/* Copyright 2024 The TensorFlow Authors. All Rights Reserved.

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

    http://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 <cstdint>
#include <vector>

#include <gtest/gtest.h>
#include "tensorflow/lite/c/c_api_types.h"
#include "tensorflow/lite/experimental/genai/genai_ops.h"
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"

namespace tflite {
namespace {

static const int kDefaultMaxNumCacheEntries = 2048;

class SimpleCacheOpModel : public SingleOpModel {
 public:
  SimpleCacheOpModel(const TensorData& pos_tensor, const TensorData& k_tensor,
                     const TensorData& v_tensor) {
    pos_ = AddInput(pos_tensor);
    k_ = AddInput(k_tensor);
    v_ = AddInput(v_tensor);
    kfull_ = AddOutput(k_tensor.type);
    vfull_ = AddOutput(v_tensor.type);
    SetCustomOp("KV_Cache", {}, ops::custom::Register_KV_CACHE);

    BuildInterpreter({GetShape(pos_), GetShape(k_), GetShape(v_)});
  }

  void SetPosition(const std::vector<int64_t>& data) {
    PopulateTensor(pos_, data);
  }
  void SetKey(const std::vector<float>& data) { PopulateTensor(k_, data); }
  void SetValue(const std::vector<float>& data) { PopulateTensor(v_, data); }

  void ResizePosition(const std::vector<int>& dims) {
    interpreter_->ResizeInputTensor(pos_, dims);
  }
  void ResizeKey(const std::vector<int>& dims) {
    interpreter_->ResizeInputTensor(k_, dims);
  }
  void ResizeValue(const std::vector<int>& dims) {
    interpreter_->ResizeInputTensor(v_, dims);
  }

  std::vector<float> GetFullK() {
    const auto output = ExtractVector<float>(kfull_);
    return output;
  }

  std::vector<float> GetFullV() {
    const auto output = ExtractVector<float>(vfull_);
    return output;
  }

  TfLiteStatus ReAllocate() { return interpreter_->AllocateTensors(); }

 protected:
  int pos_;
  int k_;
  int v_;
  int kfull_;
  int vfull_;
};

TEST(SimpleCacheOp1Test, BasicTest) {
  SimpleCacheOpModel m({TensorType_INT64, {2}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}});

  m.SetPosition({0, 1});
  m.SetKey({{1, 0, -6, 2, 4, 3, 1, 0, -6, 2, 4, 3}});
  m.SetValue({{4, 2, -4, 2, 4, 2, 4, 2, -4, 2, 4, 2}});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);
  std::vector<float> fullk = m.GetFullK();
  std::vector<float> fullv = m.GetFullV();
  ASSERT_EQ(fullk.size(), 2 * 3 * kDefaultMaxNumCacheEntries);
  ASSERT_EQ(fullv.size(), 2 * 3 * kDefaultMaxNumCacheEntries);
}

TEST(SimpleCacheOp2Test, AddToCache) {
  SimpleCacheOpModel m({TensorType_INT64, {2}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}});

  m.SetPosition({0, 1});
  std::vector<float> key = {1, 5, -6, 2, 4, 3, 8, 9, -8, 7, 2, 11};
  m.SetKey(key);
  std::vector<float> value = {2, 3, -4, 5, 6, 7, 1, 8, -12, 11, 14, 21};
  m.SetValue(value);
  const int key_size = 2 * 3;
  ASSERT_EQ(m.Invoke(), kTfLiteOk);
  std::vector<float> fullk = m.GetFullK();
  std::vector<float> fullv = m.GetFullV();
  for (int i = 0; i < key.size(); ++i) {
    ASSERT_EQ(fullk[i], key[i]);
    ASSERT_EQ(fullv[i], value[i]);
  }
  for (int i = key.size(); i < fullk.size(); ++i) {
    ASSERT_EQ(fullk[i], 0.);
    ASSERT_EQ(fullv[i], 0.);
  }

  ASSERT_EQ(fullk.size(), 2 * 3 * kDefaultMaxNumCacheEntries);
  ASSERT_EQ(fullv.size(), 2 * 3 * kDefaultMaxNumCacheEntries);

  for (int i = 0; i < 510; i++) {
    int offset = 2 * i + 2;
    m.SetPosition({offset, offset + 1});
    ASSERT_EQ(m.Invoke(), kTfLiteOk);
  }

  fullk = m.GetFullK();
  fullv = m.GetFullV();

  for (int i = 0; i < 1022 * key_size; ++i) {
    ASSERT_NE(fullv[i], 0);
  }

  for (int i = 1022 * key_size; i < fullk.size(); ++i) {
    ASSERT_EQ(fullv[i], 0);
  }
}

TEST(SimpleCacheOp2Test, ShiftSlotsInCache) {
  SimpleCacheOpModel m({TensorType_INT64, {2}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}},
                       {TensorType_FLOAT32, {1, 2, 2, 3}});

  m.SetPosition({0, 1});
  std::vector<float> key = {1, 5, -6, 2, 4, 3, 2, 6, -7, 3, 5, 4};
  m.SetKey(key);
  std::vector<float> value = {4, 2, -4, 2, 4, 2, 9, 8, -9, 8, 9, 1};
  m.SetValue(value);
  ASSERT_EQ(m.Invoke(), kTfLiteOk);
  std::vector<float> fullk = m.GetFullK();
  std::vector<float> fullv = m.GetFullV();
  for (int i = 0; i < key.size(); ++i) {
    ASSERT_EQ(fullk[i], key[i]);
    ASSERT_EQ(fullv[i], value[i]);
  }
  for (int i = key.size(); i < fullk.size(); ++i) {
    ASSERT_EQ(fullk[i], 0.);
    ASSERT_EQ(fullv[i], 0.);
  }
  ASSERT_EQ(fullk.size(), 2 * 3 * kDefaultMaxNumCacheEntries);
  ASSERT_EQ(fullv.size(), 2 * 3 * kDefaultMaxNumCacheEntries);

  // Now fill up the cache
  for (int i = 0; i < 1023; i++) {
    ASSERT_EQ(m.Invoke(), kTfLiteOk);
    int offset = 2 * i + 2;
    m.SetPosition({offset, offset + 1});
    ASSERT_EQ(m.Invoke(), kTfLiteOk);
  }

  fullk = m.GetFullK();
  fullv = m.GetFullV();

  for (int i = 0; i < fullk.size(); ++i) {
    ASSERT_NE(fullk[i], 0);
    ASSERT_NE(fullv[i], 0);
  }

  for (int j = 0; j < 6; ++j) {
    int idxfull = fullk.size() - 6 + j;
    int idx = 6 + j;
    ASSERT_EQ(fullk[idxfull], key[idx]);
    ASSERT_EQ(fullv[idxfull], value[idx]);
  }

  std::vector<float> key2 = {1, 1, 1, 1, 1, 1, 7, 7, 7, 7, 7, 7};
  m.SetKey(key2);
  std::vector<float> value2 = {8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9};
  m.SetValue(value2);
  m.SetPosition({2048, 2049});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  fullk = m.GetFullK();
  fullv = m.GetFullV();

  for (int j = 0; j < 12; ++j) {
    int idxfull = fullk.size() - 12 + j;
    ASSERT_EQ(fullk[idxfull], key2[j]);
    ASSERT_EQ(fullv[idxfull], value2[j]);
  }

  // Resize to a single entry. Add to a full cache and verify
  // the cached contents.
  m.ResizeKey({1, 1, 2, 3});
  m.ResizeValue({1, 1, 2, 3});
  m.ResizePosition({1});
  m.ReAllocate();

  std::vector<float> key3 = {4, 4, 4, 4, 4, 4};
  m.SetKey(key3);
  std::vector<float> value3 = {2, 2, 2, 2, 2, 2};
  m.SetValue(value3);
  m.SetPosition({2050});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  fullk = m.GetFullK();
  fullv = m.GetFullV();

  for (int j = 0; j < 6; ++j) {
    int idxfull = fullk.size() - 6 + j;
    ASSERT_EQ(fullk[idxfull], key3[j]);
    ASSERT_EQ(fullv[idxfull], value3[j]);
  }

  // Verify that other cache entries got shifted up 1.
  for (int j = 0; j < 6; ++j) {
    int idxfull = fullk.size() - 12 + j;
    ASSERT_EQ(fullk[idxfull], key2[6 + j]);
    ASSERT_EQ(fullv[idxfull], value2[6 + j]);
  }

  std::vector<float> key4 = {5, 5, 5, 5, 5, 5};
  m.SetKey(key3);
  std::vector<float> value4 = {3, 3, 3, 3, 3, 3};
  m.SetValue(value3);
  m.SetPosition({0});
  ASSERT_EQ(m.Invoke(), kTfLiteError);
}

}  // namespace
}  // namespace tflite