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DialectBuilder.hpp
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/*
* SPDX-License-Identifier: Apache-2.0
*/
//===---- DialectBuilder.hpp - Helper functions for MLIR dialects -----===//
//
// Copyright 2019-2024 The IBM Research Authors.
//
// =============================================================================
//
// This file contains helper functions for building MLIR operations.
//
//===----------------------------------------------------------------------===//
#ifndef ONNX_MLIR_DIALECT_BUILDER_MLIR_H
#define ONNX_MLIR_DIALECT_BUILDER_MLIR_H
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/Utils/ReshapeOpsUtils.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/IntegerSet.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Value.h"
// Please do not add dependences on ONNX or KRNL dialects.
#include "src/Dialect/Mlir/IndexExpr.hpp"
#include "src/Dialect/Mlir/VectorMachineSupport.hpp"
#include <functional>
namespace onnx_mlir {
struct DialectBuilder {
// Constructor for analysis (no code generation, get builder disabled).
DialectBuilder(mlir::Location loc) : builder(nullptr), location(loc) {}
// Constructors for code generation.
DialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: builder(&b), location(loc) {}
DialectBuilder(const DialectBuilder &db)
: builder(db.builder), location(db.location) {}
virtual ~DialectBuilder() {}
DialectBuilder(DialectBuilder &&) = delete;
DialectBuilder &operator=(const DialectBuilder &) = delete;
DialectBuilder &&operator=(const DialectBuilder &&) = delete;
// Public getters of builder and location.
mlir::OpBuilder &getBuilder() const { return b(); }
mlir::OpBuilder *getBuilderPtr() const { return builder; } // Possibly null.
mlir::Location getLoc() const { return loc(); }
protected:
// Private getters of builder and location (concise version).
mlir::OpBuilder &b() const {
assert(builder);
return *builder;
}
mlir::Location loc() const { return location; }
private:
mlir::OpBuilder *builder;
mlir::Location location;
};
//===----------------------------------------------------------------------===//
// Math Builder
//===----------------------------------------------------------------------===//
/// Helper struct to build simple arithmetic quantities with minimal type
/// inference support. Code is adapted to support the DialectBuilder super-class
/// that facilitate the building of other dialect builders using another dialect
/// builder.
//===----------------------------------------------------------------------===//
// Original code for MathBuilder is copied from LLVM MLIR Utils.cpp
// Modified here to add operations, add super class.
// License added here for this class for completeness.
// 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
//===----------------------------------------------------------------------===//
struct MathBuilder final : DialectBuilder {
MathBuilder(mlir::Location loc) : DialectBuilder(loc) {}
MathBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
MathBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~MathBuilder() {}
// Support for vectors: we provide queries that work regardless of if we have
// (1) a scalar or (2) a vector of a basic element type.
static bool isVector(mlir::Value val);
static bool isVector(mlir::Type type);
// The method belows ignore the vectors part of the type to provide answer on
// the basic element types alone.
static bool isScalarOrVectorInteger(mlir::Value val);
static bool isScalarOrVectorInteger(mlir::Type elementOrVectorType);
static bool isScalarOrVectorUnsignedInteger(mlir::Value val);
static bool isScalarOrVectorUnsignedInteger(mlir::Type elementOrVectorType);
static bool isScalarOrVectorFloat(mlir::Value val);
static bool isScalarOrVectorFloat(mlir::Type elementOrVectorType);
// Return the basic element type regardless of if we are given (1) a scalar or
// (2) a vector of a basic element type.
static mlir::Type elementTypeOfScalarOrVector(mlir::Value val);
static mlir::Type elementTypeOfScalarOrVector(mlir::Type elementOrVectorType);
// Return a type of the same vector shape as vectorType with a basic element
// type of elementType. When vectorType is not a vector, then the returned
// type is simply a scalar of elementType. ElementType should not be a scalar
// type.
static mlir::Type getTypeWithVector(
mlir::Type vectorType, mlir::Type elementType);
// "B" below indicates that the operation will splat scalar values if one of
// the input value is itself a vector.
// "B" below indicates that the operation will splat scalar values if one of
// the input value is itself a vector.
// "B" below indicates that the operation will splat scalar values if one of
// the input value is itself a vector.
mlir::Value abs(mlir::Value val) const;
mlir::Value add(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value andi(mlir::Value lhs, mlir::Value rhs) const; // B/Int only.
mlir::Value ceil(mlir::Value val) const; // Float only.
mlir::Value ceilDiv(mlir::Value lhs, mlir::Value rhs) const; // B/Int only.
mlir::Value clip(mlir::Value val, mlir::Value lb, mlir::Value ub) const; // B.
mlir::Value copySign(mlir::Value rem, mlir::Value div) const; // B/Float only.
mlir::Value div(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value erf(mlir::Value val) const;
mlir::Value exp(mlir::Value val) const; // Float only.
mlir::Value exp2(mlir::Value val) const; // Float only.
mlir::Value floor(mlir::Value val) const; // Float only.
mlir::Value floorDiv(mlir::Value lhs, mlir::Value rhs) const; // B/Int only.
mlir::Value fma(
mlir::Value lhs, mlir::Value rhs, mlir::Value acc) const; // B.
mlir::Value log(mlir::Value val) const; // Float only.
mlir::Value log2(mlir::Value val) const; // Float only.
mlir::Value mul(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value neg(mlir::Value val) const;
mlir::Value ori(mlir::Value lhs, mlir::Value rhs) const; // B/Int only.
mlir::Value pow(mlir::Value base, mlir::Value exp) const; // B/Float only.
mlir::Value rem(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value round(mlir::Value) const; // Float only.
mlir::Value roundEven(mlir::Value) const; // Float only.
mlir::Value roundEvenEmulation(mlir::Value) const; // Float only.
mlir::Value sqrt(mlir::Value val) const; // Float only.
mlir::Value sub(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value tanh(mlir::Value val) const; // Float only.
mlir::Value xori(mlir::Value lhs, mlir::Value rhs) const; // B/Int only.
mlir::Value select(
mlir::Value cmp, mlir::Value trueVal, mlir::Value valseVal) const; // B.
mlir::Value gt(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value ge(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value lt(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value le(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value eq(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value neq(mlir::Value lhs, mlir::Value rhs) const; // B.
// Signed versions (index/signless/signed int or float)
mlir::Value sgt(mlir::Value lhs, mlir::Value rhs) const; // B/No unsigned.
mlir::Value sge(mlir::Value lhs, mlir::Value rhs) const; // B/No unsigned.
mlir::Value slt(mlir::Value lhs, mlir::Value rhs) const; // B/No unsigned.
mlir::Value sle(mlir::Value lhs, mlir::Value rhs) const; // B/No unsigned.
// Unsigned versions
mlir::Value ugt(mlir::Value lhs, mlir::Value rhs) const; // B/Unsigned only.
mlir::Value uge(mlir::Value lhs, mlir::Value rhs) const; // B/Unsigned only.
mlir::Value ult(mlir::Value lhs, mlir::Value rhs) const; // B/Unsigned only.
mlir::Value ule(mlir::Value lhs, mlir::Value rhs) const; // B/Unsigned only.
mlir::Value min(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value max(mlir::Value lhs, mlir::Value rhs) const; // B.
mlir::Value constant(mlir::Type type, double val) const;
mlir::Value constantIndex(int64_t val) const;
mlir::TypedAttr negativeInfAttr(mlir::Type type) const;
mlir::TypedAttr positiveInfAttr(mlir::Type type) const;
/// Emit a negative infinity constant of a specific type. Supported types:
/// F16, F32, F64, Int8, Int16, Int32, Int64. In case of Float, emit the
/// negative of the positive infinity. In case of Integer, emit the minimum
/// mlir::Value.
mlir::Value negativeInf(mlir::Type type) const;
/// Emit a positive infinity constant of a specific type. Supported types:
/// F16, F32, F64, Int8, Int16, Int32, Int64. In case of Integer, emit the
/// maximum mlir::Value.
mlir::Value positiveInf(mlir::Type type) const;
// Cast handle bool/int/float/index elementary types. Do not convert
// signed/index to unsigned.
mlir::Value cast(mlir::Type destType, mlir::Value val) const; // B.
mlir::Value castToIndex(mlir::Value val) const;
// Add indexOffsets to the least significant indices. So if indices are (i, j,
// k, l) and offsets are (K, L), the results will be (i, j, k+K, l+L).
void addOffsetToLeastSignificant(mlir::ValueRange indices,
mlir::ValueRange offsets,
llvm::SmallVectorImpl<mlir::Value> &computedIndices) const;
void addOffsetToLeastSignificant(mlir::ArrayRef<IndexExpr> indices,
mlir::ValueRange offsets,
llvm::SmallVectorImpl<mlir::Value> &computedIndices) const;
// Perform splat to match (see below), accepting up to 3 values at most.
void splatToMatch(llvm::SmallVectorImpl<mlir::Value> &vals) const;
private:
mlir::Value createArithCmp(
mlir::Value lhs, mlir::Value rhs, mlir::arith::CmpIPredicate pred) const;
mlir::Value createArithCmp(
mlir::Value lhs, mlir::Value rhs, mlir::arith::CmpFPredicate pred) const;
mlir::Value castToSignless(mlir::Value source, int64_t width) const;
mlir::Value castToUnsigned(mlir::Value source, int64_t width) const;
// If any of the first, second, or third values are vector types, splat the
// other ones to the same VL. Return true if one or more values were splatted.
bool splatToMatch(mlir::Value &first, mlir::Value &second) const;
bool splatToMatch(
mlir::Value &first, mlir::Value &second, mlir::Value &third) const;
};
//===----------------------------------------------------------------------===//
// Shape Builder
//===----------------------------------------------------------------------===//
struct ShapeBuilder final : DialectBuilder {
ShapeBuilder(mlir::Location loc) : DialectBuilder(loc) {}
ShapeBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
ShapeBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~ShapeBuilder() {}
mlir::Value dim(mlir::Value val, int64_t index) const;
mlir::Value shapeOf(mlir::Value val) const;
mlir::Value fromExtents(mlir::ValueRange extents) const;
mlir::Value getExtent(mlir::Value val, int64_t index) const;
mlir::Value toExtentTensor(mlir::Type type, mlir::Value shape) const;
};
//===----------------------------------------------------------------------===//
// MemRef Builder with added support for aligned memory
//===----------------------------------------------------------------------===//
// Default alignment attribute for all allocation of memory. On most system, it
// numElems is 16 bytes.
static constexpr int64_t gDefaultAllocAlign = 16;
struct MemRefBuilder final : DialectBuilder {
MemRefBuilder(mlir::Location loc) : DialectBuilder(loc) {}
MemRefBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
MemRefBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~MemRefBuilder() {}
// Constants
static const int64_t defaultAlign;
// Common load/store interface (krnl/affine/memref)
// Add offsets (if any) to the least significant memref dims.
mlir::Value load(mlir::Value memref, mlir::ValueRange indices = {},
mlir::ValueRange offsets = {}) const;
mlir::Value loadIE(mlir::Value memref, mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
void store(mlir::Value val, mlir::Value memref, mlir::ValueRange indices = {},
mlir::ValueRange offsets = {}) const;
void storeIE(mlir::Value val, mlir::Value memref,
mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
// Info: get static and dynamic size of memory in number of elementary type.
// Array of vector types are not supported at this time.
//
// If range r>0: include dims in range [ 0, min(r, rank) ).
// Range r==0: noop, look at no data.
// If r<=: include dims from [ max(0, r+rank) to rank ).
//
// Default value includes the entire range. Return true if static only within
// the specified range.
bool getStaticAndDynamicMemSize(mlir::MemRefType type,
mlir::ValueRange dynSymbols, int64_t &staticSize, IndexExpr &dynSize,
int64_t range = 1000) const;
bool getStaticAndDynamicMemSize(mlir::MemRefType type, DimsExprRef dims,
int64_t &staticSize, IndexExpr &dynSize, int64_t range = 1000) const;
// Same as above, but does not track of dynamic size.
static bool getStaticMemSize(
mlir::MemRefType type, int64_t &staticSize, int64_t range = 1000);
// Alloc for static shapes without alignment.
mlir::memref::AllocOp alloc(mlir::MemRefType type) const;
// Alloc for static/dynamic shapes without alignment.
mlir::memref::AllocOp alloc(
mlir::MemRefType type, mlir::ValueRange dynSymbols) const;
mlir::memref::AllocOp alloc(
mlir::Value operandOfSameType, mlir::MemRefType type) const;
mlir::memref::AllocOp alloc(mlir::MemRefType type, DimsExprRef dims) const;
// Alloc for static shapes with alignment.
// Minimum alignment is gDefaultAllocAlign.
mlir::memref::AllocOp alignedAlloc(
mlir::MemRefType type, int64_t align = defaultAlign) const;
// Alloc for static/dynamic shapes with alignment.
mlir::memref::AllocOp alignedAlloc(mlir::MemRefType type,
mlir::ValueRange dynSymbols, int64_t align = defaultAlign) const;
mlir::memref::AllocOp alignedAlloc(mlir::Value operandOfSameType,
mlir::MemRefType type, int64_t align = defaultAlign) const;
mlir::memref::AllocOp alignedAlloc(mlir::MemRefType type, DimsExprRef dims,
int64_t align = defaultAlign) const;
// Alloc for shapes with alignment and padding for safe full SIMD
// operations. Padding may be added so that every values in the shape may
// safely be computed by a SIMD operation (or possibly multiple ones if
// unrollSIMD>1). Minimum alignment is gDefaultAllocAlign. Operation does
// not support layouts at this time.
//
// Alloc for static shapes with alignment and SIMD padding.
mlir::Value alignedAllocWithSimdPadding(mlir::MemRefType type, int64_t VL = 1,
int64_t align = defaultAlign) const;
// Alloc for static/dynamic shapes with alignment and SIMD padding.
mlir::Value alignedAllocWithSimdPadding(mlir::MemRefType type,
mlir::ValueRange dynSymbols, int64_t VL = 1,
int64_t align = defaultAlign) const;
mlir::Value alignedAllocWithSimdPadding(mlir::Value operandOfSameType,
mlir::MemRefType type, int64_t VL = 1,
int64_t align = defaultAlign) const;
mlir::Value alignedAllocWithSimdPadding(mlir::MemRefType type,
DimsExprRef dims, int64_t VL = 1, int64_t align = defaultAlign) const;
// The alloca instruction allocates memory on the stack frame of the
// currently executing function, to be automatically released when this
// function returns to its caller. It is strongly suggested to place alloca
// instructions outside of a loop.
//
// When possible, DO NOT USE ALLOCA except for a few scalars.
//
mlir::memref::AllocaOp alloca(mlir::MemRefType type) const;
mlir::memref::AllocaOp alignedAlloca(
mlir::MemRefType type, int64_t align = defaultAlign) const;
mlir::memref::DeallocOp dealloc(mlir::Value val) const;
// Reshapes.
mlir::memref::ReshapeOp reshape(mlir::MemRefType outputType,
mlir::Value valToReshape, mlir::Value outputShapeStoredInMem) const;
// Reshape to dimensions passed by destDims. Will create data-structure to
// hold the dims, save into it, and the perform the actual reshape.
mlir::memref::ReshapeOp reshape(
DimsExpr &outputDims, mlir::Value valToReshape) const;
// Flatten innermost dimensions of a MemRef. User provide the value to
// reshape (valToReshape), its dims (dims), and the number of innermost
// loops to collapse (dimsToFlatten). The function computes the new
// flattened dimensions (flattenDims) and return the flattened value. Values
// of dimsToFlatten are in the [1, rank of input] range. Legal only on types
// with identity layouts.
mlir::Value reshapeToFlatInnermost(mlir::Value valToReshape, DimsExprRef dims,
DimsExpr &flattenDims, int64_t dimsToFlatten) const;
// Flatten to a 2D MemRef, with outer dim including outermost dim to axis
// -1, and inner dim including the remaining innermost dims. Values of axis
// are in the [1, rank of input) range. Negative axis values are taken from
// the back. Legal only on types with identity layouts.
mlir::Value reshapeToFlat2D(mlir::Value valToReshape, DimsExprRef dims,
DimsExpr &flattenDims, int64_t axis) const;
// Perform the reverse operation; given a flattened value, unflatten it by
// giving the function its original unflattened dimensions (outputDims) and
// type (outputType). Legal only on types with identity layouts.
mlir::memref::ReshapeOp reshapeFromFlat(mlir::Value valToReshape,
DimsExpr &outputDims, mlir::MemRefType outputType) const;
// Casts.
mlir::memref::CastOp cast(
mlir::Value input, mlir::MemRefType outputType) const;
mlir::Value reinterpretCast(mlir::Value input, DimsExpr &outputDims) const;
mlir::Value reinterpretCast(
mlir::Value input, mlir::Value offset, DimsExpr &outputDims) const;
// Does not support layouts at this time. Does only work for values that are
// then loaded with affine or memref scalar load/store (MLIR limitations).
mlir::Value collapseShape(mlir::Value input,
mlir::ArrayRef<mlir::ReassociationIndices> reassociation);
// Create a view of input value (<byte size>xi8) starting at byteOffset and
// shaped by outputType.
mlir::memref::ViewOp view(mlir::Value input, int64_t byteOffset,
mlir::MemRefType outputType, mlir::ValueRange outputDynSymbols) const;
// Create a subview of val.
mlir::memref::SubViewOp subView(mlir::Value val,
mlir::ArrayRef<int64_t> offsets, // Offset for each val dims.
mlir::ArrayRef<int64_t> sizes, // Sizes for each val dims.
mlir::ArrayRef<int64_t> strides) // Stride for each val dims.
const;
// Create a subview of val.
mlir::memref::SubViewOp subView(mlir::MemRefType outputType, mlir::Value val,
mlir::ArrayRef<int64_t> offsets, // Offset for each val dims.
mlir::ArrayRef<int64_t> sizes, // Sizes for each val dims.
mlir::ArrayRef<int64_t> strides) // Stride for each val dims.
const;
// Create a subview of val. Size of 1 => remove that dim.
mlir::memref::SubViewOp subView(mlir::Value val,
mlir::ArrayRef<IndexExpr> offsets, // Offset for each val dims.
mlir::ArrayRef<IndexExpr> sizes, // Sizes for each val dims.
mlir::ArrayRef<IndexExpr> strides) // Stride for each val dims.
const;
mlir::Value dim(mlir::Value val, int64_t index) const;
mlir::Value dim(mlir::Value val, mlir::Value index) const;
void prefetchIE(mlir::Value memref, mlir::ArrayRef<IndexExpr> indices,
bool isWrite, unsigned locality, bool isData = true);
void prefetch(mlir::Value memref, mlir::ValueRange indices, bool isWrite,
unsigned locality, bool isData = true);
// Queries about memory
static bool isNoneValue(mlir::Value value);
// Check if "innerDims" innermost dims are scalar (size 1).
static bool hasOneElementInInnermostDims(mlir::Value value, int64_t innerDim);
private:
mlir::IntegerAttr computeAlignment(int64_t alignment) const;
void computeDynSymbols(
mlir::MemRefType type, // Use type to determine dynamic dimensions.
DimsExprRef dims, // Get dyn syms from index expr.
llvm::SmallVectorImpl<mlir::Value> &dynSymbols) // Output dim symbols.
const;
void computeDynSymbols(
mlir::Value operandOfSameType, // Extract dyn symbols from this value.
mlir::MemRefType type, // Use type to determine dynamic dimensions.
llvm::SmallVectorImpl<mlir::Value> &dynSymbols) // Output dim symbols.
const;
};
//===----------------------------------------------------------------------===//
// Functions definitions for SIMD methods (simdIterate & simdReduce)
//===----------------------------------------------------------------------===//
namespace impl {
// For simdIterate: given a list of inputs, create one output value.
template <class BUILDER>
using SimdIterateBodyFn = std::function<mlir::Value(
const BUILDER &b, mlir::ArrayRef<mlir::Value> inputVals, int64_t VL)>;
// For simdReduce: take one input & one temp reduction value, and generate the
// new reduction value.
template <class BUILDER>
using SimdReductionBodyFn = std::function<mlir::Value(
const BUILDER &b, mlir::Value inputVal, mlir::Value tmpVal, int64_t VL)>;
// For simdReduce: take one temp simd reduction value, create a scalar
// reduction, and possibly apply post processing to it (e.g. div by number of
// elements).
//
// For simdReduce2D: only the post processing. Reduction is done before.
template <class BUILDER>
using SimdPostReductionBodyFn = std::function<mlir::Value(
const BUILDER &b, mlir::Value tmpVal, int64_t VL)>;
// Function used for (nearly) all loops, where there is typically one value in
// the provided ValueRange per loop nest.
template <class BUILDER>
using LoopBodyFn = mlir::function_ref<void(const BUILDER &, mlir::ValueRange)>;
} // namespace impl
//===----------------------------------------------------------------------===//
// Structured Control Flow (SCF) Builder
//===----------------------------------------------------------------------===//
struct SCFBuilder final : DialectBuilder {
SCFBuilder(mlir::Location loc) : DialectBuilder(loc) {}
SCFBuilder(mlir::OpBuilder &b, mlir::Location loc) : DialectBuilder(b, loc) {}
SCFBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~SCFBuilder() {}
/// Create an if then with optional else. Construct does not generate a
/// result (unlike some scf::if) and introduces the yields automatically.
using SCFThenElseBodyFn = mlir::function_ref<void(const SCFBuilder &)>;
void ifThenElse(mlir::Value cond, SCFThenElseBodyFn thenFn,
SCFThenElseBodyFn elseFn = nullptr) const;
// Common loop interface (krnl/affine/scf).
using SCFLoopBodyFn = impl::LoopBodyFn<SCFBuilder>;
void forLoopIE(IndexExpr lb, IndexExpr ub, int64_t step, bool useParallel,
SCFLoopBodyFn bodyFn) const;
void forLoopsIE(mlir::ArrayRef<IndexExpr> lbs, mlir::ArrayRef<IndexExpr> ubs,
mlir::ArrayRef<int64_t> steps, mlir::ArrayRef<bool> useParallel,
SCFLoopBodyFn builderFn) const;
// Custom interface
void forLoop(
mlir::Value lb, mlir::Value ub, int64_t step, SCFLoopBodyFn bodyFn) const;
void parallelLoops(mlir::ValueRange lbs, mlir::ValueRange ubs,
mlir::ValueRange steps, SCFLoopBodyFn bodyFn) const;
void yield() const;
// Common simd loop interface (krnl/affine/scf).
// For detailed description, see KrnlBuilder.hpp file.
using SCFSimdIterateBodyFn = impl::SimdIterateBodyFn<SCFBuilder>;
void simdIterateIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
bool useParallel, mlir::ArrayRef<mlir::Value> inputs,
mlir::ArrayRef<DimsExpr> inputAFs, mlir::ArrayRef<mlir::Value> outputs,
mlir::ArrayRef<DimsExpr> outputAFs,
mlir::ArrayRef<SCFSimdIterateBodyFn> simdIterateBodyList) const;
// For detailed description, see KrnlBuilder.hpp file.
using SCFSimdReductionBodyFn = impl::SimdReductionBodyFn<SCFBuilder>;
using SCFSimdPostReductionBodyFn = impl::SimdPostReductionBodyFn<SCFBuilder>;
void simdReduceIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
mlir::ArrayRef<mlir::Value> inputs, mlir::ArrayRef<DimsExpr> inputAFs,
mlir::ArrayRef<mlir::Value> temps, mlir::ArrayRef<DimsExpr> tempAFs,
mlir::ArrayRef<mlir::Value> outputs, mlir::ArrayRef<DimsExpr> outputAFs,
mlir::ArrayRef<mlir::Value> initVals,
/* reduction function (simd or scalar) */
mlir::ArrayRef<SCFSimdReductionBodyFn> simdReductionBodyFnList,
/* post reduction function (simd to scalar + post processing)*/
mlir::ArrayRef<SCFSimdPostReductionBodyFn> simdPostReductionBodyFnList)
const;
void simdReduce2DIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
mlir::Value input, DimsExpr inputAF, mlir::Value tmp, DimsExpr tmpAF,
mlir::Value output, DimsExpr outputAF, mlir::Value initVal,
/* reduction functions (simd or scalar) */
SCFSimdReductionBodyFn reductionBodyFn,
/* post reduction functions (post processing ONLY)*/
SCFSimdPostReductionBodyFn postReductionBodyFn) const;
};
//===----------------------------------------------------------------------===//
// Vector Builder
//===----------------------------------------------------------------------===//
struct VectorBuilder final : DialectBuilder {
VectorBuilder(mlir::Location loc) : DialectBuilder(loc) {}
VectorBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
VectorBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~VectorBuilder() {}
using F2 = std::function<mlir::Value(mlir::Value const, mlir::Value const)>;
enum CombiningKind { ADD, MUL, MAX, MIN, AND, OR, XOR };
// Check if two types have compatible shapes (assuming that scalar will be
// splatted to the proper vector shape),
static bool compatibleShapes(const mlir::Type t1, const mlir::Type t2);
// Check that the two types have identical elementary types and shapes.
static bool compatibleTypes(const mlir::Type t1, const mlir::Type t2);
// Get the machine SIMD vector length for the given elementary type.
// This can help guide certain optimizations.
int64_t getArchVectorLength(const mlir::Type &elementType) const;
int64_t getArchVectorLength(const mlir::VectorType &vecType) const;
int64_t getArchVectorLength(mlir::Value vecValue) const;
// Vector load: memref is expected to be scalar, will load a vector's worth
// of values: e.g. %result = vector.load %base[%i, %j] :
// memref<100x100xf32>, vector<8xf32>.
// Add offsets (if any) to the least significant memref dims.
mlir::Value load(mlir::VectorType vecType, mlir::Value memref,
mlir::ValueRange indices = {}, mlir::ValueRange offsets = {}) const;
mlir::Value loadIE(mlir::VectorType vecType, mlir::Value memref,
mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
// Vector store: memref can be a scalar, will store the vector values.
// Add offsets (if any) to the least significant memref dims.
void store(mlir::Value val, mlir::Value memref, mlir::ValueRange indices = {},
mlir::ValueRange offsets = {}) const;
void storeIE(mlir::Value val, mlir::Value memref,
mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
// Splat: a single value is copied.
mlir::Value splat(mlir::VectorType vecType, mlir::Value val) const;
// Broadcast: possibly a N dim vector is copied to M>N dim vector.
mlir::Value broadcast(mlir::VectorType vecType, mlir::Value val) const;
// Shuffle: use mask to determine which value to write to the output.
mlir::Value shuffle(mlir::Value lhs, mlir::Value rhs,
llvm::SmallVectorImpl<int64_t> &mask) const;
mlir::Value fma(mlir::Value lhs, mlir::Value rhs, mlir::Value acc) const;
mlir::Value typeCast(mlir::Type resTy, mlir::Value val) const;
// Composite functions.
mlir::Value mergeHigh(mlir::Value lhs, mlir::Value rhs, int64_t step) const;
mlir::Value mergeLow(mlir::Value lhs, mlir::Value rhs, int64_t step) const;
mlir::Value reduction(CombiningKind kind, mlir::Value value) const;
void multiReduction(mlir::ArrayRef<mlir::Value> inputVecArray,
F2 reductionFct, llvm::SmallVectorImpl<mlir::Value> &outputVecArray);
// Cast vectors to vectors of different shape (e.g. 1D to 2D and back).
mlir::Value shapeCast(mlir::VectorType newType, mlir::Value vector) const;
// Extract and insert 1D vector from/to 2D vector.
mlir::Value extractFrom2D(mlir::Value vector2D, int64_t position) const;
mlir::Value insertInto2D(
mlir::Value vector, mlir::Value vector2D, int64_t position) const;
// Insert and extract one element (scalar).
mlir::Value extractElement(mlir::Value vector, int64_t position) const;
mlir::Value insertElement(
mlir::Value vector, mlir::Value element, int64_t position) const;
private:
bool isPowerOf2(uint64_t num) const;
uint64_t getLengthOf1DVector(mlir::Value vec) const;
};
//===----------------------------------------------------------------------===//
// Affine Builder
//===----------------------------------------------------------------------===//
template <class LOAD_OP, class STORE_OP>
struct GenericAffineBuilder final : DialectBuilder {
GenericAffineBuilder(mlir::Location loc) : DialectBuilder(loc) {}
GenericAffineBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
GenericAffineBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~GenericAffineBuilder() {}
// Common load/store interface (krnl/affine/memref)
// Add offsets (if any) to the least significant memref dims.
mlir::Value load(mlir::Value memref, mlir::ValueRange indices = {},
mlir::ValueRange offsets = {}) const;
mlir::Value loadIE(mlir::Value memref, mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
void store(mlir::Value val, mlir::Value memref, mlir::ValueRange indices = {},
mlir::ValueRange offsets = {}) const;
void storeIE(mlir::Value val, mlir::Value memref,
mlir::ArrayRef<IndexExpr> indices = {},
mlir::ValueRange offsets = {}) const;
mlir::Operation *prefetch(mlir::Value memref, mlir::AffineMap map,
mlir::ValueRange indices, bool isWrite, unsigned localityHint,
bool isDataCache = true);
// Common loop interface (krnl/affine/scf).
using GenericAffineLoopBodyFn = impl::LoopBodyFn<GenericAffineBuilder>;
void forLoopIE(IndexExpr lb, IndexExpr ub, int64_t step, bool useParallel,
GenericAffineLoopBodyFn builderFn) const;
void forLoopsIE(mlir::ArrayRef<IndexExpr> lbs, mlir::ArrayRef<IndexExpr> ubs,
mlir::ArrayRef<int64_t> steps, mlir::ArrayRef<bool> useParallel,
GenericAffineLoopBodyFn builderFn) const;
// Custom interface
void forLoopIE(IndexExpr lb, IndexExpr ub, int64_t step,
GenericAffineLoopBodyFn builderFn) const; // Sequential only.
// Common simd loop interface (krnl/affine/scf).
using GenericAffineSimdIterateBodyFn =
impl::SimdIterateBodyFn<GenericAffineBuilder<LOAD_OP, STORE_OP>>;
void simdIterateIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
bool useParallel, mlir::ArrayRef<mlir::Value> inputs,
mlir::ArrayRef<DimsExpr> inputAFs, mlir::ArrayRef<mlir::Value> outputs,
mlir::ArrayRef<DimsExpr> outputAFs,
mlir::ArrayRef<GenericAffineSimdIterateBodyFn> simdIterateBodyList) const;
using GenericAffineSimdReductionBodyFn =
impl::SimdReductionBodyFn<GenericAffineBuilder<LOAD_OP, STORE_OP>>;
using GenericAffineSimdPostReductionBodyFn =
impl::SimdPostReductionBodyFn<GenericAffineBuilder<LOAD_OP, STORE_OP>>;
void simdReduceIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
mlir::ArrayRef<mlir::Value> inputs, mlir::ArrayRef<DimsExpr> inputAFs,
mlir::ArrayRef<mlir::Value> temps, mlir::ArrayRef<DimsExpr> tempAFs,
mlir::ArrayRef<mlir::Value> outputs, mlir::ArrayRef<DimsExpr> outputAFs,
mlir::ArrayRef<mlir::Value> initVals,
/* reduction function (simd or scalar) */
mlir::ArrayRef<GenericAffineSimdReductionBodyFn> simdReductionBodyFnList,
/* post reduction function (simd to scalar + post processing)*/
mlir::ArrayRef<GenericAffineSimdPostReductionBodyFn>
simdPostReductionBodyFnList) const;
void simdReduce2DIE(IndexExpr lb, IndexExpr ub, int64_t VL, bool fullySimd,
mlir::Value input, DimsExpr inputAF, mlir::Value tmp, DimsExpr tmpAF,
mlir::Value output, DimsExpr outputAF, mlir::Value initVal,
/* reduction functions (simd or scalar) */
GenericAffineSimdReductionBodyFn reductionBodyFn,
/* post reduction functions (post processing ONLY)*/
GenericAffineSimdPostReductionBodyFn postReductionBodyFn) const;
// This if then else construct has no arguments to the blocks.
using GenericAffineThenElseBodyFn =
mlir::function_ref<void(const GenericAffineBuilder<LOAD_OP, STORE_OP> &)>;
void ifThenElseIE(IndexExprScope &scope, mlir::ArrayRef<IndexExpr> conditions,
GenericAffineThenElseBodyFn thenFn,
GenericAffineThenElseBodyFn elseFn) const;
// AffineApplyOp
mlir::Value apply(mlir::AffineMap map, mlir::ValueRange operands) const;
void yield() const;
private:
// Support for adding blocks.
void appendToBlock(mlir::Block *block,
mlir::function_ref<void(mlir::ValueRange)> builderFn) const;
};
// Affine builder uses affine load and store for memory operations. A later
// definition of AffineBuilderKrnlMem will use Krnl load and store for memory
// operations. We recommend to use AffineBuilderKrnlMem when converting the
// Krnl dialect into the affine dialect.
using AffineBuilder = GenericAffineBuilder<mlir::affine::AffineLoadOp,
mlir::affine::AffineStoreOp>;
//===----------------------------------------------------------------------===//
// LLVM Builder
//===----------------------------------------------------------------------===//
struct LLVMBuilder final : DialectBuilder {
using voidFuncRef = mlir::function_ref<void(const LLVMBuilder &createLLVM)>;
using valueFuncRef =
mlir::function_ref<mlir::Value(const LLVMBuilder &createLLVM)>;
LLVMBuilder(mlir::Location loc) : DialectBuilder(loc) {}
LLVMBuilder(mlir::OpBuilder &b, mlir::Location loc)
: DialectBuilder(b, loc) {}
LLVMBuilder(const DialectBuilder &db) : DialectBuilder(db) {}
virtual ~LLVMBuilder() {}
// AddOp
mlir::Value add(mlir::Value lhs, mlir::Value rhs) const;
// AddressOfOp
mlir::Value addressOf(mlir::LLVM::GlobalOp op) const;
// AllocaOp
mlir::Value _alloca(mlir::Type resultType, mlir::Type elementType,
mlir::Value size, int64_t alignment) const;
// AndOp
mlir::Value andi(mlir::Value lhs, mlir::Value rhs) const;
// BitcastOp
mlir::Value bitcast(mlir::Type type, mlir::Value val) const;
// BrOp
void br(
mlir::ArrayRef<mlir::Value> destOperands, mlir::Block *destBlock) const;
// CallOp
mlir::Value call(mlir::ArrayRef<mlir::Type> resultTypes,
llvm::StringRef funcName, mlir::ArrayRef<mlir::Value> inputs,
bool isVarArg = false) const;
mlir::Value call(mlir::ArrayRef<mlir::Type> resultTypes,
mlir::FlatSymbolRefAttr funcSymbol, mlir::ArrayRef<mlir::Value> inputs,
bool isVarArg = false) const;
// CondBrOp
void condBr(mlir::Value cond, mlir::Block *trueBlock,
mlir::ArrayRef<mlir::Value> trueOperands, mlir::Block *falseBlock,
mlir::ArrayRef<mlir::Value> falseOperands) const;
// ConstantOp
mlir::Value constant(mlir::Type type, int64_t val) const;
mlir::Value constant(mlir::Type type, double val) const;
// ExtractElementOp
mlir::Value extractElement(
mlir::Type resultType, mlir::Value container, int64_t position) const;
// ExtractValueOp
mlir::Value extractValue(mlir::Type resultType, mlir::Value container,
mlir::ArrayRef<int64_t> position) const;
// FuncOp (assume non-variadic functions, otherwise add support like in
// seen in `call` in this file).
mlir::LLVM::LLVMFuncOp func(llvm::StringRef name, mlir::Type type,
bool createUniqueFunc = false) const;
// GEPOp
mlir::Value getElemPtr(mlir::Type resultType, mlir::Type elemType,
mlir::Value base, mlir::ArrayRef<mlir::LLVM::GEPArg> indices) const;
// GlobalOp
mlir::LLVM::GlobalOp globalOp(mlir::Type resultType, bool isConstant,
mlir::LLVM::Linkage, llvm::StringRef name, mlir::Attribute attr,
uint64_t alignment = 0, bool uniqueName = true) const;
// ICmpOp
mlir::Value icmp(
mlir::LLVM::ICmpPredicate cond, mlir::Value lhs, mlir::Value rhs) const;
// InsertElementOp
mlir::Value insertElement(
mlir::Value vec, mlir::Value val, int64_t position) const;
// InsertValueOp
mlir::Value insertValue(mlir::Type resultType, mlir::Value container,
mlir::Value val, mlir::ArrayRef<int64_t> position) const;
// Inttoptr
mlir::Value inttoptr(mlir::Type type, mlir::Value val) const;
// LShrOp
mlir::Value lshr(mlir::Value lhs, mlir::Value rhs) const;
// LoadOp
mlir::Value load(mlir::Type elementType, mlir::Value addr) const;
// MulOp
mlir::Value mul(mlir::Value lhs, mlir::Value rhs) const;
// NullOp
mlir::Value null(mlir::Type type) const;
// OrOp
mlir::Value ori(mlir::Value lhs, mlir::Value rhs) const;
// Ptrtoint
mlir::Value ptrtoint(mlir::Type type, mlir::Value val) const;
// ReturnOp
void _return() const;
void _return(mlir::Value val) const;
// SelectOp
mlir::Value select(mlir::Value cmp, mlir::Value lhs, mlir::Value rhs) const;
// SExtOp
mlir::Value sext(mlir::Type type, mlir::Value val) const;
// ShlOp
mlir::Value shl(mlir::Value lhs, mlir::Value rhs) const;
// StoreOp
void store(mlir::Value val, mlir::Value addr) const;
// TruncOp
mlir::Value trunc(mlir::Type type, mlir::Value val) const;
// ZExtOp
mlir::Value zext(mlir::Type type, mlir::Value val) const;
//===--------------------------------------------------------------------===//
// Helper functions
//===--------------------------------------------------------------------===//
// Get or insert a function declaration at the beginning of the module.
mlir::FlatSymbolRefAttr getOrInsertSymbolRef(mlir::ModuleOp module,
llvm::StringRef symName, mlir::Type resultType,
mlir::ArrayRef<mlir::Type> operandTypes, bool isVarArg = false) const;
/// Generate code that looks like "if then with optional else" at LLVM.
/// The following prototype code will be generated:
/// ```
/// llvm.condBr cond, ^thenBlock, ^elseBlock
/// ^thenBlock:
/// thenBody
/// ^elseBlock:
/// elseBody
/// ^mainBlock
/// ...
/// ```
void ifThenElse(valueFuncRef cond, voidFuncRef thenFn,
voidFuncRef elseFn = nullptr) const;
/// Postfix a symbol with the value of `onnx-mlir.symbol-postfix` in the
/// module attribute.
static inline std::string SymbolPostfix(
mlir::ModuleOp &module, std::string symbol) {
std::string postfix = "";
if (mlir::StringAttr postfixAttr = module->getAttrOfType<mlir::StringAttr>(
"onnx-mlir.symbol-postfix")) {
if (!postfixAttr.getValue().empty())
postfix = "_" + postfixAttr.getValue().lower();
}
return symbol + postfix;
}
private:
// Support for calling vararg functions; add the necessary type.
void handleVarArgCall(mlir::LLVM::CallOp &callOp,
mlir::ArrayRef<mlir::Type> resultTypes,
mlir::ArrayRef<mlir::Value> inputs) const;
};
//===----------------------------------------------------------------------===//
// Multi Dialect Builder
//===----------------------------------------------------------------------===//
/*
Instead of creating multiple builders, e.g.
KrnlBuilder createKrnl(rewriter, loc);
MathBuilder createMath(createKrnl);
MemRefBuilder createMemRef(createKrnl);
createKrnl.defineLoop(1);
createMath.add(i1, i2);
createMemRef.alloca(type);
We can create a single builder composed of multiple types
MultiDialectBuilder<KrnlBuilder, MathBuilder, MemRefBuilder>
create(rewriter, loc);
create.krnl.defineLoop(1);
create.math.add(i1, i2);
create.mem.alloca(type);
Types that can be used here are
* AffineBuilder, access field with affine
* AffineBuilderKrnlMem, access field with affineKMem
* KrnlBuilder, access field with krnl
* MathBuilder, access field with math
* MemRefBuilder, access field with mem
* ONNXBuilder, access field with onnx
* SCFBuilder, access field with scf
* VectorBuilder, access field with vec
*/
// Anchor class.
template <class... Ts>
struct MultiDialectBuilder {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: builder(&b), location(loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: builder(db.getBuilderPtr()), location(db.getLoc()) {}
// Public getters of builder and location.
mlir::OpBuilder &getBuilder() const {
assert(builder);
return *builder;
}
mlir::OpBuilder *getBuilderPtr() const { return builder; }
mlir::Location getLoc() const { return location; }
private:
mlir::OpBuilder *builder;
mlir::Location location;
};
// Recursive class specialized for MathBuilder refereed to as math.
template <class... Ts>
struct MultiDialectBuilder<MathBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), math(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), math(db) {}
MathBuilder math;
};
// Recursive class specialized for ShapeBuilder refereed to as shape.
template <class... Ts>
struct MultiDialectBuilder<ShapeBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), shape(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), shape(db) {}
ShapeBuilder shape;
};
// Recursive class specialized for MemRefBuilder refereed to as mem.
template <class... Ts>
struct MultiDialectBuilder<MemRefBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), mem(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), mem(db) {}
MemRefBuilder mem;
};
// Recursive class specialized for AffineBuilder refereed to as affine.
template <class... Ts>
struct MultiDialectBuilder<AffineBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), affine(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), affine(db) {}
AffineBuilder affine;
};
// Recursive class specialized for SCFBuilder refereed to as scf.
template <class... Ts>
struct MultiDialectBuilder<SCFBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), scf(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), scf(db) {}
SCFBuilder scf;
};
// Recursive class specialized for VectorBuilder refereed to as vec.
template <class... Ts>
struct MultiDialectBuilder<VectorBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), vec(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), vec(db) {}
VectorBuilder vec;
};
// Recursive class specialized for LLVMBuilder refereed to as llvm.
template <class... Ts>
struct MultiDialectBuilder<LLVMBuilder, Ts...> : MultiDialectBuilder<Ts...> {
MultiDialectBuilder(mlir::OpBuilder &b, mlir::Location loc)
: MultiDialectBuilder<Ts...>(b, loc), llvm(b, loc) {}
MultiDialectBuilder(const DialectBuilder &db)
: MultiDialectBuilder<Ts...>(db), llvm(db) {}
LLVMBuilder llvm;