STT-tensorflow/tensorflow/lite/kernels/internal/reference/div.h

/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_

#include <algorithm>

#include "tensorflow/lite/kernels/internal/common.h"

namespace tflite {

namespace reference_ops {

// Element-wise div that can often be used for inner loop of broadcast Div as
// well as the non-broadcast Div.
inline void DivElementwise(int size, const ArithmeticParams& params,
                           const uint8* input1_data, const uint8* input2_data,
                           uint8* output_data) {
  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);
  TFLITE_DCHECK_GT(params.output_offset, -256);
  TFLITE_DCHECK_LT(params.output_offset, 256);

  for (int i = 0; i < size; ++i) {
    const int32 input1_val = params.input1_offset + input1_data[i];
    const int32 input2_val = params.input2_offset + input2_data[i];
    TFLITE_DCHECK_NE(input2_val, 0);
    int recip_shift;
    const int32 input2_inv =
        (input2_val > 0) ? GetReciprocal(input2_val, 31, &recip_shift)
                         : -GetReciprocal(-input2_val, 31, &recip_shift);
    const int headroom = CountLeadingSignBits(input1_val);
    const int32 unscaled_quotient = MultiplyByQuantizedMultiplierGreaterThanOne(
        input1_val, input2_inv, headroom);
    const int total_shift = params.output_shift - recip_shift - headroom;
    const int32 unclamped_result =
        params.output_offset +
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            unscaled_quotient, params.output_multiplier, total_shift);
    const int32 clamped_output =
        std::min(params.quantized_activation_max,
                 std::max(params.quantized_activation_min, unclamped_result));
    output_data[i] = static_cast<uint8>(clamped_output);
  }
}

inline void Div(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const uint8* input1_data,
                const RuntimeShape& input2_shape, const uint8* input2_data,
                const RuntimeShape& output_shape, uint8* output_data) {
  TFLITE_DCHECK_LE(params.quantized_activation_min,
                   params.quantized_activation_max);
  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);

  DivElementwise(flat_size, params, input1_data, input2_data, output_data);
}

template <int N = 5>
inline void BroadcastDivSlow(const ArithmeticParams& params,
                             const RuntimeShape& unextended_input1_shape,
                             const uint8* input1_data,
                             const RuntimeShape& unextended_input2_shape,
                             const uint8* input2_data,
                             const RuntimeShape& unextended_output_shape,
                             uint8* output_data) {
  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);

  NdArrayDesc<N> desc1;
  NdArrayDesc<N> desc2;
  NdArrayDesc<N> output_desc;
  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
                                      unextended_input2_shape, &desc1, &desc2);
  CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
                 &output_desc);

  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);
  TFLITE_DCHECK_GT(params.output_offset, -256);
  TFLITE_DCHECK_LT(params.output_offset, 256);

  auto div_func = [&](int indexes[N]) {
    const int32 input1_val =
        params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)];
    const int32 input2_val =
        params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)];
    TFLITE_DCHECK_NE(input2_val, 0);
    int recip_shift;
    const int32 input2_inv =
        (input2_val > 0) ? GetReciprocal(input2_val, 31, &recip_shift)
                         : -GetReciprocal(-input2_val, 31, &recip_shift);
    const int headroom = CountLeadingSignBits(input1_val);
    const int32 unscaled_quotient = MultiplyByQuantizedMultiplierGreaterThanOne(
        input1_val, input2_inv, headroom);
    const int total_shift = params.output_shift - recip_shift - headroom;
    const int32 unclamped_result =
        params.output_offset +
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            unscaled_quotient, params.output_multiplier, total_shift);
    const int32 clamped_output =
        std::min(params.quantized_activation_max,
                 std::max(params.quantized_activation_min, unclamped_result));
    output_data[SubscriptToIndex(output_desc, indexes)] =
        static_cast<uint8>(clamped_output);
  };
  NDOpsHelper<N>(output_desc, div_func);
}

// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary
// dimensionality if the runtime code does a single loop over one dimension
// that handles broadcasting as the base case. The code generator would then
// generate max(D1, D2) nested for loops.
template <typename T, int N = 5>
void BroadcastDivSlow(const ArithmeticParams& params,
                      const RuntimeShape& unextended_input1_shape,
                      const T* input1_data,
                      const RuntimeShape& unextended_input2_shape,
                      const T* input2_data,
                      const RuntimeShape& unextended_output_shape,
                      T* output_data) {
  T output_activation_min;
  T output_activation_max;
  GetActivationParams(params, &output_activation_min, &output_activation_max);

  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N);
  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N);
  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N);

  NdArrayDesc<N> desc1;
  NdArrayDesc<N> desc2;
  NdArrayDesc<N> output_desc;
  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
                                      unextended_input2_shape, &desc1, &desc2);
  CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape),
                 &output_desc);

  // In Tensorflow, the dimensions are canonically named (batch_number, row,
  // col, channel), with extents (batches, height, width, depth), with the
  // trailing dimension changing most rapidly (channels has the smallest
  // stride, typically 1 element).
  //
  // In generated C code, we store arrays with the dimensions reversed. The
  // first dimension has smallest stride.

  auto div_func = [&](int indexes[N]) {
    output_data[SubscriptToIndex(output_desc, indexes)] =
        ActivationFunctionWithMinMax(
            input1_data[SubscriptToIndex(desc1, indexes)] /
                input2_data[SubscriptToIndex(desc2, indexes)],
            output_activation_min, output_activation_max);
  };
  NDOpsHelper<N>(output_desc, div_func);
}

template <typename T>
inline void Div(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const T* input1_data,
                const RuntimeShape& input2_shape, const T* input2_data,
                const RuntimeShape& output_shape, T* output_data) {
  T output_activation_min;
  T output_activation_max;
  GetActivationParams(params, &output_activation_min, &output_activation_max);

  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);
  for (int i = 0; i < flat_size; ++i) {
    output_data[i] = ActivationFunctionWithMinMax(
        input1_data[i] / input2_data[i], output_activation_min,
        output_activation_max);
  }
}

}  // namespace reference_ops
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_