VectorUtils.h

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//===- VectorUtils.h - Vector Utilities -------------------------*- 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_VECTOR_UTILS_VECTORUTILS_H_
#define MLIR_DIALECT_VECTOR_UTILS_VECTORUTILS_H_
 
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/UB/IR/UBOps.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/Support/LLVM.h"
 
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/TypeSwitch.h"
 
namespace mlir {
 
// Forward declarations.
class AffineMap;
class Block;
class Location;
class OpBuilder;
class Operation;
class ShapedType;
class Value;
class VectorType;
class VectorTransferOpInterface;
 
namespace affine {
class AffineApplyOp;
class AffineForOp;
} // namespace affine
 
namespace vector {
/// Helper function that creates a memref::DimOp or tensor::DimOp depending on
/// the type of `source`.
Value createOrFoldDimOp(OpBuilder &b, Location loc, Value source, int64_t dim);
 
/// Returns two dims that are greater than one if the transposition is applied
/// on a 2D slice. Otherwise, returns a failure.
FailureOr<std::pair<int, int>> isTranspose2DSlice(vector::TransposeOp op);
 
/// Return true if `vectorType` is a contiguous slice of `memrefType`,
/// in the sense that it can be read/written from/to a contiguous area
/// of the memref.
///
/// The leading unit dimensions of the vector type are ignored as they
/// are not relevant to the result. Let N be the number of the vector
/// dimensions after ignoring a leading sequence of unit ones.
///
/// For `vectorType` to be a contiguous slice of `memrefType`
///   a) the N trailing dimensions of `memrefType` must be contiguous, and
///   b) the N-1 trailing dimensions of `vectorType` and `memrefType`
///      must match.
///
/// Examples:
///
///   Ex.1 contiguous slice, perfect match
///     vector<4x3x2xi32> from memref<5x4x3x2xi32>
///   Ex.2 contiguous slice, the leading dim does not match (2 != 4)
///     vector<2x3x2xi32> from memref<5x4x3x2xi32>
///   Ex.3 non-contiguous slice, 2 != 3
///     vector<2x2x2xi32> from memref<5x4x3x2xi32>
///   Ex.4 contiguous slice, leading unit dimension of the vector ignored,
///        2 != 3 (allowed)
///     vector<1x2x2xi32> from memref<5x4x3x2xi32>
///   Ex.5. contiguous slice, leading two unit dims of the vector ignored,
///         2 != 3 (allowed)
///     vector<1x1x2x2xi32> from memref<5x4x3x2xi32>
///   Ex.6. non-contiguous slice, 2 != 3, no leading sequence of unit dims
///     vector<2x1x2x2xi32> from memref<5x4x3x2xi32>)
///   Ex.7 contiguous slice, memref needs to be contiguous only in the last
///        dimension
///     vector<1x1x2xi32> from memref<2x2x2xi32, strided<[8, 4, 1]>>
///   Ex.8 non-contiguous slice, memref needs to be contiguous in the last
///        two dimensions, and it isn't
///     vector<1x2x2xi32> from memref<2x2x2xi32, strided<[8, 4, 1]>>
bool isContiguousSlice(MemRefType memrefType, VectorType vectorType);
 
/// Returns an iterator for all positions in the leading dimensions of `vType`
/// up to the `targetRank`. If any leading dimension before the `targetRank` is
/// scalable (so cannot be unrolled), it will return an iterator for positions
/// up to the first scalable dimension.
///
/// If no leading dimensions can be unrolled an empty optional will be returned.
///
/// Examples:
///
///   For vType = vector<2x3x4> and targetRank = 1
///
///   The resulting iterator will yield:
///     [0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2]
///
///   For vType = vector<3x[4]x5> and targetRank = 0
///
///   The scalable dimension blocks unrolling so the iterator yields only:
///     [0], [1], [2]
///
std::optional<StaticTileOffsetRange>
createUnrollIterator(VectorType vType, int64_t targetRank = 1);
 
/// Returns a functor (int64_t -> Value) which returns a constant vscale
/// multiple.
///
/// Example:
/// ```c++
/// auto createVscaleMultiple = makeVscaleConstantBuilder(rewriter, loc);
/// auto c4Vscale = createVscaleMultiple(4); // 4 * vector.vscale
/// ```
inline auto makeVscaleConstantBuilder(PatternRewriter &rewriter, Location loc) {
  Value vscale = nullptr;
  return [loc, vscale, &rewriter](int64_t multiplier) mutable {
    if (!vscale)
      vscale = vector::VectorScaleOp::create(rewriter, loc);
    return arith::MulIOp::create(
        rewriter, loc, vscale,
        arith::ConstantIndexOp::create(rewriter, loc, multiplier));
  };
}
 
/// Returns a range over the dims (size and scalability) of a VectorType.
inline auto getDims(VectorType vType) {
  return llvm::zip_equal(vType.getShape(), vType.getScalableDims());
}
 
/// A wrapper for getMixedSizes for vector.transfer_read and
/// vector.transfer_write Ops (for source and destination, respectively).
///
/// Tensor and MemRef types implement their own, very similar version of
/// getMixedSizes. This method will call the appropriate version (depending on
/// `hasTensorSemantics`). It will also automatically extract the operand for
/// which to call it on (source for "read" and destination for "write" ops).
SmallVector<OpFoldResult> getMixedSizesXfer(bool hasTensorSemantics,
                                            Operation *xfer,
                                            RewriterBase &rewriter);
 
/// A pattern for ops that implement `MaskableOpInterface` and that _might_ be
/// masked (i.e. inside `vector.mask` Op region). In particular:
///   1. Matches `SourceOp` operation, Op.
///   2.1. If Op is masked, retrieves the masking Op, maskOp, and updates the
///     insertion point to avoid inserting new ops into the `vector.mask` Op
///     region (which only allows one Op).
///   2.2 If Op is not masked, this step is skipped.
///   3. Invokes `matchAndRewriteMaskableOp` on Op and optionally maskOp if
///     found in step 2.1.
///
/// This wrapper frees patterns from re-implementing the logic to update the
/// insertion point when a maskable Op is masked. Such patterns are still
/// responsible for providing an updated ("rewritten") version of:
///   a. the source Op when mask _is not_ present,
///   b. the source Op and the masking Op when mask _is_ present.
/// To use this pattern, implement `matchAndRewriteMaskableOp`. Note that
/// the return value will depend on the case above.
template <class SourceOp>
struct MaskableOpRewritePattern : OpRewritePattern<SourceOp> {
  using OpRewritePattern<SourceOp>::OpRewritePattern;
 
private:
  LogicalResult matchAndRewrite(SourceOp sourceOp,
                                PatternRewriter &rewriter) const final {
    auto maskableOp = dyn_cast<MaskableOpInterface>(sourceOp.getOperation());
    if (!maskableOp)
      return failure();
 
    Operation *rootOp = sourceOp;
 
    // If this Op is masked, update the insertion point to avoid inserting into
    // the vector.mask Op region.
    OpBuilder::InsertionGuard guard(rewriter);
    MaskingOpInterface maskOp;
    if (maskableOp.isMasked()) {
      maskOp = maskableOp.getMaskingOp();
      rewriter.setInsertionPoint(maskOp);
      rootOp = maskOp;
    }
 
    FailureOr<Value> newOp =
        matchAndRewriteMaskableOp(sourceOp, maskOp, rewriter);
    if (failed(newOp))
      return failure();
 
    // Rewriting succeeded but there are no values to replace.
    if (rootOp->getNumResults() == 0) {
      rewriter.eraseOp(rootOp);
    } else {
      assert(*newOp != Value() &&
             "Cannot replace an op's use with an empty value.");
      rewriter.replaceOp(rootOp, *newOp);
    }
    return success();
  }
 
public:
  // Matches `sourceOp` that can potentially be masked with `maskingOp`. If the
  // latter is present, returns a replacement for `maskingOp`. Otherwise,
  // returns a replacement for `sourceOp`.
  virtual FailureOr<Value>
  matchAndRewriteMaskableOp(SourceOp sourceOp, MaskingOpInterface maskingOp,
                            PatternRewriter &rewriter) const = 0;
};
 
/// Returns true if the input Vector type can be linearized.
///
/// Linearization is meant in the sense of flattening vectors, e.g.:
///   * vector<NxMxKxi32> -> vector<N*M*Kxi32>
/// In this sense, Vectors that are either:
///   * already linearized, or
///   * contain more than 1 scalable dimensions,
/// are not linearizable.
bool isLinearizableVector(VectorType type);
 
/// Creates a TransferReadOp from `source`.
///
/// If the shape of vector to read differs from the shape of the value being
/// read, masking is used to avoid out-of-bounds accesses. Set
/// `useInBoundsInsteadOfMasking` to `true` to use the "in_bounds" attribute
/// instead of explicit masks.
///
/// Note: all read offsets are set to 0.
Value createReadOrMaskedRead(OpBuilder &builder, Location loc, Value source,
                             const VectorType &vecToReadTy,
                             std::optional<Value> padValue = std::nullopt,
                             bool useInBoundsInsteadOfMasking = false);
 
Value createReadOrMaskedRead(OpBuilder &builder, Location loc, Value source,
                             ArrayRef<int64_t> inputVectorSizes,
                             std::optional<Value> padValue = std::nullopt,
                             bool useInBoundsInsteadOfMasking = false,
                             ArrayRef<bool> inputScalableVecDims = {});
 
/// Returns success if `inputVectorSizes` is a valid masking configuraion for
/// given `shape`, i.e., it meets:
///   1. The numbers of elements in both array are equal.
///   2. `inputVectorSizes` does not have dynamic dimensions.
///   3. All the values in `inputVectorSizes` are greater than or equal to
///      static sizes in `shape`.
LogicalResult isValidMaskedInputVector(ArrayRef<int64_t> shape,
                                       ArrayRef<int64_t> inputVectorSizes);
 
/// Generic utility for unrolling n-D vector operations to (n-1)-D operations.
/// This handles the common pattern of:
/// 1. Check if already 1-D. If so, return failure.
/// 2. Check for scalable dimensions. If so, return failure.
/// 3. Create poison initialized result.
/// 4. Loop through the outermost dimension, execute the UnrollVectorOpFn to
/// create sub vectors.
/// 5. Insert the sub vectors back into the final vector.
/// 6. Replace the original op with the new result.
using UnrollVectorOpFn =
    function_ref<Value(PatternRewriter &, Location, VectorType, int64_t)>;
 
LogicalResult unrollVectorOp(Operation *op, PatternRewriter &rewriter,
                             UnrollVectorOpFn unrollFn);
 
/// Generic utility for unrolling values of type vector<NxAxBx...>
/// to N values of type vector<AxBx...> using vector.extract. If the input
/// is rank-1 or has leading scalable dimension, failure is returned.
FailureOr<SmallVector<Value>> unrollVectorValue(TypedValue<VectorType>,
                                                RewriterBase &);
 
} // namespace vector
 
/// Constructs a permutation map of invariant memref indices to vector
/// dimension.
///
/// If no index is found to be invariant, 0 is added to the permutation_map and
/// corresponds to a vector broadcast along that dimension.
///
/// The implementation uses the knowledge of the mapping of loops to
/// vector dimension. `loopToVectorDim` carries this information as a map with:
///   - keys representing "vectorized enclosing loops";
///   - values representing the corresponding vector dimension.
/// Note that loopToVectorDim is a whole function map from which only enclosing
/// loop information is extracted.
///
/// Prerequisites: `indices` belong to a vectorizable load or store operation
/// (i.e. at most one invariant index along each AffineForOp of
/// `loopToVectorDim`). `insertPoint` is the insertion point for the vectorized
/// load or store operation.
///
/// Example 1:
/// The following MLIR snippet:
///
/// ```mlir
///    affine.for %i3 = 0 to %0 {
///      affine.for %i4 = 0 to %1 {
///        affine.for %i5 = 0 to %2 {
///          %a5 = load %arg0[%i4, %i5, %i3] : memref<?x?x?xf32>
///    }}}
/// ```
///
/// may vectorize with {permutation_map: (d0, d1, d2) -> (d2, d1)} into:
///
/// ```mlir
///    affine.for %i3 = 0 to %0 step 32 {
///      affine.for %i4 = 0 to %1 {
///        affine.for %i5 = 0 to %2 step 256 {
///          %4 = vector.transfer_read %arg0, %i4, %i5, %i3
///               {permutation_map: (d0, d1, d2) -> (d2, d1)} :
///               (memref<?x?x?xf32>, index, index) -> vector<32x256xf32>
///    }}}
/// ```
///
/// Meaning that vector.transfer_read will be responsible for reading the slice:
/// `%arg0[%i4, %i5:%15+256, %i3:%i3+32]` into vector<32x256xf32>.
///
/// Example 2:
/// The following MLIR snippet:
///
/// ```mlir
///    %cst0 = arith.constant 0 : index
///    affine.for %i0 = 0 to %0 {
///      %a0 = load %arg0[%cst0, %cst0] : memref<?x?xf32>
///    }
/// ```
///
/// may vectorize with {permutation_map: (d0) -> (0)} into:
///
/// ```mlir
///    affine.for %i0 = 0 to %0 step 128 {
///      %3 = vector.transfer_read %arg0, %c0_0, %c0_0
///           {permutation_map: (d0, d1) -> (0)} :
///           (memref<?x?xf32>, index, index) -> vector<128xf32>
///    }
/// ````
///
/// Meaning that vector.transfer_read will be responsible of reading the slice
/// `%arg0[%c0, %c0]` into vector<128xf32> which needs a 1-D vector broadcast.
///
AffineMap
makePermutationMap(Block *insertPoint, ArrayRef<Value> indices,
                   const DenseMap<Operation *, unsigned> &loopToVectorDim);
AffineMap
makePermutationMap(Operation *insertPoint, ArrayRef<Value> indices,
                   const DenseMap<Operation *, unsigned> &loopToVectorDim);
 
namespace matcher {
 
/// Matches vector.transfer_read, vector.transfer_write and ops that return a
/// vector type that is a multiple of the sub-vector type. This allows passing
/// over other smaller vector types in the function and avoids interfering with
/// operations on those.
/// This is a first approximation, it can easily be extended in the future.
/// TODO: this could all be much simpler if we added a bit that a vector type to
/// mark that a vector is a strict super-vector but it still does not warrant
/// adding even 1 extra bit in the IR for now.
bool operatesOnSuperVectorsOf(Operation &op, VectorType subVectorType);
 
} // namespace matcher
} // namespace mlir
 
#endif // MLIR_DIALECT_VECTOR_UTILS_VECTORUTILS_H_