/* 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 <array>
#include <cstdint>
#include <functional>
#include <random>
#include <vector>

#include <gtest/gtest.h>
#include <fp16.h>
#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/schema/schema_utils.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);

  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_tensor<float>(
      default_interpreter->inputs()[0]);
  std::generate(default_input_data,
                default_input_data + BatchSize() * InputHeight() *
                                         InputWidth() * InputChannels(),
                input_rng);

  float* delegate_input_data = delegate_interpreter->typed_tensor<float>(
      delegate_interpreter->inputs()[0]);
  std::copy(default_input_data,
            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_tensor<float>(
      default_interpreter->outputs()[0]);
  float* delegate_output_data = delegate_interpreter->typed_tensor<float>(
      delegate_interpreter->outputs()[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));

  flatbuffers::FlatBufferBuilder builder;
  std::vector<flatbuffers::Offset<OperatorCode>> operator_codes{
      {CreateOperatorCode(builder, BuiltinOperator_DEPTHWISE_CONV_2D)}};
  std::vector<flatbuffers::Offset<tflite::Operator>> operators;
  std::vector<flatbuffers::Offset<tflite::Buffer>> buffers{
      {CreateBuffer(builder, builder.CreateVector({}))}};

  if (FP16Weights()) {
    operator_codes.emplace_back(
        CreateOperatorCode(builder, BuiltinOperator_DEQUANTIZE));

    std::vector<uint16_t> filter_data(KernelHeight() * KernelWidth() *
                                      OutputChannels());
    std::vector<uint16_t> 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();
      auto value_rng =
          std::bind(fp16_ieee_from_fp32_value,
                    std::bind(std::uniform_real_distribution<float>(
                                  std::min(range, 0.0f), std::max(range, 0.0f)),
                              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();
          }
        }
      }
    }

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

    const std::array<int32_t, 1> dequantize_filter_inputs{{0}};
    const std::array<int32_t, 1> dequantize_filter_outputs{{3}};
    operators.emplace_back(CreateOperator(
        builder, /*opcode_index=*/1,
        builder.CreateVector<int32_t>(dequantize_filter_inputs.data(),
                                      dequantize_filter_inputs.size()),
        builder.CreateVector<int32_t>(dequantize_filter_outputs.data(),
                                      dequantize_filter_outputs.size())));
    const std::array<int32_t, 1> dequantize_bias_inputs{{1}};
    const std::array<int32_t, 1> dequantize_bias_outputs{{4}};
    operators.emplace_back(CreateOperator(
        builder, /*opcode_index=*/1,
        builder.CreateVector<int32_t>(dequantize_bias_inputs.data(),
                                      dequantize_bias_inputs.size()),
        builder.CreateVector<int32_t>(dequantize_bias_outputs.data(),
                                      dequantize_bias_outputs.size())));
  } else {
    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();
      auto value_rng =
          std::bind(std::uniform_real_distribution<float>(
                        std::min(range, 0.0f), std::max(range, 0.0f)),
                    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();
          }
        }
      }
    }

    buffers.emplace_back(CreateBuffer(
        builder, builder.CreateVector(
                     reinterpret_cast<const uint8_t*>(filter_data.data()),
                     sizeof(float) * filter_data.size())));
    buffers.emplace_back(CreateBuffer(
        builder,
        builder.CreateVector(reinterpret_cast<const uint8_t*>(bias_data.data()),
                             sizeof(float) * bias_data.size())));

    if (SparseWeights()) {
      operator_codes.emplace_back(
          CreateOperatorCode(builder, BuiltinOperator_DENSIFY));
      const std::array<int32_t, 1> densify_filter_inputs{{0}};
      const std::array<int32_t, 1> densify_filter_outputs{{2}};
      operators.emplace_back(CreateOperator(
          builder, /*opcode_index=*/1,
          builder.CreateVector<int32_t>(densify_filter_inputs.data(),
                                        densify_filter_inputs.size()),
          builder.CreateVector<int32_t>(densify_filter_outputs.data(),
                                        densify_filter_outputs.size())));
    }
  }

  const std::array<int32_t, 4> input_shape{
      {BatchSize(), InputHeight(), InputWidth(), InputChannels()}};
  const std::array<int32_t, 4> output_shape{
      {BatchSize(), OutputHeight(), OutputWidth(), OutputChannels()}};
  const std::array<int32_t, 4> filter_shape{
      {1, KernelHeight(), KernelWidth(), OutputChannels()}};
  const std::array<int32_t, 1> bias_shape{{OutputChannels()}};

  std::vector<flatbuffers::Offset<tflite::Tensor>> tensors;
  if (FP16Weights()) {
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
        TensorType_FLOAT16, /*buffer=*/1));
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(bias_shape.data(), bias_shape.size()),
        TensorType_FLOAT16, /*buffer=*/2));
  } else 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.
    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]);
    }
    flatbuffers::Offset<SparsityParameters> sparsity_param =
        CreateSparsityParameters(builder, builder.CreateVector(traversal_order),
                                 0, builder.CreateVector(dim_metadata));
    tensors.emplace_back(CreateTensor(
        builder,
        builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
        TensorType_FLOAT32, /*buffer=*/1, /*name=*/0, /*quantization=*/0,
        /*is_variable=*/false, /*sparsity=*/sparsity_param));
  }
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
      TensorType_FLOAT32));
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(filter_shape.data(), filter_shape.size()),
      TensorType_FLOAT32, /*buffer=*/FP16Weights() || SparseWeights() ? 0 : 1));
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(bias_shape.data(), bias_shape.size()),
      TensorType_FLOAT32, /*buffer=*/FP16Weights() ? 0 : 2));
  tensors.emplace_back(CreateTensor(
      builder,
      builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
      TensorType_FLOAT32));

  const std::array<int32_t, 3> op_inputs{
      {static_cast<int>(tensors.size()) - 4,
       static_cast<int>(tensors.size()) - 3,
       static_cast<int>(tensors.size()) - 2}};
  const std::array<int32_t, 1> op_outputs{
      {static_cast<int>(tensors.size()) - 1}};

  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()));

  const std::array<int32_t, 1> subgraph_inputs{
      {static_cast<int>(tensors.size()) - 4}};
  const std::array<int32_t, 1> subgraph_outputs{
      {static_cast<int>(tensors.size()) - 1}};
  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()));

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

  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
