Imported Upstream version 1.21.0upstream/1.21.0tizen_7.0_m2_releaseaccepted/tizen/unified/20220912.170817accepted/tizen/unified/20220912.164738accepted/tizen/7.0/unified/hotfix/20221116.105341accepted/tizen/7.0/unified/20221110.060236tizen_7.0_hotfixtizen_7.0accepted/tizen_7.0_unified_hotfixaccepted/tizen_7.0_unified

2 files changed, 441 insertions, 0 deletions

diff --git a/compiler/luci/pass/src/helpers/SparsityFormatConverter.cpp b/compiler/luci/pass/src/helpers/SparsityFormatConverter.cpp
new file mode 100644
index 000000000..72b7d60ff
--- /dev/null
+++ b/compiler/luci/pass/src/helpers/SparsityFormatConverter.cpp
@@ -0,0 +1,312 @@
+/*
+ * Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2020 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.
+ */
+
+// codes under namespace sparsity referenced from
+// https://github.com/tensorflow/tensorflow/blob/3f878cff5b698b82eea85db2b60d65a2e320850e/
+//       tensorflow/lite/kernels/internal/utils/sparsity_format_converter.h
+//       tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc
+
+#include "SparsityFormatConverter.h"
+
+#include <oops/InternalExn.h>
+
+#include <cassert>
+
+namespace sparsity
+{
+
+namespace
+{
+
+uint64_t GetFlattenedIndex(const std::vector<int> &indices, const std::vector<int> &shape)
+{
+  uint64_t index = 0;
+  int sub_elements = 1;
+  for (int i = shape.size() - 1; i >= 0; i--)
+  {
+    index += indices[i] * sub_elements;
+    sub_elements *= shape[i];
+  }
+  return index;
+}
+
+std::vector<int> TfLiteIntArrayToVector(const TfLiteIntArray *int_array)
+{
+  std::vector<int> values;
+  if (!int_array)
+  {
+    return values;
+  }
+
+  values.resize(int_array->size);
+  for (int i = 0; i < int_array->size; i++)
+  {
+    values[i] = int_array->data[i];
+  }
+
+  return values;
+}
+
+} // namespace
+
+template <typename T>
+FormatConverter<T>::FormatConverter(const std::vector<int> &shape, const TfLiteSparsity &sparsity)
+{
+  auto traversal_order = TfLiteIntArrayToVector(sparsity.traversal_order);
+  auto block_map = TfLiteIntArrayToVector(sparsity.block_map);
+
+  std::vector<TfLiteDimensionType> format(sparsity.dim_metadata_size);
+  std::vector<int> dense_size(sparsity.dim_metadata_size);
+  std::vector<std::vector<int>> segments(sparsity.dim_metadata_size);
+  std::vector<std::vector<int>> indices(sparsity.dim_metadata_size);
+  for (int i = 0; i < sparsity.dim_metadata_size; i++)
+  {
+    format[i] = sparsity.dim_metadata[i].format;
+    dense_size[i] = sparsity.dim_metadata[i].dense_size;
+    segments[i] = TfLiteIntArrayToVector(sparsity.dim_metadata[i].array_segments);
+    indices[i] = TfLiteIntArrayToVector(sparsity.dim_metadata[i].array_indices);
+  }
+
+  InitSparseToDenseConverter(shape, std::move(traversal_order), std::move(format),
+                             std::move(dense_size), std::move(segments), std::move(indices),
+                             std::move(block_map));
+}
+
+template <typename T>
+void FormatConverter<T>::InitSparseToDenseConverter(
+  std::vector<int> shape, std::vector<int> traversal_order, std::vector<TfLiteDimensionType> format,
+  std::vector<int> dense_size, std::vector<std::vector<int>> segments,
+  std::vector<std::vector<int>> indices, std::vector<int> block_map)
+{
+  dense_shape_ = std::move(shape);
+  traversal_order_ = std::move(traversal_order);
+  block_map_ = std::move(block_map);
+  format_ = std::move(format);
+
+  dense_size_ = 1;
+  for (size_t i = 0; i < dense_shape_.size(); i++)
+  {
+    dense_size_ *= dense_shape_[i];
+  }
+
+  dim_metadata_.resize(2 * format_.size());
+  for (size_t i = 0; i < format_.size(); i++)
+  {
+    if (format_[i] == kTfLiteDimDense)
+    {
+      dim_metadata_[2 * i] = {dense_size[i]};
+    }
+    else
+    {
+      dim_metadata_[2 * i] = std::move(segments[i]);
+      dim_metadata_[2 * i + 1] = std::move(indices[i]);
+    }
+  }
+
+  int original_rank = dense_shape_.size();
+  int block_dim = 0;
+
+  blocked_shape_.resize(original_rank);
+  block_size_.resize(block_map_.size());
+  for (int i = 0; i < original_rank; i++)
+  {
+    if (block_dim < (int)block_map_.size() && block_map_[block_dim] == i)
+    {
+      if (original_rank + block_dim < (int)traversal_order_.size())
+      {
+        int orig_dim = traversal_order_[original_rank + block_dim];
+        block_size_[block_dim] = dense_size[orig_dim];
+        blocked_shape_[i] = dense_shape_[i] / dense_size[orig_dim];
+        block_dim++;
+      }
+    }
+    else
+    {
+      blocked_shape_[i] = dense_shape_[i];
+    }
+  }
+}
+
+template <typename T>
+void FormatConverter<T>::Populate(const T *src_data, std::vector<int> indices, int level,
+                                  int prev_idx, int *src_data_ptr, T *dest_data)
+{
+  if (static_cast<size_t>(level) == indices.size())
+  {
+    int orig_rank = dense_shape_.size();
+    std::vector<int> orig_idx;
+    orig_idx.resize(orig_rank);
+    int i = 0;
+    for (; static_cast<size_t>(i) < orig_idx.size(); i++)
+    {
+      int orig_dim = traversal_order_[i];
+      orig_idx[orig_dim] = indices[i];
+    }
+
+    for (; static_cast<size_t>(i) < indices.size(); i++)
+    {
+      const int block_idx = traversal_order_[i] - orig_rank;
+      const int orig_dim = block_map_[block_idx];
+      orig_idx[orig_dim] = orig_idx[orig_dim] * block_size_[block_idx] + indices[i];
+    }
+
+    dest_data[GetFlattenedIndex(orig_idx, dense_shape_)] = src_data[*src_data_ptr];
+
+    *src_data_ptr = *src_data_ptr + 1;
+    return;
+  }
+
+  const int metadata_idx = 2 * level;
+  const int shape_of_level = dim_metadata_[metadata_idx][0];
+  if (format_[level] == kTfLiteDimDense)
+  {
+    for (int i = 0; i < shape_of_level; i++)
+    {
+      indices[level] = i;
+      Populate(src_data, indices, level + 1, prev_idx * shape_of_level + i, src_data_ptr,
+               dest_data);
+    }
+  }
+  else if (static_cast<size_t>(prev_idx + 1) < dim_metadata_[metadata_idx].size())
+  {
+    const auto &array_segments = dim_metadata_[metadata_idx];
+    const auto &array_indices = dim_metadata_[metadata_idx + 1];
+    for (int i = array_segments[prev_idx]; i < array_segments[prev_idx + 1]; i++)
+    {
+      if (static_cast<size_t>(i) < array_indices.size() &&
+          static_cast<size_t>(level) < indices.size())
+      {
+        indices[level] = array_indices[i];
+        Populate(src_data, indices, level + 1, i, src_data_ptr, dest_data);
+      }
+    }
+  }
+}
+
+template <typename T> bool FormatConverter<T>::SparseToDense(const T *src_data)
+{
+  data_.resize(dense_size_);
+  std::fill(data_.begin(), data_.end(), T(0));
+
+  int total_rank = traversal_order_.size();
+  int src_data_ptr = 0;
+  std::vector<int> indices(total_rank);
+  Populate(src_data, indices, 0, 0, &src_data_ptr, data_.data());
+
+  return true;
+}
+
+template class FormatConverter<float>;
+template class FormatConverter<uint16_t>;
+
+} // namespace sparsity
+
+#include <luci/IR/SparsityParam.h>
+
+namespace luci
+{
+
+sparsity::TfLiteDimensionType to_tflite_sparsity(luci::DimensionType dt)
+{
+  switch (dt)
+  {
+    case luci::DimensionType::DENSE:
+      return sparsity::TfLiteDimensionType::kTfLiteDimDense;
+    case luci::DimensionType::SPARSE_CSR:
+      return sparsity::TfLiteDimensionType::kTfLiteDimSparseCSR;
+  }
+  return sparsity::TfLiteDimensionType::kTfLiteDimDense;
+}
+
+sparsity::TfLiteIntArray *to_tflite_sparsity(const luci::SparseIndexVector &data)
+{
+  auto type = data.type();
+  switch (type)
+  {
+    case luci::SparseIndexVectorType::NONE:
+    {
+      std::vector<int32_t> empty;
+      return makeTfLiteArray(empty);
+    }
+    case luci::SparseIndexVectorType::I32:
+      return makeTfLiteArray<int32_t>(*data.as_int32_vector());
+    case luci::SparseIndexVectorType::U16:
+      return makeTfLiteArray<uint16_t>(*data.as_uint16_vector());
+    case luci::SparseIndexVectorType::U8:
+      return makeTfLiteArray<uint8_t>(*data.as_uint8_vector());
+    default:
+      INTERNAL_EXN_V("unsupported SparseIndexVectorType", oops::to_uint32(type));
+  }
+}
+
+sparsity::TfLiteSparsity to_tflite_sparsity(const luci::SparsityParam *sp)
+{
+  sparsity::TfLiteSparsity tflsp;
+  tflsp.traversal_order = makeTfLiteArray(sp->traversal_order);
+  tflsp.block_map = makeTfLiteArray(sp->block_map);
+  tflsp.dim_metadata = makeTfLiteDimensionMetadata(sp->dim_metadata);
+  tflsp.dim_metadata_size = sp->dim_metadata.size();
+  return tflsp;
+}
+
+template <typename T> sparsity::TfLiteIntArray *makeTfLiteArray(const std::vector<T> &data)
+{
+  size_t cn = data.size();
+  size_t sz = 1 + data.size();
+  sparsity::TfLiteIntArray *sp = (sparsity::TfLiteIntArray *)(new int[sz]);
+  sp->size = cn;
+  for (size_t i = 0; i < cn; ++i)
+  {
+    sp->data[i] = data[i];
+  }
+  return sp;
+}
+
+sparsity::TfLiteDimensionMetadata *
+makeTfLiteDimensionMetadata(const std::vector<luci::DimMetaData> &data)
+{
+  size_t cn = data.size();
+  sparsity::TfLiteDimensionMetadata *tfldm = new sparsity::TfLiteDimensionMetadata[cn];
+
+  for (size_t i = 0; i < cn; ++i)
+  {
+    tfldm[i].format = to_tflite_sparsity(data[i].format());
+    tfldm[i].dense_size = data[i].dense_size();
+    tfldm[i].array_segments = to_tflite_sparsity(data[i].array_segments());
+    tfldm[i].array_indices = to_tflite_sparsity(data[i].array_indices());
+  }
+
+  return tfldm;
+}
+
+void freeTfLiteSparsity(sparsity::TfLiteSparsity &tflsp)
+{
+  assert(tflsp.traversal_order);
+  assert(tflsp.block_map);
+  delete[] tflsp.traversal_order;
+  delete[] tflsp.block_map;
+
+  for (int i = 0; i < tflsp.dim_metadata_size; ++i)
+  {
+    assert(tflsp.dim_metadata[i].array_segments);
+    assert(tflsp.dim_metadata[i].array_indices);
+    delete[] tflsp.dim_metadata[i].array_segments;
+    delete[] tflsp.dim_metadata[i].array_indices;
+  }
+}
+
+} // namespace luci
diff --git a/compiler/luci/pass/src/helpers/SparsityFormatConverter.h b/compiler/luci/pass/src/helpers/SparsityFormatConverter.h
new file mode 100644
index 000000000..fcd9bbcd0
--- /dev/null
+++ b/compiler/luci/pass/src/helpers/SparsityFormatConverter.h
@@ -0,0 +1,129 @@
+/*
+ * Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2020 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.
+ */
+
+#ifndef __LUCI_PASS_HELPERS_SPARSITY_FORMAT_CONVERTER_H__
+#define __LUCI_PASS_HELPERS_SPARSITY_FORMAT_CONVERTER_H__
+
+#include <cstdint>
+#include <vector>
+
+// codes under namespace sparsity referenced from
+// https://github.com/tensorflow/tensorflow/blob/3f878cff5b698b82eea85db2b60d65a2e320850e/
+//       tensorflow/lite/kernels/internal/utils/sparsity_format_converter.h
+//       tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc
+
+namespace sparsity
+{
+
+// Storage format of each dimension in a sparse tensor.
+typedef enum TfLiteDimensionType
+{
+  kTfLiteDimDense = 0,
+  kTfLiteDimSparseCSR,
+} TfLiteDimensionType;
+
+// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
+// indices
+typedef struct TfLiteIntArray
+{
+  int size;
+  int data[];
+} TfLiteIntArray;
+
+// Metadata to encode each dimension in a sparse tensor.
+typedef struct TfLiteDimensionMetadata
+{
+  TfLiteDimensionType format;
+  int dense_size;
+  TfLiteIntArray *array_segments;
+  TfLiteIntArray *array_indices;
+} TfLiteDimensionMetadata;
+
+// Parameters used to encode a sparse tensor. For detailed explanation of each
+// field please refer to lite/schema/schema.fbs.
+typedef struct TfLiteSparsity
+{
+  TfLiteIntArray *traversal_order;
+  TfLiteIntArray *block_map;
+  TfLiteDimensionMetadata *dim_metadata;
+  int dim_metadata_size;
+} TfLiteSparsity;
+
+// A converter that keeps an internal representation of sparse tensor parameters
+// and converts tensors between dense and sparse formats.
+template <typename T> class FormatConverter
+{
+public:
+  /* Creates a sparse to dense converter.
+   * @param shape      Shape of the target dense tensor.
+   * @param sparsity   Sparsity parameter of the sparse TfLiteTensor.
+   */
+  FormatConverter(const std::vector<int> &shape, const TfLiteSparsity &sparsity);
+
+  const std::vector<T> &GetData() { return data_; }
+  const std::vector<std::vector<int>> &GetDimMetadata() { return dim_metadata_; }
+
+  bool SparseToDense(const T *src_data);
+
+private:
+  // Helper function for initializing this converter for sparse to dense
+  // conversion.
+  void InitSparseToDenseConverter(std::vector<int> shape, std::vector<int> traversal_order,
+                                  std::vector<TfLiteDimensionType> format,
+                                  std::vector<int> dense_size,
+                                  std::vector<std::vector<int>> segments,
+                                  std::vector<std::vector<int>> indices,
+                                  std::vector<int> block_map);
+
+  void Populate(const T *src_data, std::vector<int> indices, int level, int prev_idx,
+                int *src_data_ptr, T *dest_data);
+
+private:
+  std::vector<int> dense_shape_;
+  std::vector<int> blocked_shape_;
+  size_t dense_size_;
+  std::vector<int> traversal_order_;
+  std::vector<TfLiteDimensionType> format_;
+  std::vector<int> block_size_;
+  std::vector<int> block_map_;
+  std::vector<std::vector<int>> dim_metadata_;
+  std::vector<T> data_;
+};
+
+extern template class FormatConverter<float>;
+extern template class FormatConverter<uint16_t>;
+
+} // namespace sparsity
+
+#include <luci/IR/SparsityParam.h>
+
+namespace luci
+{
+
+sparsity::TfLiteDimensionType to_tflite_sparsity(luci::DimensionType dt);
+sparsity::TfLiteIntArray *to_tflite_sparsity(const luci::SparseIndexVector &data);
+sparsity::TfLiteSparsity to_tflite_sparsity(const luci::SparsityParam *sp);
+
+template <typename T> sparsity::TfLiteIntArray *makeTfLiteArray(const std::vector<T> &data);
+sparsity::TfLiteDimensionMetadata *
+makeTfLiteDimensionMetadata(const std::vector<luci::DimMetaData> &data);
+
+void freeTfLiteSparsity(sparsity::TfLiteSparsity &tflsp);
+
+} // namespace luci
+
+#endif // __LUCI_PASS_HELPERS_SPARSITY_FORMAT_CONVERTER_H__