// Copyright 2025 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <stddef.h>
#include <stdint.h>

#include <algorithm>
#include <cmath>
#include <limits>

#include "compression/types.h"  // GEMMA_DISABLED_TARGETS
#include "util/threading_context.h"
#ifndef HWY_DISABLED_TARGETS
#define HWY_DISABLED_TARGETS GEMMA_DISABLED_TARGETS
#endif  // HWY_DISABLED_TARGETS

#include "gemma/activations.h"
#include "gemma/configs.h"  // kMaxQKVDim
#include "gemma/gemma.h"
#include "gemma/weights.h"
#include "util/threading.h"
#include "hwy/profiler.h"

// Compiles this file for multiple architectures via "foreach_target.h", to
// which we pass the filename via macro 'argument'.
// clang-format off
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "gemma/flash_attention.cc"  // NOLINT
// clang-format on
#include "hwy/foreach_target.h"  // IWYU pragma: keep
#include "hwy/highway.h"
// After highway.h
#include "compression/compress-inl.h"
#include "gemma/attention.h"
#include "ops/matmul-inl.h"
#include "ops/ops-inl.h"

HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {

// Transposes q into q_t.
// Both are 4D tensors stuffed into a 2-D MatPtrT.
// q has shape [batch, qbatch][head, qkv_dim].
// q_t has shape [qkv_dim][qbatch, head, batch] in order to make the maximum
// possible consecutive elements have the same KV.
static void TransposeQ(const MatPtrT<float>& q, MatPtrT<float>& q_t,
                       const size_t qbatch_size, ThreadingContext& ctx) {
  static const auto zone = ctx.profiler.AddZone("Gen.Attention.TransposeQ");
  const size_t num_heads = q.Cols() / q_t.Rows();
  const size_t batch_size = q.Rows() / qbatch_size;
  const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
    PROFILER_ZONE3(ctx.profiler, worker, zone);
    float* HWY_RESTRICT qt_row = q_t.Row(task);
    for (size_t qi = 0; qi < qbatch_size; ++qi)
      for (size_t h = 0; h < num_heads; ++h) {
        for (size_t b = 0; b < batch_size; ++b) {
          qt_row[(qi * num_heads + h) * batch_size + b] =
              q.Row(b * qbatch_size + qi)[h * q_t.Rows() + task];
        }
      }
  };
  {
    // Full parallelism is helpful, SmallParallelFor is insufficient.
    HierarchicalParallelFor(q_t.Rows(), ctx.pools, func);
  }
}

// Updates q in place for RMSNorm and positional encoding.
void RMSNormAndPositionalEncoding(const size_t num_tokens, const QBatch& qbatch,
                                  MatPtrT<KV_t>& q, const size_t layer_idx,
                                  const LayerWeightsPtrs& layer,
                                  const AttentionActivations& activations,
                                  ThreadingContext& ctx) {
  static const auto zone =
      ctx.profiler.AddZone("Gen.Attention.RMSNormAndPositionalEncoding");
  const float query_scale = activations.query_scale;
  const hwy::Divisor div_qbatch(qbatch.Size());
  const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
    PROFILER_ZONE3(ctx.profiler, worker, zone);
    size_t qi = div_qbatch.Remainder(task);
    size_t batch_idx = div_qbatch.Divide(task);
    for (size_t h = 0; h < layer.layer_config.heads; ++h) {
      const size_t tq_idx = qbatch.Size() * batch_idx + qi;
      // Find the token position in the query and calculate
      // the range of cache positions to attend to.
      const size_t pos = qbatch.Pos(qi) + batch_idx;
      float* HWY_RESTRICT q_row =
          q.Row(tq_idx) + h * layer.layer_config.qkv_dim;
      // Apply rope and scaling to Q.
      if (layer.query_norm_scale.HasPtr()) {
        CallUpcasted(&layer.query_norm_scale, [&](const auto* weights_t) {
          RMSNormInplace(weights_t->PackedScale1(), q_row,
                          layer.layer_config.qkv_dim, ctx.profiler, worker);
        });
      }
      PositionalEncodingQK(q_row, layer_idx, layer, activations, ctx.profiler,
                            worker, pos, query_scale);
    }
  };
  {
    // Full parallelism is helpful, SmallParallelFor is insufficient.
    HierarchicalParallelFor(num_tokens * qbatch.Size(), ctx.pools, func);
  }
}

// Handles a single v row of flash attention for a single q.k dot product.
void HWY_INLINE SingleFlashAttentionStep(
    float x, float cap, float& old_max, float& old_d,
    const float* HWY_RESTRICT v, const size_t v_cols,
    float* HWY_RESTRICT att_out, hwy::Profiler& p, const size_t worker) {
  if (cap > 0.0f) {
    // Compute tanh(x / cap) * cap, being LogitsSoftCap on the scalar x.
    x = cap * std::tanh(x / cap);
  }
  float m = std::max(x, old_max);
  x = std::exp(x - m);
  float scale = old_d * std::exp(old_max - m);
  old_d = x + scale;
  old_max = m;
  float one_over_d = 1.0f / old_d;
  scale *= one_over_d;
  x *= one_over_d;
  MulByConst(scale, att_out, v_cols, p, worker);
  MulByConstAndAdd(x, v, att_out, v_cols, p, worker);
}

// Calculates the complete attention outputs for a single row of q.
void SingleFlashAttention(const size_t start_pos, const size_t last_pos,
                          const float* HWY_RESTRICT q, const MatPtrT<KV_t>& k,
                          const MatPtrT<KV_t>& v, const size_t layer_idx,
                          const LayerWeightsPtrs& layer,
                          const AttentionActivations& activations,
                          float* HWY_RESTRICT att_out, hwy::Profiler& p,
                          const size_t worker) {
  static const auto zone = p.AddZone("Gen.Attention.SingleFlashAttention");
  PROFILER_ZONE3(p, worker, zone);
  const size_t pos_mod = activations.div_seq_len.Remainder(start_pos);
  float m = Dot(q, k.Row(pos_mod), k.Cols());
  if (float cap = activations.config.att_cap; cap > 0.0f) {
    // Compute tanh(x / cap) * cap, being LogitsSoftCap on the scalar x.
    m = cap * std::tanh(m / cap);
  }
  float d = 1.0f;
  // This is just a copy of the first token.
  MulByConstTo(d, v.Row(pos_mod), att_out, v.Cols(), p, worker);
  for (size_t pos = start_pos + 1; pos <= last_pos; ++pos) {
    const size_t pos_mod = activations.div_seq_len.Remainder(pos);
    float x = Dot(q, k.Row(pos_mod), k.Cols());
    SingleFlashAttentionStep(x, activations.config.att_cap, m, d,
                             v.Row(pos_mod), v.Cols(), att_out, p, worker);
  }
}

// Computes and returns a single vector of NF Q.K dot products, which represents
// the dot products of NF rows of Q for a single K timestep.
template <class DF, class VF = hn::Vec<DF>>
VF QDotKVector(DF df, const uint32_t* HWY_RESTRICT q_offsets,
               const size_t k_pos, const MatPtrT<KV_t>& q,
               const MatPtrT<KV_t>& k, hwy::Profiler& p, const size_t worker) {
  hn::TFromD<DF> results[hn::MaxLanes(df)];
  for (size_t i = 0; i < hn::Lanes(df); ++i) {
    results[i] = Dot(q.Row(0) + q_offsets[i], k.Row(k_pos), k.Cols());
  }
  return hn::LoadU(df, results);
}

// Returns an NF Q rows by 8 K rows tile of Q.K dot products, in single
// precision.
// This is the result of NF rows of Q against 8 K timesteps, with positions
// given by k_pos[0..7]. Q has been transposed so that the NF rows are read in
// consecutive elements, and other columns by adding q_stride.
template <class DF, class VF = hn::Vec<DF>>
void QDotKTileFloat(DF df, const float* HWY_RESTRICT q, const size_t q_stride,
                    const MatPtrT<KV_t>& k, const size_t* k_pos,
                    hwy::Profiler& p, const size_t worker, VF& sum0, VF& sum1,
                    VF& sum2, VF& sum3, VF& sum4, VF& sum5, VF& sum6,
                    VF& sum7) {
  constexpr size_t kHTileSize = 8;
  sum0 = hn::Zero(df);
  sum1 = hn::Zero(df);
  sum2 = hn::Zero(df);
  sum3 = hn::Zero(df);
  sum4 = hn::Zero(df);
  sum5 = hn::Zero(df);
  sum6 = hn::Zero(df);
  sum7 = hn::Zero(df);
  const float* HWY_RESTRICT k_row[kHTileSize];
  for (int i = 0; i < kHTileSize; ++i) {
    k_row[i] = k.Row(k_pos[i]);
  }
  for (size_t i = 0; i < k.Cols(); ++i) {
    VF q_vec = hn::Load(df, q);
    VF k_0 = hn::Set(df, k_row[0][i]);
    sum0 = hn::MulAdd(q_vec, k_0, sum0);
    VF k_1 = hn::Set(df, k_row[1][i]);
    sum1 = hn::MulAdd(q_vec, k_1, sum1);
    VF k_2 = hn::Set(df, k_row[2][i]);
    sum2 = hn::MulAdd(q_vec, k_2, sum2);
    VF k_3 = hn::Set(df, k_row[3][i]);
    sum3 = hn::MulAdd(q_vec, k_3, sum3);
    VF k_4 = hn::Set(df, k_row[4][i]);
    sum4 = hn::MulAdd(q_vec, k_4, sum4);
    VF k_5 = hn::Set(df, k_row[5][i]);
    sum5 = hn::MulAdd(q_vec, k_5, sum5);
    VF k_6 = hn::Set(df, k_row[6][i]);
    sum6 = hn::MulAdd(q_vec, k_6, sum6);
    VF k_7 = hn::Set(df, k_row[7][i]);
    sum7 = hn::MulAdd(q_vec, k_7, sum7);
    q += q_stride;
  }
}

// Returns the element-wise maximum of 8 vectors, in a single vector.
template <class DF, class VF = hn::Vec<DF>>
VF HWY_INLINE ElementwiseMaxOf8(DF df, const VF& x0, const VF& x1, const VF& x2,
                                const VF& x3, const VF& x4, const VF& x5,
                                const VF& x6, const VF& x7) {
  VF m0 = hn::Max(x0, x1);
  VF m1 = hn::Max(x2, x3);
  VF m2 = hn::Max(x4, x5);
  VF m3 = hn::Max(x6, x7);
  m0 = hn::Max(m0, m1);
  m2 = hn::Max(m2, m3);
  return hn::Max(m0, m2);
}

// Returns the element-wise sum of 8 vectors, in a single vector.
template <class DF, class VF = hn::Vec<DF>>
VF HWY_INLINE ElementwiseSumOf8(DF df, const VF& x0, const VF& x1, const VF& x2,
                                const VF& x3, const VF& x4, const VF& x5,
                                const VF& x6, const VF& x7) {
  VF sum0 = hn::Add(x0, x1);
  VF sum1 = hn::Add(x2, x3);
  VF sum2 = hn::Add(x4, x5);
  VF sum3 = hn::Add(x6, x7);
  sum0 = hn::Add(sum0, sum1);
  sum2 = hn::Add(sum2, sum3);
  return hn::Add(sum0, sum2);
}

// Sweeps a tile of NF Q rows by 8 K timesteps accumulators from start_pos to
// min_last_pos, then sweeps the remaining timesteps in the range (min_last_pos,
// max_last_pos].
void TileFlashAttention(
    const MatPtrT<float>& q, const uint32_t* HWY_RESTRICT q_offsets,
    const StridedView<float>& qT, const MatPtrT<KV_t>& k,
    const size_t start_pos, const uint32_t* HWY_RESTRICT last_pos,
    const size_t min_last_pos, const size_t max_last_pos,
    const MatPtrT<KV_t>& v, const size_t layer_idx,
    const LayerWeightsPtrs& layer, const AttentionActivations& activations,
    MatPtrT<float>& att_out, const uint32_t* HWY_RESTRICT out_offsets,
    hwy::Profiler& p, const size_t worker) {
  static const auto zone = p.AddZone("Gen.Attention.TileFlashAttention");
  PROFILER_ZONE3(p, worker, zone);
  constexpr int kHTileSize = 8;
  using DF = hn::ScalableTag<float>;
  const DF df;
  using VF = hn::Vec<DF>;
  using DI = hn::ScalableTag<uint32_t>;
  const DI di;
  using VI = hn::Vec<DI>;
  const int kVTileSize = hn::Lanes(df);
  for (int i = 0; i < kVTileSize; ++i) {
    hwy::ZeroBytes(att_out.Row(0) + out_offsets[i],
                   v.Cols() * sizeof(att_out.Row(0)[0]));
  }
  VI lasts = hn::LoadU(di, last_pos);
  VF old_m = hn::Set(df, -std::numeric_limits<float>::max() / 2.0f);
  VF old_d = hn::Zero(df);
  const float* HWY_RESTRICT qT_row = qT.Row(0);
  const size_t qT_stride = qT.Stride();
  size_t position = start_pos;
  while (position + kHTileSize - 1 <= min_last_pos) {
    size_t k_pos[kHTileSize];
    for (size_t i = 0; i < kHTileSize; ++i) {
      k_pos[i] = activations.div_seq_len.Remainder(position + i);
    }
    VF x0, x1, x2, x3, x4, x5, x6, x7;
    QDotKTileFloat(df, qT_row, qT_stride, k, k_pos, p, worker, x0, x1, x2, x3,
                   x4, x5, x6, x7);
    if (activations.config.att_cap > 0.0f) {
      // Compute tanh(x / cap) * cap, being LogitsSoftCap on the tile.
      VF cap = hn::Set(df, activations.config.att_cap);
      VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
      x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
      x1 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x1, one_over_cap)));
      x2 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x2, one_over_cap)));
      x3 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x3, one_over_cap)));
      x4 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x4, one_over_cap)));
      x5 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x5, one_over_cap)));
      x6 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x6, one_over_cap)));
      x7 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x7, one_over_cap)));
    }
    VF m = ElementwiseMaxOf8(df, x0, x1, x2, x3, x4, x5, x6, x7);
    m = hn::Max(old_m, m);
    x0 = hn::Exp(df, x0 - m);
    x1 = hn::Exp(df, x1 - m);
    x2 = hn::Exp(df, x2 - m);
    x3 = hn::Exp(df, x3 - m);
    x4 = hn::Exp(df, x4 - m);
    x5 = hn::Exp(df, x5 - m);
    x6 = hn::Exp(df, x6 - m);
    x7 = hn::Exp(df, x7 - m);
    VF scale = hn::Mul(old_d, hn::Exp(df, old_m - m));
    old_d = ElementwiseSumOf8(df, x0, x1, x2, x3, x4, x5, x6, x7);
    old_d = hn::Add(scale, old_d);
    old_m = m;
    VF one_over_d = hn::Div(hn::Set(df, 1.0f), old_d);
    scale = hn::Mul(scale, one_over_d);
    x0 = hn::Mul(x0, one_over_d);
    x1 = hn::Mul(x1, one_over_d);
    x2 = hn::Mul(x2, one_over_d);
    x3 = hn::Mul(x3, one_over_d);
    x4 = hn::Mul(x4, one_over_d);
    x5 = hn::Mul(x5, one_over_d);
    x6 = hn::Mul(x6, one_over_d);
    x7 = hn::Mul(x7, one_over_d);
    MulByConstAndAddTile(df, scale, x0, x1, x2, x3, x4, x5, x6, x7, v, k_pos,
                         att_out.Row(0), out_offsets, v.Cols(), p, worker);
    position += kHTileSize;
  }
  while (position <= max_last_pos) {
    size_t k_pos = activations.div_seq_len.Remainder(position);
    VF x0 = QDotKVector(df, q_offsets, k_pos, q, k, p, worker);
    if (activations.config.att_cap > 0.0f) {
      // Compute tanh(x / cap) * cap, being LogitsSoftCap on the vector.
      VF cap = hn::Set(df, activations.config.att_cap);
      VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
      x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
    }
    // Past the last position, x0 doesn't count.
    auto mask = hn::Gt(hn::Set(di, position), lasts);
    VF causal_offset = hn::MaskedSet(df, RebindMask(df, mask),
                                     std::numeric_limits<float>::max() / 2.0f);
    x0 = hn::Sub(x0, causal_offset);
    VF m = hn::Max(old_m, x0);
    x0 = hn::Exp(df, x0 - m);
    VF scale = hn::Mul(old_d, hn::Exp(df, old_m - m));
    old_m = m;
    old_d = hn::Add(scale, x0);
    VF one_over_d = hn::Div(hn::Set(df, 1.0f), old_d);
    x0 = hn::Mul(x0, one_over_d);
    scale = hn::Mul(scale, one_over_d);
    MulByConstAndAddVector(df, scale, x0, v, k_pos, att_out.Row(0), out_offsets,
                           v.Cols(), p, worker);
    ++position;
  }
}

// Returns an 4 Q rows by NF K tile of Q.K dot products, in single precision.
// This is the result of 4 rows of Q against NF K timesteps, with positions
// given by k_offsets[0..NF]. Q has been transposed so that the 4 rows are read
// in consecutive elements, and other columns by adding q_stride.
template <class DF, class VF = hn::Vec<DF>>
void QDotKTilex4(DF df, const float* HWY_RESTRICT q, const size_t q_stride,
                 const MatPtrT<KV_t>& k, const int32_t* HWY_RESTRICT k_offsets,
                 hwy::Profiler& p, const size_t worker, VF& sum0, VF& sum1,
                 VF& sum2, VF& sum3) {
  sum0 = hn::Zero(df);
  sum1 = hn::Zero(df);
  sum2 = hn::Zero(df);
  sum3 = hn::Zero(df);
  const float* HWY_RESTRICT k_base = k.Row(0);
  using DI = hn::ScalableTag<int32_t>;
  const DI di;
  using VI = hn::Vec<DI>;
  VI k_offsets_vec = hn::LoadU(di, k_offsets);
  for (size_t i = 0; i < k.Cols(); ++i) {
    VF k_vec = hn::GatherIndex(df, k_base + i, k_offsets_vec);
    VF q_0 = hn::Set(df, q[0]);
    sum0 = hn::MulAdd(q_0, k_vec, sum0);
    VF q_1 = hn::Set(df, q[1]);
    sum1 = hn::MulAdd(q_1, k_vec, sum1);
    VF q_2 = hn::Set(df, q[2]);
    sum2 = hn::MulAdd(q_2, k_vec, sum2);
    VF q_3 = hn::Set(df, q[3]);
    sum3 = hn::MulAdd(q_3, k_vec, sum3);
    q += q_stride;
  }
}

// Handles NF v rows of flash attention for NF q.k dot products from one q row.
template <class DF, class VF = hn::Vec<DF>>
float HWY_INLINE SingleFlashAttentionRowVector(DF df, VF& x, float& old_max,
                                               float& old_d) {
  float m = hn::ReduceMax(df, x);
  m = std::max(m, old_max);
  x = hn::Exp(df, x - hn::Set(df, m));
  float scale = old_d * std::exp(old_max - m);
  old_d = hn::ReduceSum(df, x) + scale;
  old_max = m;
  float one_over_d = 1.0f / old_d;
  scale *= one_over_d;
  x = hn::Mul(x, hn::Set(df, one_over_d));
  return scale;
}

// Sweeps a tile of 4 Q rows by NF K timesteps accumulators from start_pos to
// min_last_pos, then sweeps the remaining timesteps in the range (min_last_pos,
// max_last_pos].
void TileFlashAttention4(
    const MatPtrT<float>& q, const uint32_t* HWY_RESTRICT q_offsets,
    const StridedView<float>& qT, const MatPtrT<KV_t>& k,
    const size_t start_pos, const uint32_t* HWY_RESTRICT last_pos,
    const size_t min_last_pos, const size_t max_last_pos,
    const MatPtrT<KV_t>& v, const size_t layer_idx,
    const LayerWeightsPtrs& layer, const AttentionActivations& activations,
    MatPtrT<float>& att_out, const uint32_t* HWY_RESTRICT out_offsets,
    hwy::Profiler& p, const size_t worker) {
  static const auto zone = p.AddZone("Gen.Attention.TileFlashAttention4");
  PROFILER_ZONE3(p, worker, zone);
  using DF = hn::ScalableTag<float>;
  const DF df;
  using VF = hn::Vec<DF>;
  constexpr size_t kMaxNF = hn::MaxLanes(df);
  const size_t kHTileSize = hn::Lanes(df);
  HWY_DASSERT(kHTileSize <= kMaxNF);
  constexpr size_t kVTileSize = 4;
  float scales[kVTileSize];
  for (size_t i = 0; i < kVTileSize; ++i) {
    hwy::ZeroBytes(att_out.Row(0) + out_offsets[i],
                   v.Cols() * sizeof(att_out.Row(0)[0]));
  }
  float old_m0 = -std::numeric_limits<float>::max() / 2.0f;
  float old_m1 = -std::numeric_limits<float>::max() / 2.0f;
  float old_m2 = -std::numeric_limits<float>::max() / 2.0f;
  float old_m3 = -std::numeric_limits<float>::max() / 2.0f;
  float old_d0 = 0.0f;
  float old_d1 = 0.0f;
  float old_d2 = 0.0f;
  float old_d3 = 0.0f;
  const float* HWY_RESTRICT qT_row = qT.Row(0);
  const size_t qT_stride = qT.Stride();
  size_t position = start_pos;
  while (position + kHTileSize - 1 <= min_last_pos) {
    int32_t k_offsets[kMaxNF];
    size_t v_pos[kMaxNF];
    for (size_t i = 0; i < kHTileSize; ++i) {
      v_pos[i] = activations.div_seq_len.Remainder(position + i);
      k_offsets[i] = k.Row(v_pos[i]) - k.Row(0);
    }
    VF x0, x1, x2, x3;
    QDotKTilex4(df, qT_row, qT_stride, k, k_offsets, p, worker, x0, x1, x2, x3);
    if (activations.config.att_cap > 0.0f) {
      // Compute tanh(x / cap) * cap, being LogitsSoftCap on the tile.
      VF cap = hn::Set(df, activations.config.att_cap);
      VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
      x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
      x1 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x1, one_over_cap)));
      x2 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x2, one_over_cap)));
      x3 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x3, one_over_cap)));
    }
    scales[0] = SingleFlashAttentionRowVector(df, x0, old_m0, old_d0);
    scales[1] = SingleFlashAttentionRowVector(df, x1, old_m1, old_d1);
    scales[2] = SingleFlashAttentionRowVector(df, x2, old_m2, old_d2);
    scales[3] = SingleFlashAttentionRowVector(df, x3, old_m3, old_d3);
    MulByConstAndAddTile4(df, scales, x0, x1, x2, x3, v, v_pos, att_out.Row(0),
                          out_offsets, v.Cols(), p, worker);
    position += kHTileSize;
  }
  while (position <= max_last_pos) {
    size_t k_pos = activations.div_seq_len.Remainder(position);
    if (position <= last_pos[0]) {
      // Past the last position, x0 doesn't count.
      float x0 = Dot(q.Row(0) + q_offsets[0], k.Row(k_pos), k.Cols());
      SingleFlashAttentionStep(x0, activations.config.att_cap, old_m0, old_d0,
                               v.Row(k_pos), v.Cols(),
                               att_out.Row(0) + out_offsets[0], p, worker);
    }
    if (position <= last_pos[1]) {
      // Past the last position, x1 doesn't count.
      float x1 = Dot(q.Row(0) + q_offsets[1], k.Row(k_pos), k.Cols());
      SingleFlashAttentionStep(x1, activations.config.att_cap, old_m1, old_d1,
                               v.Row(k_pos), v.Cols(),
                               att_out.Row(0) + out_offsets[1], p, worker);
    }
    if (position <= last_pos[2]) {
      // Past the last position, x2 doesn't count.
      float x2 = Dot(q.Row(0) + q_offsets[2], k.Row(k_pos), k.Cols());
      SingleFlashAttentionStep(x2, activations.config.att_cap, old_m2, old_d2,
                               v.Row(k_pos), v.Cols(),
                               att_out.Row(0) + out_offsets[2], p, worker);
    }
    if (position <= last_pos[3]) {
      // Past the last position, x3 doesn't count.
      float x3 = Dot(q.Row(0) + q_offsets[3], k.Row(k_pos), k.Cols());
      SingleFlashAttentionStep(x3, activations.config.att_cap, old_m3, old_d3,
                               v.Row(k_pos), v.Cols(),
                               att_out.Row(0) + out_offsets[3], p, worker);
    }
    ++position;
  }
}

// Rounds n to a number that can be used as the number of Q rows in a tile
// of flash attention.
static size_t RoundToSuitablePowerOf2(size_t n) {
  if (n < 4) return 1;
  if (n < 8) return 4;
  if (n < 16) return 8;
  if (n < 32) return 16;
  return 32;
}

// The nominal aim of attention is to combine 3 inputs Q[L,D], K[L,D], V[L,D]
// into a single output O[L,D].
// Conventional attention first computes A[L,L] = Q . KT
// followed by A = softmax(A) (over invididual rows).
// Then A is multiplied by V to get O[L,D].
// For each row of O, this takes a read of one row of Q L times, all of K,
// 3 write/reads of one row of A, read all of V, and read/write the one row of O
// L times. Ignoring the computation for now, and focusing just on memory,
// the one row of O takes L(4D+3) reads and L(D+3) writes.
// For the whole of Q, this is L^2(4D+3) reads and L^2(D+3) writes.
//
// Flash attention fuses these operations together, and has 3 operating modes:
// 1. NF rows of the result computed using tiles of registers of shape NFx8.
// 2. 4 rows of the result computed using tiles of registers of shape 4xNF.
// 3. One row (of Q and the result) at a time.
// In all cases the intermediate result (Q.KT) is never stored to memory.
// NF is the number of float lanes in a register, being 16 for AVX3. The softmax
// is converted to streaming form using the algorithm from:
// https://courses.cs.washington.edu/courses/cse599m/23sp/notes/flashattn.pdf.
// Q is transposed to Q_T[D,L] to make the dot product computation efficient.
//
// In mode 1:
// QDotKTileFloat computes NF Q rows x 8 K timesteps of Q.K dot products in one
// go, reducing reads of Q by 8 and reads of K by NF. The streaming softmax is
// computed entirely in registers, and a further NF registers to accumulate the
// results of the product of the softmax and V, reduce the number of reads of V
// by NF, and the reads/writes of O by 8.
// The reads are thus reduced to 2DL^2(1/8+1/NF) and writes reduced to DL^2/8,
// which on AVX3 is an overall reduction by about a factor of 10.
// Mode 1 can only be accessed if there is a large Qbatch size, or in multi-turn
// prefill, since in other cases, there is either a single K timestep (prefill)
// or a single num_heads set of Q rows (decode).
//
// In mode 2, the 4 rows of Q are computed against NF K timesteps in a tile,
// reducing the reads of Q by NF, and the reads of K by 4. The softmax and
// accumulation of the result is done in registers, cutting the reads of V by 4.
// The reads/writes of O are reduced by a factor of NF.
// The overall reduction is limited by the need to use gather to load K.
// Transposing K would be possible, but is complicated by the wraparound.
// Mode 2 can be used in all cases when there are at least 4 attention heads,
// but it may be prefereable to use mode 3 when the batch size is small to
// maximise parallelism.
//
// In mode 3, a single row of Q is computed against a single K timestep at a
// time, using SingleFlashAttention. In this case there is no reduction in the
// reads of Q or K, or V, or O, but the reads/writes of the intermediate A are
// still eliminated.
//
// A further complication is that real attention is not as simple as documented
// in the paper and above. There are multiple query heads, differing KV, and
// different sequence lengths, so a lot of the work in FlashAttention is making
// sure that a collection of q rows with the same KV and sequence length are
// grouped together so that mode 1 or 2 can be used, and choosing which of the
// 3 modes to use for best efficiency.
void FlashAttention(const size_t num_tokens, const size_t target_parallelism,
                    const size_t layer_idx, const LayerWeightsPtrs& layer,
                    AttentionActivations& activations, QBatch& qbatch,
                    ThreadingContext& ctx) {
  static const auto zone = ctx.profiler.AddZone("Gen.Attention.FlashAttention");
  RMSNormAndPositionalEncoding(num_tokens, qbatch, activations.q, layer_idx,
                               layer, activations, ctx);
  const hwy::Divisor div_qbatch(qbatch.Size());
  const LayerConfig& layer_config = layer.layer_config;
  const size_t qkv_dim = layer_config.qkv_dim;

  // A "head group" in the context of GQA refers to a collection of query
  // heads that share the same key and value heads.
  const size_t kHeadGroups = layer_config.heads / layer_config.kv_heads;
  const size_t cache_layer_size = layer_config.CacheLayerSize();
  const size_t seq_len =
      static_cast<size_t>(activations.div_seq_len.GetDivisor());
  const size_t token_batch = num_tokens * div_qbatch.GetDivisor();
  const size_t total_tasks = token_batch * layer_config.heads;

  using DF = hn::ScalableTag<float>;
  const DF df;
  const size_t kNF = hn::Lanes(df);
  constexpr size_t kMaxNF = hn::MaxLanes(df);
  HWY_DASSERT(kNF <= kMaxNF);
  // The vertical tile size is determined by the ability to use tiling and the
  // target_parallelism. In practice the possible tile sizes in order of
  // preference for efficiency are kNF, 4, 1, where kNF is likely to be 4 8 or
  // 16. The final tile size is chosen to be the largest possible that allows
  // for target_parallelism parallel tasks.
  const size_t kMaxEqualK = RoundToSuitablePowerOf2(kHeadGroups * num_tokens);
  const size_t kMinTileSize = (total_tasks / 4 >= target_parallelism) ? 4 : 1;
  const size_t kVTileSize =
      (kNF <= kMaxEqualK && total_tasks / kNF >= target_parallelism)
          ? kNF
          : std::min(kMinTileSize, kMaxEqualK);
  // q has shape [batch, qbatch][head, qkv_dim].
  // We transpose it to [qkv_dim][qbatch, head, batch] in order to make the
  // maximum possible number of consecutive columns have the same KV matrices.
  // Each thread will process a tile of NF columns of QT so the starting column
  // index of QT is just the task index * kVTileSize.
  TransposeQ(activations.q, activations.q_T, qbatch.Size(), ctx);
  const size_t num_thread_tasks = hwy::DivCeil(total_tasks, kVTileSize);
  const hwy::Divisor div_tokens(num_tokens);
  // All layers should have the same number of heads.
  HWY_DASSERT(activations.div_heads.GetDivisor() == layer_config.heads);

  // For each head/token/query, compute fused flash Q.K, softmax and weighted V.
  const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
    PROFILER_ZONE3(ctx.profiler, worker, zone);
    // Offsets into original Q for each row in the tile.
    uint32_t q_offsets[kMaxNF];
    // Offsets into att_out for each row in the tile.
    uint32_t out_offsets[kMaxNF];
    // Start positions for each row in the tile.
    size_t start_positions[kMaxNF];
    // Last positions for each row in the tile. Inclusive.
    uint32_t last_pos[kMaxNF];
    // min and max last positions across all rows in the tile determines when
    // TileFlashAttention switches to single vector mode to handle the
    // ragged sequence lengths.
    size_t min_last_pos = std::numeric_limits<size_t>::max();
    size_t max_last_pos = 0;
    // Indices into the qbatch.KV for each row in the tile.
    size_t qi_indices[kMaxNF];
    // Indices into the kv_cache for each row in the tile.
    size_t kv_offsets[kMaxNF];
    // first_task is [qbatch, head, token].
    const size_t first_task = task * kVTileSize;
    const size_t last_task = first_task + kVTileSize - 1;
    bool use_tile_attention = kVTileSize > 1 && last_task < total_tasks;
    for (size_t offset = 0;
         offset < kVTileSize && first_task + offset < total_tasks; ++offset) {
      const size_t batch_idx = div_tokens.Remainder(first_task + offset);
      const size_t qh = div_tokens.Divide(first_task + offset);
      const size_t head = activations.div_heads.Remainder(qh);
      const size_t qi = activations.div_heads.Divide(qh);
      const size_t tq_idx = div_qbatch.GetDivisor() * batch_idx + qi;
      qi_indices[offset] = qi;

      // Find the token position in the query and calculate
      // the range of cache positions to attend to.
      const size_t pos = qbatch.Pos(qi) + batch_idx;
      const size_t start_pos = StartPos(pos, activations.config, layer_idx);
      start_positions[offset] = start_pos;
      size_t last = pos;
      const size_t prefix_end = qbatch.PrefixEnd(qi);
      if (prefix_end > 0 && prefix_end - 1 > last) {
        // last_pos in QDotK and WeightedSumV is inclusive.
        last = prefix_end - 1;
      }
      last_pos[offset] = last;
      min_last_pos = HWY_MIN(min_last_pos, last);
      max_last_pos = HWY_MAX(max_last_pos, last);
      q_offsets[offset] =
          activations.q.Row(tq_idx) + head * qkv_dim - activations.q.Row(0);
      out_offsets[offset] = activations.att_out.Row(tq_idx) + head * qkv_dim -
                            activations.att_out.Row(0);
      const size_t kv_index = head / kHeadGroups;
      const size_t head_offset = kv_index * qkv_dim * 2;
      kv_offsets[offset] = layer_idx * cache_layer_size + head_offset;
      // If any of the parameters in this if statement differ within this task,
      // then we can't use TileFlashAttention. TileFlashAttention requires that
      // all rows in the tile have the same K and V matrices, and Q starts at
      // the same position. The end positions do not have to be the equal.
      if (start_positions[offset] != start_positions[0] ||
          qi_indices[offset] != qi_indices[0] ||
          kv_offsets[offset] != kv_offsets[0]) {
        use_tile_attention = false;
      }
    }
    for (size_t offset = 0;
         offset < kVTileSize && first_task + offset < total_tasks; ++offset) {
      auto& kv_cache = qbatch.KV(qi_indices[offset]).kv_cache;
      MatPtrT<KV_t> k("k_view", Extents2D(seq_len, qkv_dim));
      k.SetPtr(kv_cache.Row(0) + kv_offsets[offset], kv_cache.Stride());
      MatPtrT<KV_t> v("v_view", Extents2D(seq_len, qkv_dim));
      v.SetPtr(kv_cache.Row(0) + kv_offsets[offset] + qkv_dim,
               kv_cache.Stride());
      if (use_tile_attention) {
        // To avoid duplicating the code to setup K and V, the call to
        // TileFlashAttention is inside the loop over tasks, even though it
        // handles all rows in the task at once.
        StridedView<float> qT =
            StridedView<float>(activations.q_T.Row(0) + first_task, kVTileSize,
                               activations.q_T.Stride());
        if (kVTileSize == kNF) {
          TileFlashAttention(activations.q, q_offsets, qT, k,
                             start_positions[offset], last_pos, min_last_pos,
                             max_last_pos, v, layer_idx, layer, activations,
                             activations.att_out, out_offsets, ctx.profiler,
                             worker);
        } else if (kVTileSize == 4) {
          TileFlashAttention4(activations.q, q_offsets, qT, k,
                              start_positions[offset], last_pos, min_last_pos,
                              max_last_pos, v, layer_idx, layer, activations,
                              activations.att_out, out_offsets, ctx.profiler,
                              worker);
        } else {
          HWY_UNREACHABLE;
        }
        break;
      } else {
        SingleFlashAttention(start_positions[offset], last_pos[offset],
                             activations.q.Row(0) + q_offsets[offset], k, v,
                             layer_idx, layer, activations,
                             activations.att_out.Row(0) + out_offsets[offset],
                             ctx.profiler, worker);
      }
    }
  };

  {
    PROFILER_ZONE("Gen.Attention.DotSoftmax.ForkJoin");
    // Full parallelism is helpful, SmallParallelFor is insufficient.
    HierarchicalParallelFor(num_thread_tasks, ctx.pools, func);
  }
}

// NOLINTNEXTLINE(google-readability-namespace-comments)
}  // namespace HWY_NAMESPACE
}  // namespace gcpp
HWY_AFTER_NAMESPACE();