/* Copyright 2017 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 <unistd.h>
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <iostream>
#include <limits>

#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/context.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h"
#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"

namespace tflite {
namespace ops {
namespace builtin {
namespace activations {

struct OpData {
  int32_t input_multiplier = 0;
  int input_left_shift = 0;
  int32_t input_range_radius = 0;
  int diff_min = 0;
};

void* Init(TfLiteContext* context, const char* buffer, size_t length) {
  // This is a builtin op, so we don't use the contents in 'buffer', if any.
  // Instead, we allocate a new object to carry information from Prepare() to
  // Eval().
  return new OpData;
}

void Free(TfLiteContext* context, void* buffer) {
  delete reinterpret_cast<OpData*>(buffer);
}

TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE_EQ(context, input->type, output->type);

  return context->ResizeTensor(context, output,
                               TfLiteIntArrayCopy(input->dims));
}

TfLiteStatus SigmoidPrepare(TfLiteContext* context, TfLiteNode* node) {
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE_EQ(context, input->type, output->type);

  if (input->type == kTfLiteUInt8) {
    TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
    TF_LITE_ENSURE(context, output->params.scale == 1. / 256);

    static constexpr int kInputIntegerBits = 4;

    const double input_real_multiplier =
        input->params.scale *
        static_cast<double>(1 << (31 - kInputIntegerBits));

    QuantizeMultiplierGreaterThanOne(input_real_multiplier,
                                     &data->input_multiplier,
                                     &data->input_left_shift);
    data->input_range_radius =
        CalculateInputRadius(kInputIntegerBits, data->input_left_shift);
  }

  return context->ResizeTensor(context, output,
                               TfLiteIntArrayCopy(input->dims));
}

TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(node->builtin_data);
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE_EQ(context, input->type, output->type);

  TF_LITE_ENSURE(context,
                 NumDimensions(input) == 2 || NumDimensions(input) == 4);

  if (input->type == kTfLiteUInt8) {
    TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
    TF_LITE_ENSURE(context, output->params.scale == 1. / 256);

    static const int kScaledDiffIntegerBits = 5;

    tflite::PreprocessSoftmaxScaling(
        params->beta, input->params.scale, kScaledDiffIntegerBits,
        &data->input_multiplier, &data->input_left_shift);
    data->diff_min = -1.0 * tflite::CalculateInputRadius(
                                kScaledDiffIntegerBits, data->input_left_shift);
  }

  return context->ResizeTensor(context, output,
                               TfLiteIntArrayCopy(input->dims));
}

TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  switch (input->type) {
    case kTfLiteFloat32: {
      size_t elements = input->bytes / sizeof(float);
      float* in = input->data.f;
      float* in_end = in + elements;
      float* out = output->data.f;
      for (; in < in_end; in++, out++) *out = std::max(0.f, *in);
      return kTfLiteOk;
    } break;
    default:
      context->ReportError(context, "Only float32 supported currently.");
      return kTfLiteError;
  }
}

TfLiteStatus Relu1Eval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  switch (input->type) {
    case kTfLiteFloat32: {
      size_t elements = input->bytes / sizeof(float);
      float* in = input->data.f;
      float* in_end = in + elements;
      float* out = output->data.f;
      for (; in < in_end; in++, out++) {
        *out = std::min(std::max(-1.f, *in), 1.f);
      }
      return kTfLiteOk;
    } break;
    default:
      context->ReportError(context, "Only float32 supported currently.");
      return kTfLiteError;
  }
}

TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  switch (input->type) {
    case kTfLiteFloat32: {
      size_t elements = input->bytes / sizeof(float);
      float* in = input->data.f;
      float* in_end = in + elements;
      float* out = output->data.f;
      for (; in < in_end; in++, out++) *out = std::min(std::max(0.f, *in), 6.f);
      return kTfLiteOk;
    } break;
    default:
      context->ReportError(context, "Only float32 supported currently.");
      return kTfLiteError;
  }
}

TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  switch (input->type) {
    case kTfLiteFloat32: {
      size_t elements = input->bytes / sizeof(float);
      float* in = input->data.f;
      float* in_end = in + elements;
      float* out = output->data.f;
      for (; in < in_end; in++, out++) *out = std::tanh(*in);
      return kTfLiteOk;
    } break;
    default:
      context->ReportError(context, "Only float32 supported currently.");
      return kTfLiteError;
  }
}

// Sigmoid is also know as "Logistic".
TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) {
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);
  switch (input->type) {
    case kTfLiteFloat32: {
      size_t elements = input->bytes / sizeof(float);
      float* in = input->data.f;
      float* in_end = in + elements;
      float* out = output->data.f;
      for (; in < in_end; in++, out++) *out = 1.f / (1.f + std::exp(-*in));
      break;
    }
    case kTfLiteUInt8: {
      optimized_ops::Logistic(
          GetTensorData<uint8_t>(input), GetTensorDims(input),
          input->params.zero_point, data->input_range_radius,
          data->input_multiplier, data->input_left_shift,
          GetTensorData<uint8_t>(output), GetTensorDims(output));
      break;
    }
    default:
      context->ReportError(context, "Only float32 supported currently.");
      return kTfLiteError;
  }
  return kTfLiteOk;
}

// Takes a 2D tensor and perform softmax along the second dimension.
void Softmax2DFloat(TfLiteTensor* input, TfLiteTensor* output,
                    TfLiteSoftmaxParams* params) {
  const int batch_size = input->dims->data[0];
  const int input_size = input->dims->data[1];
  float* in = input->data.f;
  float* out = output->data.f;
  TF_LITE_ASSERT(input_size > 0);

  // For each batch
  for (int b = 0; b < batch_size; b++) {
    // Find the max coeff.
    float max_coeff = in[0];
    for (int i = 1; i < input_size; i++) {
      if (in[i] > max_coeff) max_coeff = in[i];
    }

    // Compute the normalized sum of exps.
    float exp_sum = 0.0;
    for (int i = 0; i < input_size; i++) {
      out[i] = std::exp((in[i] - max_coeff) * params->beta);
      exp_sum += out[i];
    }

    // Divide by the sum of exps.
    float reciprocal_sum_exp = 1.f / exp_sum;
    for (int i = 0; i < input_size; i++) {
      out[i] *= reciprocal_sum_exp;
    }

    // Advance in and out pointers for the next batch.
    in += input_size;
    out += input_size;
  }
}

void Softmax2DQuantized(TfLiteTensor* input, TfLiteTensor* output,
                        TfLiteSoftmaxParams* params, OpData* data) {
  // TODO(ahentz): this is arguably a dirty trick. Since the implementation
  // always traverses the last dimension of a 4D tensor, we will pretend our 2D
  // tensor is 4D in a special way. We will convert a (X, Y) shape into a (X,
  // 1, 1, Y) shape.
  const int batch_size = input->dims->data[0];
  const int input_size = input->dims->data[1];
  optimized_ops::Softmax(GetTensorData<uint8_t>(input),
                         GetTensorDims({batch_size, 1, 1, input_size}),
                         data->input_multiplier, data->input_left_shift,
                         data->diff_min, GetTensorData<uint8_t>(output),
                         GetTensorDims({batch_size, 1, 1, input_size}));
}

// Takes a 4D tensor and perform softmax along the forth dimension.
void Softmax4DFloat(TfLiteTensor* input, TfLiteTensor* output,
                    TfLiteSoftmaxParams* params) {
  optimized_ops::Softmax(GetTensorData<float>(input), GetTensorDims(input),
                         params->beta, GetTensorData<float>(output),
                         GetTensorDims(output));
}

void Softmax4DQuantized(TfLiteTensor* input, TfLiteTensor* output,
                        TfLiteSoftmaxParams* params, OpData* data) {
  optimized_ops::Softmax(GetTensorData<uint8_t>(input), GetTensorDims(input),
                         data->input_multiplier, data->input_left_shift,
                         data->diff_min, GetTensorData<uint8_t>(output),
                         GetTensorDims(output));
}

TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(node->builtin_data);
  OpData* data = reinterpret_cast<OpData*>(node->user_data);

  TfLiteTensor* input = GetInput(context, node, 0);
  TfLiteTensor* output = GetOutput(context, node, 0);

  // TODO(ahentz): consider an implementation that works for many (all?)
  // dimensions.
  switch (input->type) {
    case kTfLiteFloat32: {
      if (NumDimensions(input) == 2) {
        Softmax2DFloat(input, output, params);
        return kTfLiteOk;
      }
      if (NumDimensions(input) == 4) {
        Softmax4DFloat(input, output, params);
        return kTfLiteOk;
      }
      context->ReportError(context,
                           "Only 2D and 4D tensors supported currently.");
      return kTfLiteError;
    }
    case kTfLiteUInt8: {
      if (NumDimensions(input) == 2) {
        Softmax2DQuantized(input, output, params, data);
        return kTfLiteOk;
      }
      if (NumDimensions(input) == 4) {
        Softmax4DQuantized(input, output, params, data);
        return kTfLiteOk;
      }
      context->ReportError(context,
                           "Only 2D and 4D tensors supported currently.");
      return kTfLiteError;
    }
    default:
      context->ReportError(context,
                           "Only float32 and uint8_t supported currently.");
      return kTfLiteError;
  }
}

}  // namespace activations

TfLiteRegistration* Register_RELU() {
  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
                                 activations::GenericPrepare,
                                 activations::ReluEval};
  return &r;
}

TfLiteRegistration* Register_RELU_N1_TO_1() {
  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
                                 activations::GenericPrepare,
                                 activations::Relu1Eval};
  return &r;
}

TfLiteRegistration* Register_RELU6() {
  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
                                 activations::GenericPrepare,
                                 activations::Relu6Eval};
  return &r;
}

TfLiteRegistration* Register_TANH() {
  static TfLiteRegistration r = {/*init=*/nullptr, /*free=*/nullptr,
                                 activations::GenericPrepare,
                                 activations::TanhEval};
  return &r;
}

TfLiteRegistration* Register_LOGISTIC() {
  static TfLiteRegistration r = {activations::Init, activations::Free,
                                 activations::SigmoidPrepare,
                                 activations::SigmoidEval};
  return &r;
}

TfLiteRegistration* Register_SOFTMAX() {
  static TfLiteRegistration r = {activations::Init, activations::Free,
                                 activations::SoftmaxPrepare,
                                 activations::SoftmaxEval};
  return &r;
}

}  // namespace builtin
}  // namespace ops
}  // namespace tflite
