/* Copyright 2020 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 "tensorflow/lite/delegates/xnnpack/depthwise_conv_2d_tester.h"

#include <algorithm>
#include <array>
#include <cstdint>
#include <cstdlib>
#include <functional>
#include <memory>
#include <random>
#include <vector>

#include <gtest/gtest.h>
#include "fp16.h"  // from @FP16
#include "flatbuffers/buffer.h"  // from @flatbuffers
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "flatbuffers/string.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_conversion_utils.h"
#include "tensorflow/lite/core/interpreter_builder.h"
#include "tensorflow/lite/core/kernels/register.h"
#include "tensorflow/lite/delegates/xnnpack/test_util.h"
#include "tensorflow/lite/delegates/xnnpack/xnnpack_delegate.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

namespace tflite {
namespace xnnpack {

void DepthwiseConv2DTester::Test(TfLiteDelegate* delegate) const {
  std::vector<char> buffer = CreateTfLiteModel();
  const Model* model = GetModel(buffer.data());

  std::unique_ptr<Interpreter> delegate_interpreter;
  ASSERT_EQ(
      InterpreterBuilder(
          model,
          ::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
          &delegate_interpreter),
      kTfLiteOk);
  std::unique_ptr<Interpreter> default_interpreter;
  ASSERT_EQ(
      InterpreterBuilder(
          model,
          ::tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates())(
          &default_interpreter),
      kTfLiteOk);

  ASSERT_TRUE(delegate_interpreter);
  ASSERT_TRUE(default_interpreter);

  ASSERT_EQ(delegate_interpreter->inputs().size(), 1);
  ASSERT_EQ(default_interpreter->inputs().size(), 1);

  ASSERT_EQ(delegate_interpreter->outputs().size(), 1);
  ASSERT_EQ(default_interpreter->outputs().size(), 1);

  ASSERT_EQ(delegate_interpreter->AllocateTensors(), kTfLiteOk);
  ASSERT_EQ(default_interpreter->AllocateTensors(), kTfLiteOk);

  ASSERT_EQ(delegate_interpreter->ModifyGraphWithDelegate(delegate), kTfLiteOk);

  if (weights_cache_ != nullptr) {
    TfLiteXNNPackDelegateWeightsCacheFinalizeHard(weights_cache_);
  }

  std::random_device random_device;
  auto rng = std::mt19937(random_device());
  auto input_rng =
      std::bind(std::uniform_real_distribution<float>(), std::ref(rng));
  float* default_input_data = default_interpreter->typed_input_tensor<float>(0);
  std::generate_n(default_input_data,
                  BatchSize() * InputHeight() * InputWidth() * InputChannels(),
                  input_rng);

  float* delegate_input_data =
      delegate_interpreter->typed_input_tensor<float>(0);
  std::copy_n(default_input_data,
              BatchSize() * InputHeight() * InputWidth() * InputChannels(),
              delegate_input_data);

  ASSERT_EQ(default_interpreter->Invoke(), kTfLiteOk);
  ASSERT_EQ(delegate_interpreter->Invoke(), kTfLiteOk);

  float* default_output_data =
      default_interpreter->typed_output_tensor<float>(0);
  float* delegate_output_data =
      delegate_interpreter->typed_output_tensor<float>(0);

  for (int32_t i = 0; i < BatchSize(); i++) {
    for (int32_t y = 0; y < OutputHeight(); y++) {
      for (int32_t x = 0; x < OutputWidth(); x++) {
        for (int32_t c = 0; c < OutputChannels(); c++) {
          const int32_t index = ((i * OutputHeight() + y) * OutputWidth() + x) *
                                    OutputChannels() +
                                c;
          ASSERT_NEAR(default_output_data[index], delegate_output_data[index],
                      std::abs(default_output_data[index]) * 3.0e-6f)
              << "batch " << i << " / " << BatchSize() << ", y position " << y
              << " / " << OutputHeight() << ", x position " << x << " / "
              << OutputWidth() << ", channel " << c << " / "
              << OutputChannels();
        }
      }
    }
  }
}

std::vector<char> DepthwiseConv2DTester::CreateTfLiteModel() const {
  std::random_device random_device;
  auto rng = std::mt19937(random_device());
  auto range_rng = std::bind(
      std::uniform_real_distribution<float>(-25.0f, 25.0f), std::ref(rng));

  /*************************** Define operator codes **************************/
  flatbuffers::FlatBufferBuilder builder;
  std::vector<flatbuffers::Offset<OperatorCode>> operator_codes{
      {CreateOperatorCode(builder, BuiltinOperator_DEPTHWISE_CONV_2D)}};
  int dequantize_operator_code = -1;
  switch (WeightsType()) {
    case WeightsType::kFP32:
      break;
    case WeightsType::kFP16:
    case WeightsType::kTensorWiseQuantizedInt8:
    case WeightsType::kChannelWiseQuantizedInt8:
      dequantize_operator_code = operator_codes.size();
      operator_codes.emplace_back(
          CreateOperatorCode(builder, BuiltinOperator_DEQUANTIZE));
      break;
  }
  int densify_operator_code = -1;
  if (SparseWeights()) {
    densify_operator_code = operator_codes.size();
    operator_codes.emplace_back(
        CreateOperatorCode(builder, BuiltinOperator_DENSIFY));
  }

  /*********************** Generate filter and bias data **********************/
  std::vector<float> filter_data(KernelHeight() * KernelWidth() *
                                 OutputChannels());
  std::vector<float> bias_data(OutputChannels());
  for (int32_t ic = 0; ic < InputChannels(); ic++) {
    // Use the same range of all-positive or all-negative values to generate
    // all pixels within the same batch index & channel, but different ranges
    // for different channels or batches. This ensures that no catastrophic
    // cancellation occur, but test covers both positive and negative inputs.
    const float range = range_rng();
    const auto value_dist = std::uniform_real_distribution<float>(
        std::min(range, 0.0f), std::max(range, 0.0f));
    auto value_rng = std::bind(value_dist, std::ref(rng));
    for (int32_t m = 0; m < DepthMultiplier(); m++) {
      const int32_t oc = ic * DepthMultiplier() + m;
      bias_data[oc] = value_rng();
      for (int32_t y = 0; y < KernelHeight(); y++) {
        for (int32_t x = 0; x < KernelWidth(); x++) {
          const int32_t index = (y * KernelWidth() + x) * OutputChannels() + oc;
          filter_data[index] = value_rng();
        }
      }
    }
  }

  /************************ Define sparsity parameters ************************/
  flatbuffers::Offset<SparsityParameters> filter_sparsity_params = 0;
  const std::vector<int32_t> filter_shape = {1, KernelHeight(), KernelWidth(),
                                             OutputChannels()};
  if (SparseWeights()) {
    // Sparse tensor in TFLite can be in different formats. Here we choose the
    // simplest configuration that
    //   1. all dimensions are dense,
    //   2. in-order traversal, and
    //   3. no block configuration.
    const int dims_count = filter_shape.size();
    std::vector<flatbuffers::Offset<DimensionMetadata>> dim_metadata(
        dims_count);
    std::vector<int> traversal_order(dims_count);
    for (int i = 0; i < dims_count; i++) {
      traversal_order[i] = i;
      dim_metadata[i] = CreateDimensionMetadata(builder, DimensionType_DENSE,
                                                filter_shape[i]);
    }
    filter_sparsity_params =
        CreateSparsityParameters(builder, builder.CreateVector(traversal_order),
                                 0, builder.CreateVector(dim_metadata));
  }

  /****************************** Define buffers ******************************/
  std::vector<flatbuffers::Offset<tflite::Buffer>> buffers{
      {CreateBuffer(builder, builder.CreateVector({}))}};
  tflite::TensorType quantized_filter_type = TensorType_FLOAT32;
  flatbuffers::Offset<tflite::QuantizationParameters>
      filter_quantization_params = 0;
  int filter_buffer_id = 0, quantized_filter_buffer_id = 0;
  switch (WeightsType()) {
    case WeightsType::kFP32:
      filter_buffer_id = buffers.size();
      buffers.emplace_back(CreateBuffer(
          builder, builder.CreateVector(
                       reinterpret_cast<const uint8_t*>(filter_data.data()),
                       sizeof(float) * filter_data.size())));
      break;
    case WeightsType::kFP16: {
      std::vector<uint16_t> quantized_filter_data(filter_data.size());
      std::transform(filter_data.begin(), filter_data.end(),
                     quantized_filter_data.begin(), fp16_ieee_from_fp32_value);

      quantized_filter_buffer_id = buffers.size();
      buffers.emplace_back(CreateBuffer(
          builder,
          builder.CreateVector(
              reinterpret_cast<const uint8_t*>(quantized_filter_data.data()),
              sizeof(uint16_t) * quantized_filter_data.size())));

      quantized_filter_type = TensorType_FLOAT16;
      break;
    }
    case WeightsType::kTensorWiseQuantizedInt8:
    case WeightsType::kChannelWiseQuantizedInt8: {
      std::vector<float> filter_scales;
      std::vector<int64_t> filter_zero_points;
      int32_t filter_quantized_dimension = 0;

      std::vector<int8_t> quantized_filter_data(filter_data.size());
      if (WeightsType() == WeightsType::kChannelWiseQuantizedInt8) {
        filter_quantized_dimension =
            static_cast<int32_t>(filter_shape.size()) - 1;
        const int32_t num_scales = filter_shape[filter_quantized_dimension];
        filter_scales = GetInt8QuantizationScalePerChannel(
            filter_data.data(), filter_quantized_dimension, filter_shape);
        filter_zero_points.resize(num_scales, 0);
        QuantizeInt8PerChannel(filter_scales.data(), filter_zero_points.data(),
                               filter_quantized_dimension, filter_data.data(),
                               quantized_filter_data.data(), filter_shape);
      } else {
        filter_scales.resize(1, GetInt8QuantizationScale(filter_data));
        filter_zero_points.resize(1, 0);
        std::transform(filter_data.begin(), filter_data.end(),
                       quantized_filter_data.begin(),
                       std::bind(QuantizeInt8, std::placeholders::_1, 0,
                                 filter_scales[0]));
      }

      quantized_filter_buffer_id = buffers.size();
      buffers.emplace_back(CreateBuffer(
          builder,
          builder.CreateVector(
              reinterpret_cast<const uint8_t*>(quantized_filter_data.data()),
              sizeof(int8_t) * quantized_filter_data.size())));

      quantized_filter_type = TensorType_INT8;
      filter_quantization_params = CreateQuantizationParameters(
          builder, /*min=*/0, /*max=*/0,
          builder.CreateVector<float>(filter_scales),
          builder.CreateVector<int64_t>(filter_zero_points),
          /*details_type=*/QuantizationDetails_NONE,
          /*details=*/0, filter_quantized_dimension);
      break;
    }
  }
  tflite::TensorType quantized_bias_type = TensorType_FLOAT32;
  int bias_buffer_id = 0, quantized_bias_buffer_id = 0;
  switch (BiasType()) {
    case BiasType::kFP32:
      bias_buffer_id = buffers.size();
      buffers.emplace_back(CreateBuffer(
          builder, builder.CreateVector(
                       reinterpret_cast<const uint8_t*>(bias_data.data()),
                       sizeof(float) * bias_data.size())));
      break;
    case BiasType::kFP16: {
      std::vector<uint16_t> quantized_bias_data(bias_data.size());
      std::transform(bias_data.begin(), bias_data.end(),
                     quantized_bias_data.begin(), fp16_ieee_from_fp32_value);

      quantized_bias_buffer_id = buffers.size();
      buffers.emplace_back(CreateBuffer(
          builder,
          builder.CreateVector(
              reinterpret_cast<const uint8_t*>(quantized_bias_data.data()),
              sizeof(uint16_t) * quantized_bias_data.size())));

      quantized_bias_type = TensorType_FLOAT16;
      break;
    }
  }

  /****************************** Define tensors ******************************/
  std::vector<flatbuffers::Offset<tflite::Tensor>> tensors;
  int sparse_filter_tensor_id = -1;
  if (SparseWeights()) {
    sparse_filter_tensor_id = tensors.size();
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
        /*type=*/quantized_filter_type,
        /*buffer=*/std::max(filter_buffer_id, quantized_filter_buffer_id),
        /*name=*/0, filter_quantization_params,
        /*is_variable=*/false, filter_sparsity_params));
  }
  int quantized_filter_tensor_id = -1;
  if (quantized_filter_type != TensorType_FLOAT32) {
    quantized_filter_tensor_id = tensors.size();
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
        /*type=*/quantized_filter_type,
        /*buffer=*/SparseWeights() ? 0 : quantized_filter_buffer_id,
        /*name=*/0, filter_quantization_params));
  }
  int quantized_bias_tensor_id = -1;
  const std::vector<int32_t> bias_shape = {OutputChannels()};
  if (quantized_bias_type != TensorType_FLOAT32) {
    quantized_bias_tensor_id = tensors.size();
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(bias_shape.data(), bias_shape.size()),
        quantized_bias_type, /*buffer=*/quantized_bias_buffer_id));
  }

  const int input_tensor_id = tensors.size();
  const std::array<int32_t, 4> input_shape{
      {BatchSize(), InputHeight(), InputWidth(), InputChannels()}};
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
      TensorType_FLOAT32));

  const int filter_tensor_id = tensors.size();
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
      TensorType_FLOAT32,
      /*buffer=*/SparseWeights() ? 0 : filter_buffer_id));

  const int bias_tensor_id = tensors.size();
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(bias_shape.data(), bias_shape.size()),
      TensorType_FLOAT32, bias_buffer_id));

  const int output_tensor_id = tensors.size();
  const std::array<int32_t, 4> output_shape{
      {BatchSize(), OutputHeight(), OutputWidth(), OutputChannels()}};
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
      TensorType_FLOAT32));

  /***************************** Define operators *****************************/
  std::vector<flatbuffers::Offset<tflite::Operator>> operators;
  if (SparseWeights()) {
    const std::array<int32_t, 1> densify_filter_inputs{
        {sparse_filter_tensor_id}};
    const std::array<int32_t, 1> densify_filter_outputs{
        {quantized_filter_tensor_id >= 0 ? quantized_filter_tensor_id
                                         : filter_tensor_id}};
    operators.emplace_back(CreateOperator(
        builder, /*opcode_index=*/densify_operator_code,
        builder.CreateVector<int32_t>(densify_filter_inputs.data(),
                                      densify_filter_inputs.size()),
        builder.CreateVector<int32_t>(densify_filter_outputs.data(),
                                      densify_filter_outputs.size())));
  }

  if (quantized_filter_tensor_id >= 0) {
    const std::array<int32_t, 1> dequantize_filter_inputs{
        {quantized_filter_tensor_id}};
    const std::array<int32_t, 1> dequantize_filter_outputs{{filter_tensor_id}};
    operators.emplace_back(CreateOperator(
        builder, /*opcode_index=*/dequantize_operator_code,
        builder.CreateVector<int32_t>(dequantize_filter_inputs.data(),
                                      dequantize_filter_inputs.size()),
        builder.CreateVector<int32_t>(dequantize_filter_outputs.data(),
                                      dequantize_filter_outputs.size())));
  }

  if (quantized_bias_tensor_id >= 0) {
    const std::array<int32_t, 1> dequantize_bias_inputs{
        {quantized_bias_tensor_id}};
    const std::array<int32_t, 1> dequantize_bias_outputs{{bias_tensor_id}};
    operators.emplace_back(CreateOperator(
        builder, /*opcode_index=*/dequantize_operator_code,
        builder.CreateVector<int32_t>(dequantize_bias_inputs.data(),
                                      dequantize_bias_inputs.size()),
        builder.CreateVector<int32_t>(dequantize_bias_outputs.data(),
                                      dequantize_bias_outputs.size())));
  }

  const std::array<int32_t, 3> op_inputs{
      {input_tensor_id, filter_tensor_id, bias_tensor_id}};
  const std::array<int32_t, 1> op_outputs{{output_tensor_id}};
  const flatbuffers::Offset<DepthwiseConv2DOptions> depthwise_conv2d_options =
      CreateDepthwiseConv2DOptions(
          builder, Padding(), StrideWidth(), StrideHeight(), DepthMultiplier(),
          Activation(), DilationWidth(), DilationHeight());
  operators.emplace_back(CreateOperator(
      builder, /*opcode_index=*/0,
      builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
      builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()),
      BuiltinOptions_DepthwiseConv2DOptions, depthwise_conv2d_options.Union()));

  /****************************** Define subgraph *****************************/
  const std::array<int32_t, 1> subgraph_inputs{{input_tensor_id}};
  const std::array<int32_t, 1> subgraph_outputs{{output_tensor_id}};
  const flatbuffers::Offset<SubGraph> subgraph = CreateSubGraph(
      builder, builder.CreateVector(tensors.data(), tensors.size()),
      builder.CreateVector<int32_t>(subgraph_inputs.data(),
                                    subgraph_inputs.size()),
      builder.CreateVector<int32_t>(subgraph_outputs.data(),
                                    subgraph_outputs.size()),
      builder.CreateVector(operators.data(), operators.size()));

  const flatbuffers::Offset<flatbuffers::String> description =
      builder.CreateString("DepthwiseConv2D model");

  const flatbuffers::Offset<Model> model_buffer = CreateModel(
      builder, TFLITE_SCHEMA_VERSION,
      builder.CreateVector(operator_codes.data(), operator_codes.size()),
      builder.CreateVector(&subgraph, 1), description,
      builder.CreateVector(buffers.data(), buffers.size()));

  builder.Finish(model_buffer);

  return std::vector<char>(builder.GetBufferPointer(),
                           builder.GetBufferPointer() + builder.GetSize());
}

}  // namespace xnnpack
}  // namespace tflite
