1 files changed, 173 insertions, 0 deletions
diff --git a/runtime/onert/backend/cpu/kernel/SoftMaxLayer.cc b/runtime/onert/backend/cpu/kernel/SoftMaxLayer.cc
new file mode 100644
index 000000000..7a1ba48aa
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+++ b/runtime/onert/backend/cpu/kernel/SoftMaxLayer.cc
@@ -0,0 +1,173 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. 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 "SoftMaxLayer.h"
+
+#include "OperationUtils.h"
+
+#include <cker/operation/SoftMax.h>
+
+namespace onert
+{
+namespace backend
+{
+namespace cpu
+{
+namespace kernel
+{
+
+SoftMaxLayer::SoftMaxLayer() : _input(nullptr), _output(nullptr), _beta(0.0)
+{
+  // DO NOTHING
+}
+
+// Performs softmax along the input of size (input_size * batch_size).
+void Softmax(const float *in, const int input_size, const int batch_size, const float beta,
+             float *out)
+{
+  assert(input_size > 0);
+
+  // For each batch
+  for (int b = 0; b < batch_size; b++)
+  {
+    // Find the max coeff.
+    float max_coeff = in[0];
+    for (int i = 1; i < input_size; i++)
+    {
+      if (in[i] > max_coeff)
+        max_coeff = in[i];
+    }
+
+    // Compute the normalized sum of exps.
+    float exp_sum = 0.0;
+    for (int i = 0; i < input_size; i++)
+    {
+      out[i] = std::exp((in[i] - max_coeff) * beta);
+      exp_sum += out[i];
+    }
+
+    // Divide by the sum of exps.
+    float reciprocal_sum_exp = 1.f / exp_sum;
+    for (int i = 0; i < input_size; i++)
+    {
+      out[i] *= reciprocal_sum_exp;
+    }
+
+    // Advance in and out pointers for the next batch.
+    in += input_size;
+    out += input_size;
+  }
+}
+
+void SoftMaxLayer::softmaxFloat32()
+{
+  if (getNumberOfDimensions(_input) == 2)
+  {
+    uint32_t batch_size = getSizeOfDimension(_input, 0);
+    if (batch_size == 0)
+      throw std::runtime_error("batch_size should not be 0");
+
+    uint32_t input_size = getNumberOfElements(_input) / batch_size;
+    Softmax(reinterpret_cast<const float *>(_input->buffer()), input_size, batch_size, _beta,
+            reinterpret_cast<float *>(_output->buffer()));
+  }
+  else if (getNumberOfDimensions(_input) == 4)
+  {
+    nnfw::cker::SoftmaxParams op_params;
+    op_params.beta = _beta;
+    nnfw::cker::Softmax(op_params, convertTensorToCkerShape(_input),
+                        reinterpret_cast<const float *>(_input->buffer()),
+                        convertTensorToCkerShape(_output),
+                        reinterpret_cast<float *>(_output->buffer()));
+  }
+  else
+  {
+    throw std::runtime_error{"only 2D and 4D tensors supported"};
+  }
+}
+
+void SoftMaxLayer::softmaxQuant8()
+{
+  nnfw::cker::Shape descrIn4D(4);
+
+  if (getNumberOfDimensions(_input) == 2)
+  {
+    auto batch_size = getSizeOfDimension(_input, 0);
+    if (batch_size == 0)
+      throw std::runtime_error("batch_size should not be 0");
+
+    auto input_size = getNumberOfElements(_input) / batch_size;
+    descrIn4D.SetDim(0, batch_size);
+    descrIn4D.SetDim(1, 1);
+    descrIn4D.SetDim(2, 1);
+    descrIn4D.SetDim(3, input_size);
+  }
+  else if (getNumberOfDimensions(_input) == 4)
+  {
+    descrIn4D.SetDim(0, _input->dimension(0));
+    descrIn4D.SetDim(1, _input->dimension(1));
+    descrIn4D.SetDim(2, _input->dimension(2));
+    descrIn4D.SetDim(3, _input->dimension(3));
+  }
+  else
+  {
+    throw std::runtime_error{"only 2D and 4D tensors supported"};
+  }
+  if (_output->offset() != 0 || _output->scale() != 1.f / 256)
+  {
+    throw std::runtime_error{"incorrect scale / offset for output"};
+  }
+  static const int32_t kScaledDiffIntegerBits = 5;
+  const double input_beta_real_multiplier = std::min(
+      1.0 * _beta * _input->scale() * (1 << (31 - kScaledDiffIntegerBits)), (1ll << 31) - 1.0);
+  int32_t input_multiplier = 0;
+  int32_t input_left_shift = 0;
+  QuantizeMultiplierGreaterThanOne(input_beta_real_multiplier, &input_multiplier,
+                                   &input_left_shift);
+  float diff_min = -1.0f * CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift);
+
+  nnfw::cker::SoftmaxParams op_params;
+  op_params.input_multiplier = input_multiplier;
+  op_params.input_left_shift = input_left_shift;
+  op_params.diff_min = diff_min;
+  nnfw::cker::Softmax(op_params, descrIn4D, reinterpret_cast<const uint8_t *>(_input->buffer()),
+                      descrIn4D, reinterpret_cast<uint8_t *>(_output->buffer()));
+}
+
+void SoftMaxLayer::configure(const operand::Tensor *input, const float beta,
+                             operand::Tensor *output)
+{
+  _input = input;
+  _output = output;
+  _beta = beta;
+}
+
+void SoftMaxLayer::run()
+{
+  if (_input->data_type() == OperandType::FLOAT32)
+  {
+    softmaxFloat32();
+  }
+  else if (_input->data_type() == OperandType::QUANT8_ASYMM)
+  {
+    softmaxQuant8();
+  }
+}
+
+} // namespace kernel
+} // namespace cpu
+} // namespace backend
+} // namespace onert