//===- Utils.h - Utilities to support the Linalg dialect --------*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#ifndef MLIR_DIALECT_LINALG_UTILS_UTILS_H
#define MLIR_DIALECT_LINALG_UTILS_UTILS_H

#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "llvm/ADT/StringSet.h"
#include <optional>

namespace mlir {
class AffineExpr;
class AffineMap;
class PatternRewriter;

namespace affine {
class AffineForOp;
} // namespace affine

namespace tensor {
class ExtractSliceOp;
} // namespace tensor

namespace linalg {

//===----------------------------------------------------------------------===//
// Utilities for inferring various semantics properties of Linalg ops.
//===----------------------------------------------------------------------===//

/// Compute inverse permutation for the destination tensor (i.e. in the packed
/// domain).
SmallVector<int64_t> getPackInverseDestPerm(linalg::PackOp packOp,
                                            PackingMetadata &metadata);

/// Compute inverse permutation for the source tensor (i.e. in the packed
/// domain).
SmallVector<int64_t> getUnPackInverseSrcPerm(linalg::UnPackOp,
                                             PackingMetadata &metadata);

//===----------------------------------------------------------------------===//
// General utilities
//===----------------------------------------------------------------------===//

/// Check if all indexing maps are projected permutations.
bool allIndexingsAreProjectedPermutation(LinalgOp op);

/// Detect whether `r` has only ConstantOp, ElementwiseMappable and YieldOp.
bool hasOnlyScalarElementwiseOp(Region &r);

/// Check if a LinalgOp is an element-wise operation.
bool isElementwise(LinalgOp op);

/// Check if iterator type has "parallel" semantics.
bool isParallelIterator(utils::IteratorType iteratorType);

/// Check if iterator type  has "reduction" semantics.
bool isReductionIterator(utils::IteratorType iteratorType);

/// Create a tensor::PadOp that pads `source` to the shape of `type` whose sizes
/// are assumed to be greater than the dynamic `source` size. If `typeDynDims`
/// is specified, then it must contain the sizes of all the dynamic dimensions
/// in order of appearance in `type`, otherwise the function will pad those
/// values to `0`. The padding introduces trailing `pad` values until the target
/// size is met. If `source` is defined by one or more LinalgOps that have been
/// padded with the same  value and sizes, return their padded result instead of
/// creating a tensor::PadOp.
///
/// Example:
/// ```
/// %0 = tensor.extract_slice %arg0 [%iv0, %iv1] [%sz0, %sz1]
/// %1 = tensor.pad %0 low[0, 0] high[...] { tensor.yield %cst }
/// %2 = linalg.matmul ins(...) outs(%1)
/// %3 = tensor.extract_slice %2 [0, 0] [%sz0, %sz1]
/// ```
/// makeComposedPadHighOp(source=%3, pad=%cst) returns %2
/// makeComposedPadHighOp(source=%3, pad=%other_cst) returns %4
/// ```
/// %4 = tensor.pad %3 low[0, 0] high[...] { tensor.yield %other_cst }
/// ```
Value makeComposedPadHighOp(OpBuilder &b, Location loc, RankedTensorType type,
                            Value source, Value padding, bool nofold,
                            ValueRange typeDynDims = {});

/// Returns GenericOp that copies an n-D memref. Unlike the current
/// implementation of memref::CopyOp, this op can further tile, lower to loops
/// or vectorize.
GenericOp makeMemRefCopyOp(OpBuilder &b, Location loc, Value from, Value to);

/// Get the reassociation maps to fold the result of a extract_slice (or
/// source of a insert_slice) operation with given offsets, and sizes to its
/// rank-reduced version. This is only done for the cases where the size is 1
/// and offset is 0. Strictly speaking the offset 0 is not required in
/// general, but non-zero offsets are not handled by SPIR-V backend at this
/// point (and potentially cannot be handled).
std::optional<SmallVector<ReassociationIndices>>
getReassociationMapForFoldingUnitDims(ArrayRef<OpFoldResult> mixedSizes);

//===----------------------------------------------------------------------===//
// Convolution matcher utility
//===----------------------------------------------------------------------===//

/// A struct containing dilations and strides inferred from convolution ops.
struct DilationsAndStrides {
  SmallVector<int64_t> dilations;
  SmallVector<int64_t> strides;
};

/// Given a linalg `op` this function returns DilationsAndStrides if it is a
/// convolution op of type `ConvOpTy`, otherwise returns std::nullopt. The
/// dilations and strides are inferred from the indexing maps. For ops like
/// Conv1DOp, Conv2DOp and Conv3DOp that have no strides/dilations attributes,
/// defaults of [1, ...] are returned for both.
template <typename ConvOpTy>
std::optional<DilationsAndStrides> matchConvolutionOpOfType(LinalgOp op);

/// Returns true if the linalg `op` is a convolution op of type `ConvOpTy`.
/// This is a convenience wrapper around matchConvolutionOpOfType.
template <typename ConvOpTy>
bool isaConvolutionOpOfType(LinalgOp op) {
  return matchConvolutionOpOfType<ConvOpTy>(op).has_value();
}

//===----------------------------------------------------------------------===//
// Fusion / Tiling utilities
//===----------------------------------------------------------------------===//

/// The type of loops to be generated during tiling.
enum class LinalgTilingLoopType {
  Loops = 0,
  AffineLoops = 1,
  ParallelLoops = 2
};

/// Computes tile offsets, given a list of loop `ivs` and `tileSizes`. In case
/// a tile size is zero (i.e., no tiling), the corresponding offset is also
/// zero.
SmallVector<OpFoldResult> computeTileOffsets(OpBuilder &b, Location loc,
                                             ArrayRef<OpFoldResult> ivs,
                                             ArrayRef<OpFoldResult> tileSizes);

/// Computes tile sizes, given a list of `tileSizes` and dimension
/// sizes (`sizeBounds`). In case a tile size is zero (i.e., no tiling), the
/// corresponding result size is the corresponding value from `sizeBounds`.
/// Note: The returned tile sizes are closed intervals.
SmallVector<OpFoldResult> computeTileSizes(OpBuilder &b, Location loc,
                                           ArrayRef<OpFoldResult> tileSizes,
                                           ArrayRef<OpFoldResult> sizeBounds);

/// Returns the list of tensor output types produced when the given structured
/// operation `op` is applied to the given `operands`. Note that `operands`
/// are not necessarily the actual operands of `op`.
SmallVector<Type> getTensorOutputTypes(LinalgOp op, ValueRange operands);

/// Creates `insert_slice` ops that insert `results` back into larger tensors
/// they were originally extracted from with `extract_slice` before being
/// passed as `operands` to the given structured operation `op` or its clone.
/// Note that `operands` are not necessarily the actual operands of `op`, the
/// operation serves only as metadata container for operand types and
/// positions.
SmallVector<Value> insertSlicesBack(OpBuilder &builder, Location loc,
                                    LinalgOp op, ValueRange operands,
                                    ValueRange results);

/// A struct containg offsets-sizes-strides arguments of the tiled shape.
struct SliceParameters {
  SmallVector<OpFoldResult> offsets;
  SmallVector<OpFoldResult> sizes;
  SmallVector<OpFoldResult> strides;
};

/// Computes SliceParameters for a single `valueToTile` assuming that its user
/// is being tiled with the given loop bounds `lbs` and `ubs` and the tile
/// sizes `tileSizes`.
///
/// `omitPartialTileCheck` controls whether to omit the partial/boundary tile
/// condition check in cases where we statically know that it is unnecessary.
SliceParameters
computeSliceParameters(OpBuilder &builder, Location loc, Value valueToTile,
                       ArrayRef<OpFoldResult> tileSizes, AffineMap map,
                       ArrayRef<OpFoldResult> lbs, ArrayRef<OpFoldResult> ubs,
                       ArrayRef<OpFoldResult> subShapeSizes,
                       bool omitPartialTileCheck);

/// Computes SliceParamaters for all `valuesToTile` of the given `linalgOp`,
/// assuming `linalgOp` is being fused into a loop nest. Calls
/// `computeSliceParameters` for every individual value.
///
/// Note that a constant zero in `tileSizes` means no tiling at that implicit
/// loop. The number of non-zero values in `tileSizes` should be equal to the
/// number of values in `ivs`.
///
/// Some of the `valuesToTile` won't be affected by tiling. For these values,
/// std::nullopt will be returned.
SmallVector<std::optional<SliceParameters>>
computeAllSliceParameters(OpBuilder &builder, Location loc, LinalgOp linalgOp,
                          ValueRange valuesToTile, ArrayRef<OpFoldResult> ivs,
                          ArrayRef<OpFoldResult> tileSizes,
                          ArrayRef<OpFoldResult> sizeBounds,
                          bool omitPartialTileCheck);

/// Creates an extract_slice/subview op for a single `valueToTile` with
/// `builder`. This new operation extracts a tile of `valueToTile`, starting
/// at offsets `lbs` and with sizes `subShapeSizes`. `omitPartialTileCheck`
/// controls whether to omit the partial/boundary tile condition check in
/// cases where we statically know that it is unnecessary.
Operation *makeTiledShape(OpBuilder &builder, Location loc, Value valueToTile,
                          ArrayRef<OpFoldResult> tileSizes, AffineMap map,
                          ArrayRef<OpFoldResult> lbs,
                          ArrayRef<OpFoldResult> ubs,
                          ArrayRef<OpFoldResult> subShapeSizes,
                          bool omitPartialTileCheck);

/// Creates extract_slice/subview ops for all `valuesToTile` of the given
/// `linalgOp` with `builder`, assuming `linalgOp` is being fused into a loop
/// nest for tiling with the given induction variables `ivs` and tile sizes
/// `tileSizes`. `sizeBounds` are the iteration space bounds for *all* the
/// implicit loops in `linalgOp`. `omitPartialTileCheck` controls whether to
/// omit the partial/boundary tile condition check in cases where we
/// statically know that it is unnecessary.
///
/// Note that a constant zero in `tileSizes` means no tiling at that implicit
/// loop. The number of non-zero values in `tileSizes` should be equal to the
/// number of values in `ivs`.
SmallVector<Value> makeTiledShapes(OpBuilder &builder, Location loc,
                                   LinalgOp linalgOp, ValueRange valuesToTile,
                                   ArrayRef<OpFoldResult> ivs,
                                   ArrayRef<OpFoldResult> tileSizes,
                                   ArrayRef<OpFoldResult> sizeBounds,
                                   bool omitPartialTileCheck);

/// Add the specified offsets to any `linalg.index` ops contained in the given
/// `linalgOp`. The offsets are provided in the same order as iteration space
/// dimensions. Null offests are assumed to be zero.
void offsetIndices(OpBuilder &b, LinalgOp linalgOp,
                   ArrayRef<OpFoldResult> offests);
void offsetIndices(RewriterBase &b, LinalgOp linalgOp,
                   ArrayRef<OpFoldResult> offests);

/// A struct containing the Linalg producer before and after fusion.
/// When operating on tensors, `fusedProducer` may feed into a `tensor.cast`
/// op before the consumer Linalg op, until enough canonicalizations have
/// applied.
struct FusionInfo {
  LinalgOp originalProducer;
  LinalgOp fusedProducer;
};

/// This implements the fusion part of the "tileAndFuse on tensors"
/// transformation and thus requires the `consumerOpOperand` to be a
/// `extract_slice` op (generally obtained by applying the tiling
/// transformation).
FailureOr<FusionInfo> fuseProducerOfTensor(OpBuilder &b,
                                           OpOperand &consumerOpOperand);

/// This implements the fusion part of the "tileAndFuse on tensors"
/// transformation and thus requires the `consumerOpOperand` to be a
/// `extract_slice` op (generally obtained by applying the tiling
/// transformation). Assumes `producerOfTensor` is a Linalg op that produces
/// `consumerOpOperand`.
FailureOr<FusionInfo> fuseProducerOfTensor(OpBuilder &b,
                                           OpResult producerOpResult,
                                           OpOperand &consumerOpOperand);

//===----------------------------------------------------------------------===//
// Distribution utilities
//===----------------------------------------------------------------------===//

/// Scheme used to distribute loops to processors.
enum class DistributionMethod {
  /// Cyclic distribution where no assumption is made about the dynamic
  /// relationship between number of processors and number of iterations of
  /// the
  /// distributed loop. Distributes the following loop
  ///
  /// scf.parallel (%iv) = (%lb) to (%ub) step (%step)
  ///
  /// to
  ///
  /// scf.parallel(%iv)= (%lb + %procId * %step) to (%ub) step (%step *
  /// %nprocs)
  Cyclic = 0,

  /// Cyclic distribution where the number of processors can be assumed to be
  /// more than or equal to the number of iterations of the distributed loop.
  /// In
  /// such cases, a simple in-bounds check is enough (instead of materializing
  /// a
  /// loop). Distributes the following loop
  ///
  /// scf.parallel (%iv) = (%lb) to (%ub) step (%step)
  ///
  /// to
  ///
  /// %iv = %lb + %procId * %step
  /// %cond = arith.cmpi "slt", %iv, %ub
  /// scf.if %cond {
  ///   ...
  /// }
  CyclicNumProcsGeNumIters = 1,

  /// Cyclic distribution where the number of processors can be assumed to be
  ///  equal to the number of iterations of the distributed loop. In such
  ///  cases,
  ///  no bounds check is needed. Distributes the following loop
  ///
  /// scf.parallel (%iv) = (%lb) to (%ub) step (%step)
  ///
  /// to
  ///
  /// %iv = %lb + %procId * %step
  CyclicNumProcsEqNumIters = 2,

  /// No Distribution.
  None = 3
};

/// Callback function type used to get processor ID, and number of processors
/// used for distribution for all parallel loops generated.
struct ProcInfo {
  Value procId;
  Value nprocs;
  DistributionMethod distributionMethod;
};
using ProcInfoCallBackFn = std::function<SmallVector<ProcInfo>(
    OpBuilder &b, Location loc, ArrayRef<Range> parallelLoopRanges)>;

/// Options that allow distribution of loops generated in Linalg transforms to
/// processors while generating the loops.
struct LinalgLoopDistributionOptions {
  /// Callback function that returns the Values for processor ID (`procId`),
  /// and number of processors (`nprocs`) used to execute the parallel loops.
  /// The number of `{procId, nprocs}` pairs returned must be equal to the
  /// number of `parallelLoopRanges` passed into the callback. The
  /// `parallelLoopRanges` are ranges of the outer parallel loops of the
  /// operation that do have non-zero tile sizes specified.
  ProcInfoCallBackFn procInfo;
};

/// Update the `lb`, `ub` and `step` to get per processor `lb`, `ub` and
/// `step`.
void updateBoundsForCyclicDistribution(OpBuilder &builder, Location loc,
                                       Value procId, Value nprocs, Value &lb,
                                       Value &ub, Value &step);

//===----------------------------------------------------------------------===//
// Fusion on tensor utilities
//===----------------------------------------------------------------------===//

//===----------------------------------------------------------------------===//
// Generic op region utilities
//===----------------------------------------------------------------------===//

/// A struct containing common matchers over linalg op's region.
struct RegionMatcher {
  enum class BinaryOpKind {
    IAdd,
  };

  /// Matches the given linalg op if its body is performing binary operation
  /// on int or float scalar values and returns the binary op kind.
  ///
  /// The linalg op's region is expected to be
  /// ```
  /// {
  ///   ^bb(%a: <scalar-type>, %b: <scalar-type>):
  ///     %0 = <binary-op> %a, %b: <scalar-type>
  ///     linalg.yield %0: <scalar-type>
  /// }
  /// ```
  static std::optional<BinaryOpKind> matchAsScalarBinaryOp(GenericOp op);
};

//===----------------------------------------------------------------------===//
// Loop nest utilities
//===----------------------------------------------------------------------===//

/// Utility class used to generate nested loops with ranges described by
/// `loopRanges` and loop type described by the `iteratorTypes`.
/// `bodyBuilderFn` is used to generate the body of the innermost loop. It is
/// passed a range of loop induction variables and a range of operand values
/// to use.
template <typename LoopTy>
struct GenerateLoopNest {
  static void doit(OpBuilder &b, Location loc, ArrayRef<Range> loopRanges,
                   LinalgOp linalgOp,
                   ArrayRef<utils::IteratorType> iteratorTypes,
                   function_ref<scf::ValueVector(OpBuilder &, Location,
                                                 ValueRange, ValueRange)>
                       bodyBuilderFn,
                   ArrayRef<linalg::ProcInfo> procInfo = {});
};

/// Returns an attribute list that excludes pre-defined attributes.
template <typename OpTy>
SmallVector<NamedAttribute> getPrunedAttributeList(OpTy op) {
  auto elidedAttrs = llvm::to_vector(op.getAttributeNames());
  if (isa<linalg::LinalgOp>(op.getOperation()))
    elidedAttrs.push_back(LinalgDialect::kMemoizedIndexingMapsAttrName);
  return getPrunedAttributeList(op, elidedAttrs);
}

} // namespace linalg
} // namespace mlir

#endif // MLIR_DIALECT_LINALG_UTILS_UTILS_H