//===- AVXTranspose.cpp - Lower Vector transpose to AVX -------------------===//
//
// 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 vector.transpose rewrites as AVX patterns for particular
// sizes of interest.
//
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Vector/VectorOps.h"
#include "mlir/Dialect/X86Vector/Transforms.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/PatternMatch.h"

using namespace mlir;
using namespace mlir::vector;
using namespace mlir::x86vector;
using namespace mlir::x86vector::avx2;

Value mlir::x86vector::avx2::mm256UnpackLoPs(ImplicitLocOpBuilder &b, Value v1,
                                             Value v2) {
  return b.create<vector::ShuffleOp>(
      v1, v2, ArrayRef<int64_t>{0, 8, 1, 9, 4, 12, 5, 13});
}

Value mlir::x86vector::avx2::mm256UnpackHiPs(ImplicitLocOpBuilder &b, Value v1,
                                             Value v2) {
  return b.create<vector::ShuffleOp>(
      v1, v2, ArrayRef<int64_t>{2, 10, 3, 11, 6, 14, 7, 15});
}
///                            a  a   b   b  a  a   b   b
/// Takes an 8 bit mask, 2 bit for each position of a[0, 3)  **and** b[0, 4):
///                                 0:127    |         128:255
///                            b01  b23  C8  D8  |  b01+4 b23+4 C8+4 D8+4
Value mlir::x86vector::avx2::mm256ShufflePs(ImplicitLocOpBuilder &b, Value v1,
                                            Value v2, int8_t mask) {
  int8_t b01, b23, b45, b67;
  MaskHelper::extractShuffle(mask, b01, b23, b45, b67);
  SmallVector<int64_t> shuffleMask{b01,     b23,     b45 + 8,     b67 + 8,
                                   b01 + 4, b23 + 4, b45 + 8 + 4, b67 + 8 + 4};
  return b.create<vector::ShuffleOp>(v1, v2, shuffleMask);
}

// imm[0:1] out of imm[0:3] is:
//    0             1           2             3
// a[0:127] or a[128:255] or b[0:127] or b[128:255]    |
//          a[0:127] or a[128:255] or b[0:127] or b[128:255]
//             0             1           2             3
// imm[0:1] out of imm[4:7].
Value mlir::x86vector::avx2::mm256Permute2f128Ps(ImplicitLocOpBuilder &b,
                                                 Value v1, Value v2,
                                                 int8_t mask) {
  SmallVector<int64_t> shuffleMask;
  auto appendToMask = [&](int8_t control) {
    if (control == 0)
      llvm::append_range(shuffleMask, ArrayRef<int64_t>{0, 1, 2, 3});
    else if (control == 1)
      llvm::append_range(shuffleMask, ArrayRef<int64_t>{4, 5, 6, 7});
    else if (control == 2)
      llvm::append_range(shuffleMask, ArrayRef<int64_t>{8, 9, 10, 11});
    else if (control == 3)
      llvm::append_range(shuffleMask, ArrayRef<int64_t>{12, 13, 14, 15});
    else
      llvm_unreachable("control > 3 : overflow");
  };
  int8_t b03, b47;
  MaskHelper::extractPermute(mask, b03, b47);
  appendToMask(b03);
  appendToMask(b47);
  return b.create<vector::ShuffleOp>(v1, v2, shuffleMask);
}

/// AVX2 4x8xf32-specific transpose lowering using a "C intrinsics" model.
void mlir::x86vector::avx2::transpose4x8xf32(ImplicitLocOpBuilder &ib,
                                             MutableArrayRef<Value> vs) {
#ifndef NDEBUG
  auto vt = VectorType::get({8}, Float32Type::get(ib.getContext()));
  assert(vs.size() == 4 && "expects 4 vectors");
  assert(llvm::all_of(ValueRange{vs}.getTypes(),
                      [&](Type t) { return t == vt; }) &&
         "expects all types to be vector<8xf32>");
#endif

  Value T0 = mm256UnpackLoPs(ib, vs[0], vs[1]);
  Value T1 = mm256UnpackHiPs(ib, vs[0], vs[1]);
  Value T2 = mm256UnpackLoPs(ib, vs[2], vs[3]);
  Value T3 = mm256UnpackHiPs(ib, vs[2], vs[3]);
  Value S0 = mm256ShufflePs(ib, T0, T2, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S1 = mm256ShufflePs(ib, T0, T2, MaskHelper::shuffle<3, 2, 3, 2>());
  Value S2 = mm256ShufflePs(ib, T1, T3, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S3 = mm256ShufflePs(ib, T1, T3, MaskHelper::shuffle<3, 2, 3, 2>());
  vs[0] = mm256Permute2f128Ps(ib, S0, S1, MaskHelper::permute<2, 0>());
  vs[1] = mm256Permute2f128Ps(ib, S2, S3, MaskHelper::permute<2, 0>());
  vs[2] = mm256Permute2f128Ps(ib, S0, S1, MaskHelper::permute<3, 1>());
  vs[3] = mm256Permute2f128Ps(ib, S2, S3, MaskHelper::permute<3, 1>());
}

/// AVX2 8x8xf32-specific transpose lowering using a "C intrinsics" model.
void mlir::x86vector::avx2::transpose8x8xf32(ImplicitLocOpBuilder &ib,
                                             MutableArrayRef<Value> vs) {
  auto vt = VectorType::get({8}, Float32Type::get(ib.getContext()));
  (void)vt;
  assert(vs.size() == 8 && "expects 8 vectors");
  assert(llvm::all_of(ValueRange{vs}.getTypes(),
                      [&](Type t) { return t == vt; }) &&
         "expects all types to be vector<8xf32>");

  Value T0 = mm256UnpackLoPs(ib, vs[0], vs[1]);
  Value T1 = mm256UnpackHiPs(ib, vs[0], vs[1]);
  Value T2 = mm256UnpackLoPs(ib, vs[2], vs[3]);
  Value T3 = mm256UnpackHiPs(ib, vs[2], vs[3]);
  Value T4 = mm256UnpackLoPs(ib, vs[4], vs[5]);
  Value T5 = mm256UnpackHiPs(ib, vs[4], vs[5]);
  Value T6 = mm256UnpackLoPs(ib, vs[6], vs[7]);
  Value T7 = mm256UnpackHiPs(ib, vs[6], vs[7]);
  Value S0 = mm256ShufflePs(ib, T0, T2, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S1 = mm256ShufflePs(ib, T0, T2, MaskHelper::shuffle<3, 2, 3, 2>());
  Value S2 = mm256ShufflePs(ib, T1, T3, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S3 = mm256ShufflePs(ib, T1, T3, MaskHelper::shuffle<3, 2, 3, 2>());
  Value S4 = mm256ShufflePs(ib, T4, T6, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S5 = mm256ShufflePs(ib, T4, T6, MaskHelper::shuffle<3, 2, 3, 2>());
  Value S6 = mm256ShufflePs(ib, T5, T7, MaskHelper::shuffle<1, 0, 1, 0>());
  Value S7 = mm256ShufflePs(ib, T5, T7, MaskHelper::shuffle<3, 2, 3, 2>());
  vs[0] = mm256Permute2f128Ps(ib, S0, S4, MaskHelper::permute<2, 0>());
  vs[1] = mm256Permute2f128Ps(ib, S1, S5, MaskHelper::permute<2, 0>());
  vs[2] = mm256Permute2f128Ps(ib, S2, S6, MaskHelper::permute<2, 0>());
  vs[3] = mm256Permute2f128Ps(ib, S3, S7, MaskHelper::permute<2, 0>());
  vs[4] = mm256Permute2f128Ps(ib, S0, S4, MaskHelper::permute<3, 1>());
  vs[5] = mm256Permute2f128Ps(ib, S1, S5, MaskHelper::permute<3, 1>());
  vs[6] = mm256Permute2f128Ps(ib, S2, S6, MaskHelper::permute<3, 1>());
  vs[7] = mm256Permute2f128Ps(ib, S3, S7, MaskHelper::permute<3, 1>());
}

/// Rewrite avx2-specific 2-D vector.transpose, for the supported cases and
/// depending on the `TransposeLoweringOptions`.
class TransposeOpLowering : public OpRewritePattern<vector::TransposeOp> {
public:
  using OpRewritePattern<vector::TransposeOp>::OpRewritePattern;

  TransposeOpLowering(LoweringOptions loweringOptions, MLIRContext *context,
                      int benefit)
      : OpRewritePattern<vector::TransposeOp>(context, benefit),
        loweringOptions(loweringOptions) {}

  LogicalResult matchAndRewrite(vector::TransposeOp op,
                                PatternRewriter &rewriter) const override {
    auto loc = op.getLoc();

    VectorType srcType = op.getVectorType();
    if (srcType.getRank() != 2)
      return rewriter.notifyMatchFailure(op, "Not a 2-D transpose");

    SmallVector<int64_t, 4> transp;
    for (auto attr : op.transp())
      transp.push_back(attr.cast<IntegerAttr>().getInt());
    if (transp[0] != 1 && transp[1] != 0)
      return rewriter.notifyMatchFailure(op, "Not a 2-D transpose permutation");

    int64_t m = srcType.getShape().front(), n = srcType.getShape().back();

    auto applyRewrite = [&]() {
      ImplicitLocOpBuilder ib(loc, rewriter);
      SmallVector<Value> vs;
      for (int64_t i = 0; i < m; ++i)
        vs.push_back(ib.create<vector::ExtractOp>(op.vector(), i));
      if (m == 4)
        transpose4x8xf32(ib, vs);
      if (m == 8)
        transpose8x8xf32(ib, vs);
      auto flattenedType =
          VectorType::get({n * m}, op.getVectorType().getElementType());
      auto transposedType =
          VectorType::get({n, m}, op.getVectorType().getElementType());
      Value res = ib.create<arith::ConstantOp>(
          op.getVectorType(), ib.getZeroAttr(op.getVectorType()));
      // The transposed form is still 4x8 and needs to be reinterpreted as 8x4
      // via shape_casts.
      for (int64_t i = 0; i < m; ++i)
        res = ib.create<vector::InsertOp>(vs[i], res, i);
      if (m == 4) {
        res = ib.create<vector::ShapeCastOp>(flattenedType, res);
        res = ib.create<vector::ShapeCastOp>(transposedType, res);
      }

      rewriter.replaceOp(op, res);
      return success();
    };

    if (loweringOptions.transposeOptions.lower4x8xf32_ && m == 4 && n == 8)
      return applyRewrite();
    if (loweringOptions.transposeOptions.lower8x8xf32_ && m == 8 && n == 8)
      return applyRewrite();
    return failure();
  }

private:
  LoweringOptions loweringOptions;
};

void mlir::x86vector::avx2::populateSpecializedTransposeLoweringPatterns(
    RewritePatternSet &patterns, LoweringOptions options, int benefit) {
  patterns.add<TransposeOpLowering>(options, patterns.getContext(), benefit);
}