/* Copyright 2019 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/micro/kernels/svdf.h"

#include <cmath>

#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/activation_utils.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/micro_log.h"

namespace tflite {
namespace {

#if defined(HIFI4) || defined(HIFI4_INTERNAL) || defined(HIFI5)

TfLiteStatus EvalIntegerSvdfHifi(TfLiteContext* context, TfLiteNode* node,
                                 const TfLiteEvalTensor* input_tensor,
                                 const TfLiteEvalTensor* weights_feature_tensor,
                                 const TfLiteEvalTensor* weights_time_tensor,
                                 const TfLiteEvalTensor* bias_tensor,
                                 const TfLiteSVDFParams* params,
                                 TfLiteEvalTensor* activation_state_tensor,
                                 TfLiteEvalTensor* output_tensor,
                                 const OpDataSvdf& data) {
  const int n_rank = params->rank;
  const int n_batch = input_tensor->dims->data[0];
  const int n_input = input_tensor->dims->data[1];
  const int n_filter = weights_feature_tensor->dims->data[0];
  const int n_unit = n_filter / n_rank;
  const int n_memory = weights_time_tensor->dims->data[1];

  TFLITE_DCHECK(context != nullptr);
  TFLITE_DCHECK(context->GetScratchBuffer != nullptr);

  // Shift states.
  int16_t* const state_ptr =
      tflite::micro::GetTensorData<int16_t>(activation_state_tensor);

  // Left shift the activation_state.
  int num_bytes = sizeof(*state_ptr) * (n_batch * n_filter * n_memory - 1);
#if defined(HIFI5)
  memcpy(state_ptr, state_ptr + 1, num_bytes);
#else
  xa_nn_memmove_16(state_ptr, state_ptr + 1, num_bytes);
#endif

  // Note: no need to clear the latest activation, matmul is not accumulative.

  // Feature matmul.
  const int8_t* input = tflite::micro::GetTensorData<int8_t>(input_tensor);
  const int8_t* weight_feature =
      tflite::micro::GetTensorData<int8_t>(weights_feature_tensor);
  int16_t* result_in_batch = state_ptr + (n_memory - 1);

  for (int b = 0; b < n_batch; b++) {
    TF_LITE_ENSURE_EQ(context,
                      xa_nn_matXvec_out_stride_sym8sxasym8s_16(
                          &result_in_batch[b * n_filter * n_memory],
                          weight_feature, &input[b * n_input], NULL, n_filter,
                          n_input, n_input, n_memory, -data.input_zero_point,
                          (data.effective_scale_1_a), data.effective_scale_1_b),
                      0);
  }

  // Time weights dot product + activation
  for (int b = 0; b < n_batch; ++b) {
    const int16_t* vector1_ptr =
        tflite::micro::GetTensorData<int16_t>(weights_time_tensor);
    const int16_t* vector2_ptr =
        tflite::micro::GetTensorData<int16_t>(activation_state_tensor) +
        b * n_memory * n_filter;
    const int32_t* bias_ptr =
        tflite::micro::GetTensorData<int32_t>(bias_tensor);
    int8_t* output_ptr =
        tflite::micro::GetTensorData<int8_t>(output_tensor) + b * n_unit;

    TF_LITE_ENSURE_EQ(
        context,
        xa_nn_dot_prod_16x16_asym8s(
            output_ptr, vector1_ptr, vector2_ptr, bias_ptr, n_memory * n_rank,
            (data.effective_scale_2_a), data.effective_scale_2_b,
            data.output_zero_point, n_unit),
        0);
  }
  return kTfLiteOk;
}
#endif  // defined(HIFI4) || defined (HIFI4_INTERNAL) || defined(HIFI5)

void* Init(TfLiteContext* context, const char* buffer, size_t length) {
  TFLITE_DCHECK(context != nullptr);
  return context->AllocatePersistentBuffer(context, sizeof(OpDataSvdf));
}

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
#if defined(HIFI4) || defined(HIFI4_INTERNAL) || defined(HIFI5)
  TFLITE_DCHECK(node->builtin_data != nullptr);
  const auto* params = static_cast<const TfLiteSVDFParams*>(node->builtin_data);

  // Validate Tensor Inputs (dtype depends on quantization):
  // [0] = Input, {2, batch_size, input_size}
  // [1] = Weights Feature, {2, num_filters, input_size}
  // [2] = Weights Time, {2, num_filters, memory_size}
  // [3] = Bias (optional), {1, num_units}
  // [4] = Activation State (variable),
  //         {2, batch_size, memory_size * num_filters}
  MicroContext* micro_context = GetMicroContext(context);
  TfLiteTensor* input =
      micro_context->AllocateTempInputTensor(node, kSvdfInputTensor);
  TfLiteTensor* weights_feature =
      micro_context->AllocateTempInputTensor(node, kSvdfWeightsFeatureTensor);
  TfLiteTensor* weights_time =
      micro_context->AllocateTempInputTensor(node, kSvdfWeightsTimeTensor);
  TfLiteTensor* bias =
      micro_context->AllocateTempInputTensor(node, kSvdfBiasTensor);
  TfLiteTensor* activation_state = micro_context->AllocateTempInputTensor(
      node, kSvdfInputActivationStateTensor);

  // Define input constants based on input tensor definition above:
  const int rank = params->rank;
  const int input_size = input->dims->data[1];
  const int batch_size = input->dims->data[0];

  const int num_filters = weights_feature->dims->data[0];
  TF_LITE_ENSURE_EQ(context, num_filters % rank, 0);
  const int num_units = num_filters / rank;
  const int memory_size = weights_time->dims->data[1];

  if (input->type != kTfLiteInt8) {
    MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
                input->type);
    return kTfLiteError;
  }

  // Validate Input Tensor:
  TF_LITE_ENSURE(context, input->type == kTfLiteInt8);
  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 2);

  // Validate Tensor Output:
  // [0] = float/int8_t, {2, batch_size, num_units}
  TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kSvdfOutputTensor);
  TF_LITE_ENSURE_EQ(context, NumDimensions(output), 2);
  TF_LITE_ENSURE_EQ(context, output->dims->data[0], batch_size);
  TF_LITE_ENSURE_EQ(context, output->dims->data[1], num_units);

  // Validate Weights Feature Input Tensor:
  TF_LITE_ENSURE_EQ(context, NumDimensions(weights_feature), 2);
  TF_LITE_ENSURE_EQ(context, weights_feature->dims->data[1], input_size);

  // Validate Weights Time Input Tensor:
  TF_LITE_ENSURE_EQ(context, NumDimensions(weights_time), 2);
  TF_LITE_ENSURE_EQ(context, weights_time->dims->data[0], num_filters);
  TF_LITE_ENSURE_EQ(context, weights_time->dims->data[1], memory_size);

  // Validate Optional Bias Input Tensor:
  if (bias != nullptr) {
    TF_LITE_ENSURE_EQ(context, bias->dims->data[0], num_units);
    TF_LITE_ENSURE_EQ(context, bias->type, kTfLiteInt32);
  }

  // Validate Activation State Input Tensor:
  TF_LITE_ENSURE_EQ(context, NumDimensions(activation_state), 2);
  TF_LITE_ENSURE_EQ(context, activation_state->dims->data[0], batch_size);
  TF_LITE_ENSURE_EQ(context, activation_state->dims->data[1],
                    memory_size * num_filters);

  TF_LITE_ENSURE_EQ(context, node->inputs->size, 5);
  TF_LITE_ENSURE_EQ(context, weights_feature->type, kTfLiteInt8);
  TF_LITE_ENSURE_EQ(context, weights_time->type, kTfLiteInt16);
  TF_LITE_ENSURE_EQ(context, activation_state->type, kTfLiteInt16);

  // Validate output tensor:
  TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt8);

  const double effective_scale_1 =
      static_cast<double>(input->params.scale * weights_feature->params.scale /
                          activation_state->params.scale);
  const double effective_scale_2 =
      static_cast<double>(activation_state->params.scale *
                          weights_time->params.scale / output->params.scale);

  TF_LITE_ENSURE_NEAR(context, static_cast<double>(bias->params.scale),
                      static_cast<double>(activation_state->params.scale *
                                          weights_time->params.scale),
                      1e-5);

  TFLITE_DCHECK(node->user_data != nullptr);
  OpDataSvdf* data = static_cast<OpDataSvdf*>(node->user_data);

  QuantizeMultiplier(effective_scale_1, &(data->effective_scale_1_a),
                     &(data->effective_scale_1_b));
  QuantizeMultiplier(effective_scale_2, &(data->effective_scale_2_a),
                     &(data->effective_scale_2_b));

  data->input_zero_point = input->params.zero_point;
  data->output_zero_point = output->params.zero_point;

  const TfLiteStatus scratch_status = context->RequestScratchBufferInArena(
      context, batch_size * num_filters * sizeof(int32_t),
      &(data->scratch_tensor_index));
  TF_LITE_ENSURE_OK(context, scratch_status);
  const TfLiteStatus scratch_output_status =
      context->RequestScratchBufferInArena(
          context, batch_size * num_units * sizeof(int32_t),
          &(data->scratch_output_tensor_index));
  TF_LITE_ENSURE_OK(context, scratch_output_status);

  micro_context->DeallocateTempTfLiteTensor(input);
  micro_context->DeallocateTempTfLiteTensor(weights_time);
  micro_context->DeallocateTempTfLiteTensor(weights_feature);
  if (bias != nullptr) {
    micro_context->DeallocateTempTfLiteTensor(bias);
  }
  micro_context->DeallocateTempTfLiteTensor(activation_state);
  micro_context->DeallocateTempTfLiteTensor(output);

  return kTfLiteOk;
#else
  return PrepareSvdf(context, node);
#endif  // defined(HIFI4) || defined(HIFI4_INTERNAL) || defined(HIFI5)
}

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = static_cast<TfLiteSVDFParams*>(node->builtin_data);

  const TfLiteEvalTensor* input =
      tflite::micro::GetEvalInput(context, node, kSvdfInputTensor);
  const TfLiteEvalTensor* weights_feature =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsFeatureTensor);
  const TfLiteEvalTensor* weights_time =
      tflite::micro::GetEvalInput(context, node, kSvdfWeightsTimeTensor);
  const TfLiteEvalTensor* bias =
      (NumInputs(node) == 5)
          ? tflite::micro::GetEvalInput(context, node, kSvdfBiasTensor)
          : nullptr;
  TfLiteEvalTensor* activation_state = tflite::micro::GetMutableEvalInput(
      context, node, kSvdfInputActivationStateTensor);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kSvdfOutputTensor);

  TFLITE_DCHECK(node->user_data != nullptr);
  const OpDataSvdf& data = *(static_cast<const OpDataSvdf*>(node->user_data));

#if defined(HIFI4) || defined(HIFI4_INTERNAL) || defined(HIFI5)
  return EvalIntegerSvdfHifi(context, node, input, weights_feature,
                             weights_time, bias, params, activation_state,
                             output, data);
#else
  switch (weights_feature->type) {
    case kTfLiteFloat32: {
      EvalFloatSvdfReference(
          context, node, input, weights_feature, weights_time, bias, params,
          data.scratch_tensor_index, activation_state, output);
      return kTfLiteOk;
      break;
    }

    case kTfLiteInt8: {
      switch (weights_time->type) {
        case kTfLiteInt16: {
          EvalInt16SvdfReference(context, node, input, weights_feature,
                                 weights_time, bias, params, activation_state,
                                 output, data);
          return kTfLiteOk;
          break;
        }
        case kTfLiteInt8: {
          EvalInt8SvdfReference(context, node, input, weights_feature,
                                weights_time, bias, params, activation_state,
                                output, data);
          return kTfLiteOk;
          break;
        }
        default:
          MicroPrintf("Type %s not currently supported.",
                      TfLiteTypeGetName(weights_time->type));
          return kTfLiteError;
      }
    }

    default:
      MicroPrintf("Type %s not currently supported.",
                  TfLiteTypeGetName(weights_feature->type));
      return kTfLiteError;
  }
  return kTfLiteOk;
#endif  // defined(HIFI4) || defined(HIFI4_INTERNAL) || defined(HIFI5)
}

}  // namespace

TfLiteRegistration Register_SVDF() {
  return tflite::micro::RegisterOp(Init, Prepare, Eval);
}

}  // namespace tflite