/
BuiltinTypes.cpp
952 lines (808 loc) · 33.6 KB
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BuiltinTypes.cpp
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//===- BuiltinTypes.cpp - MLIR Builtin Type Classes -----------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/IR/BuiltinTypes.h"
#include "TypeDetail.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/BuiltinDialect.h"
#include "mlir/IR/Diagnostics.h"
#include "mlir/IR/Dialect.h"
#include "mlir/IR/TensorEncoding.h"
#include "llvm/ADT/APFloat.h"
#include "llvm/ADT/BitVector.h"
#include "llvm/ADT/Sequence.h"
#include "llvm/ADT/Twine.h"
#include "llvm/ADT/TypeSwitch.h"
using namespace mlir;
using namespace mlir::detail;
//===----------------------------------------------------------------------===//
/// Tablegen Type Definitions
//===----------------------------------------------------------------------===//
#define GET_TYPEDEF_CLASSES
#include "mlir/IR/BuiltinTypes.cpp.inc"
//===----------------------------------------------------------------------===//
// BuiltinDialect
//===----------------------------------------------------------------------===//
void BuiltinDialect::registerTypes() {
addTypes<
#define GET_TYPEDEF_LIST
#include "mlir/IR/BuiltinTypes.cpp.inc"
>();
}
//===----------------------------------------------------------------------===//
/// ComplexType
//===----------------------------------------------------------------------===//
/// Verify the construction of an integer type.
LogicalResult ComplexType::verify(function_ref<InFlightDiagnostic()> emitError,
Type elementType) {
if (!elementType.isIntOrFloat())
return emitError() << "invalid element type for complex";
return success();
}
//===----------------------------------------------------------------------===//
// Integer Type
//===----------------------------------------------------------------------===//
// static constexpr must have a definition (until in C++17 and inline variable).
constexpr unsigned IntegerType::kMaxWidth;
/// Verify the construction of an integer type.
LogicalResult IntegerType::verify(function_ref<InFlightDiagnostic()> emitError,
unsigned width,
SignednessSemantics signedness) {
if (width > IntegerType::kMaxWidth) {
return emitError() << "integer bitwidth is limited to "
<< IntegerType::kMaxWidth << " bits";
}
return success();
}
unsigned IntegerType::getWidth() const { return getImpl()->width; }
IntegerType::SignednessSemantics IntegerType::getSignedness() const {
return getImpl()->signedness;
}
IntegerType IntegerType::scaleElementBitwidth(unsigned scale) {
if (!scale)
return IntegerType();
return IntegerType::get(getContext(), scale * getWidth(), getSignedness());
}
//===----------------------------------------------------------------------===//
// Float Type
//===----------------------------------------------------------------------===//
unsigned FloatType::getWidth() {
if (isa<Float16Type, BFloat16Type>())
return 16;
if (isa<Float32Type>())
return 32;
if (isa<Float64Type>())
return 64;
if (isa<Float80Type>())
return 80;
if (isa<Float128Type>())
return 128;
llvm_unreachable("unexpected float type");
}
/// Returns the floating semantics for the given type.
const llvm::fltSemantics &FloatType::getFloatSemantics() {
if (isa<BFloat16Type>())
return APFloat::BFloat();
if (isa<Float16Type>())
return APFloat::IEEEhalf();
if (isa<Float32Type>())
return APFloat::IEEEsingle();
if (isa<Float64Type>())
return APFloat::IEEEdouble();
if (isa<Float80Type>())
return APFloat::x87DoubleExtended();
if (isa<Float128Type>())
return APFloat::IEEEquad();
llvm_unreachable("non-floating point type used");
}
FloatType FloatType::scaleElementBitwidth(unsigned scale) {
if (!scale)
return FloatType();
MLIRContext *ctx = getContext();
if (isF16() || isBF16()) {
if (scale == 2)
return FloatType::getF32(ctx);
if (scale == 4)
return FloatType::getF64(ctx);
}
if (isF32())
if (scale == 2)
return FloatType::getF64(ctx);
return FloatType();
}
//===----------------------------------------------------------------------===//
// FunctionType
//===----------------------------------------------------------------------===//
unsigned FunctionType::getNumInputs() const { return getImpl()->numInputs; }
ArrayRef<Type> FunctionType::getInputs() const {
return getImpl()->getInputs();
}
unsigned FunctionType::getNumResults() const { return getImpl()->numResults; }
ArrayRef<Type> FunctionType::getResults() const {
return getImpl()->getResults();
}
/// Helper to call a callback once on each index in the range
/// [0, `totalIndices`), *except* for the indices given in `indices`.
/// `indices` is allowed to have duplicates and can be in any order.
inline void iterateIndicesExcept(unsigned totalIndices,
ArrayRef<unsigned> indices,
function_ref<void(unsigned)> callback) {
llvm::BitVector skipIndices(totalIndices);
for (unsigned i : indices)
skipIndices.set(i);
for (unsigned i = 0; i < totalIndices; ++i)
if (!skipIndices.test(i))
callback(i);
}
/// Returns a new function type without the specified arguments and results.
FunctionType
FunctionType::getWithoutArgsAndResults(ArrayRef<unsigned> argIndices,
ArrayRef<unsigned> resultIndices) {
ArrayRef<Type> newInputTypes = getInputs();
SmallVector<Type, 4> newInputTypesBuffer;
if (!argIndices.empty()) {
unsigned originalNumArgs = getNumInputs();
iterateIndicesExcept(originalNumArgs, argIndices, [&](unsigned i) {
newInputTypesBuffer.emplace_back(getInput(i));
});
newInputTypes = newInputTypesBuffer;
}
ArrayRef<Type> newResultTypes = getResults();
SmallVector<Type, 4> newResultTypesBuffer;
if (!resultIndices.empty()) {
unsigned originalNumResults = getNumResults();
iterateIndicesExcept(originalNumResults, resultIndices, [&](unsigned i) {
newResultTypesBuffer.emplace_back(getResult(i));
});
newResultTypes = newResultTypesBuffer;
}
return get(getContext(), newInputTypes, newResultTypes);
}
//===----------------------------------------------------------------------===//
// OpaqueType
//===----------------------------------------------------------------------===//
/// Verify the construction of an opaque type.
LogicalResult OpaqueType::verify(function_ref<InFlightDiagnostic()> emitError,
Identifier dialect, StringRef typeData) {
if (!Dialect::isValidNamespace(dialect.strref()))
return emitError() << "invalid dialect namespace '" << dialect << "'";
// Check that the dialect is actually registered.
MLIRContext *context = dialect.getContext();
if (!context->allowsUnregisteredDialects() &&
!context->getLoadedDialect(dialect.strref())) {
return emitError()
<< "`!" << dialect << "<\"" << typeData << "\">"
<< "` type created with unregistered dialect. If this is "
"intended, please call allowUnregisteredDialects() on the "
"MLIRContext, or use -allow-unregistered-dialect with "
"mlir-opt";
}
return success();
}
//===----------------------------------------------------------------------===//
// ShapedType
//===----------------------------------------------------------------------===//
constexpr int64_t ShapedType::kDynamicSize;
constexpr int64_t ShapedType::kDynamicStrideOrOffset;
ShapedType ShapedType::clone(ArrayRef<int64_t> shape, Type elementType) {
if (auto other = dyn_cast<MemRefType>()) {
MemRefType::Builder b(other);
b.setShape(shape);
b.setElementType(elementType);
return b;
}
if (auto other = dyn_cast<UnrankedMemRefType>()) {
MemRefType::Builder b(shape, elementType);
b.setMemorySpace(other.getMemorySpace());
return b;
}
if (isa<TensorType>())
return RankedTensorType::get(shape, elementType);
if (isa<VectorType>())
return VectorType::get(shape, elementType);
llvm_unreachable("Unhandled ShapedType clone case");
}
ShapedType ShapedType::clone(ArrayRef<int64_t> shape) {
if (auto other = dyn_cast<MemRefType>()) {
MemRefType::Builder b(other);
b.setShape(shape);
return b;
}
if (auto other = dyn_cast<UnrankedMemRefType>()) {
MemRefType::Builder b(shape, other.getElementType());
b.setShape(shape);
b.setMemorySpace(other.getMemorySpace());
return b;
}
if (isa<TensorType>())
return RankedTensorType::get(shape, getElementType());
if (isa<VectorType>())
return VectorType::get(shape, getElementType());
llvm_unreachable("Unhandled ShapedType clone case");
}
ShapedType ShapedType::clone(Type elementType) {
if (auto other = dyn_cast<MemRefType>()) {
MemRefType::Builder b(other);
b.setElementType(elementType);
return b;
}
if (auto other = dyn_cast<UnrankedMemRefType>()) {
return UnrankedMemRefType::get(elementType, other.getMemorySpace());
}
if (isa<TensorType>()) {
if (hasRank())
return RankedTensorType::get(getShape(), elementType);
return UnrankedTensorType::get(elementType);
}
if (isa<VectorType>())
return VectorType::get(getShape(), elementType);
llvm_unreachable("Unhandled ShapedType clone hit");
}
Type ShapedType::getElementType() const {
return TypeSwitch<Type, Type>(*this)
.Case<VectorType, RankedTensorType, UnrankedTensorType, MemRefType,
UnrankedMemRefType>([](auto ty) { return ty.getElementType(); });
}
unsigned ShapedType::getElementTypeBitWidth() const {
return getElementType().getIntOrFloatBitWidth();
}
int64_t ShapedType::getNumElements() const {
assert(hasStaticShape() && "cannot get element count of dynamic shaped type");
auto shape = getShape();
int64_t num = 1;
for (auto dim : shape) {
num *= dim;
assert(num >= 0 && "integer overflow in element count computation");
}
return num;
}
int64_t ShapedType::getRank() const {
assert(hasRank() && "cannot query rank of unranked shaped type");
return getShape().size();
}
bool ShapedType::hasRank() const {
return !isa<UnrankedMemRefType, UnrankedTensorType>();
}
int64_t ShapedType::getDimSize(unsigned idx) const {
assert(idx < getRank() && "invalid index for shaped type");
return getShape()[idx];
}
bool ShapedType::isDynamicDim(unsigned idx) const {
assert(idx < getRank() && "invalid index for shaped type");
return isDynamic(getShape()[idx]);
}
unsigned ShapedType::getDynamicDimIndex(unsigned index) const {
assert(index < getRank() && "invalid index");
assert(ShapedType::isDynamic(getDimSize(index)) && "invalid index");
return llvm::count_if(getShape().take_front(index), ShapedType::isDynamic);
}
/// Get the number of bits require to store a value of the given shaped type.
/// Compute the value recursively since tensors are allowed to have vectors as
/// elements.
int64_t ShapedType::getSizeInBits() const {
assert(hasStaticShape() &&
"cannot get the bit size of an aggregate with a dynamic shape");
auto elementType = getElementType();
if (elementType.isIntOrFloat())
return elementType.getIntOrFloatBitWidth() * getNumElements();
if (auto complexType = elementType.dyn_cast<ComplexType>()) {
elementType = complexType.getElementType();
return elementType.getIntOrFloatBitWidth() * getNumElements() * 2;
}
// Tensors can have vectors and other tensors as elements, other shaped types
// cannot.
assert(isa<TensorType>() && "unsupported element type");
assert((elementType.isa<VectorType, TensorType>()) &&
"unsupported tensor element type");
return getNumElements() * elementType.cast<ShapedType>().getSizeInBits();
}
ArrayRef<int64_t> ShapedType::getShape() const {
if (auto vectorType = dyn_cast<VectorType>())
return vectorType.getShape();
if (auto tensorType = dyn_cast<RankedTensorType>())
return tensorType.getShape();
return cast<MemRefType>().getShape();
}
int64_t ShapedType::getNumDynamicDims() const {
return llvm::count_if(getShape(), isDynamic);
}
bool ShapedType::hasStaticShape() const {
return hasRank() && llvm::none_of(getShape(), isDynamic);
}
bool ShapedType::hasStaticShape(ArrayRef<int64_t> shape) const {
return hasStaticShape() && getShape() == shape;
}
//===----------------------------------------------------------------------===//
// VectorType
//===----------------------------------------------------------------------===//
LogicalResult VectorType::verify(function_ref<InFlightDiagnostic()> emitError,
ArrayRef<int64_t> shape, Type elementType) {
if (shape.empty())
return emitError() << "vector types must have at least one dimension";
if (!isValidElementType(elementType))
return emitError() << "vector elements must be int/index/float type";
if (any_of(shape, [](int64_t i) { return i <= 0; }))
return emitError() << "vector types must have positive constant sizes";
return success();
}
VectorType VectorType::scaleElementBitwidth(unsigned scale) {
if (!scale)
return VectorType();
if (auto et = getElementType().dyn_cast<IntegerType>())
if (auto scaledEt = et.scaleElementBitwidth(scale))
return VectorType::get(getShape(), scaledEt);
if (auto et = getElementType().dyn_cast<FloatType>())
if (auto scaledEt = et.scaleElementBitwidth(scale))
return VectorType::get(getShape(), scaledEt);
return VectorType();
}
//===----------------------------------------------------------------------===//
// TensorType
//===----------------------------------------------------------------------===//
// Check if "elementType" can be an element type of a tensor.
static LogicalResult
checkTensorElementType(function_ref<InFlightDiagnostic()> emitError,
Type elementType) {
if (!TensorType::isValidElementType(elementType))
return emitError() << "invalid tensor element type: " << elementType;
return success();
}
/// Return true if the specified element type is ok in a tensor.
bool TensorType::isValidElementType(Type type) {
// Note: Non standard/builtin types are allowed to exist within tensor
// types. Dialects are expected to verify that tensor types have a valid
// element type within that dialect.
return type.isa<ComplexType, FloatType, IntegerType, OpaqueType, VectorType,
IndexType>() ||
!type.getDialect().getNamespace().empty();
}
//===----------------------------------------------------------------------===//
// RankedTensorType
//===----------------------------------------------------------------------===//
LogicalResult
RankedTensorType::verify(function_ref<InFlightDiagnostic()> emitError,
ArrayRef<int64_t> shape, Type elementType,
Attribute encoding) {
for (int64_t s : shape)
if (s < -1)
return emitError() << "invalid tensor dimension size";
if (auto v = encoding.dyn_cast_or_null<VerifiableTensorEncoding>())
if (failed(v.verifyEncoding(shape, elementType, emitError)))
return failure();
return checkTensorElementType(emitError, elementType);
}
//===----------------------------------------------------------------------===//
// UnrankedTensorType
//===----------------------------------------------------------------------===//
LogicalResult
UnrankedTensorType::verify(function_ref<InFlightDiagnostic()> emitError,
Type elementType) {
return checkTensorElementType(emitError, elementType);
}
//===----------------------------------------------------------------------===//
// BaseMemRefType
//===----------------------------------------------------------------------===//
Attribute BaseMemRefType::getMemorySpace() const {
if (auto rankedMemRefTy = dyn_cast<MemRefType>())
return rankedMemRefTy.getMemorySpace();
return cast<UnrankedMemRefType>().getMemorySpace();
}
unsigned BaseMemRefType::getMemorySpaceAsInt() const {
if (auto rankedMemRefTy = dyn_cast<MemRefType>())
return rankedMemRefTy.getMemorySpaceAsInt();
return cast<UnrankedMemRefType>().getMemorySpaceAsInt();
}
//===----------------------------------------------------------------------===//
// MemRefType
//===----------------------------------------------------------------------===//
/// Given an `originalShape` and a `reducedShape` assumed to be a subset of
/// `originalShape` with some `1` entries erased, return the set of indices
/// that specifies which of the entries of `originalShape` are dropped to obtain
/// `reducedShape`. The returned mask can be applied as a projection to
/// `originalShape` to obtain the `reducedShape`. This mask is useful to track
/// which dimensions must be kept when e.g. compute MemRef strides under
/// rank-reducing operations. Return None if reducedShape cannot be obtained
/// by dropping only `1` entries in `originalShape`.
llvm::Optional<llvm::SmallDenseSet<unsigned>>
mlir::computeRankReductionMask(ArrayRef<int64_t> originalShape,
ArrayRef<int64_t> reducedShape) {
size_t originalRank = originalShape.size(), reducedRank = reducedShape.size();
llvm::SmallDenseSet<unsigned> unusedDims;
unsigned reducedIdx = 0;
for (unsigned originalIdx = 0; originalIdx < originalRank; ++originalIdx) {
// Greedily insert `originalIdx` if no match.
if (reducedIdx < reducedRank &&
originalShape[originalIdx] == reducedShape[reducedIdx]) {
reducedIdx++;
continue;
}
unusedDims.insert(originalIdx);
// If no match on `originalIdx`, the `originalShape` at this dimension
// must be 1, otherwise we bail.
if (originalShape[originalIdx] != 1)
return llvm::None;
}
// The whole reducedShape must be scanned, otherwise we bail.
if (reducedIdx != reducedRank)
return llvm::None;
return unusedDims;
}
bool mlir::detail::isSupportedMemorySpace(Attribute memorySpace) {
// Empty attribute is allowed as default memory space.
if (!memorySpace)
return true;
// Supported built-in attributes.
if (memorySpace.isa<IntegerAttr, StringAttr, DictionaryAttr>())
return true;
// Allow custom dialect attributes.
if (!::mlir::isa<BuiltinDialect>(memorySpace.getDialect()))
return true;
return false;
}
Attribute mlir::detail::wrapIntegerMemorySpace(unsigned memorySpace,
MLIRContext *ctx) {
if (memorySpace == 0)
return nullptr;
return IntegerAttr::get(IntegerType::get(ctx, 64), memorySpace);
}
Attribute mlir::detail::skipDefaultMemorySpace(Attribute memorySpace) {
IntegerAttr intMemorySpace = memorySpace.dyn_cast_or_null<IntegerAttr>();
if (intMemorySpace && intMemorySpace.getValue() == 0)
return nullptr;
return memorySpace;
}
unsigned mlir::detail::getMemorySpaceAsInt(Attribute memorySpace) {
if (!memorySpace)
return 0;
assert(memorySpace.isa<IntegerAttr>() &&
"Using `getMemorySpaceInteger` with non-Integer attribute");
return static_cast<unsigned>(memorySpace.cast<IntegerAttr>().getInt());
}
MemRefType::Builder &
MemRefType::Builder::setMemorySpace(unsigned newMemorySpace) {
memorySpace =
wrapIntegerMemorySpace(newMemorySpace, elementType.getContext());
return *this;
}
unsigned MemRefType::getMemorySpaceAsInt() const {
return detail::getMemorySpaceAsInt(getMemorySpace());
}
LogicalResult MemRefType::verify(function_ref<InFlightDiagnostic()> emitError,
ArrayRef<int64_t> shape, Type elementType,
ArrayRef<AffineMap> affineMapComposition,
Attribute memorySpace) {
if (!BaseMemRefType::isValidElementType(elementType))
return emitError() << "invalid memref element type";
// Negative sizes are not allowed except for `-1` that means dynamic size.
for (int64_t s : shape)
if (s < -1)
return emitError() << "invalid memref size";
// Check that the structure of the composition is valid, i.e. that each
// subsequent affine map has as many inputs as the previous map has results.
// Take the dimensionality of the MemRef for the first map.
size_t dim = shape.size();
for (auto it : llvm::enumerate(affineMapComposition)) {
AffineMap map = it.value();
if (map.getNumDims() == dim) {
dim = map.getNumResults();
continue;
}
return emitError() << "memref affine map dimension mismatch between "
<< (it.index() == 0 ? Twine("memref rank")
: "affine map " + Twine(it.index()))
<< " and affine map" << it.index() + 1 << ": " << dim
<< " != " << map.getNumDims();
}
if (!isSupportedMemorySpace(memorySpace)) {
return emitError() << "unsupported memory space Attribute";
}
return success();
}
//===----------------------------------------------------------------------===//
// UnrankedMemRefType
//===----------------------------------------------------------------------===//
unsigned UnrankedMemRefType::getMemorySpaceAsInt() const {
return detail::getMemorySpaceAsInt(getMemorySpace());
}
LogicalResult
UnrankedMemRefType::verify(function_ref<InFlightDiagnostic()> emitError,
Type elementType, Attribute memorySpace) {
if (!BaseMemRefType::isValidElementType(elementType))
return emitError() << "invalid memref element type";
if (!isSupportedMemorySpace(memorySpace))
return emitError() << "unsupported memory space Attribute";
return success();
}
// Fallback cases for terminal dim/sym/cst that are not part of a binary op (
// i.e. single term). Accumulate the AffineExpr into the existing one.
static void extractStridesFromTerm(AffineExpr e,
AffineExpr multiplicativeFactor,
MutableArrayRef<AffineExpr> strides,
AffineExpr &offset) {
if (auto dim = e.dyn_cast<AffineDimExpr>())
strides[dim.getPosition()] =
strides[dim.getPosition()] + multiplicativeFactor;
else
offset = offset + e * multiplicativeFactor;
}
/// Takes a single AffineExpr `e` and populates the `strides` array with the
/// strides expressions for each dim position.
/// The convention is that the strides for dimensions d0, .. dn appear in
/// order to make indexing intuitive into the result.
static LogicalResult extractStrides(AffineExpr e,
AffineExpr multiplicativeFactor,
MutableArrayRef<AffineExpr> strides,
AffineExpr &offset) {
auto bin = e.dyn_cast<AffineBinaryOpExpr>();
if (!bin) {
extractStridesFromTerm(e, multiplicativeFactor, strides, offset);
return success();
}
if (bin.getKind() == AffineExprKind::CeilDiv ||
bin.getKind() == AffineExprKind::FloorDiv ||
bin.getKind() == AffineExprKind::Mod)
return failure();
if (bin.getKind() == AffineExprKind::Mul) {
auto dim = bin.getLHS().dyn_cast<AffineDimExpr>();
if (dim) {
strides[dim.getPosition()] =
strides[dim.getPosition()] + bin.getRHS() * multiplicativeFactor;
return success();
}
// LHS and RHS may both contain complex expressions of dims. Try one path
// and if it fails try the other. This is guaranteed to succeed because
// only one path may have a `dim`, otherwise this is not an AffineExpr in
// the first place.
if (bin.getLHS().isSymbolicOrConstant())
return extractStrides(bin.getRHS(), multiplicativeFactor * bin.getLHS(),
strides, offset);
return extractStrides(bin.getLHS(), multiplicativeFactor * bin.getRHS(),
strides, offset);
}
if (bin.getKind() == AffineExprKind::Add) {
auto res1 =
extractStrides(bin.getLHS(), multiplicativeFactor, strides, offset);
auto res2 =
extractStrides(bin.getRHS(), multiplicativeFactor, strides, offset);
return success(succeeded(res1) && succeeded(res2));
}
llvm_unreachable("unexpected binary operation");
}
LogicalResult mlir::getStridesAndOffset(MemRefType t,
SmallVectorImpl<AffineExpr> &strides,
AffineExpr &offset) {
auto affineMaps = t.getAffineMaps();
if (!affineMaps.empty() && affineMaps.back().getNumResults() != 1)
return failure();
AffineMap m;
if (!affineMaps.empty()) {
m = affineMaps.back();
for (size_t i = affineMaps.size() - 1; i > 0; --i)
m = m.compose(affineMaps[i - 1]);
assert(!m.isIdentity() && "unexpected identity map");
}
auto zero = getAffineConstantExpr(0, t.getContext());
auto one = getAffineConstantExpr(1, t.getContext());
offset = zero;
strides.assign(t.getRank(), zero);
// Canonical case for empty map.
if (!m) {
// 0-D corner case, offset is already 0.
if (t.getRank() == 0)
return success();
auto stridedExpr =
makeCanonicalStridedLayoutExpr(t.getShape(), t.getContext());
if (succeeded(extractStrides(stridedExpr, one, strides, offset)))
return success();
assert(false && "unexpected failure: extract strides in canonical layout");
}
// Non-canonical case requires more work.
auto stridedExpr =
simplifyAffineExpr(m.getResult(0), m.getNumDims(), m.getNumSymbols());
if (failed(extractStrides(stridedExpr, one, strides, offset))) {
offset = AffineExpr();
strides.clear();
return failure();
}
// Simplify results to allow folding to constants and simple checks.
unsigned numDims = m.getNumDims();
unsigned numSymbols = m.getNumSymbols();
offset = simplifyAffineExpr(offset, numDims, numSymbols);
for (auto &stride : strides)
stride = simplifyAffineExpr(stride, numDims, numSymbols);
/// In practice, a strided memref must be internally non-aliasing. Test
/// against 0 as a proxy.
/// TODO: static cases can have more advanced checks.
/// TODO: dynamic cases would require a way to compare symbolic
/// expressions and would probably need an affine set context propagated
/// everywhere.
if (llvm::any_of(strides, [](AffineExpr e) {
return e == getAffineConstantExpr(0, e.getContext());
})) {
offset = AffineExpr();
strides.clear();
return failure();
}
return success();
}
LogicalResult mlir::getStridesAndOffset(MemRefType t,
SmallVectorImpl<int64_t> &strides,
int64_t &offset) {
AffineExpr offsetExpr;
SmallVector<AffineExpr, 4> strideExprs;
if (failed(::getStridesAndOffset(t, strideExprs, offsetExpr)))
return failure();
if (auto cst = offsetExpr.dyn_cast<AffineConstantExpr>())
offset = cst.getValue();
else
offset = ShapedType::kDynamicStrideOrOffset;
for (auto e : strideExprs) {
if (auto c = e.dyn_cast<AffineConstantExpr>())
strides.push_back(c.getValue());
else
strides.push_back(ShapedType::kDynamicStrideOrOffset);
}
return success();
}
//===----------------------------------------------------------------------===//
/// TupleType
//===----------------------------------------------------------------------===//
/// Return the elements types for this tuple.
ArrayRef<Type> TupleType::getTypes() const { return getImpl()->getTypes(); }
/// Accumulate the types contained in this tuple and tuples nested within it.
/// Note that this only flattens nested tuples, not any other container type,
/// e.g. a tuple<i32, tensor<i32>, tuple<f32, tuple<i64>>> is flattened to
/// (i32, tensor<i32>, f32, i64)
void TupleType::getFlattenedTypes(SmallVectorImpl<Type> &types) {
for (Type type : getTypes()) {
if (auto nestedTuple = type.dyn_cast<TupleType>())
nestedTuple.getFlattenedTypes(types);
else
types.push_back(type);
}
}
/// Return the number of element types.
size_t TupleType::size() const { return getImpl()->size(); }
//===----------------------------------------------------------------------===//
// Type Utilities
//===----------------------------------------------------------------------===//
AffineMap mlir::makeStridedLinearLayoutMap(ArrayRef<int64_t> strides,
int64_t offset,
MLIRContext *context) {
AffineExpr expr;
unsigned nSymbols = 0;
// AffineExpr for offset.
// Static case.
if (offset != MemRefType::getDynamicStrideOrOffset()) {
auto cst = getAffineConstantExpr(offset, context);
expr = cst;
} else {
// Dynamic case, new symbol for the offset.
auto sym = getAffineSymbolExpr(nSymbols++, context);
expr = sym;
}
// AffineExpr for strides.
for (auto en : llvm::enumerate(strides)) {
auto dim = en.index();
auto stride = en.value();
assert(stride != 0 && "Invalid stride specification");
auto d = getAffineDimExpr(dim, context);
AffineExpr mult;
// Static case.
if (stride != MemRefType::getDynamicStrideOrOffset())
mult = getAffineConstantExpr(stride, context);
else
// Dynamic case, new symbol for each new stride.
mult = getAffineSymbolExpr(nSymbols++, context);
expr = expr + d * mult;
}
return AffineMap::get(strides.size(), nSymbols, expr);
}
/// Return a version of `t` with identity layout if it can be determined
/// statically that the layout is the canonical contiguous strided layout.
/// Otherwise pass `t`'s layout into `simplifyAffineMap` and return a copy of
/// `t` with simplified layout.
/// If `t` has multiple layout maps or a multi-result layout, just return `t`.
MemRefType mlir::canonicalizeStridedLayout(MemRefType t) {
auto affineMaps = t.getAffineMaps();
// Already in canonical form.
if (affineMaps.empty())
return t;
// Can't reduce to canonical identity form, return in canonical form.
if (affineMaps.size() > 1 || affineMaps[0].getNumResults() > 1)
return t;
// Corner-case for 0-D affine maps.
auto m = affineMaps[0];
if (m.getNumDims() == 0 && m.getNumSymbols() == 0) {
if (auto cst = m.getResult(0).dyn_cast<AffineConstantExpr>())
if (cst.getValue() == 0)
return MemRefType::Builder(t).setAffineMaps({});
return t;
}
// 0-D corner case for empty shape that still have an affine map. Example:
// `memref<f32, affine_map<()[s0] -> (s0)>>`. This is a 1 element memref whose
// offset needs to remain, just return t.
if (t.getShape().empty())
return t;
// If the canonical strided layout for the sizes of `t` is equal to the
// simplified layout of `t` we can just return an empty layout. Otherwise,
// just simplify the existing layout.
AffineExpr expr =
makeCanonicalStridedLayoutExpr(t.getShape(), t.getContext());
auto simplifiedLayoutExpr =
simplifyAffineExpr(m.getResult(0), m.getNumDims(), m.getNumSymbols());
if (expr != simplifiedLayoutExpr)
return MemRefType::Builder(t).setAffineMaps({AffineMap::get(
m.getNumDims(), m.getNumSymbols(), simplifiedLayoutExpr)});
return MemRefType::Builder(t).setAffineMaps({});
}
AffineExpr mlir::makeCanonicalStridedLayoutExpr(ArrayRef<int64_t> sizes,
ArrayRef<AffineExpr> exprs,
MLIRContext *context) {
assert(!sizes.empty() && !exprs.empty() &&
"expected non-empty sizes and exprs");
// Size 0 corner case is useful for canonicalizations.
if (llvm::is_contained(sizes, 0))
return getAffineConstantExpr(0, context);
auto maps = AffineMap::inferFromExprList(exprs);
assert(!maps.empty() && "Expected one non-empty map");
unsigned numDims = maps[0].getNumDims(), nSymbols = maps[0].getNumSymbols();
AffineExpr expr;
bool dynamicPoisonBit = false;
int64_t runningSize = 1;
for (auto en : llvm::zip(llvm::reverse(exprs), llvm::reverse(sizes))) {
int64_t size = std::get<1>(en);
// Degenerate case, no size =-> no stride
if (size == 0)
continue;
AffineExpr dimExpr = std::get<0>(en);
AffineExpr stride = dynamicPoisonBit
? getAffineSymbolExpr(nSymbols++, context)
: getAffineConstantExpr(runningSize, context);
expr = expr ? expr + dimExpr * stride : dimExpr * stride;
if (size > 0) {
runningSize *= size;
assert(runningSize > 0 && "integer overflow in size computation");
} else {
dynamicPoisonBit = true;
}
}
return simplifyAffineExpr(expr, numDims, nSymbols);
}
/// Return a version of `t` with a layout that has all dynamic offset and
/// strides. This is used to erase the static layout.
MemRefType mlir::eraseStridedLayout(MemRefType t) {
auto val = ShapedType::kDynamicStrideOrOffset;
return MemRefType::Builder(t).setAffineMaps(makeStridedLinearLayoutMap(
SmallVector<int64_t, 4>(t.getRank(), val), val, t.getContext()));
}
AffineExpr mlir::makeCanonicalStridedLayoutExpr(ArrayRef<int64_t> sizes,
MLIRContext *context) {
SmallVector<AffineExpr, 4> exprs;
exprs.reserve(sizes.size());
for (auto dim : llvm::seq<unsigned>(0, sizes.size()))
exprs.push_back(getAffineDimExpr(dim, context));
return makeCanonicalStridedLayoutExpr(sizes, exprs, context);
}
/// Return true if the layout for `t` is compatible with strided semantics.
bool mlir::isStrided(MemRefType t) {
int64_t offset;
SmallVector<int64_t, 4> strides;
auto res = getStridesAndOffset(t, strides, offset);
return succeeded(res);
}
/// Return the layout map in strided linear layout AffineMap form.
/// Return null if the layout is not compatible with a strided layout.
AffineMap mlir::getStridedLinearLayoutMap(MemRefType t) {
int64_t offset;
SmallVector<int64_t, 4> strides;
if (failed(getStridesAndOffset(t, strides, offset)))
return AffineMap();
return makeStridedLinearLayoutMap(strides, offset, t.getContext());
}