Files
clang-p2996/mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h
Tres Popp 5550c82189 [mlir] Move casting calls from methods to function calls
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.

Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.

Caveats include:
- This clang-tidy script probably has more problems.
- This only touches C++ code, so nothing that is being generated.

Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
  for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443

Implementation:
This first patch was created with the following steps. The intention is
to only do automated changes at first, so I waste less time if it's
reverted, and so the first mass change is more clear as an example to
other teams that will need to follow similar steps.

Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
   additional check:
   https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
   and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
   them to a pure state.
4. Some changes have been deleted for the following reasons:
   - Some files had a variable also named cast
   - Some files had not included a header file that defines the cast
     functions
   - Some files are definitions of the classes that have the casting
     methods, so the code still refers to the method instead of the
     function without adding a prefix or removing the method declaration
     at the same time.

```
ninja -C $BUILD_DIR clang-tidy

run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
               -header-filter=mlir/ mlir/* -fix

rm -rf $BUILD_DIR/tools/mlir/**/*.inc

git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\
            mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\
            mlir/lib/**/IR/\
            mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\
            mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\
            mlir/test/lib/Dialect/Test/TestTypes.cpp\
            mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\
            mlir/test/lib/Dialect/Test/TestAttributes.cpp\
            mlir/unittests/TableGen/EnumsGenTest.cpp\
            mlir/test/python/lib/PythonTestCAPI.cpp\
            mlir/include/mlir/IR/
```

Differential Revision: https://reviews.llvm.org/D150123
2023-05-12 11:21:25 +02:00

389 lines
17 KiB
C++

//===- CodegenUtils.h - Utilities for generating MLIR -----------*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This header file defines utilities for generating MLIR.
//
//===----------------------------------------------------------------------===//
#ifndef MLIR_DIALECT_SPARSETENSOR_TRANSFORMS_CODEGENUTILS_H_
#define MLIR_DIALECT_SPARSETENSOR_TRANSFORMS_CODEGENUTILS_H_
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/LLVMIR/LLVMDialect.h"
#include "mlir/Dialect/SparseTensor/IR/Enums.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/IR/Builders.h"
namespace mlir {
class Location;
class Type;
class Value;
namespace sparse_tensor {
/// Shorthand aliases for the `emitCInterface` argument to `getFunc()`,
/// `createFuncCall()`, and `replaceOpWithFuncCall()`.
enum class EmitCInterface : bool { Off = false, On = true };
//===----------------------------------------------------------------------===//
// ExecutionEngine/SparseTensorUtils helper functions.
//===----------------------------------------------------------------------===//
/// Converts an overhead storage bitwidth to its internal type-encoding.
OverheadType overheadTypeEncoding(unsigned width);
/// Converts an overhead storage type to its internal type-encoding.
OverheadType overheadTypeEncoding(Type tp);
/// Converts the internal type-encoding for overhead storage to an mlir::Type.
Type getOverheadType(Builder &builder, OverheadType ot);
/// Returns the OverheadType for position overhead storage.
OverheadType posTypeEncoding(SparseTensorEncodingAttr enc);
/// Returns the OverheadType for coordinate overhead storage.
OverheadType crdTypeEncoding(SparseTensorEncodingAttr enc);
/// Convert OverheadType to its function-name suffix.
StringRef overheadTypeFunctionSuffix(OverheadType ot);
/// Converts an overhead storage type to its function-name suffix.
StringRef overheadTypeFunctionSuffix(Type overheadTp);
/// Converts a primary storage type to its internal type-encoding.
PrimaryType primaryTypeEncoding(Type elemTp);
/// Convert PrimaryType to its function-name suffix.
StringRef primaryTypeFunctionSuffix(PrimaryType pt);
/// Converts a primary storage type to its function-name suffix.
StringRef primaryTypeFunctionSuffix(Type elemTp);
//===----------------------------------------------------------------------===//
// Misc code generators and utilities.
//===----------------------------------------------------------------------===//
/// Add type casting between arith and index types when needed.
Value genCast(OpBuilder &builder, Location loc, Value value, Type dstTy);
/// Generates a pointer/index load from the sparse storage scheme. Narrower
/// data types need to be zero extended before casting the value into the
/// index type used for looping and indexing.
Value genIndexLoad(OpBuilder &builder, Location loc, Value mem, Value s);
/// Generates a 1-valued attribute of the given type. This supports
/// all the same types as `getZeroAttr`; however, unlike `getZeroAttr`,
/// for unsupported types we raise `llvm_unreachable` rather than
/// returning a null attribute.
TypedAttr getOneAttr(Builder &builder, Type tp);
/// Generates the comparison `v != 0` where `v` is of numeric type.
/// For floating types, we use the "unordered" comparator (i.e., returns
/// true if `v` is NaN).
Value genIsNonzero(OpBuilder &builder, Location loc, Value v);
/// Computes the shape of destination tensor of a reshape operator. This is only
/// used when operands have dynamic shape. The shape of the destination is
/// stored into dstShape.
void genReshapeDstShape(OpBuilder &builder, Location loc,
SmallVectorImpl<Value> &dstShape,
ArrayRef<Value> srcShape,
ArrayRef<StaticSize> staticDstShape,
ArrayRef<ReassociationIndices> reassociation);
/// Reshape coordinates during a reshaping operation.
void reshapeCvs(OpBuilder &builder, Location loc,
ArrayRef<ReassociationIndices> reassociation,
ValueRange srcSizes, ValueRange srcCvs, // NOLINT
ValueRange dstSizes, SmallVectorImpl<Value> &dstCvs);
/// Returns a function reference (first hit also inserts into module). Sets
/// the "_emit_c_interface" on the function declaration when requested,
/// so that LLVM lowering generates a wrapper function that takes care
/// of ABI complications with passing in and returning MemRefs to C functions.
FlatSymbolRefAttr getFunc(ModuleOp module, StringRef name, TypeRange resultType,
ValueRange operands, EmitCInterface emitCInterface);
/// Creates a `CallOp` to the function reference returned by `getFunc()` in
/// the builder's module.
func::CallOp createFuncCall(OpBuilder &builder, Location loc, StringRef name,
TypeRange resultType, ValueRange operands,
EmitCInterface emitCInterface);
/// Returns the equivalent of `void*` for opaque arguments to the
/// execution engine.
Type getOpaquePointerType(MLIRContext *ctx);
Type getOpaquePointerType(Builder &builder);
/// Generates an uninitialized temporary buffer of the given size and
/// type, but returns it as type `memref<? x $tp>` (rather than as type
/// `memref<$sz x $tp>`).
Value genAlloca(OpBuilder &builder, Location loc, Value sz, Type tp);
/// Generates an uninitialized temporary buffer of the given size and
/// type, and returns it as type `memref<? x $tp>` (staticShape=false) or
/// `memref<$sz x $tp>` (staticShape=true).
Value genAlloca(OpBuilder &builder, Location loc, unsigned sz, Type tp,
bool staticShape = false);
/// Generates an uninitialized temporary buffer with room for one value
/// of the given type, and returns the `memref<$tp>`.
Value genAllocaScalar(OpBuilder &builder, Location loc, Type tp);
/// Generates a temporary buffer, initializes it with the given contents,
/// and returns it as type `memref<? x $tp>` (rather than specifying the
/// size of the buffer).
Value allocaBuffer(OpBuilder &builder, Location loc, ValueRange values);
/// Generates code to allocate a buffer of the given type, and zero
/// initialize it. If the buffer type has any dynamic sizes, then the
/// `sizes` parameter should be as filled by sizesFromPtr(); that way
/// we can reuse the genDimSizeCall() results generated by sizesFromPtr().
Value allocDenseTensor(OpBuilder &builder, Location loc,
RankedTensorType tensorTp, ValueRange sizes);
/// Generates code to deallocate a dense buffer.
void deallocDenseTensor(OpBuilder &builder, Location loc, Value buffer);
/// Generates code to read the value from `tensor[ivs]`. The generated code
/// looks like the following and the insertion point after this routine is
/// inside the then-branch.
/// if (tensor[ivs] != 0)
/// insert_point
Value genValueForDense(OpBuilder &builder, Location loc, Value tensor,
ValueRange ivs);
/// Generates the loop structure to iterate over a dense tensor or a sparse
/// tensor constant to support the lowering of dense-to-sparse convert operator.
//
// The loop to iterate a dense tensor:
// for i1 in dim1
// ..
// for ik in dimk
// val = a[i1,..,ik]
// if val != 0
// loop-body
//
// The loop to iterate a sparse tensor constant:
// for i in range(NNZ)
// val = values[i]
// [i1,..,ik] = coordinates[i]
// loop-body
void genDenseTensorOrSparseConstantIterLoop(
OpBuilder &builder, Location loc, Value src, unsigned rank,
function_ref<void(OpBuilder &, Location, Value, ValueRange)> bodyBuilder);
/// Populates given sizes array from dense tensor or sparse tensor constant.
void sizesFromSrc(OpBuilder &builder, SmallVectorImpl<Value> &sizes,
Location loc, Value src);
/// Generates a 1D MemRefType with a dynamic size. When withLayout is set, the
/// returned memref has a layout has unknown strides and offsets. Otherwise,
/// a memref with a standard unit stride zero offset layout is returned.
inline MemRefType get1DMemRefType(Type etp, bool withLayout) {
auto layout = withLayout ? StridedLayoutAttr::StridedLayoutAttr::get(
etp.getContext(), ShapedType::kDynamic,
{ShapedType::kDynamic})
: StridedLayoutAttr();
return MemRefType::get(ShapedType::kDynamic, etp, layout);
}
/// Scans to top of generated loop.
Operation *getTop(Operation *op);
/// Iterate over a sparse constant, generates constantOp for value
/// and coordinates. E.g.,
/// sparse<[ [0], [28], [31] ],
/// [ (-5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] >
/// =>
/// %c1 = arith.constant 0
/// %v1 = complex.constant (5.13, 2.0)
/// callback({%c1}, %v1)
///
/// %c2 = arith.constant 28
/// %v2 = complex.constant (3.0, 4.0)
/// callback({%c2}, %v2)
///
/// %c3 = arith.constant 31
/// %v3 = complex.constant (5.0, 6.0)
/// callback({%c3}, %v3)
void foreachInSparseConstant(
OpBuilder &builder, Location loc, SparseElementsAttr attr, AffineMap order,
function_ref<void(ArrayRef<Value>, Value)> callback);
/// Loads `size`-many values from the memref, which must have rank-1 and
/// size greater-or-equal to `size`. If the optional `(offsetIdx,offsetVal)`
/// arguments are provided, then the `offsetVal` will be added to the
/// `offsetIdx`-th value after loading.
SmallVector<Value> loadAll(OpBuilder &builder, Location loc, size_t size,
Value mem, size_t offsetIdx = 0,
Value offsetVal = Value());
/// Stores all the values of `vs` into the memref `mem`, which must have
/// rank-1 and size greater-or-equal to `vs.size()`. If the optional
/// `(offsetIdx,offsetVal)` arguments are provided, then the `offsetVal`
/// will be added to the `offsetIdx`-th value before storing.
void storeAll(OpBuilder &builder, Location loc, Value mem, ValueRange vs,
size_t offsetIdx = 0, Value offsetVal = Value());
/// Reshapes the linear values buffer for an annotated all dense sparse tensor
/// to match the shape of the corresponding dense tensor to support direct
/// access of the buffer through `lvlCoords`.
Value reshapeValuesToLevels(OpBuilder &builder, Location loc,
SparseTensorEncodingAttr enc, ValueRange dimSizes,
Value valuesBuffer, Value lvlCoords);
//===----------------------------------------------------------------------===//
// Inlined constant generators.
//
// All these functions are just wrappers to improve code legibility;
// therefore, we mark them as `inline` to avoid introducing any additional
// overhead due to the legibility.
//
// TODO: Ideally these should move upstream, so that we don't
// develop a design island. However, doing so will involve
// substantial design work. For related prior discussion, see
// <https://llvm.discourse.group/t/evolving-builder-apis-based-on-lessons-learned-from-edsc/879>
//===----------------------------------------------------------------------===//
/// Generates a 0-valued constant of the given type. In addition to
/// the scalar types (`ComplexType`, `FloatType`, `IndexType`,
/// `IntegerType`), this also works for `RankedTensorType` and `VectorType`
/// (for which it generates a constant `DenseElementsAttr` of zeros).
inline Value constantZero(OpBuilder &builder, Location loc, Type tp) {
if (auto ctp = dyn_cast<ComplexType>(tp)) {
auto zeroe = builder.getZeroAttr(ctp.getElementType());
auto zeroa = builder.getArrayAttr({zeroe, zeroe});
return builder.create<complex::ConstantOp>(loc, tp, zeroa);
}
return builder.create<arith::ConstantOp>(loc, tp, builder.getZeroAttr(tp));
}
/// Generates a 1-valued constant of the given type. This supports all
/// the same types as `constantZero`.
inline Value constantOne(OpBuilder &builder, Location loc, Type tp) {
if (auto ctp = dyn_cast<ComplexType>(tp)) {
auto zeroe = builder.getZeroAttr(ctp.getElementType());
auto onee = getOneAttr(builder, ctp.getElementType());
auto zeroa = builder.getArrayAttr({onee, zeroe});
return builder.create<complex::ConstantOp>(loc, tp, zeroa);
}
return builder.create<arith::ConstantOp>(loc, tp, getOneAttr(builder, tp));
}
/// Generates a constant of `index` type.
inline Value constantIndex(OpBuilder &builder, Location loc, int64_t i) {
return builder.create<arith::ConstantIndexOp>(loc, i);
}
/// Generates a constant of `i64` type.
inline Value constantI64(OpBuilder &builder, Location loc, int64_t i) {
return builder.create<arith::ConstantIntOp>(loc, i, 64);
}
/// Generates a constant of `i32` type.
inline Value constantI32(OpBuilder &builder, Location loc, int32_t i) {
return builder.create<arith::ConstantIntOp>(loc, i, 32);
}
/// Generates a constant of `i16` type.
inline Value constantI16(OpBuilder &builder, Location loc, int16_t i) {
return builder.create<arith::ConstantIntOp>(loc, i, 16);
}
/// Generates a constant of `i8` type.
inline Value constantI8(OpBuilder &builder, Location loc, int8_t i) {
return builder.create<arith::ConstantIntOp>(loc, i, 8);
}
/// Generates a constant of `i1` type.
inline Value constantI1(OpBuilder &builder, Location loc, bool b) {
return builder.create<arith::ConstantIntOp>(loc, b, 1);
}
/// Generates a constant of the given `Action`.
inline Value constantAction(OpBuilder &builder, Location loc, Action action) {
return constantI32(builder, loc, static_cast<uint32_t>(action));
}
/// Generates a constant of the internal type-encoding for overhead storage.
inline Value constantOverheadTypeEncoding(OpBuilder &builder, Location loc,
unsigned width) {
return constantI32(builder, loc,
static_cast<uint32_t>(overheadTypeEncoding(width)));
}
/// Generates a constant of the internal type-encoding for position
/// overhead storage.
inline Value constantPosTypeEncoding(OpBuilder &builder, Location loc,
SparseTensorEncodingAttr enc) {
return constantOverheadTypeEncoding(builder, loc, enc.getPosWidth());
}
/// Generates a constant of the internal type-encoding for coordinate
/// overhead storage.
inline Value constantCrdTypeEncoding(OpBuilder &builder, Location loc,
SparseTensorEncodingAttr enc) {
return constantOverheadTypeEncoding(builder, loc, enc.getCrdWidth());
}
/// Generates a constant of the internal type-encoding for primary storage.
inline Value constantPrimaryTypeEncoding(OpBuilder &builder, Location loc,
Type elemTp) {
return constantI32(builder, loc,
static_cast<uint32_t>(primaryTypeEncoding(elemTp)));
}
/// Generates a constant of the internal dimension level type encoding.
inline Value constantDimLevelTypeEncoding(OpBuilder &builder, Location loc,
DimLevelType dlt) {
return constantI8(builder, loc, static_cast<uint8_t>(dlt));
}
inline bool isZeroRankedTensorOrScalar(Type type) {
auto rtp = dyn_cast<RankedTensorType>(type);
return !rtp || rtp.getRank() == 0;
}
/// Infers the result type and generates `ToPositionsOp`.
Value genToPositions(OpBuilder &builder, Location loc, Value tensor, Level lvl);
/// Infers the result type and generates `ToCoordinatesOp`. If the
/// level is within a COO region, the result type is a memref with unknown
/// stride and offset. Otherwise, the result type is a memref without
/// any specified layout.
Value genToCoordinates(OpBuilder &builder, Location loc, Value tensor,
Level lvl, Level cooStart);
/// Infers the result type and generates `ToCoordinatesBufferOp`.
Value genToCoordinatesBuffer(OpBuilder &builder, Location loc, Value tensor);
/// Infers the result type and generates `ToValuesOp`.
Value genToValues(OpBuilder &builder, Location loc, Value tensor);
/// Generates code to retrieve the values size for the sparse tensor.
Value genValMemSize(OpBuilder &builder, Location loc, Value tensor);
/// Generates code to retrieve the slice offset for the sparse tensor slice,
/// return a constant if the offset is statically known.
Value createOrFoldSliceOffsetOp(OpBuilder &builder, Location loc, Value tensor,
Dimension dim);
/// Generates code to retrieve the slice slice for the sparse tensor slice,
/// return a constant if the offset is statically known.
Value createOrFoldSliceStrideOp(OpBuilder &builder, Location loc, Value tensor,
Dimension dim);
} // namespace sparse_tensor
} // namespace mlir
#endif // MLIR_DIALECT_SPARSETENSOR_TRANSFORMS_CODEGENUTILS_H_