From: 이한종/동작제어Lab(SR)/Engineer/삼성전자 <hanjoung.lee@samsung.com>
Date: Thu, 8 Nov 2018 08:16:57 +0000 (+0900)
Subject: [neurun] Remove IInitializerGenerator (#3525)
X-Git-Tag: 0.3~438
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=9cf99a1f2050b992c645bec85ad8053270e6fff5;p=platform%2Fcore%2Fml%2Fnnfw.git

[neurun] Remove IInitializerGenerator (#3525)

Remove IInitializerGenerator and its implementations. We do not need
this backend API anymore.

Signed-off-by: Hanjoung Lee <hanjoung.lee@samsung.com>
---

diff --git a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc b/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc
deleted file mode 100644
index be3d280..0000000
--- a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.cc
+++ /dev/null
@@ -1,163 +0,0 @@
-/*
- * 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 "backend/acl_cl/InitializerGenerator.h"
-
-#include <arm_compute/core/Coordinates.h>
-
-#include "backend/acl_cl/kernel/View.h"
-#include "internal/nnapi/kernel/Reader.h"
-#include "util/kernel/IndexIterator.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-InitializerGenerator::InitializerGenerator(const neurun::graph::operand::Set &ctx) : _ctx(ctx)
-{
-  // DO NOTHING
-}
-
-Initializer InitializerGenerator::generate(const graph::operation::Conv2DNode &node)
-{
-  using graph::operation::Conv2DNode;
-
-  Initializer ret;
-  ret.push_back({node.getInputs().at(Conv2DNode::Input::KERNEL), generateWeight(node)});
-  ret.push_back({node.getInputs().at(Conv2DNode::Input::BIAS), generateBias(node)});
-  return ret;
-}
-
-Initializer InitializerGenerator::generate(const graph::operation::FullyConnectedNode &node)
-{
-  using graph::operation::FullyConnectedNode;
-
-  Initializer ret;
-  ret.push_back({node.getInputs().at(FullyConnectedNode::Input::WEIGHT), generateWeight(node)});
-  ret.push_back({node.getInputs().at(FullyConnectedNode::Input::BIAS), generateBias(node)});
-  return ret;
-}
-
-InitializerFn InitializerGenerator::generateWeight(const graph::operation::Conv2DNode &node)
-{
-  const auto ker_index{node.getInputs().at(1)};
-
-  const auto ker_shape = _ctx.at(ker_index).shape().asKernel();
-  auto ker_base = _ctx.at(ker_index).data().base();
-  auto ker_size = _ctx.at(ker_index).data().size();
-
-  return [ker_shape, ker_base, ker_size](::arm_compute::ITensor &tensor) {
-    const ::internal::nnapi::kernel::Reader<float> from{ker_shape, ker_base, ker_size};
-    ::internal::arm_compute::kernel::View<float> into{&tensor};
-
-    ::nnfw::util::kernel::iterate(ker_shape)
-        << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) {
-             const auto value = from.at(nth, ch, row, col);
-             into.at(nth, ch, row, col) = value;
-           };
-  };
-}
-
-InitializerFn InitializerGenerator::generateWeight(const graph::operation::FullyConnectedNode &node)
-{
-  const auto weight_index{node.getInputs().at(1)};
-  const auto input_index{node.getInputs().at(0)};
-
-  const auto num_output = _ctx.at(weight_index).shape().dim(0);
-  auto weight_base = _ctx.at(weight_index).data().base();
-  auto weight_size = _ctx.at(weight_index).data().size();
-
-  // NOTE We assume that input is a feature map
-  // TODO Remove this restriction!
-  const auto ifm_shape = _ctx.at(input_index).shape().asFeature();
-
-  return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) {
-    const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H, ifm_shape.W};
-    const ::internal::nnapi::kernel::Reader<float> from{ker_shape, weight_base, weight_size};
-
-    ::nnfw::util::kernel::iterate(ker_shape)
-        << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) {
-             const auto value = from.at(nth, ch, row, col);
-
-             uint32_t offset = 0;
-
-             // ARM Compute Library uses 'NCHW' ordering
-             offset += nth * ifm_shape.C * ifm_shape.H * ifm_shape.W;
-             offset += ch * ifm_shape.H * ifm_shape.W;
-             offset += row * ifm_shape.W;
-             offset += col;
-
-             const ::arm_compute::Coordinates coordinate{offset};
-
-             auto into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-             *into = value;
-           };
-  };
-}
-
-InitializerFn InitializerGenerator::generateBias(const graph::operation::Conv2DNode &node)
-{
-  // TODO Refactor so we can reuse the common code
-
-  const auto bias_index{node.getInputs().at(2)};
-
-  auto bias_base = _ctx.at(bias_index).data().base();
-  const auto bias_size = _ctx.at(bias_index).shape().asVector();
-
-  return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
-    for (int32_t n = 0; n < bias_size; ++n)
-    {
-      const ::arm_compute::Coordinates coordinate{n};
-
-      float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-      const float *from = reinterpret_cast<const float *>(bias_base) + n;
-      const auto value = *from;
-
-      *into = value;
-    }
-  };
-}
-
-InitializerFn InitializerGenerator::generateBias(const graph::operation::FullyConnectedNode &node)
-{
-  const auto bias_index{node.getInputs().at(2)};
-
-  auto bias_base = _ctx.at(bias_index).data().base();
-  const auto bias_size = _ctx.at(bias_index).shape().asVector();
-
-  return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
-    for (int32_t n = 0; n < bias_size; ++n)
-    {
-      const ::arm_compute::Coordinates coordinate{n};
-
-      float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-      const float *from = reinterpret_cast<const float *>(bias_base) + n;
-      const auto value = *from;
-
-      *into = value;
-    }
-  };
-}
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
diff --git a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h b/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h
deleted file mode 100644
index bf076f6..0000000
--- a/runtimes/neurun/src/backend/acl_cl/InitializerGenerator.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * 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.
- */
-
-#ifndef __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__
-#define __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__
-
-#include "backend/interface/IInitializerGenerator.h"
-
-#include "graph/operand/Set.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace acl_cl
-{
-
-class InitializerGenerator : public IInitializerGenerator
-{
-public:
-  InitializerGenerator(const neurun::graph::operand::Set &ctx);
-
-public:
-  Initializer generate(const graph::operation::Conv2DNode &node) override;
-  Initializer generate(const graph::operation::FullyConnectedNode &node) override;
-
-private:
-  InitializerFn generateWeight(const graph::operation::Conv2DNode &node);
-  InitializerFn generateWeight(const graph::operation::FullyConnectedNode &node);
-
-  InitializerFn generateBias(const graph::operation::Conv2DNode &node);
-  InitializerFn generateBias(const graph::operation::FullyConnectedNode &node);
-
-private:
-  const neurun::graph::operand::Set &_ctx;
-};
-
-} // namespace acl_cl
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_ACL_CL_INITIALIZER_GENERATOR_H__
diff --git a/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc b/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc
deleted file mode 100644
index f415c71..0000000
--- a/runtimes/neurun/src/backend/cpu/InitializerGenerator.cc
+++ /dev/null
@@ -1,227 +0,0 @@
-/*
- * 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 "InitializerGenerator.h"
-
-#include "internal/nnapi/kernel/Reader.h"
-#include "internal/nnapi/kernel/View.h"
-#include "util/kernel/IndexIterator.h"
-
-#include "NeuralNetworks.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-InitializerGenerator::InitializerGenerator(const neurun::graph::operand::Set &ctx) : _ctx(ctx)
-{
-  // DO NOTHING
-}
-
-Initializer InitializerGenerator::generate(const graph::operation::Conv2DNode &node)
-{
-  using graph::operation::Conv2DNode;
-
-  Initializer ret;
-  ret.push_back({node.getInputs().at(Conv2DNode::Input::KERNEL), generateWeight(node)});
-  ret.push_back({node.getInputs().at(Conv2DNode::Input::BIAS), generateBias(node)});
-  return ret;
-}
-
-Initializer InitializerGenerator::generate(const graph::operation::FullyConnectedNode &node)
-{
-  using graph::operation::FullyConnectedNode;
-
-  Initializer ret;
-  ret.push_back({node.getInputs().at(FullyConnectedNode::Input::WEIGHT), generateWeight(node)});
-  ret.push_back({node.getInputs().at(FullyConnectedNode::Input::BIAS), generateBias(node)});
-  return ret;
-}
-
-InitializerFn InitializerGenerator::generateWeight(const graph::operation::Conv2DNode &node)
-{
-  const auto ker_index{node.getInputs().at(1)};
-
-  const auto ker_shape = _ctx.at(ker_index).shape().asKernel();
-  auto ker_base = _ctx.at(ker_index).data().base();
-  auto ker_size = _ctx.at(ker_index).data().size();
-
-  return [ker_shape, ker_base, ker_size](::arm_compute::ITensor &tensor) {
-    const ::internal::nnapi::kernel::Reader<float> from{ker_shape, ker_base, ker_size};
-    ::internal::nnapi::kernel::View<float> into{&tensor};
-
-    ::nnfw::util::kernel::iterate(ker_shape)
-        << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) {
-             const auto value = from.at(nth, ch, row, col);
-             into.at(nth, row, col, ch) = value;
-           };
-  };
-}
-
-InitializerFn InitializerGenerator::generateWeight(const graph::operation::FullyConnectedNode &node)
-{
-  const auto weight_index{node.getInputs().at(1)};
-  const auto input_index{node.getInputs().at(0)};
-
-  const auto num_output = _ctx.at(weight_index).shape().dim(0);
-  auto weight_base = _ctx.at(weight_index).data().base();
-  auto weight_size = _ctx.at(weight_index).data().size();
-  auto weight_type = _ctx.at(weight_index).typeInfo().type();
-
-  // NOTE We assume that input is a feature map
-  // TODO Remove this restriction!
-  const auto ifm_shape = _ctx.at(input_index).shape().asFeature();
-
-  switch (weight_type)
-  {
-    case ::neurun::graph::operand::DataType::TENSOR_FLOAT32:
-    {
-      return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) {
-        const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H,
-                                                    ifm_shape.W};
-        const ::internal::nnapi::kernel::Reader<float> from{ker_shape, weight_base, weight_size};
-
-        ::nnfw::util::kernel::iterate(ker_shape)
-            << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) {
-                 const auto value = from.at(nth, ch, row, col);
-
-                 uint32_t offset = 0;
-
-                 // NNAPI uses NHWC ordering
-                 offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C;
-                 offset += row * ifm_shape.W * ifm_shape.C;
-                 offset += col * ifm_shape.C;
-                 offset += ch;
-
-                 const ::arm_compute::Coordinates coordinate{offset};
-
-                 auto into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-                 *into = value;
-               };
-      };
-    }
-    case ::neurun::graph::operand::DataType::TENSOR_QUANT8_ASYMM:
-    {
-      return [num_output, ifm_shape, weight_base, weight_size](::arm_compute::ITensor &tensor) {
-        const ::nnfw::util::kernel::Shape ker_shape{num_output, ifm_shape.C, ifm_shape.H,
-                                                    ifm_shape.W};
-        const ::internal::nnapi::kernel::Reader<uint8_t> from{ker_shape, weight_base, weight_size};
-        ::nnfw::util::kernel::iterate(ker_shape)
-            << [&](uint32_t nth, uint32_t ch, uint32_t row, uint32_t col) {
-                 const auto value = from.at(nth, ch, row, col);
-                 uint32_t offset = 0;
-
-                 // NNAPI uses NHWC ordering
-                 offset += nth * ifm_shape.H * ifm_shape.W * ifm_shape.C;
-                 offset += row * ifm_shape.W * ifm_shape.C;
-                 offset += col * ifm_shape.C;
-                 offset += ch;
-
-                 const ::arm_compute::Coordinates coordinate{offset};
-
-                 auto into = reinterpret_cast<uint8_t *>(tensor.ptr_to_element(coordinate));
-
-                 *into = value;
-               };
-      };
-    }
-    default:
-    {
-      throw std::runtime_error("Not supported weight type");
-    }
-  }
-}
-
-InitializerFn InitializerGenerator::generateBias(const graph::operation::Conv2DNode &node)
-{
-  // TODO Refactor so we can reuse the common code
-
-  const auto bias_index{node.getInputs().at(2)};
-
-  auto bias_base = _ctx.at(bias_index).data().base();
-  const auto bias_size = _ctx.at(bias_index).shape().asVector();
-
-  return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
-    for (int32_t n = 0; n < bias_size; ++n)
-    {
-      const ::arm_compute::Coordinates coordinate{n};
-
-      float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-      const float *from = reinterpret_cast<const float *>(bias_base) + n;
-      const auto value = *from;
-
-      *into = value;
-    }
-  };
-}
-
-InitializerFn InitializerGenerator::generateBias(const graph::operation::FullyConnectedNode &node)
-{
-  const auto bias_index{node.getInputs().at(2)};
-
-  auto bias_base = _ctx.at(bias_index).data().base();
-  auto bias_type = _ctx.at(bias_index).typeInfo().type();
-  const auto bias_size = _ctx.at(bias_index).shape().asVector();
-
-  switch (bias_type)
-  {
-    case ::neurun::graph::operand::DataType::TENSOR_FLOAT32:
-    {
-      return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
-        for (int32_t n = 0; n < bias_size; ++n)
-        {
-          const ::arm_compute::Coordinates coordinate{n};
-
-          float *into = reinterpret_cast<float *>(tensor.ptr_to_element(coordinate));
-
-          const float *from = reinterpret_cast<const float *>(bias_base) + n;
-          const auto value = *from;
-
-          *into = value;
-        }
-      };
-    }
-    case ::neurun::graph::operand::DataType::TENSOR_QUANT8_ASYMM:
-    {
-      return [bias_base, bias_size](::arm_compute::ITensor &tensor) {
-        for (int32_t n = 0; n < bias_size; ++n)
-        {
-          const ::arm_compute::Coordinates coordinate{n};
-
-          uint8_t *into = reinterpret_cast<uint8_t *>(tensor.ptr_to_element(coordinate));
-
-          const uint8_t *from = reinterpret_cast<const uint8_t *>(bias_base) + n;
-          const auto value = *from;
-
-          *into = value;
-        }
-      };
-    }
-    default:
-    {
-      throw std::runtime_error("Not supported bias type");
-    }
-  }
-}
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
diff --git a/runtimes/neurun/src/backend/cpu/InitializerGenerator.h b/runtimes/neurun/src/backend/cpu/InitializerGenerator.h
deleted file mode 100644
index 859504a..0000000
--- a/runtimes/neurun/src/backend/cpu/InitializerGenerator.h
+++ /dev/null
@@ -1,55 +0,0 @@
-/*
- * 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.
- */
-
-#ifndef __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__
-#define __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__
-
-#include "backend/interface/IInitializerGenerator.h"
-
-#include "graph/operand/Set.h"
-
-namespace neurun
-{
-namespace backend
-{
-namespace cpu
-{
-
-class InitializerGenerator : public IInitializerGenerator
-{
-public:
-  InitializerGenerator(const neurun::graph::operand::Set &ctx);
-
-public:
-  Initializer generate(const graph::operation::Conv2DNode &node) override;
-  Initializer generate(const graph::operation::FullyConnectedNode &node) override;
-
-private:
-  InitializerFn generateWeight(const graph::operation::Conv2DNode &node);
-  InitializerFn generateWeight(const graph::operation::FullyConnectedNode &node);
-
-  InitializerFn generateBias(const graph::operation::Conv2DNode &node);
-  InitializerFn generateBias(const graph::operation::FullyConnectedNode &node);
-
-private:
-  const neurun::graph::operand::Set &_ctx;
-};
-
-} // namespace cpu
-} // namespace backend
-} // namespace neurun
-
-#endif // __NEURUN_BACKEND_CPU_INITIALIZER_GENERATOR_H__
diff --git a/runtimes/neurun/src/backend/interface/IInitializerGenerator.h b/runtimes/neurun/src/backend/interface/IInitializerGenerator.h
deleted file mode 100644
index 6aca1e4..0000000
--- a/runtimes/neurun/src/backend/interface/IInitializerGenerator.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
- * 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.
- */
-
-#ifndef __NEURUN_BACKEND_IINITIALIZER_GENERATOR_H__
-#define __NEURUN_BACKEND_IINITIALIZER_GENERATOR_H__
-
-#include <list>
-
-#include "arm_compute/core/ITensor.h"
-
-#include "graph/operand/Index.h"
-#include "graph/operation/Conv2D.h"
-#include "graph/operation/MaxPool2D.h"
-#include "graph/operation/AvgPool2D.h"
-#include "graph/operation/Concat.h"
-#include "graph/operation/FullyConnected.h"
-#include "graph/operation/Reshape.h"
-#include "graph/operation/Softmax.h"
-#include "graph/operation/NOP.h"
-#include "graph/operation/Permute.h"
-#include "graph/operation/Add.h"
-
-using InitializerFn = std::function<void(::arm_compute::ITensor &)>;
-
-struct InitializerEntry
-{
-  neurun::graph::operand::Index index;
-  InitializerFn fn;
-};
-
-using Initializer = std::list<InitializerEntry>;
-
-namespace neurun
-{
-namespace backend
-{
-
-struct IInitializerGenerator
-{
-  virtual ~IInitializerGenerator() = default;
-
-  virtual Initializer generate(const graph::operation::Conv2DNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::MaxPool2DNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::AvgPool2DNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::ConcatNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::FullyConnectedNode &)
-  {
-    return Initializer{};
-  }
-  virtual Initializer generate(const graph::operation::ReshapeNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::SoftmaxNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::NOPNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::PermuteNode &) { return Initializer{}; }
-  virtual Initializer generate(const graph::operation::AddNode &) { return Initializer{}; }
-};
-
-} // namespace backend
-} // namespace neurun
-#endif // __NEURUN_BACKEND_IINITIALIZER_GENERATOR_H__
diff --git a/runtimes/neurun/src/codegen/IPlanBuilder.h b/runtimes/neurun/src/codegen/IPlanBuilder.h
index e37766a..8ada9a5 100644
--- a/runtimes/neurun/src/codegen/IPlanBuilder.h
+++ b/runtimes/neurun/src/codegen/IPlanBuilder.h
@@ -19,7 +19,6 @@
 
 #include "arm_compute/core/TensorInfo.h"
 #include "backend/interface/IStageGenerator.h"
-#include "backend/interface/IInitializerGenerator.h"
 
 namespace neurun
 {
@@ -32,7 +31,6 @@ struct IPlanBuilder
 
   virtual void addShapeConstr(const ::neurun::graph::operand::Index &ind,
                               const ::arm_compute::TensorInfo &info) = 0;
-  virtual void addInitializer(const Initializer &initializer) = 0;
   virtual void addStage(const Stage &) = 0;
 };
 
diff --git a/runtimes/neurun/src/codegen/PlanBuilder.cc b/runtimes/neurun/src/codegen/PlanBuilder.cc
index 76187c7..c1f92b0 100644
--- a/runtimes/neurun/src/codegen/PlanBuilder.cc
+++ b/runtimes/neurun/src/codegen/PlanBuilder.cc
@@ -29,14 +29,6 @@ void PlanBuilder::addShapeConstr(const ::neurun::graph::operand::Index &ind,
   _tensor_info_ctx[ind.asInt()] = info;
 }
 
-void PlanBuilder::addInitializer(const Initializer &initializer)
-{
-  for (auto &entry : initializer)
-  {
-    _initializer_ctx[entry.index.asInt()] = entry.fn;
-  }
-}
-
 void PlanBuilder::addStage(const Stage &stage) { _stages.emplace_back(stage); }
 
 void PlanBuilder::finalize(const backend::TensorBuilderSet &tensor_builders)
diff --git a/runtimes/neurun/src/codegen/PlanBuilder.h b/runtimes/neurun/src/codegen/PlanBuilder.h
index 243d2a4..a3a7844 100644
--- a/runtimes/neurun/src/codegen/PlanBuilder.h
+++ b/runtimes/neurun/src/codegen/PlanBuilder.h
@@ -58,9 +58,6 @@ public:
                       const ::arm_compute::TensorInfo &info) override;
 
 public:
-  void addInitializer(const Initializer &initializer) override;
-
-public:
   void addStage(const Stage &stage) override;
 
 public:
@@ -75,7 +72,6 @@ private:
 
 private:
   std::map<int, ::arm_compute::TensorInfo> _tensor_info_ctx;
-  std::map<int, InitializerFn> _initializer_ctx;
   std::vector<Stage> _stages;
 };