AI-on-the-edge-device/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize_common.cc

/* 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 <limits>

#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/quantize.h"
#include "tensorflow/lite/kernels/internal/reference/requantize.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/kernels/quantize.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_utils.h"

namespace tflite {

TfLiteStatus PrepareQuantizeReference(TfLiteContext* context,
                                      TfLiteNode* node) {
  TFLITE_DCHECK(node->user_data != nullptr);
  auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  MicroContext* micro_context = GetMicroContext(context);

  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
  TF_LITE_ENSURE(context, input != nullptr);
  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
  TF_LITE_ENSURE(context, output != nullptr);

  // TODO(b/128934713): Add support for fixed-point per-channel quantization.
  // Currently this only support affine per-layer quantization.
  TF_LITE_ENSURE_EQ(context, output->quantization.type,
                    kTfLiteAffineQuantization);
  const auto* affine_quantization =
      reinterpret_cast<TfLiteAffineQuantization*>(output->quantization.params);
  TF_LITE_ENSURE(context, affine_quantization);
  TF_LITE_ENSURE(context, affine_quantization->scale);
  TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);

  TF_LITE_ENSURE(
      context, input->type == kTfLiteFloat32 || input->type == kTfLiteInt32 ||
                   input->type == kTfLiteInt16 || input->type == kTfLiteInt8 ||
                   input->type == kTfLiteUInt8);
  TF_LITE_ENSURE(context, output->type == kTfLiteInt8 ||
                              output->type == kTfLiteInt16 ||
                              output->type == kTfLiteInt32 ||
                              output->type == kTfLiteUInt8);

  if ((input->type == kTfLiteInt16 && output->type == kTfLiteInt8) ||
      (input->type == kTfLiteInt8 && output->type == kTfLiteInt8) ||
      (input->type == kTfLiteInt8 && output->type == kTfLiteUInt8) ||
      (input->type == kTfLiteUInt8 && output->type == kTfLiteInt8) ||
      (input->type == kTfLiteInt8 && output->type == kTfLiteInt16) ||
      (input->type == kTfLiteInt8 && output->type == kTfLiteInt32) ||
      (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) ||
      (input->type == kTfLiteInt16 && output->type == kTfLiteInt32) ||
      (input->type == kTfLiteInt32 && output->type == kTfLiteInt8) ||
      (input->type == kTfLiteInt32 && output->type == kTfLiteInt16)) {
    double effective_scale = static_cast<double>(input->params.scale) /
                             static_cast<double>(output->params.scale);

    QuantizeMultiplier(effective_scale, &data->requantize_output_multiplier,
                       &data->requantize_output_shift);
  }

  data->quantization_params.zero_point = output->params.zero_point;
  data->quantization_params.scale = static_cast<double>(output->params.scale);

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

  micro_context->DeallocateTempTfLiteTensor(input);
  micro_context->DeallocateTempTfLiteTensor(output);
  return kTfLiteOk;
}

TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) {
  TFLITE_DCHECK(node->user_data != nullptr);
  auto* data = static_cast<OpDataQuantizeReference*>(node->user_data);

  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);

  if (input->type == kTfLiteFloat32) {
    switch (output->type) {
      case kTfLiteInt8:
        reference_ops::AffineQuantize(
            data->quantization_params, tflite::micro::GetTensorShape(input),
            tflite::micro::GetTensorData<float>(input),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int8_t>(output));
        break;
      case kTfLiteInt16:
        reference_ops::AffineQuantize(
            data->quantization_params, tflite::micro::GetTensorShape(input),
            tflite::micro::GetTensorData<float>(input),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int16_t>(output));
        return kTfLiteOk;
      default:
        MicroPrintf("Input %s, output %s not supported.",
                    TfLiteTypeGetName(input->type),
                    TfLiteTypeGetName(output->type));
        return kTfLiteError;
    }
  } else if (input->type == kTfLiteInt32) {
    size_t size = ElementCount(*input->dims);
    switch (output->type) {
      case kTfLiteInt8:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int32_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int8_t>(output));
        break;
      case kTfLiteInt16:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int32_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int16_t>(output));
        break;
      default:
        MicroPrintf("Input %s, output %s not supported.",
                    TfLiteTypeGetName(input->type),
                    TfLiteTypeGetName(output->type));
        return kTfLiteError;
    }
  } else if (input->type == kTfLiteInt16) {
    size_t size = ElementCount(*input->dims);
    switch (output->type) {
      case kTfLiteInt8:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int16_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int8_t>(output));
        break;
      case kTfLiteInt16:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int16_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int16_t>(output));
        return kTfLiteOk;
      case kTfLiteInt32:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int16_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int32_t>(output));
        return kTfLiteOk;
      default:
        MicroPrintf("Input %s, output %s not supported.",
                    TfLiteTypeGetName(input->type),
                    TfLiteTypeGetName(output->type));
        return kTfLiteError;
    }
  } else if (input->type == kTfLiteInt8) {
    // Int8 to Int8 requantization, required if the input and output tensors
    // have different scales and/or zero points.
    size_t size = ElementCount(*input->dims);
    switch (output->type) {
      case kTfLiteInt8:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int8_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int8_t>(output));
        break;
      case kTfLiteUInt8:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int8_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<uint8_t>(output));
        break;
      case kTfLiteInt16:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int8_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int16_t>(output));
        break;
      case kTfLiteInt32:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<int8_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int32_t>(output));
        break;
      default:
        MicroPrintf("Input %s, output %s not supported.",
                    TfLiteTypeGetName(input->type),
                    TfLiteTypeGetName(output->type));
        return kTfLiteError;
    }
  } else if (input->type == kTfLiteUInt8) {
    size_t size = ElementCount(*input->dims);
    switch (output->type) {
      case kTfLiteInt8:
        reference_ops::Requantize(
            tflite::micro::GetTensorData<uint8_t>(input), size,
            data->requantize_output_multiplier, data->requantize_output_shift,
            data->input_zero_point, data->quantization_params.zero_point,
            tflite::micro::GetTensorData<int8_t>(output));
        break;
      default:
        MicroPrintf("Input %s, output %s not supported.",
                    TfLiteTypeGetName(input->type),
                    TfLiteTypeGetName(output->type));
        return kTfLiteError;
    }
  } else {
    MicroPrintf("Input %s, output %s not supported.",
                TfLiteTypeGetName(input->type),
                TfLiteTypeGetName(output->type));
    return kTfLiteError;
  }

  return kTfLiteOk;
}

}  // namespace tflite