1 files changed, 137 insertions, 0 deletions
diff --git a/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc b/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc
new file mode 100644
index 000000000..9c1c5d4e2
--- /dev/null
+++ b/runtime/neurun/core/src/exec/interp/operations/FullyConnected.cc
@@ -0,0 +1,137 @@
+/*
+ * Copyright (c) 2019 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 <cker/operation/FullyConnected.h>
+
+#include "OperationUtil.h"
+
+#include "exec/interp/Registration.h"
+#include "ir/operation/FullyConnected.h"
+#include "misc/polymorphic_downcast.h"
+
+namespace neurun
+{
+namespace exec
+{
+namespace interp
+{
+namespace fc
+{
+
+void prepareFC(ExecEnv *env, const ir::Operation &node)
+{
+  const auto in_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
+  const auto kernel_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
+  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
+  const auto out_index = node.getOutputs().at(0);
+
+  const auto in_tensor = env->tensorAt(in_index);
+  const auto kernel_tensor = env->tensorAt(kernel_index);
+  const auto bias_tensor = env->tensorAt(bias_index);
+
+  UNUSED_RELEASE(in_tensor);
+  UNUSED_RELEASE(kernel_tensor);
+  UNUSED_RELEASE(bias_tensor);
+
+  assert(in_tensor->num_dimensions() >= 2);
+  assert(kernel_tensor->num_dimensions() == 2);
+  assert(bias_tensor->num_dimensions() == 1);
+
+  const auto input_size_with_batch = in_tensor->num_elements();
+  const auto num_units = kernel_tensor->dimension(0);
+  const auto input_size = kernel_tensor->dimension(1);
+  const auto batch_size = input_size_with_batch / input_size;
+  assert(input_size_with_batch % input_size == 0);
+  assert(num_units == bias_tensor->dimension(0));
+
+  // Make output tensor info
+  ir::Shape output_shape(2);
+  output_shape.dim(0) = batch_size;
+  output_shape.dim(1) = num_units;
+  const ir::OperandInfo out_info{output_shape, in_tensor->tensorInfo().typeInfo()};
+  env->allocateIfNeeded(out_index, out_info);
+
+  auto out_tensor = env->tensorAt(out_index);
+  UNUSED_RELEASE(out_tensor);
+
+  // Handle same ifm & ofm data type only
+  assert(in_tensor->data_type() == out_tensor->data_type());
+  assert(out_tensor->num_dimensions() == 2);
+  assert(out_tensor->dimension(0) == batch_size);
+  assert(out_tensor->dimension(1) == num_units);
+}
+
+void invoke(const ITensor *ifm_tensor, const ITensor *ker_tensor, const ITensor *bias_tensor,
+            const ITensor *ofm_tensor, const ir::operation::FullyConnected::Param &param)
+{
+  const auto ifm_buffer = ifm_tensor->bufferRO();
+  const auto ker_buffer = ker_tensor->bufferRO();
+  const auto bias_buffer = bias_tensor->bufferRO();
+  auto ofm_buffer = ofm_tensor->buffer();
+
+  // Calculate
+  nnfw::cker::FullyConnectedParams cker_param;
+  calculateActivationRange(param.activation, &cker_param.float_activation_min,
+                           &cker_param.float_activation_max);
+  const auto cker_ifm_shape = convertExtendShape(ifm_tensor->tensorInfo().shape());
+  const auto cker_ker_shape = convertExtendShape(ker_tensor->tensorInfo().shape());
+  const auto cker_bias_shape = convertExtendShape(bias_tensor->tensorInfo().shape());
+  const auto cker_ofm_shape = convertExtendShape(ofm_tensor->tensorInfo().shape());
+  const float *ifm_ptr = reinterpret_cast<const float *>(ifm_buffer);
+  const float *ker_ptr = reinterpret_cast<const float *>(ker_buffer);
+  const float *bias_ptr = reinterpret_cast<const float *>(bias_buffer);
+  float *ofm_ptr = reinterpret_cast<float *>(ofm_buffer);
+
+  nnfw::cker::FullyConnected(cker_param, cker_ifm_shape, ifm_ptr, cker_ker_shape, ker_ptr,
+                             cker_bias_shape, bias_ptr, cker_ofm_shape, ofm_ptr);
+}
+
+void invokeFC(const ExecEnv *env, const ir::Operation &node)
+{
+  const auto &conv_node =
+      nnfw::misc::polymorphic_downcast<const ir::operation::FullyConnected &>(node);
+
+  const auto ifm_index = node.getInputs().at(ir::operation::FullyConnected::INPUT);
+  const auto ker_index = node.getInputs().at(ir::operation::FullyConnected::WEIGHT);
+  const auto bias_index = node.getInputs().at(ir::operation::FullyConnected::BIAS);
+  const auto ofm_index = node.getOutputs().at(0);
+
+  const auto ifm_tensor = env->tensorAt(ifm_index);
+  const auto ker_tensor = env->tensorAt(ker_index);
+  const auto bias_tensor = env->tensorAt(bias_index);
+  const auto ofm_tensor = env->tensorAt(ofm_index);
+
+  const auto data_type = ifm_tensor->data_type();
+  if (data_type == ir::DataType::FLOAT32)
+  {
+    invoke(ifm_tensor, ker_tensor, bias_tensor, ofm_tensor, conv_node.param());
+  }
+  else
+  {
+    throw std::runtime_error{"NYI: Support float only"};
+  }
+}
+} // namespace fc
+
+OpKernel *getFullyConnected()
+{
+  static OpKernel kernel = {fc::prepareFC, fc::invokeFC};
+  return &kernel;
+}
+
+} // namespace interp
+} // namespace exec
+} // namespace neurun