tensorflow/lite/micro/kernels/xtensa/softmax.cc

/* Copyright 2018 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/softmax.h"

#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/reference/softmax.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/kernel_util.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h"
#include "tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h"
#include "tensorflow/lite/micro/micro_log.h"

namespace tflite {
namespace {

#if defined(HIFI4) || defined(HIFI5)
TfLiteStatus EvalHifiInt8(const XtensaSoftmaxOpData* op_data,
                          const TfLiteEvalTensor* input,
                          TfLiteEvalTensor* output, TfLiteContext* context) {
  const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input);
  const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
  const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output);
  int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
  const int trailing_dim = input_shape.DimensionsCount() - 1;
  const int outer_size =
      MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
  const int depth =
      MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);

  void* p_scratch = static_cast<void*>(
      context->GetScratchBuffer(context, op_data->scratch_tensor_index));
  for (int i = 0; i < outer_size; ++i) {
    int err = xa_nn_vec_softmax_asym8s_asym8s(
        &output_data[i * depth], &input_data[i * depth],
        op_data->params.diff_min, op_data->params.input_left_shift,
        op_data->params.input_multiplier, depth, p_scratch);
    TF_LITE_ENSURE(context, err == 0);
  }
  return kTfLiteOk;
}
#endif  // defined(HIFI4) || defined(HIFI5)

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);

  if (input->type == kTfLiteInt8 && output->type == kTfLiteInt16) {
    return XtensaEvalSoftmaxInt8Int16(context, node);
  }

  TFLITE_DCHECK(node->user_data != nullptr);

#if defined(HIFI4) || defined(HIFI5)
  XtensaSoftmaxOpData op_data =
      *static_cast<XtensaSoftmaxOpData*>(node->user_data);
  SoftmaxParams params = op_data.params;
#else
  SoftmaxParams params = *static_cast<SoftmaxParams*>(node->user_data);
#endif

  if (input->type == kTfLiteInt8 && output->type == kTfLiteInt8) {
#if defined(HIFI4) || defined(HIFI5)
    return EvalHifiInt8(static_cast<XtensaSoftmaxOpData*>(node->user_data),
                        input, output, context);
#elif defined(VISION_P6)
    return SoftmaxEvalVision(
        context, node, *(static_cast<XtensaSoftmaxOpData*>(node->user_data)),
        input, output);
#else
    tflite::reference_ops::Softmax(
        params, tflite::micro::GetTensorShape(input),
        tflite::micro::GetTensorData<int8_t>(input),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<int8_t>(output));
    return kTfLiteOk;
#endif  // defined(HIFI4) || defined(HIFI5)
  }

  if (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) {
    tflite::reference_ops::SoftmaxInt16(
        params, tflite::micro::GetTensorShape(input),
        tflite::micro::GetTensorData<int16_t>(input),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<int16_t>(output));
    return kTfLiteOk;
  }

  if (input->type == kTfLiteFloat32) {
    tflite::reference_ops::Softmax(params, tflite::micro::GetTensorShape(input),
                                   tflite::micro::GetTensorData<float>(input),
                                   tflite::micro::GetTensorShape(output),
                                   tflite::micro::GetTensorData<float>(output));
    return kTfLiteOk;
  }

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

}  // namespace

TFLMRegistration Register_SOFTMAX() {
  return tflite::micro::RegisterOp(XtensaInitSoftmax, XtensaPrepareSoftmax,
                                   Eval);
}

}  // namespace tflite