Specialize.cpp

Go to the documentation of this file.

//===- Specialize.cpp - linalg generic ops to named ops  ------------------===//
//
// 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 file implements a method to specialize generic operations to named
// operations. Conceptually it is the opposite of generalize.cpp.
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Linalg/IR/LinalgInterfaces.h"
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 
namespace mlir {
#define GEN_PASS_DEF_LINALGSPECIALIZEGENERICOPSPASS
#include "mlir/Dialect/Linalg/Passes.h.inc"
} // namespace mlir
 
#define DEBUG_TYPE "linalg-specialization"
 
#define REPLACE_BINARY_OP(NEWOP, OPERANDS_SWAP)                                \
  (rewriter.replaceOpWithNewOp<NEWOP>(                                         \
      genericOp,                                                               \
      ValueRange{genericOp.getDpsInputs()[(OPERANDS_SWAP) ? 1 : 0],            \
                 genericOp.getDpsInputs()[(OPERANDS_SWAP) ? 0 : 1]},           \
      ValueRange{genericOp.getDpsInits()[0]}))
 
#define REPLACE_UNARY_OP(NEWOP)                                                \
  (rewriter.replaceOpWithNewOp<NEWOP>(genericOp,                               \
                                      ValueRange{genericOp.getDpsInputs()[0]}, \
                                      ValueRange{genericOp.getDpsInits()[0]}))
 
using namespace mlir;
using namespace mlir::linalg;
 
// Given a elementwise single binary linalg generic op, checks whether the
// binary op accesses operands as swapped. e.g.
// this differentiates between a linalg-generic body that contains:
//    ^bb0(%a: f32, %b: f32, %c : f32):
//         %0 = arith.subf %a, %b : f32
//         linalg.yield %0: f32
// against:
//    ^bb0(%a: f32, %b: f32, %c : f32):
//         %0 = arith.subf %b, %a : f32
//         linalg.yield %0: f32
// Former is linalg.sub(a,b), latter is linalg.sub(b,a).
static bool areBinOpsSwapped(GenericOp genericOp) {
  Block *body = genericOp.getBody();
  Operation *op = &body->front();
  bool swapped = false;
  if (op->getOpOperand(0).get() != body->getArgument(0)) {
    swapped = true;
    assert(op->getOpOperand(0).get() == body->getArgument(1) &&
           op->getOpOperand(1).get() == body->getArgument(0) &&
           "binary op uses just one block arg");
  }
  return swapped;
}
 
//===----------------------------------------------------------------------===//
// Specialize linalg generic to matmul variants.
//===----------------------------------------------------------------------===//
/// Identifies linalg.generic that is essentially named op of the form:
//    ` linalg.{batch_}?matmul{_transpose_a | _transpose_b}? `
//
// It is possible that a linalg.generic may be implementing a matmul but not
// in a straight-forward way e.g. below is matrix multiply over some slice
// ```
//  %0 = linalg.generic {
//          indexing_maps = [affine_map<(d0, d1, d2) -> (3, d1, d0)>,
//                           affine_map<(d0, d1, d2) -> (d0, 5, d2)>,
//                           affine_map<(d0, d1, d2) -> (d2, d1, 13)>],
//          iterator_types = ["parallel", "parallel", "parallel"]}
//          ins(%A, %B : tensor<20x20x20xf32>,  tensor<20x20x20xf32>)
//          outs(%C : tensor<20x20x20xf32>) {
//             ^bb0(%a: f32, %b: f32, %c : f32):
//                %mul = arith.mulf %a, %b : f32
//                %add = arith.addf %mul, %c : f32
//                linalg.yield %add : f32
//       } -> tensor<20x20x20xf32>
// ```
// It is not possible to represent above as named op.
// e.g. linalg.batch_matmul(%A, %B :  tensor<20x20x20xf32>, ...) is
// not  the same as linalg.generic above.
namespace {
enum class IndexMatchResult {
  Match = 0,  // identity map.
  Transposed, // transposed map.
  Mismatch    // none of the above.
};
 
// Checks whether the input Affine `map` contains two consecutive dims that
// can be interpreted as accessing a 2D matrix. It is assumed that the row
// column dimension are adjacent axis (in this order) and start at
// `rowDimIdx` in the input map.
//
//  e.g. consider A matrix in `C[M,N] = A[M,K] * B[K,N]`. We will check
//  whether the map of A is identity (match), transposed, or something
//  completely different (mis-match). Similar for B and C.
static IndexMatchResult matchOperandMap(AffineMap map, unsigned rowDimIdx,
                                        unsigned expectedPosOfRowDim,
                                        unsigned expectedPosOfColDim) {
  // Get the matrix multiply indices. They are past the batch indices.
  auto exprOfRowDim = map.getResults()[rowDimIdx];
  auto exprOfColDim = map.getResults()[rowDimIdx + 1];
 
  // They should be pure dimension ids.
  if (exprOfRowDim.getKind() != AffineExprKind::DimId ||
      exprOfColDim.getKind() != AffineExprKind::DimId)
    return IndexMatchResult::Mismatch;
 
  auto posRowDim = cast<AffineDimExpr>(exprOfRowDim).getPosition();
  auto posColDim = cast<AffineDimExpr>(exprOfColDim).getPosition();
 
  if (expectedPosOfRowDim == posRowDim && expectedPosOfColDim == posColDim)
    return IndexMatchResult::Match;
 
  if (expectedPosOfRowDim == posColDim && expectedPosOfColDim == posRowDim)
    return IndexMatchResult::Transposed;
 
  return IndexMatchResult::Mismatch;
}
 
// Replaces genericOp with `NamedOpTy` op, supplied as a template arg.
//  All the variants expressed as pseudo regular expression:
//      `linalg.{batch_}?matmul{_transpose_a | _transpose_b}?`
//  have same number of ins/out, so its easy to stamp different versions.
template <typename NamedOpTy>
static LinalgOp replaceWithMatmulVariant(RewriterBase &rewriter, GenericOp op) {
  LinalgOp namedOp = rewriter.replaceOpWithNewOp<NamedOpTy>(
      op, ValueRange{op.getDpsInputs()[0], op.getDpsInputs()[1]},
      ValueRange{op.getDpsInits()[0]});
  return namedOp;
}
 
// Converts linalg.generic to named linalg.*matmul* where possible.
static FailureOr<LinalgOp> specializeLinalgContractions(RewriterBase &rewriter,
                                                        GenericOp genericOp) {
  if (genericOp.getNumDpsInputs() != 2 || genericOp.getNumDpsInits() != 1)
    return failure();
 
  // Early exit if not projected permutations.
  auto mapRange = genericOp.getIndexingMapsArray();
  if (llvm::any_of(mapRange,
                   [](AffineMap m) { return !m.isProjectedPermutation(); }))
    return failure();
 
  // Linalg generic contraction can be across multiple axis e.g.
  // ```
  //      linalg.generic
  //           {indexing_maps = [affine_map<(m, n, k1, k2) -> (m, k1, k2)>,
  //                             affine_map<(m, n, k1, k2) -> (k2, k1, n)>,
  //                             affine_map<(m, n, k1, k2) -> (m, n)>],
  //           iterator_types = ["parallel", "parallel",
  //                             "reduction", "reduction"]}
  //           ins(%A, %B : tensor<10x20x30xf32>, tensor<30x20x40xf32>)
  //           outs(%C : tensor<10x40xf32>) {
  //           ^bb0(%a: f32, %b: f32, %c: f32):
  //                 %1 = arith.mulf %a, %b : f32
  //                 %2 = arith.addf %c, %1 : f32
  //                 linalg.yield %2 : f32
  //      } -> tensor<10x40xf32>
  //  ```
  //  In above contraction, there are two reduction dimensions {k1, k2}
  //  and although a valid linalg contraction, it is not a named-op
  //  matrix multiply kind. Therefore, reject multi-dim reduction.
  auto res = inferContractionDims(genericOp);
  if (!succeeded(res))
    return failure();
  auto dims = *res;
  if (dims.m.size() != 1 || dims.n.size() != 1 || dims.k.size() != 1)
    return failure();
 
  if (!mlir::linalg::detail::isContractionBody(
          *genericOp.getBlock(), [](Operation *first, Operation *second) {
            return (isa<arith::MulFOp>(first) && isa<arith::AddFOp>(second)) ||
                   (isa<arith::MulIOp>(first) && isa<arith::AddIOp>(second)) ||
                   (isa<complex::MulOp>(first) && isa<complex::AddOp>(second));
          }))
    return failure();
 
  // Check rank of operands
  auto indexingMaps = genericOp.getIndexingMapsArray();
  if (llvm::any_of(indexingMaps, [&dims](AffineMap m) {
        return m.getResults().size() !=
               dims.batch.size() + 2 /* any two of {m,n,k} */;
      }))
    return failure();
 
  auto numOfBatchDims = dims.batch.size();
  if (indexingMaps[0].getNumDims() != numOfBatchDims + 3)
    return failure();
 
  if (numOfBatchDims) {
    // Each operand in a linalg generic contraction  could express different
    // permutations for its batch dimension. But for named op it must be
    // identity since separate maps are not specified.
    if (llvm::any_of(indexingMaps, [numOfBatchDims](AffineMap m) {
          for (unsigned i = 0; i < numOfBatchDims; ++i) {
            auto expr = m.getResults()[i];
            if (expr.getKind() != AffineExprKind::DimId ||
                cast<AffineDimExpr>(expr).getPosition() != i)
              return true;
          }
          return false;
        }))
      return failure();
  }
 
  auto a =
      matchOperandMap(indexingMaps[0], numOfBatchDims, dims.m[0], dims.k[0]);
  auto b =
      matchOperandMap(indexingMaps[1], numOfBatchDims, dims.k[0], dims.n[0]);
  auto c =
      matchOperandMap(indexingMaps[2], numOfBatchDims, dims.m[0], dims.n[0]);
 
  if (llvm::is_contained({a, b, c}, IndexMatchResult::Mismatch))
    return failure();
 
  if (c != IndexMatchResult::Match ||
      (a == IndexMatchResult::Transposed && b == IndexMatchResult::Transposed))
    return failure();
 
  /// Codegen the different matmul variants.
  if (numOfBatchDims) {
    return replaceWithMatmulVariant<BatchMatmulOp>(rewriter, genericOp);
  }
  return replaceWithMatmulVariant<MatmulOp>(rewriter, genericOp);
}
 
/// Utility to specialize a `genericOp` with a convolution op of type `ConvOpTy`
/// with `dilations` and `strides`.
template <typename ConvOpTy>
static FailureOr<LinalgOp>
specializeToConvOp(RewriterBase &rewriter, GenericOp genericOp,
                   ArrayRef<int64_t> dilations, ArrayRef<int64_t> strides) {
  SmallVector<Value> inputs = genericOp.getDpsInputs();
  ValueRange outputs = genericOp.getDpsInits();
  SmallVector<Type> resultTypes = genericOp.hasPureTensorSemantics()
                                      ? TypeRange(ValueRange(outputs))
                                      : TypeRange{};
  LinalgOp namedOp;
  // Ops with no dilations and no strides.
  if constexpr (std::is_same_v<ConvOpTy, linalg::Conv1DOp> ||
                std::is_same_v<ConvOpTy, linalg::Conv2DOp> ||
                std::is_same_v<ConvOpTy, linalg::Conv3DOp>) {
    namedOp = rewriter.replaceOpWithNewOp<ConvOpTy>(genericOp, resultTypes,
                                                    inputs, outputs);
  } else {
    Attribute stridesAttr = rewriter.getI64TensorAttr(strides);
    Attribute dilationsAttr = rewriter.getI64TensorAttr(dilations);
    namedOp = rewriter.replaceOpWithNewOp<ConvOpTy>(
        genericOp, resultTypes, inputs, outputs, stridesAttr, dilationsAttr);
  }
  return namedOp;
}
 
/// Converts linalg.generic to named linalg.*conv/pooling* where possible.
static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
                                                        GenericOp genericOp) {
  SmallVector<int64_t> dilations, strides;
#define CONV_OP_SPECIALIZER(ConvOpTy)                                          \
  if (isaConvolutionOpOfType<ConvOpTy>(genericOp, &dilations, &strides))       \
    return specializeToConvOp<ConvOpTy>(rewriter, genericOp, dilations,        \
                                        strides);                              \
  // -----------------------------
  // Convolution ops.
  // -----------------------------
  CONV_OP_SPECIALIZER(linalg::Conv1DOp);
  CONV_OP_SPECIALIZER(linalg::Conv1DNwcWcfOp);
  CONV_OP_SPECIALIZER(linalg::Conv1DNcwFcwOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwcHwcfOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwcHwcfQOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwcFhwcOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwcFhwcQOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNchwFchwOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNchwFchwQOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNgchwFgchwOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNgchwGfchwOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNgchwGfchwQOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcOp);
  CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcQOp);
  CONV_OP_SPECIALIZER(linalg::Conv3DOp);
  CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfOp);
  CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfQOp);
  CONV_OP_SPECIALIZER(linalg::Conv3DNcdhwFcdhwOp);
  // -----------------------------
  // Depthwise Convolution ops.
  // -----------------------------
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv1DNcwCwOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv1DNwcWcOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv1DNwcWcmOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNchwChwOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcQOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmQOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNcdhwCdhwOp);
  CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcmOp);
  // -----------------------------
  // Pooling ops.
  // -----------------------------
  CONV_OP_SPECIALIZER(linalg::PoolingNhwcMaxOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNhwcMinOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNhwcSumOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNhwcMaxUnsignedOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNhwcMinUnsignedOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNchwSumOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNchwMaxOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNwcSumOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNcwSumOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNwcMaxOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNwcMaxUnsignedOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNcwMaxOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNwcMinOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNwcMinUnsignedOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNdhwcSumOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNdhwcMaxOp);
  CONV_OP_SPECIALIZER(linalg::PoolingNdhwcMinOp);
#undef CONV_OP_SPECIALIZER
  return failure();
}
 
} // namespace
 
//===----------------------------------------------------------------------===//
// Categorize linalg generic to named op where possible.
//===----------------------------------------------------------------------===//
FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
                                                      GenericOp genericOp) {
  // Copy
  if (isaCopyOpInterface(genericOp)) {
    LinalgOp namedOp = rewriter.replaceOpWithNewOp<CopyOp>(
        genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0]);
    return namedOp;
  }
 
  // Fill
  if (std::optional<Value> fillValue = isaFillOpInterface(genericOp)) {
    // Always use the detected fill value, regardless of pattern
    LinalgOp namedOp = rewriter.replaceOpWithNewOp<FillOp>(
        genericOp, *fillValue, genericOp.getDpsInits()[0]);
    return namedOp;
  }
 
  // Broadcast
  std::optional<SmallVector<int64_t>> equivalentToBroadcast =
      isaBroadcastOpInterface(genericOp);
  if (equivalentToBroadcast) {
    auto dims = *equivalentToBroadcast;
    LinalgOp namedOp = rewriter.replaceOpWithNewOp<BroadcastOp>(
        genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0],
        dims);
    return namedOp;
  }
 
  // Transpose
  std::optional<SmallVector<int64_t>> equivalentToTranspose =
      isaTransposeOpInterface(genericOp);
  if (equivalentToTranspose) {
    auto permutation = *equivalentToTranspose;
    LinalgOp namedOp = rewriter.replaceOpWithNewOp<TransposeOp>(
        genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0],
        permutation);
    return namedOp;
  }
 
  // Elementwise Unary
  if (isaElemwiseSingleUnaryOpInterface(genericOp)) {
    Operation *op = &genericOp.getBody()->front();
    if (isa<math::ExpOp>(op)) {
      LinalgOp namedOp = REPLACE_UNARY_OP(ExpOp);
      return namedOp;
    }
  }
 
  // Elementwise Binary
  if (isaElemwiseSingleBinaryOpInterface(genericOp)) {
    bool swap = areBinOpsSwapped(genericOp);
    Operation *op = &genericOp.getBody()->front();
    if (isa<arith::AddFOp>(op)) {
      LinalgOp namedOp = REPLACE_BINARY_OP(AddOp, swap);
      return namedOp;
    }
    if (isa<arith::SubFOp>(op)) {
      LinalgOp namedOp = REPLACE_BINARY_OP(SubOp, swap);
      return namedOp;
    }
    if (isa<arith::MulFOp>(op)) {
      LinalgOp namedOp = REPLACE_BINARY_OP(MulOp, swap);
      return namedOp;
    }
    if (isa<arith::DivFOp>(op)) {
      LinalgOp namedOp = REPLACE_BINARY_OP(DivOp, swap);
      return namedOp;
    }
  }
 
  // Contraction - e.g. matmul
  if (isaContractionOpInterface(genericOp)) {
    return specializeLinalgContractions(rewriter, genericOp);
  }
 
  // Convolution - e.g. *conv/pooling*
  if (isaConvolutionOpInterface(genericOp)) {
    return specializeLinalgConvolutions(rewriter, genericOp);
  }
  return failure();
}
 
namespace {
struct LinalgSpecializeGenericOpsPass
    : public impl::LinalgSpecializeGenericOpsPassBase<
          LinalgSpecializeGenericOpsPass> {
 
  using impl::LinalgSpecializeGenericOpsPassBase<
      LinalgSpecializeGenericOpsPass>::LinalgSpecializeGenericOpsPassBase;
  void runOnOperation() override;
};
} // namespace
 
void LinalgSpecializeGenericOpsPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  populateLinalgGenericOpsSpecializationPatterns(patterns);
  populateDecomposeProjectedPermutationPatterns(patterns);
 
  if (failed(applyPatternsGreedily(getOperation(), std::move(patterns))))
    signalPassFailure();
}
 
void mlir::linalg::populateLinalgGenericOpsSpecializationPatterns(
    RewritePatternSet &patterns) {
  patterns.add<LinalgSpecializationPattern>(patterns.getContext());
}