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

/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_

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

namespace tflite {

namespace reference_ops {

// Element-wise mul that can often be used for inner loop of broadcast Mul as
// well as the non-broadcast Mul.
inline void MulElementwise(int size, const ArithmeticParams& params,
                           const uint8* input1_data, const uint8* input2_data,
                           uint8* output_data) {
  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];
    const int32 unclamped_result =
        params.output_offset +
        MultiplyByQuantizedMultiplier(input1_val * input2_val,
                                      params.output_multiplier,
                                      params.output_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);
  }
}

template <typename T>
inline void Mul(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 =
      MatchingFlatSize(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);
  }
}

inline void Mul(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 =
      MatchingFlatSize(input1_shape, input2_shape, output_shape);

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

inline void BroadcastMul4DSlow(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) {
  NdArrayDesc<4> desc1;
  NdArrayDesc<4> desc2;
  NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
                                      &desc2);
  const RuntimeShape extended_output_shape =
      RuntimeShape::ExtendedShape(4, output_shape);

  for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
    for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
      for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
        for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
          const int32 input1_val =
              params.input1_offset +
              input1_data[SubscriptToIndex(desc1, b, y, x, c)];
          const int32 input2_val =
              params.input2_offset +
              input2_data[SubscriptToIndex(desc2, b, y, x, c)];
          const int32 unclamped_result =
              params.output_offset +
              MultiplyByQuantizedMultiplier(input1_val * input2_val,
                                            params.output_multiplier,
                                            params.output_shift);
          const int32 clamped_output = std::min(
              params.quantized_activation_max,
              std::max(params.quantized_activation_min, unclamped_result));
          output_data[Offset(extended_output_shape, b, y, x, c)] =
              static_cast<uint8>(clamped_output);
        }
      }
    }
  }
}

template <typename T>
void BroadcastMul4DSlow(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(), 4);
  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
  const RuntimeShape output_shape =
      RuntimeShape::ExtendedShape(4, unextended_output_shape);

  NdArrayDesc<4> desc1;
  NdArrayDesc<4> desc2;
  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
                                      unextended_input2_shape, &desc1, &desc2);

  // 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.
  //
  // We name our variables by their Tensorflow convention, but generate C code
  // nesting loops such that the innermost loop has the smallest stride for the
  // best cache behavior.
  for (int b = 0; b < output_shape.Dims(0); ++b) {
    for (int y = 0; y < output_shape.Dims(1); ++y) {
      for (int x = 0; x < output_shape.Dims(2); ++x) {
        for (int c = 0; c < output_shape.Dims(3); ++c) {
          output_data[Offset(output_shape, b, y, x, c)] =
              ActivationFunctionWithMinMax(
                  input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
                      input2_data[SubscriptToIndex(desc2, b, y, x, c)],
                  output_activation_min, output_activation_max);
        }
      }
    }
  }
}

}  // namespace reference_ops
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_