// Copyright 2024 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.

#ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_CONFIGS_H_
#define THIRD_PARTY_GEMMA_CPP_GEMMA_CONFIGS_H_

// Model configurations

#include <stddef.h>

#include <array>

#include "compression/shared.h"  // BF16

namespace gcpp {

// Allow changing pre-allocated kv cache size as a compiler flag
#ifndef GEMMA_MAX_SEQLEN
#define GEMMA_MAX_SEQLEN 4096
#endif  // !GEMMA_MAX_SEQLEN

// Allow changing k parameter of `SampleTopK` as a compiler flag
#ifndef GEMMA_TOPK
#define GEMMA_TOPK 1
#endif  // !GEMMA_TOPK

static constexpr size_t kSeqLen = GEMMA_MAX_SEQLEN;
static constexpr size_t kTopK = GEMMA_TOPK;
static constexpr size_t kVocabSize = 256000;

using EmbedderInputT = BF16;

enum class LayerAttentionType {
  kGemma,
  kGriffinRecurrentBlock,
  kVit,
};

// Post attention and ffw normalization type.
enum class PostNormType {
  None,
  Scale,
};

// Post qk projection operation type.
enum class PostQKType {
  Rope,
};

// FFW activation function.
enum class ActivationType {
  Gelu,
};

// Attention query scale.
enum class QueryScaleType {
  SqrtKeySize,
  SqrtModelDimDivNumHeads,
};

// Residual connection type.
enum class ResidualType {
  Add,
};

template <size_t kNum>
constexpr std::array<LayerAttentionType, kNum> FixedLayerConfig(
    LayerAttentionType type) {
  std::array<LayerAttentionType, kNum> config = {};
  for (LayerAttentionType& l : config) {
    l = type;
  }
  return config;
}

template <size_t kNum>
constexpr std::array<size_t, kNum> FixedAttentionWindowSizes(
    size_t window_size) {
  std::array<size_t, kNum> window_size_configs = {};
  for (size_t& l : window_size_configs) {
    l = window_size;
  }
  return window_size_configs;
}

// Repeat window_size_pattern for kNum / kPatternSize times.
template <size_t kNum, size_t kPatternSize>
constexpr std::array<size_t, kNum> RepeatedAttentionWindowSizes(
    const std::array<size_t, kPatternSize>& window_size_pattern) {
  static_assert(kNum % kPatternSize == 0,
                "kNum must be a multiple of kPatternSize");
  std::array<size_t, kNum> window_size_configs = {};
  for (size_t i = 0; i < kNum; ++i) {
    window_size_configs[i] = window_size_pattern[i % kPatternSize];
  }
  return window_size_configs;
}

template <size_t kNumLayers>
constexpr size_t NumLayersOfTypeBefore(
    const std::array<LayerAttentionType, kNumLayers>& layers,
    LayerAttentionType type, size_t num) {
  size_t count = 0;
  for (size_t i = 0; i < num; i++) {
    if (layers[i] == type) count++;
  }
  return count;
}

template <class TConfig, typename = void>
struct CacheLayerSize {
  constexpr size_t operator()() const {
    return TConfig::kKVHeads * TConfig::kQKVDim * 2;
  }
};

template <class TConfig, typename = void>
struct CachePosSize {
  constexpr size_t operator()() const {
    return TConfig::kGemmaLayers * CacheLayerSize<TConfig>()();
  }
};

struct ConfigNoVit {
  struct VitConfig {
    // Some of these are needed to make the compiler happy when trying to
    // generate code that will actually never be used.
    using Weight = float;
    static constexpr int kLayers = 0;
    static constexpr std::array<LayerAttentionType, 0> kLayerConfig =
        FixedLayerConfig<0>(LayerAttentionType::kVit);
    static constexpr int kModelDim = 0;
    static constexpr int kFFHiddenDim = 0;
    static constexpr int kHeads = 1;  // Avoid division by 0 in griffin gate_w.
    static constexpr int kKVHeads = 0;
    static constexpr int kQKVDim = 0;
    static constexpr int kSeqLen = 0;
    static constexpr ResidualType kResidual = ResidualType::Add;
    static constexpr int kGriffinLayers = 0;
    static constexpr int kConv1dWidth = 0;
    static constexpr bool kFFBiases = false;
    static constexpr bool kSoftmaxAttnOutputBiases = false;
    static constexpr PostNormType kPostNorm = PostNormType::None;
  };
};

struct ConfigNoSSM : ConfigNoVit {
  static constexpr int kGriffinLayers = 0;

  static constexpr int kConv1dWidth = 0;
  static constexpr bool kFFBiases = false;
  static constexpr bool kSoftmaxAttnOutputBiases = false;
  static constexpr bool kUseHalfRope = false;
  static constexpr bool kUseLocalAttention = false;
  static constexpr bool kInterleaveQKV = true;
  static constexpr PostQKType kPostQK = PostQKType::Rope;
  static constexpr ActivationType kActivation = ActivationType::Gelu;
  static constexpr ResidualType kResidual = ResidualType::Add;
};

struct ConfigBaseGemmaV1 : ConfigNoSSM {
  static constexpr float kAttCap = 0.0f;
  static constexpr float kFinalCap = 0.0f;
  static constexpr PostNormType kPostNorm = PostNormType::None;
  static constexpr QueryScaleType kQueryScale = QueryScaleType::SqrtKeySize;
};

struct ConfigBaseGemmaV2 : ConfigNoSSM {
  static constexpr float kAttCap = 50.0f;
  static constexpr float kFinalCap = 30.0f;
  static constexpr PostNormType kPostNorm = PostNormType::Scale;
};

template <typename TWeight>
struct ConfigGemma2_27B : public ConfigBaseGemmaV2 {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = 8192;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 46> kLayerConfig =
      FixedLayerConfig<46>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 46> kAttentionWindowSizes =
      RepeatedAttentionWindowSizes<46, 2>({4096, kSeqLen});
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 4608;
  static constexpr int kFFHiddenDim = 16 * 4608 / 2;  // = 36864
  static constexpr int kHeads = 32;
  static constexpr int kKVHeads = 16;
  static constexpr int kQKVDim = 128;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
  static constexpr QueryScaleType kQueryScale =
      QueryScaleType::SqrtModelDimDivNumHeads;
};

template <typename TWeight>
struct ConfigGemma2_9B : public ConfigBaseGemmaV2 {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = 8192;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 42> kLayerConfig =
      FixedLayerConfig<42>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 42> kAttentionWindowSizes =
      RepeatedAttentionWindowSizes<42, 2>({4096, kSeqLen});
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 3584;
  static constexpr int kFFHiddenDim = 8 * 3584 / 2;  // = 14336
  static constexpr int kHeads = 16;
  static constexpr int kKVHeads = 8;
  static constexpr int kQKVDim = 256;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
  static constexpr QueryScaleType kQueryScale = QueryScaleType::SqrtKeySize;
};

template <typename TWeight>
struct ConfigGemma7B : public ConfigBaseGemmaV1 {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = gcpp::kSeqLen;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 28> kLayerConfig =
      FixedLayerConfig<28>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 28> kAttentionWindowSizes =
      FixedAttentionWindowSizes<28>(kSeqLen);
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 3072;
  static constexpr int kFFHiddenDim = 16 * 3072 / 2;  // = 24576
  static constexpr int kHeads = 16;
  static constexpr int kKVHeads = 16;  // standard MHA
  static constexpr int kQKVDim = 256;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
};

template <typename TWeight>
struct ConfigGemma2B : public ConfigBaseGemmaV1 {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = gcpp::kSeqLen;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 18> kLayerConfig =
      FixedLayerConfig<18>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 18> kAttentionWindowSizes =
      FixedAttentionWindowSizes<18>(kSeqLen);
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 2048;
  static constexpr int kFFHiddenDim = 16 * 2048 / 2;  // = 16384
  static constexpr int kHeads = 8;
  static constexpr int kKVHeads = 1;
  static constexpr int kQKVDim = 256;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
};

template <typename TWeight>
struct ConfigPaliGemma_224 : public ConfigGemma2B<TWeight> {
  // On the LM side, the vocab size is one difference to Gemma1-2B in the
  // architecture. PaliGemma adds 1024 <locNNNN> and 128 <segNNN> tokens.
  static constexpr int kVocabSize = 256000 + 1024 + 128;  // = 257152

  // Sub-config for the Vision-Transformer part.
  struct VitConfig : public ConfigNoSSM {
    using Weight = TWeight;
    // The ViT parts. https://arxiv.org/abs/2305.13035
    // "SoViT-400m/14 [...] has a width of 1152, depth 27, and MLP dim 4304."
    static constexpr std::array<LayerAttentionType, 27> kLayerConfig =
        FixedLayerConfig<27>(LayerAttentionType::kVit);
    static constexpr int kLayers = kLayerConfig.size();
    static constexpr int kNumTensorScales = 4 * kLayers;
    static constexpr int kModelDim = 1152;
    static constexpr int kFFHiddenDim = 4304;
    static constexpr int kHeads = 16;
    static constexpr int kKVHeads = 16;  // standard MHA
    static constexpr int kQKVDim = 72;
    static constexpr int kSeqLen = 16 * 16;  // 256
    static constexpr bool kFFBiases = true;
    // The Vit part does not have a vocabulary, the image patches are embedded.
    static constexpr int kVocabSize = 0;
    // Dimensions related to image processing.
    static constexpr int kPatchWidth = 14;
    static constexpr int kImageSize = 224;
    // Necessary constant for the layer configuration.
    static constexpr PostNormType kPostNorm = PostNormType::None;
  };
};

template <typename TWeight>
struct ConfigGemma2_2B : public ConfigBaseGemmaV2 {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = 8192;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 26> kLayerConfig =
      FixedLayerConfig<26>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 26> kAttentionWindowSizes =
      RepeatedAttentionWindowSizes<26, 2>({4096, kSeqLen});
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 2304;
  static constexpr int kFFHiddenDim = 8 * 2304 / 2;  // = 9216
  static constexpr int kHeads = 8;
  static constexpr int kKVHeads = 4;
  static constexpr int kQKVDim = 256;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
  static constexpr QueryScaleType kQueryScale = QueryScaleType::SqrtKeySize;
};

template <typename TWeight>
struct ConfigGemmaTiny : public ConfigNoSSM {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  static constexpr int kSeqLen = 32;
  static constexpr int kVocabSize = 64;
  static constexpr std::array<LayerAttentionType, 3> kLayerConfig =
      FixedLayerConfig<3>(LayerAttentionType::kGemma);
  static constexpr std::array<size_t, 3> kAttentionWindowSizes =
      FixedAttentionWindowSizes<3>(kSeqLen);
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kNumTensorScales = 4 * kLayers;
  static constexpr int kGemmaLayers = kLayers;
  static constexpr int kModelDim = 128;
  static constexpr int kFFHiddenDim = 256;
  static constexpr int kHeads = 4;
  static constexpr int kKVHeads = 1;
  static constexpr int kQKVDim = 16;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
  static constexpr PostNormType kPostNorm = PostNormType::None;
  static constexpr QueryScaleType kQueryScale = QueryScaleType::SqrtKeySize;

  static constexpr float kAttCap = 0.0f;
  // This is required for optimize_test to pass.
  static constexpr float kFinalCap = 30.0f;
};

template <typename TWeight>
struct ConfigGriffin2B : ConfigNoVit {
  using Weight = TWeight;  // make accessible where we only have a TConfig

  // Griffin uses local attention, so kSeqLen is actually the local attention
  // window.
  static constexpr int kSeqLen = 2048;
  static constexpr int kVocabSize = gcpp::kVocabSize;
  static constexpr std::array<LayerAttentionType, 26> kLayerConfig = {
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGemma,
      LayerAttentionType::kGriffinRecurrentBlock,
      LayerAttentionType::kGriffinRecurrentBlock,
  };
  static constexpr std::array<size_t, 26> kAttentionWindowSizes =
      FixedAttentionWindowSizes<26>(kSeqLen);
  static constexpr int kLayers = kLayerConfig.size();
  static constexpr int kGemmaLayers =
      NumLayersOfTypeBefore(kLayerConfig, LayerAttentionType::kGemma, kLayers);
  static constexpr int kGriffinLayers =
      NumLayersOfTypeBefore(kLayerConfig,
                            LayerAttentionType::kGriffinRecurrentBlock,
                            kLayers);
  static constexpr int kModelDim = 2560;
  static constexpr int kFFHiddenDim = 7680;
  static constexpr int kHeads = 10;
  static constexpr int kKVHeads = 1;
  static constexpr int kQKVDim = 256;  // query size == key size == value size
  static constexpr int kTopK = gcpp::kTopK;
  static constexpr bool kAbsolutePE = false;
  static constexpr PostNormType kPostNorm = PostNormType::None;

  // No SoftCap.
  static constexpr float kAttCap = 0.0f;
  static constexpr float kFinalCap = 0.0f;

  // SSM config.
  static constexpr int kConv1dWidth = 4;
  static constexpr bool kFFBiases = true;
  static constexpr bool kSoftmaxAttnOutputBiases = true;
  static constexpr bool kUseHalfRope = true;
  static constexpr bool kUseLocalAttention = true;
  static constexpr bool kInterleaveQKV = false;
  static constexpr int kNumTensorScales = 140;
  static constexpr PostQKType kPostQK = PostQKType::Rope;
  static constexpr ActivationType kActivation = ActivationType::Gelu;
  static constexpr QueryScaleType kQueryScale = QueryScaleType::SqrtKeySize;
  static constexpr ResidualType kResidual = ResidualType::Add;
};

}  // namespace gcpp

#endif  // THIRD_PARTY_GEMMA_CPP_GEMMA_CONFIGS_H_