult> getDimAndTileMappingImpl(OpTy op) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
DenseMap<int64_t, OpFoldResult> dimAndTileMapping;
ArrayRef<int64_t> dimsToTile = op.getInnerDimsPos();
SmallVector<OpFoldResult> tiles = op.getMixedTiles();
assert(tiles.size() == dimsToTile.size() &&
"tiles must match indices of dimension to block");
// bind the dimension `i` with the tile factor.
for (auto i : llvm::seq<int64_t>(0, dimsToTile.size()))
dimAndTileMapping[dimsToTile[i]] = tiles[i];
return dimAndTileMapping;
}
template <typename OpTy>
static SmallVector<OpFoldResult> getMixedTilesImpl(OpTy op) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
Builder builder(op);
SmallVector<OpFoldResult> mixedInnerTiles;
unsigned dynamicValIndex = 0;
for (int64_t staticTile : op.getStaticInnerTiles()) {
if (!ShapedType::isDynamic(staticTile))
mixedInnerTiles.push_back(builder.getI64IntegerAttr(staticTile));
else
mixedInnerTiles.push_back(op.getInnerTiles()[dynamicValIndex++]);
}
return mixedInnerTiles;
}
template <typename OpTy>
static SmallVector<int64_t> getStaticTilesImpl(OpTy op) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
SmallVector<Value> dynamicTiles;
SmallVector<int64_t> staticTiles;
dispatchIndexOpFoldResults(op.getMixedTiles(), dynamicTiles, staticTiles);
return staticTiles;
}
/// Returns true if `dimsPos` is invalid. It is invalid when:
/// a) It contains duplicate.
/// b) At least one dimension is out of bound (`dimPos` is >= 0 and < rank).
/// c) The number of elements in `dimsPos` is > than `rank`.
static bool isInvalidPackingPosSpecification(ArrayRef<int64_t> dimsPos,
size_t rank) {
size_t dimsPosSize = dimsPos.size();
if (dimsPosSize > rank)
return true;
DenseSet<int64_t> uniqued(llvm::from_range, dimsPos);
if (dimsPosSize != uniqued.size())
return true;
return llvm::any_of(dimsPos, [rank](int64_t dimPos) {
return dimPos < 0 || dimPos >= static_cast<int64_t>(rank);
});
}
/// Returns true if the dimension of `sourceShape` is smaller than the dimension
/// of the `limitShape`.
static bool areAllInBound(ArrayRef<int64_t> sourceShape,
ArrayRef<int64_t> limitShape) {
assert(
sourceShape.size() == limitShape.size() &&
"expected source shape rank, and limit of the shape to have same rank");
return llvm::all_of(
llvm::zip(sourceShape, limitShape), [](std::tuple<int64_t, int64_t> it) {
int64_t sourceExtent = std::get<0>(it);
int64_t limit = std::get<1>(it);
return ShapedType::isDynamic(sourceExtent) ||
ShapedType::isDynamic(limit) || sourceExtent <= limit;
});
}
template <typename OpTy>
static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
Operation *op = packOrUnPack.getOperation();
// Return true if we have a zero-value tile.
auto hasZeros = [&](ArrayRef<OpFoldResult> tiles) {
return llvm::any_of(
tiles, [](OpFoldResult tile) { return isConstantIntValue(tile, 0); });
};
// Verify tiles. Do not allow zero tiles.
SmallVector<OpFoldResult> mixedTiles = packOrUnPack.getMixedTiles();
if (hasZeros(mixedTiles))
return op->emitError("invalid zero tile factor");
// Verify inner_dims_pos and outer_dims_perm.
RankedTensorType unpackedType = (std::is_same<OpTy, PackOp>::value)
? packOrUnPack.getSourceType()
: packOrUnPack.getDestType();
size_t unpackedRank = unpackedType.getRank();
ArrayRef<int64_t> innerDimsPos = packOrUnPack.getInnerDimsPos();
ArrayRef<int64_t> outerDimPerm = packOrUnPack.getOuterDimsPerm();
if (isInvalidPackingPosSpecification(innerDimsPos, unpackedRank))
return op->emitError("invalid inner_dims_pos vector");
if (isInvalidPackingPosSpecification(outerDimPerm, unpackedRank))
return op->emitError("invalid outer_dims_perm vector");
if (!outerDimPerm.empty() && outerDimPerm.size() != unpackedRank)
return op->emitError("outer_dims_perm must be a permutation or empty");
// Tiling factors must be less than or equal to the input rank for pack (or
// output rank for unpack), and must match the number of `inner_dims_pos`.
if (mixedTiles.size() > unpackedRank) {
return op->emitError("tiling factors must be less than or equal to the "
"input rank for pack or output rank for unpack");
}
if (mixedTiles.size() != innerDimsPos.size()) {
return op->emitError(
"tiling factors must equal the number of dimensions to tile");
}
ShapedType packedType = (std::is_same<OpTy, PackOp>::value)
? packOrUnPack.getDestType()
: packOrUnPack.getSourceType();
size_t packedRank = packedType.getRank();
// Require output rank to match input rank + number of blocking factors.
size_t expectedPackedRank = unpackedRank + mixedTiles.size();
if (expectedPackedRank != packedRank) {
return op->emitError(
"packed rank != (unpacked rank + num tiling factors), got ")
<< packedRank << " != " << expectedPackedRank;
}
// Verify result shape is greater than the minimum expected
// by the pack operation, and that the output shape
// represents full tiles.
RankedTensorType expectedPackedType = PackOp::inferPackedType(
unpackedType, packOrUnPack.getStaticTiles(), innerDimsPos, outerDimPerm);
if (!areAllInBound(expectedPackedType.getShape(), packedType.getShape())) {
return op->emitError("the shape of output is not large enough to hold the "
"packed data. Expected at least ")
<< expectedPackedType << ", got " << packedType;
}
if (!llvm::all_of(
llvm::zip(packedType.getShape().take_back(mixedTiles.size()),
mixedTiles),
[](std::tuple<int64_t, OpFoldResult> it) {
int64_t shape = std::get<0>(it);
if (Attribute attr =
llvm::dyn_cast_if_present<Attribute>(std::get<1>(it))) {
IntegerAttr intAttr = dyn_cast_or_null<IntegerAttr>(attr);
int64_t staticTileSize = intAttr.getValue().getSExtValue();
return shape == staticTileSize;
}
return ShapedType::isDynamic(shape);
})) {
return op->emitError("mismatch in inner tile sizes specified and shaped of "
"tiled dimension in the packed type");
}
return success();
}
namespace {
/// Subset of PackOp/UnPackOp fields used to compute the result of applying
/// various permutations to the op.
// TODO: Add linalg.transpose + pack/unpack folding patterns that just reuse
// these. These may or may not become true foldings / canonicalizations
// depending on how aggressive we want to be in automatically folding
// transposes.
struct PackOrUnPackTransposeResult {
SmallVector<int64_t> innerDimsPos;
SmallVector<OpFoldResult> innerTiles;
SmallVector<int64_t> outerDimsPerm;
};
} // namespace
template <typename OpTy>
static PackOrUnPackTransposeResult
commonPermutationOfPackAndUnPackOp(OpTy packOrUnPackOp,
ArrayRef<int64_t> innerPermutation,
ArrayRef<int64_t> outerPermutation) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
assert((!innerPermutation.empty() || !outerPermutation.empty()) &&
"some permutation must be non-empty");
PackOrUnPackTransposeResult metadata;
metadata.innerDimsPos =
SmallVector<int64_t>(packOrUnPackOp.getInnerDimsPos());
metadata.innerTiles =
SmallVector<OpFoldResult>(packOrUnPackOp.getMixedTiles());
int64_t numOuterDims = std::is_same<OpTy, PackOp>::value
? packOrUnPackOp.getSourceRank()
: packOrUnPackOp.getDestRank();
metadata.outerDimsPerm =
packOrUnPackOp.getOuterDimsPerm().empty()
? llvm::to_vector(llvm::seq<int64_t>(0, numOuterDims))
: SmallVector<int64_t>(packOrUnPackOp.getOuterDimsPerm());
if (!innerPermutation.empty()) {
assert(innerPermutation.size() == metadata.innerDimsPos.size() &&
isPermutationVector(innerPermutation) &&
"invalid inner permutation");
applyPermutationToVector(metadata.innerDimsPos, innerPermutation);
applyPermutationToVector(metadata.innerTiles, innerPermutation);
}
if (!outerPermutation.empty()) {
assert(outerPermutation.size() == metadata.outerDimsPerm.size() &&
isPermutationVector(outerPermutation) &&
"invalid outer permutation");
applyPermutationToVector(metadata.outerDimsPerm, outerPermutation);
}
return metadata;
}
//===----------------------------------------------------------------------===//
// PackOp
//===----------------------------------------------------------------------===//
void PackOp::getAsmResultNames(function_ref<void(Value, StringRef)> setNameFn) {
setNameFn(getResult(), "pack");
}
void PackOp::build(OpBuilder &builder, OperationState &state, Value source,
Value dest, ArrayRef<int64_t> innerDimsPos,
ArrayRef<OpFoldResult> innerTiles,
std::optional<Value> paddingValue,
ArrayRef<int64_t> outerDimsPerm) {
assert(innerDimsPos.size() == innerTiles.size() &&
"number of tile sizes specified must match the specified number of "
"original dimensions to be tiled");
SmallVector<int64_t> staticTileSizes;
SmallVector<Value> dynamicTileSizes;
dispatchIndexOpFoldResults(innerTiles, dynamicTileSizes, staticTileSizes);
build(builder, state, dest.getType(), source, dest,
paddingValue ? *paddingValue : nullptr,
outerDimsPerm.empty() ? nullptr
: builder.getDenseI64ArrayAttr(outerDimsPerm),
builder.getDenseI64ArrayAttr(innerDimsPos), dynamicTileSizes,
builder.getDenseI64ArrayAttr(staticTileSizes));
}
LogicalResult
PackOp::reifyResultShapes(OpBuilder &builder,
ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
return reifyResultShapesImpl(*this, builder, reifiedReturnShapes);
}
DenseMap<int64_t, OpFoldResult> PackOp::getDimAndTileMapping() {
return getDimAndTileMappingImpl(*this);
}
SmallVector<OpFoldResult> PackOp::getMixedTiles() {
return getMixedTilesImpl(*this);
}
SmallVector<int64_t> PackOp::getStaticTiles() {
return getStaticTilesImpl(*this);
}
ArrayRef<int64_t> PackOp::getAllOuterDims() {
ShapedType inputType = getSourceType();
int64_t inputRank = inputType.getRank();
return getDestType().getShape().take_front(inputRank);
}
SmallVector<int64_t> PackOp::getTiledOuterDims() {
auto innerDimsPos = getInnerDimsPos();
auto packedShape = getDestType().getShape();
SmallVector<int64_t> res;
for (auto index : innerDimsPos)
res.push_back(packedShape[index]);
return res;
}
bool PackOp::requirePaddingValue(ArrayRef<int64_t> inputShape,
ArrayRef<int64_t> innerDimsPos,
ArrayRef<int64_t> outputShape,
ArrayRef<int64_t> outerDimsPerm,
ArrayRef<OpFoldResult> innerTiles) {
SmallVector<int64_t> outputTileSizes(
outputShape.take_front(inputShape.size()));
if (!outerDimsPerm.empty()) {
assert(outerDimsPerm.size() == outputTileSizes.size() &&
"expected output and outer_dims_perm to have same size");
applyPermutationToVector(outputTileSizes,
invertPermutationVector(outerDimsPerm));
}
for (auto [pos, tileSize] : llvm::zip_equal(innerDimsPos, innerTiles)) {
if (ShapedType::isDynamic(inputShape[pos]))
continue;
std::optional<int64_t> constantTile = getConstantIntValue(tileSize);
if (!constantTile) {
if (!ShapedType::isDynamic(outputTileSizes[pos]) &&
(inputShape[pos] % outputTileSizes[pos] != 0))
return true;
} else if (inputShape[pos] % (*constantTile) != 0) {
return true;
}
}
return false;
}
LogicalResult PackOp::verify() {
if (failed(commonVerifierPackAndUnPackOp(*this)))
return failure();
// Verify padding value, and bail out if the tile does not divide the
// dimension fully. In the case of dynamic tile factors or dimensions, having
// a partial tile is undefined behavior.
auto paddingValue = getPaddingValue();
if (paddingValue &&
paddingValue.getType() != getSourceType().getElementType()) {
return emitOpError("expected padding_value has ")
<< getSourceType().getElementType()
<< " but got: " << paddingValue.getType();
}
if (!paddingValue &&
requirePaddingValue(getSourceType().getShape(), getInnerDimsPos(),
getDestType().getShape(), getOuterDimsPerm(),
getMixedTiles())) {
return emitOpError(
"invalid tile factor or output size provided. Only full tiles are "
"supported when padding_value is not set");
}
return success();
}
/// Converts OpFoldResults to int64_t shape entries, unconditionally mapping all
/// Value's to kDynamic, even if they are arith.constant values.
static SmallVector<int64_t>
asShapeWithAnyValueAsDynamic(ArrayRef<OpFoldResult> ofrs) {
SmallVector<int64_t> result;
for (auto o : ofrs) {
// Have to do this first, as getConstantIntValue special-cases constants.
if (llvm::dyn_cast_if_present<Value>(o))
result.push_back(ShapedType::kDynamic);
else
result.push_back(getConstantIntValue(o).value_or(ShapedType::kDynamic));
}
return result;
}
/// Helper for PackOp::{getResultShape,inferPackedType}. Returns the shape of
/// the packed type. Having a shared helper helps implement these two methods in
/// a way that ensures that they agree on which dimensions are dynamic.
static SmallVector<int64_t> getPackOpResultTypeShape(
ArrayRef<int64_t> sourceShape, ArrayRef<int64_t> innerTileSizes,
ArrayRef<int64_t> innerDimsPos, ArrayRef<int64_t> outerDimsPerm) {
SmallVector<int64_t> resultShape = llvm::to_vector(sourceShape);
for (auto tiledDim : llvm::enumerate(llvm::to_vector(innerDimsPos))) {
if (ShapedType::isDynamic(resultShape[tiledDim.value()]))
continue;
if (ShapedType::isDynamic(innerTileSizes[tiledDim.index()])) {
resultShape[tiledDim.value()] = ShapedType::kDynamic;
continue;
}
resultShape[tiledDim.value()] = llvm::divideCeilSigned(
resultShape[tiledDim.value()], innerTileSizes[tiledDim.index()]);
}
// Swap tile loops if outer_dims_perm is available.
if (!outerDimsPerm.empty())
applyPermutationToVector(resultShape, outerDimsPerm);
// Append the inner tile dimensions.
resultShape.append(innerTileSizes.begin(), innerTileSizes.end());
return resultShape;
}
SmallVector<OpFoldResult> PackOp::getResultShape(
OpBuilder &builder, Location loc, ArrayRef<OpFoldResult> sourceDims,
ArrayRef<OpFoldResult> innerTileSizes, ArrayRef<int64_t> innerDimsPos,
ArrayRef<int64_t> outerDimsPerm) {
SmallVector<OpFoldResult> resultDims = llvm::to_vector(sourceDims);
AffineExpr s0, s1;
bindSymbols(builder.getContext(), s0, s1);
AffineExpr ceilDivExpr = s0.ceilDiv(s1);
for (auto tiledDim : llvm::enumerate(llvm::to_vector(innerDimsPos))) {
resultDims[tiledDim.value()] = affine::makeComposedFoldedAffineApply(
builder, loc, ceilDivExpr,
{resultDims[tiledDim.value()], innerTileSizes[tiledDim.index()]});
}
if (!outerDimsPerm.empty())
applyPermutationToVector(resultDims, outerDimsPerm);
resultDims.append(innerTileSizes.begin(), innerTileSizes.end());
SmallVector<int64_t> resultTypeShape =
getPackOpResultTypeShape(asShapeWithAnyValueAsDynamic(sourceDims),
asShapeWithAnyValueAsDynamic(innerTileSizes),
innerDimsPos, outerDimsPerm);
// Fix-up `resultDims` to ensure that they are Value's if and only if the
// result type shape says it's a dynamic dim. This is needed as callers may
// use dispatchIndexOpFoldResults on the result, and rely on exact number of
// dynamic dims returned by that.
for (unsigned i = 0; i < resultDims.size(); ++i) {
if (!ShapedType::isDynamic(resultTypeShape[i]))
continue;
resultDims[i] =
getValueOrCreateConstantIndexOp(builder, loc, resultDims[i]);
}
return resultDims;
}
/// Get the expected packed type based on source type, tile factors, position of
/// the inner tiles and permutation of the outer tiled loop.
RankedTensorType PackOp::inferPackedType(RankedTensorType sourceType,
ArrayRef<int64_t> innerTileSizes,
ArrayRef<int64_t> innerDimsPos,
ArrayRef<int64_t> outerDimsPerm) {
SmallVector<int64_t> resultShape = getPackOpResultTypeShape(
sourceType.getShape(), innerTileSizes, innerDimsPos, outerDimsPerm);
return RankedTensorType::get(resultShape, sourceType.getElementType());
}
Value PackOp::createDestinationTensor(OpBuilder &b, Location loc, Value source,
ArrayRef<OpFoldResult> innerTileSizes,
ArrayRef<int64_t> innerDimsPos,
ArrayRef<int64_t> outerDimsPerm) {
AffineExpr dim0, dim1;
bindDims(b.getContext(), dim0, dim1);
auto ceilDiv = [&](OpFoldResult v1, OpFoldResult v2) -> OpFoldResult {
return affine::makeComposedFoldedAffineApply(b, loc, dim0.ceilDiv(dim1),
{v1, v2});
};
SmallVector<OpFoldResult> mixedSizes;
for (auto [index, value] : llvm::enumerate(
llvm::cast<RankedTensorType>(source.getType()).getShape())) {
if (ShapedType::isDynamic(value))
mixedSizes.push_back(
b.create<tensor::DimOp>(loc, source, index).getResult());
else
mixedSizes.push_back(b.getIndexAttr(value));
}
for (auto it : llvm::zip(innerDimsPos, innerTileSizes)) {
int64_t dimPos = std::get<0>(it);
OpFoldResult tileSize = std::get<1>(it);
mixedSizes[dimPos] = ceilDiv(mixedSizes[dimPos], tileSize);
}
if (!outerDimsPerm.empty())
applyPermutationToVector<OpFoldResult>(mixedSizes, outerDimsPerm);
mixedSizes.append(innerTileSizes.begin(), innerTileSizes.end());
auto elemType = llvm::cast<ShapedType>(source.getType()).getElementType();
return b.create<tensor::EmptyOp>(loc, mixedSizes, elemType);
}
PackOp PackOp::createTransposedClone(OpBuilder &b, Location loc,
ArrayRef<int64_t> innerPermutation,
ArrayRef<int64_t> outerPermutation) {
PackOrUnPackTransposeResult metadata = commonPermutationOfPackAndUnPackOp(
*this, innerPermutation, outerPermutation);
Value transposedDest =
createDestinationTensor(b, loc, getSource(), metadata.innerTiles,
metadata.innerDimsPos, metadata.outerDimsPerm);
return b.create<PackOp>(loc, getSource(), transposedDest,
metadata.innerDimsPos, metadata.innerTiles,
getPaddingValue(), metadata.outerDimsPerm);
}
/// Returns true if the tiles and the tiled dims are constant.
template <typename OpTy>
bool areTilesAndTiledDimsAllConstant(OpTy op) {
static_assert(llvm::is_one_of<OpTy, PackOp, UnPackOp>::value,
"applies to only pack or unpack operations");
ShapedType packedType = (std::is_same<OpTy, PackOp>::value)
? op.getDestType()
: op.getSourceType();
SmallVector<OpFoldResult> mixedTiles = op.getMixedTiles();
for (auto [dimDest, tile] : llvm::zip(
packedType.getShape().take_back(mixedTiles.size()), mixedTiles)) {
std::optional<int64_t> constTileSize = getConstantIntValue(tile);
if (!constTileSize || ShapedType::isDynamic(dimDest))
return false;
}
return true;
}
Speculation::Speculatability PackOp::getSpeculatability() {
if (getPaddingValue())
return Speculation::Speculatable;
// The verifier rejects already operations if we can statically prove that the
// sizes of the tiles do not divide perfectly the dimension; thus, check only
// to have constant tiles and tiled inner dimensions.
if (!areTilesAndTiledDimsAllConstant(*this))
return Speculation::NotSpeculatable;
return Speculation::Speculatable;
}
// Return true if `inner_dims_pos` and `outer_dims_perm` target the same
// dimensions for pack and unpack.
static bool hasSameInnerOuterAttribute(PackOp packOp, UnPackOp unPackOp) {
if (packOp.getInnerDimsPos() != unPackOp.getInnerDimsPos())
return false;
if (packOp.getOuterDimsPerm() == unPackOp.getOuterDimsPerm())
return true;
// Outer dims permutation is optional.
// To compare unbalanced pack-unpack pair, treat no permutation as equal to
// identity permutation.
return isIdentityPermutation(packOp.getOuterDimsPerm()) &&
isIdentityPermutation(unPackOp.getOuterDimsPerm());
}
// Return true if pack and unpack have the same tiles.
// Same SSA values or same integer constants.
static bool haveSameTiles(PackOp packOp, UnPackOp unPackOp) {
auto packTiles = packOp.getMixedTiles();
auto unPackTiles = unPackOp.getMixedTiles();
if (packTiles.size() != unPackTiles.size())
return false;
for (size_t i = 0, e = packTiles.size(); i < e; i++) {
if (!isEqualConstantIntOrValue(packTiles[i], unPackTiles[i]))
return false;
}
return true;
}
/// Returns true if the pack op does not need a padding value.
static bool paddingIsNotNeeded(PackOp op) {
auto srcType = op.getSourceType();
if (llvm::any_of(op.getInnerDimsPos(),
[&](int64_t pos) { return srcType.isDynamicDim(pos); }))
return false;
if (ShapedType::isDynamicShape(op.getStaticInnerTiles()))
return false;
return !PackOp::requirePaddingValue(
srcType.getShape(), op.getInnerDimsPos(), op.getDestType().getShape(),
op.getOuterDimsPerm(), op.getMixedTiles());
}
/// Returns true if the `srcShape` or `destShape` is different from the one in
/// `packOp` and populates each with the inferred static shape.
static bool inferStaticShape(PackOp packOp, SmallVectorImpl<int64_t> &srcShape,
SmallVectorImpl<int64_t> &destShape) {
bool changeNeeded = false;
srcShape.assign(packOp.getSourceType().getShape().begin(),
packOp.getSourceType().getShape().end());
destShape.assign(packOp.getDestType().getShape().begin(),
packOp.getDestType().getShape().end());
llvm::SmallSetVector<int64_t, 4> innerDims;
innerDims.insert_range(packOp.getInnerDimsPos());
SmallVector<int64_t> inverseOuterDimsPerm;
if (!packOp.getOuterDimsPerm().empty())
inverseOuterDimsPerm = invertPermutationVector(packOp.getOuterDimsPerm());
int srcRank = packOp.getSourceRank();
for (auto i : llvm::seq<int64_t>(0, srcRank)) {
if (innerDims.contains(i))
continue;
int64_t srcPos = i;
int64_t destPos = i;
if (!inverseOuterDimsPerm.empty())
destPos = inverseOuterDimsPerm[srcPos];
if (ShapedType::isDynamic(srcShape[srcPos]) ==
ShapedType::isDynamic(destShape[destPos])) {
continue;
}
int64_t size = srcShape[srcPos];
if (ShapedType::isDynamic(size))
size = destShape[destPos];
srcShape[srcPos] = size;
destShape[destPos] = size;
changeNeeded = true;
}
return changeNeeded;
}
LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {
// Fold an pack(unpack(x)) to x.
if (auto unPackOp = packOp.getSource().getDefiningOp<UnPackOp>()) {
if (unPackOp.getSourceType() != packOp.getDestType())
return failure();
if (packOp.getPaddingValue() ||
!hasSameInnerOuterAttribute(packOp, unPackOp) ||
!haveSameTiles(packOp, unPackOp))
return failure();
rewriter.replaceOp(packOp, unPackOp.getSource());
return success();
}
// Fold optional PaddingValue operand away if padding is not needed.
if (packOp.getPaddingValue() && paddingIsNotNeeded(packOp)) {
rewriter.startOpModification(packOp);
packOp.getPaddingValueMutable().clear();
rewriter.finalizeOpModification(packOp);
return success();
}
// Insert tensor.cast ops if static shape inference is available..
SmallVector<int64_t> srcShape, destShape;
if (inferStaticShape(packOp, srcShape, destShape)) {
Location loc = packOp.getLoc();
Value source = packOp.getSource();
if (srcShape != packOp.getSourceType().getShape()) {
auto newSrcType = packOp.getSourceType().clone(srcShape);
source =
rewriter.create<tensor::CastOp>(loc, newSrcType, packOp.getSource());
}
Value dest = packOp.getDest();
RankedTensorType originalResultType = packOp.getDestType();
bool needUpdateDestType = (destShape != originalResultType.getShape());
if (needUpdateDestType) {
auto newDestType = packOp.getDestType().clone(destShape);
dest =
rewriter.create<tensor::CastOp>(loc, newDestType, packOp.getDest());
}
rewriter.modifyOpInPlace(packOp, [&] {
packOp.getSourceMutable().assign(source);
packOp.getDestMutable().assign(dest);
packOp.getResult().setType(cast<RankedTensorType>(dest.getType()));
});
// Insert a cast if needed
if (needUpdateDestType) {
rewriter.setInsertionPointAfter(packOp);
auto castOp =
rewriter.create<tensor::CastOp>(loc, originalResultType, packOp);
rewriter.replaceAllUsesExcept(packOp, castOp, castOp);
}
return success();
}
return failure();
}
template <typename PackOrUnpackOp>
static bool isLikePadUnPad(PackOrUnpackOp packOp,
RankedTensorType packedTensorType) {
static_assert(std::is_same<PackOrUnpackOp, PackOp>::value ||
std::is_same<PackOrUnpackOp, UnPackOp>::value,
"Function meant for pack/unpack");
// This is a pad if packing only adds ones and we don't transpose dimensions.
// Check that we are not transposing any dimensions.
ArrayRef<int64_t> innerDimsPos = packOp.getInnerDimsPos();
int64_t numPackedDims = innerDimsPos.size();
auto orderedDims = llvm::to_vector<4>(llvm::seq<int64_t>(0, numPackedDims));
if (orderedDims != innerDimsPos) {
// Dimensions don't happen in order.
return false;
}
ArrayRef<int64_t> packedShape = packedTensorType.getShape();
int64_t packedRank = packedTensorType.getRank();
// At this point we know that we are taking numPackedDims outer
// dimensions and pushing them all the way as the inner most dimensions.
// What's left on the outer most dimensions is, in this order:
// - the factor of the packed dimensions, then
// - the untouched dimensions
// This shifting inward of dimensions is a no-op (as opposed to a transpose)
// if all the dimensions that bubble outerward are ones.
// Therefore check that all the dimensions but the numPackedDims inner most
// ones are ones.
return llvm::all_of(
llvm::seq<int64_t>(0, packedRank - numPackedDims),
[&packedShape](int64_t i) { return packedShape[i] == 1; });
}
bool PackOp::isLikePad() {
auto packedTensorType =
llvm::cast<RankedTensorType>((*this)->getResultTypes().front());
return isLikePadUnPad(*this, packedTensorType);
}
OpFoldResult PackOp::fold(FoldAdaptor adaptor) {
std::optional<Attribute> paddingValue;
if (auto pad = adaptor.getPaddingValue())
paddingValue = pad;
if (OpFoldResult reshapedSource = reshapeConstantSource(
llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getSource()),
getDestType(), paddingValue))
return reshapedSource;
return {};
}
/// Folds a tensor.cast op into a consuming PackOp op if the
/// `tensor.cast` has source that is more static than the consuming op.
///
/// Example:
/// ```mlir
/// %1 = tensor.cast %0 : tensor<8x16xf32> to tensor<?x?xf32>
/// %2 = tensor.pack %1 ... : tensor<?x?xf32> ...
/// ```
///
/// folds into:
///
/// ```mlir
/// %2 = tensor.pack %0 ... : tensor<8x16xf32> ...
/// ```
struct FoldTensorCastPackOp : public OpRewritePattern<PackOp> {
using OpRewritePattern<PackOp>::OpRewritePattern;
LogicalResult matchAndRewrite(PackOp op,
PatternRewriter &rewriter) const override {
if (!tensor::hasFoldableTensorCastOperand(op))
return failure();
SmallVector<Type> newResultTypes(op->getResultTypes());
SmallVector<Value> newOperands =
tensor::getUpdatedOperandsAfterCastOpFolding(op, newResultTypes);
// Get the updated mixed-tile-sizes attribute.
SmallVector<OpFoldResult> newMixedTileSizes =
getNewMixedTileSizes(rewriter, newResultTypes[0], op.getMixedTiles());
// Clone op.
// TODO: Strictly speaking, discardable attributes should be _discarded_ at
// this point. However, in practice, we use them for things that we'd like
// to preserve. Implement a better abstraction.
PackOp newOp = rewriter.create<PackOp>(
op.getLoc(), newOperands[0], newOperands[1], op.getInnerDimsPos(),
newMixedTileSizes, op.getPaddingValue(), op.getOuterDimsPerm());
newOp->setDiscardableAttrs(op->getDiscardableAttrDictionary());
// Replace op.
Value oldResult = op.getResult();
Value newResult = newOp.getResult();
Value replacement = (newResult.getType() != oldResult.getType())
? rewriter.create<tensor::CastOp>(
op->getLoc(), oldResult.getType(), newResult)
: newResult;
rewriter.replaceOp(op, {replacement});
return success();
}
};
//===----------------------------------------------------------------------===//
// UnPackOp
//===----------------------------------------------------------------------===//
void UnPackOp::getAsmResultNames(
function_ref<void(Value, StringRef)> setNameFn) {
setNameFn(getResult(), "unpack");
}
LogicalResult
UnPackOp::reifyResultShapes(OpBuilder &builder,
ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
return reifyResultShapesImpl(*this, builder, reifiedReturnShapes);
}
DenseMap<int64_t, OpFoldResult> UnPackOp::getDimAndTileMapping() {
return getDimAndTileMappingImpl(*this);
}
SmallVector<OpFoldResult> UnPackOp::getMixedTiles() {
return getMixedTilesImpl(*this);
}
SmallVector<int64_t> UnPackOp::getStaticTiles() {
return getStaticTilesImpl(*this);
}
ArrayRef<int64_t> UnPackOp::getAllOuterDims() {
ShapedType destType = getDestType();
int64_t destRank = destType.getRank();
return getSourceType().getShape().take_front(destRank);
}
SmallVector<int64_t> UnPackOp::getTiledOuterDims() {
auto innerDimsPos = getInnerDimsPos();
auto packedShape = getSourceType().getShape();
SmallVector<int64_t> res;
for (auto index : innerDimsPos)
res.push_back(packedShape[index]);
return res;
}
LogicalResult UnPackOp::verify() {
return commonVerifierPackAndUnPackOp(*this);
}
Speculation::Speculatability UnPackOp::getSpeculatability() {
// See PackOp::getSpeculatability.
if (!areTilesAndTiledDimsAllConstant(*this))
return Speculation::NotSpeculatable;
return Speculation::Speculatable;
}
void UnPackOp::build(OpBuilder &builder, OperationState &state, Value source,
Value dest, ArrayRef<int64_t> innerDimsPos,
ArrayRef<OpFoldResult> innerTiles,
ArrayRef<int64_t> outerDimsPerm) {
assert(innerDimsPos.size() == innerTiles.size() &&
"number of tile sizes specified must match the specified number of "
"original dimensions to be tiled");
SmallVector<int64_t> staticTileSizes;
SmallVector<Value> dynamicTileSizes;
dispatchIndexOpFoldResults(innerTiles, dynamicTileSizes, staticTileSizes);
build(builder, state, dest.getType(), source, dest,
outerDimsPerm.empty() ? nullptr
: builder.getDenseI64ArrayAttr(outerDimsPerm),
builder.getDenseI64ArrayAttr(innerDimsPos), dynamicTileSizes,
builder.getDenseI64ArrayAttr(staticTileSizes));
}
Value UnPackOp::createDestinationTensor(OpBuilder &b, Location loc,
Value source,
ArrayRef<OpFoldResult> innerTileSizes,
ArrayRef<int64_t> innerDimsPos,
ArrayRef<int64_t> outerDimsPerm) {
AffineExpr sym0, sym1;
bindSymbols(b.getContext(), sym0, sym1);
auto dimMul = [&](OpFoldResult v1, OpFoldResult v2) -> OpFoldResult {
return affine::makeComposedFoldedAffineApply(b, loc, sym0 * sym1, {v1, v2});
};
SmallVector<OpFoldResult> mixedSizes;
auto srcType = llvm::cast<RankedTensorType>(source.getType());
for (auto i :
llvm::seq<unsigned>(0, srcType.getRank() - innerTileSizes.size())) {
if (srcType.isDynamicDim(i))
mixedSizes.push_back(b.create<tensor::DimOp>(loc, source, i).getResult());
else
mixedSizes.push_back(b.getIndexAttr(srcType.getDimSize(i)));
}
if (!outerDimsPerm.empty()) {
applyPermutationToVector<OpFoldResult>(
mixedSizes, invertPermutationVector(outerDimsPerm));
}
for (auto [dimPos, tileSize] : llvm::zip_equal(innerDimsPos, innerTileSizes))
mixedSizes[dimPos] = dimMul(mixedSizes[dimPos], tileSize);
auto elemType = srcType.getElementType();
return b.create<tensor::EmptyOp>(loc, mixedSizes, elemType);
}
UnPackOp UnPackOp::createTransposedClone(OpBuilder &b, Location loc,
Value transposedSource,
ArrayRef<int64_t> innerPermutation,
ArrayRef<int64_t> outerPermutation) {
PackOrUnPackTransposeResult metadata = commonPermutationOfPackAndUnPackOp(
*this, innerPermutation, outerPermutation);
return b.create<UnPackOp>(loc, transposedSource, getDest(),
metadata.innerDimsPos, metadata.innerTiles,
metadata.outerDimsPerm);
}
/// Returns true if the `srcShape` or `destShape` is different from the one in
/// `op` and populates each with the inferred static shape.
static bool inferStaticShape(UnPackOp op, SmallVectorImpl<int64_t> &srcShape,
SmallVectorImpl<int64_t> &destShape) {
bool changeNeeded = false;
srcShape.assign(op.getSourceType().getShape().begin(),
op.getSourceType().getShape().end());
destShape.assign(op.getDestType().getShape().begin(),
op.getDestType().getShape().end());
llvm::SmallSetVector<int64_t, 4> innerDims;
innerDims.insert_range(op.getInnerDimsPos());
SmallVector<int64_t> inverseOuterDimsPerm;
if (!op.getOuterDimsPerm().empty())
inverseOuterDimsPerm = invertPermutationVector(op.getOuterDimsPerm());
int destRank = op.getDestRank();
for (auto i : llvm::seq<int64_t>(0, destRank)) {
if (innerDims.contains(i))
continue;
int64_t srcPos = i;
int64_t destPos = i;
if (!inverseOuterDimsPerm.empty())
srcPos = inverseOuterDimsPerm[destPos];
if (ShapedType::isDynamic(srcShape[srcPos]) ==
ShapedType::isDynamic(destShape[destPos])) {
continue;
}
int64_t size = srcShape[srcPos];
if (ShapedType::isDynamic(size))
size = destShape[destPos];
srcShape[srcPos] = size;
destShape[destPos] = size;
changeNeeded = true;
}
return changeNeeded;
}
LogicalResult UnPackOp::canonicalize(UnPackOp unPackOp,
PatternRewriter &rewriter) {
/// unpack(pack(x)) -> x
if (PackOp packOp = unPackOp.getSource().getDefiningOp<PackOp>()) {
if (packOp.getSourceType() != unPackOp.getDestType())
return failure();
if (packOp.getPaddingValue() ||
!hasSameInnerOuterAttribute(packOp, unPackOp) ||
!haveSameTiles(packOp, unPackOp))
return failure();
rewriter.replaceOp(unPackOp, packOp.getSource());
return success();
}
/// unpack(destinationStyleOp(x)) -> unpack(x)
if (auto dstStyleOp =
unPackOp.getDest().getDefiningOp<DestinationStyleOpInterface>()) {
auto destValue = cast<OpResult>(unPackOp.getDest());
Value newDest = dstStyleOp.getDpsInits()[destValue.getResultNumber()];
rewriter.modifyOpInPlace(unPackOp,
[&]() { unPackOp.setDpsInitOperand(0, newDest); });
return success();
}
/// extract_slice(unpack(x into y)) -> unpack(x into extract_slice(y))
if (unPackOp->hasOneUse()) {
auto extractSliceUser =
dyn_cast<tensor::ExtractSliceOp>(*unPackOp->getUsers().begin());
if (extractSliceUser &&
areAllConstantIntValue(extractSliceUser.getMixedOffsets(), 0) &&
areAllConstantIntValue(extractSliceUser.getMixedStrides(), 1) &&
extractSliceUser.getSourceType().getRank() ==
extractSliceUser.getResultType().getRank()) {
OpBuilder::InsertionGuard g(rewriter);
rewriter.setInsertionPoint(unPackOp);
auto newDest = rewriter.create<tensor::ExtractSliceOp>(
unPackOp->getLoc(), unPackOp.getDest(),
extractSliceUser.getMixedOffsets(), extractSliceUser.getMixedSizes(),
extractSliceUser.getMixedStrides());
rewriter.modifyOpInPlace(unPackOp, [&]() {
unPackOp.setDpsInitOperand(0, newDest);
unPackOp.getResult().setType(newDest.getType());
});
rewriter.replaceOp(extractSliceUser, unPackOp);
return success();
}
}
// Insert tensor.cast ops if static shape inference is available..
SmallVector<int64_t> srcShape, destShape;
if (inferStaticShape(unPackOp, srcShape, destShape)) {
Location loc = unPackOp.getLoc();
Value source = unPackOp.getSource();
if (srcShape != unPackOp.getSourceType().getShape()) {
auto newSrcType = unPackOp.getSourceType().clone(srcShape);
source = rewriter.create<tensor::CastOp>(loc, newSrcType,
unPackOp.getSource());
}
Value dest = unPackOp.getDest();
if (destShape != unPackOp.getDestType().getShape()) {
auto newDestType = unPackOp.getDestType().clone(destShape);
dest =
rewriter.create<tensor::CastOp>(loc, newDestType, unPackOp.getDest());
}
Value newOp = rewriter.create<UnPackOp>(
loc, source, dest, unPackOp.getInnerDimsPos(), unPackOp.getMixedTiles(),
unPackOp.getOuterDimsPerm());
rewriter.replaceOpWithNewOp<tensor::CastOp>(
unPackOp, unPackOp.getResult().getType(), newOp);
return success();
}
return failure();
}
bool UnPackOp::isLikeUnPad() {
RankedTensorType packedTensorType = getSourceType();
return isLikePadUnPad(*this, packedTensorType);
}
OpFoldResult UnPackOp::fold(FoldAdaptor adaptor) {
if (OpFoldResult reshapedSource = reshapeConstantSource(
llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getSource()),
getResult().getType()))
return reshapedSource;
return {};
}
/// Folds a tensor.cast op into a consuming UnPackOp op if the
/// `tensor.cast` has source that is more static than the consuming op.
///
/// Example:
/// ```mlir
/// %1 = tensor.cast %0 : tensor<1x1x8x1xi32> to tensor<1x1x?x1xi32>
/// %2 = tensor.unpack %1 ... : tensor<1x1x?x1xi32> -> tensor<7x?xi32>
/// ```
///
/// folds into:
///
/// ```mlir
/// %2 = tensor.unpack %0 ... tensor<1x1x8x1xi32> -> tensor<7x?xi32>
/// ```
struct FoldTensorCastUnPackOp : public OpRewritePattern<UnPackOp> {
using OpRewritePattern<UnPackOp>::OpRewritePattern;
LogicalResult matchAndRewrite(UnPackOp op,
PatternRewriter &rewriter) const override {
if (!tensor::hasFoldableTensorCastOperand(op))
return failure();
SmallVector<Type> newResultTypes(op->getResultTypes());
SmallVector<Value> newOperands =
tensor::getUpdatedOperandsAfterCastOpFolding(op, newResultTypes);
Value sourceTensor = newOperands[0];
// Get the updated mixed-tile-sizes attribute.
SmallVector<OpFoldResult> newMixedTileSizes = getNewMixedTileSizes(
rewriter, sourceTensor.getType(), op.getMixedTiles());
// Clone op.
// TODO: Strictly speaking, discardable attributes should be _discarded_ at
// this point. However, in practice, we use them for things that we'd like
// to preserve. Implement a better abstraction.
UnPackOp newOp = rewriter.create<UnPackOp>(
op.getLoc(), sourceTensor, newOperands[1], op.getInnerDimsPos(),
newMixedTileSizes, op.getOuterDimsPerm());
newOp->setDiscardableAttrs(op->getDiscardableAttrDictionary());
// Replace op.
Value oldResult = op.getResult();
Value newResult = newOp.getResult();
Value replacement = (newResult.getType() != oldResult.getType())
? rewriter.create<tensor::CastOp>(
op->getLoc(), oldResult.getType(), newResult)
: newResult;
rewriter.replaceOp(op, {replacement});
return success();
}
};
//===----------------------------------------------------------------------===//
// BatchReduceMatmulOp
//===----------------------------------------------------------------------===//
SmallVector<utils::IteratorType> BatchReduceMatmulOp::getIteratorTypesArray() {
return SmallVector<utils::IteratorType>{
utils::IteratorType::reduction, utils::IteratorType::parallel,
utils::IteratorType::parallel, utils::IteratorType::reduction};
}
SmallVector<AffineMap>
BatchReduceMatmulOp::getDefaultIndexingMaps(MLIRContext *context) {
AffineExpr d0, d1, d2, d3;
SmallVector<AffineMap> indexingMaps;
bindDims(context, d0, d1, d2, d3);
indexingMaps.push_back(AffineMap::get(4, 0, {d0, d1, d3}, context));
indexingMaps.push_back(AffineMap::get(4, 0, {d0, d3, d2}, context));
indexingMaps.push_back(AffineMap::get(4, 0, {d1, d2}, context));
return indexingMaps;
}
unsigned BatchReduceMatmulOp::getNumRegionArgs() { return 3; }
std::string BatchReduceMatmulOp::getLibraryCallName() {
return generateLibraryCallName(getOperation());
}
/// Check if the op has broadcast and/or transpose semantic. Returns true if
/// the user defined indexing maps are not equal to default map.
bool BatchReduceMatmulOp::hasUserDefinedMaps() {
SmallVector<AffineMap, 3> defaultMaps =
getDefaultIndexingMaps(this->getContext());
SmallVector<AffineMap, 3> explicitMaps = getIndexingMapsArray();
return defaultMaps != explicitMaps;
}
/// Returns true if the given bcastMap map is a valid broadcast map. A valid
/// broadcast map must include K dimension.
/// TODO: Strict inclusion of K dimension in the broadcast map is not
/// necessary for both input matrices simultaneously. We can relax this
/// condition to have K dimension for one input matrix map and infer the K
/// dimension for other input matrix map from the one already having K
/// dimension.
bool BatchReduceMatmulOp::isValidLhsRhsBroadcastMap(AffineMap bcastMap,
bool isLHS) {
assert(bcastMap.getNumResults() < 3 &&
"Expected less than 3 result dim expr.");
bool isValid = false;
enum Indices { batchPos, mPos, nPos, kPos };
if (bcastMap.getNumResults() == 1) {
AffineExpr expr = bcastMap.getResult(0);
isValid = expr.isFunctionOfDim(kPos);
} else if (bcastMap.getNumResults() == 2) {
AffineExpr expr0 = bcastMap.getResult(0);
AffineExpr expr1 = bcastMap.getResult(1);
isValid =
isLHS ? ((expr0.isFunctionOfDim(batchPos) ||
expr0.isFunctionOfDim(mPos)) &&
expr1.isFunctionOfDim(kPos))
: ((expr0.isFunctionOfDim(batchPos) &&
expr1.isFunctionOfDim(kPos)) ||
(expr0.isFunctionOfDim(kPos) && expr1.isFunctionOfDim(nPos)));
}
return isValid;
}
void BatchReduceMatmulOp::regionBuilder(ImplicitLocOpBuilder &b, Block &block,
ArrayRef<NamedAttribute> attrs) {
assert(block.getNumArguments() == 3 &&
"BatchReduceMatmulOp regionBuilder expects 3 (>=0) args");
RegionBuilderHelper helper(b, block);
SmallVector<Value> yields;
auto toType = block.getArgument(2).getType();
Value castValA =
helper.buildTypeFn(TypeFn::cast_signed, toType, block.getArgument(0));
Value castValB =
helper.buildTypeFn(TypeFn::cast_signed, toType, block.getArgument(1));
Value mulVal = helper.buildBinaryFn(BinaryFn::mul, castValA, castValB);
Value addVal =
helper.buildBinaryFn(BinaryFn::add, block.getArgument(2), mulVal);
yields.push_back(addVal);
helper.yieldOutputs(yields);
}
ParseResult BatchReduceMatmulOp::parse(OpAsmParser &parser,
OperationState &result) {
SmallVector<Attribute, 3> indexingMapsAttr;
Attribute mapAttr;
if (succeeded(parser.parseOptionalKeyword("indexing_maps"))) {
if (parser.parseEqual())
return failure();
if (parser.parseLSquare())
return failure();
do {
if (parser.parseAttribute(mapAttr))
return failure();
if (!isa<AffineMapAttr>(mapAttr)) {
return parser.emitError(parser.getCurrentLocation(),
"expected affine map attribute");
}
indexingMapsAttr.push_back(mapAttr);
if (parser.parseOptionalComma())
break;
} while (true);
if (parser.parseRSquare())
return failure();
}
// Initialize indexingMaps, if not supplied explicitly.
if (indexingMapsAttr.empty()) {
indexingMapsAttr = llvm::map_to_vector(
BatchReduceMatmulOp::getDefaultIndexingMaps(parser.getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
}
result.addAttribute("indexing_maps",
parser.getBuilder().getArrayAttr(indexingMapsAttr));
return ::parseNamedStructuredOp(parser, result,
BatchReduceMatmulOp::getNumRegionArgs(),
BatchReduceMatmulOp::getRegionBuilder());
}
void BatchReduceMatmulOp::print(OpAsmPrinter &p) {
SmallVector<Attribute, 3> indexingMaps = llvm::map_to_vector(
BatchReduceMatmulOp::getDefaultIndexingMaps(getContext()),
[](AffineMap map) -> Attribute { return AffineMapAttr::get(map); });
if (!llvm::equal(getIndexingMaps(), indexingMaps)) {
p << " indexing_maps = [";
llvm::interleaveComma(getIndexingMaps(), p,
[&](Attribute attr) { p.printAttribute(attr); });
p << "]";
}
SmallVector<StringRef, 3> elidedAttrs = {
"operandSegmentSizes", "linalg.memoized_indexing_maps", "indexing_maps"};
::printNamedStructuredOp(p, getOperation(), getInputs(), getOutputs(),
elidedAttrs);
}
/// Verify the user defined indexing maps.
LogicalResult BatchReduceMatmulOp::verify() {
// Verification of pure batch_reduce_matmul is handled by
// verifyStructuredOpInterface().
if (!hasUserDefinedMaps())
return success();
for (unsigned opIndex = 0; opIndex < 3; opIndex++) {
if (failed(verifyExtendedBatchVariantMatmulSemantic(*this, opIndex)))
return failure();
}
return success();
}
LogicalResult BatchReduceMatmulOp::fold(FoldAdaptor,
SmallVectorImpl<OpFoldResult> &) {
return memref::foldMemRefCast(*this);
}
void BatchReduceMatmulOp::getEffects(
SmallVectorImpl<SideEffects::EffectInstance<MemoryEffects::Effect>>
&effects) {
if (hasPureTensorSemantics())
return;
getGenericEffectsImpl(effects, cast<LinalgOp>(getOperation()));
}
Speculation::Speculatability BatchReduceMatmulOp::getSpeculatability() {
return getGenericSpeculatabilityImpl(cast<LinalgOp>(getOperation()));
}
} // namespace linalg
} // namespace mlir
//===----------------------------------------------------------------------===//
// LinalgDialect
//===----------------------------------------------------------------------===//
void LinalgDialect::getCanonicalizationPatterns(
RewritePatternSet &results) const {
results.add<EraseDeadLinalgOp, FoldTensorCastConsumerOp, FoldTensorCastPackOp,
FoldTensorCastUnPackOp, InferStaticShapeOfOperands>(getContext());
}
Operation *LinalgDialect::materializeConstant(OpBuilder &builder,
Attribute value, Type type,
Location loc) {
return arith::ConstantOp::materialize(builder, value, type, loc);
}