1 files changed, 92 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/loader/nodes/SVDF.cpp b/compiler/luci-interpreter/src/loader/nodes/SVDF.cpp
new file mode 100644
index 000000000..d172ef438
--- /dev/null
+++ b/compiler/luci-interpreter/src/loader/nodes/SVDF.cpp
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2022 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 "Builders.h"
+
+#include "kernels/SVDF.h"
+
+namespace luci_interpreter
+{
+
+std::unique_ptr<Kernel> build_kernel_CircleSVDF(const luci::CircleNode *circle_node,
+                                                KernelBuilderHelper &helper)
+{
+  const auto *node = loco::must_cast<const luci::CircleSVDF *>(circle_node);
+  assert(node->arity() == 5);
+
+  const Tensor *input = helper.getInputTensor(node->input());
+  const Tensor *feature = helper.getInputTensor(node->weight_feature());
+  const Tensor *time = helper.getInputTensor(node->weight_time());
+  const Tensor *bias = helper.getOptionalInputTensor(node->bias());
+  const Tensor *input_activation_state = helper.getInputTensor(node->input_activation_state());
+  Tensor *output = helper.getOutputTensor(node);
+
+  auto scratchpad_tensor = std::make_unique<Tensor>(input_activation_state->element_type(),
+                                                    Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  DataType data_type = input->element_type() == DataType::S8 ? DataType::S32 : DataType::FLOAT32;
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_1 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  if (data_type == DataType::FLOAT32 &&
+      (feature->element_type() == DataType::S8 || feature->element_type() == DataType::U8))
+  {
+    data_type = feature->element_type();
+  }
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_2 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  data_type = DataType::FLOAT32;
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_3 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_4 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_5 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  scratchpad_tensor = std::make_unique<Tensor>(data_type, Shape({}), AffineQuantization{}, "");
+  scratchpad_tensor->set_observable(false);
+  scratchpad_tensor->set_data_buffer(nullptr);
+  Tensor *tmp_6 = helper.getRuntimeGraph(node->graph())->addTensor(std::move(scratchpad_tensor));
+
+  SVDFParams params{};
+  params.activation = node->fusedActivationFunction();
+  params.svdf_rank = node->svdf_rank();
+  params.asymmetric_quantize_inputs = node->asymmetric_quantize_inputs();
+
+  return std::make_unique<kernels::SVDF>(input, feature, time, bias, input_activation_state, output,
+                                         tmp, tmp_1, tmp_2, tmp_3, tmp_4, tmp_5, tmp_6, params);
+}
+
+} // namespace luci_interpreter