[nnpackage] Move nnpackage out of runtimes/contrib (#6848)

Move nnpackage out of runtimes/contrib

Signed-off-by: Sanggyu Lee <sg5.lee@samsung.com>

7 files changed, 1016 insertions, 0 deletions

diff --git a/nnpackage/examples/one_op_in_tflite/add.tflite b/nnpackage/examples/one_op_in_tflite/add.tflite
new file mode 100644
index 000000000..e748b6843
--- /dev/null
+++ b/nnpackage/examples/one_op_in_tflite/add.tflite
diff --git a/nnpackage/examples/one_op_in_tflite/metadata/MANIFEST b/nnpackage/examples/one_op_in_tflite/metadata/MANIFEST
new file mode 100644
index 000000000..1d96cce1b
--- /dev/null
+++ b/nnpackage/examples/one_op_in_tflite/metadata/MANIFEST
@@ -0,0 +1,7 @@
+{
+  "major-version" : "1",
+  "minor-version" : "0",
+  "patch-version" : "0",
+  "models"      : [ "add.tflite" ],
+  "model-types" : [ "tflite" ]
+}
diff --git a/nnpackage/schema/nnpackage_model.fbs b/nnpackage/schema/nnpackage_model.fbs
new file mode 100644
index 000000000..dbb0d3526
--- /dev/null
+++ b/nnpackage/schema/nnpackage_model.fbs
@@ -0,0 +1,804 @@
+// Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+// Copyright 2017 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.
+
+// Revision History
+// Version 0: Initial version. Based on TensorFlow Lite v1.13.1 schema.
+
+namespace circle;
+
+// This corresponds to the version.
+file_identifier "CIR0";
+// File extension of any written files.
+file_extension "circle";
+
+// IMPORTANT: All new members of tables, enums and unions must be added at the
+// end to ensure backwards compatibility.
+
+// The type of data stored in a tensor.
+enum TensorType : byte {
+  FLOAT32 = 0,
+  FLOAT16 = 1,
+  INT32 = 2,
+  UINT8 = 3,
+  INT64 = 4,
+  STRING = 5,
+  BOOL = 6,
+  INT16 = 7,
+  COMPLEX64 = 8,
+  INT8 = 9,
+}
+
+// Custom quantization parameters for experimenting with new quantization
+// techniques.
+table CustomQuantization {
+  custom:[ubyte] (force_align: 16);
+}
+
+// Represents a specific quantization technique's parameters.
+union QuantizationDetails {
+  CustomQuantization,
+}
+
+// Parameters for converting a quantized tensor back to float.
+table QuantizationParameters {
+  // These four parameters are the asymmetric linear quantization parameters.
+  // Given a quantized value q, the corresponding float value f should be:
+  //   f = scale * (q - zero_point)
+  // For other quantization types, the QuantizationDetails below is used.
+  min:[float];  // For importing back into tensorflow.
+  max:[float];  // For importing back into tensorflow.
+  scale:[float];  // For dequantizing the tensor's values.
+  zero_point:[long];
+
+  // If this is not none, the quantization parameters above are ignored and the
+  // value of the QuantizationDetails union below should be used.
+  details:QuantizationDetails;
+}
+
+table Tensor {
+  // The tensor shape. The meaning of each entry is operator-specific but
+  // builtin ops use: [batch size, height, width, number of channels] (That's
+  // Tensorflow's NHWC).
+  shape:[int];
+  type:TensorType;
+  // An index that refers to the buffers table at the root of the model. Or,
+  // if there is no data buffer associated (i.e. intermediate results), then
+  // this is 0 (which refers to an always existent empty buffer).
+  //
+  // The data_buffer itself is an opaque container, with the assumption that the
+  // target device is little-endian. In addition, all builtin operators assume
+  // the memory is ordered such that if `shape` is [4, 3, 2], then index
+  // [i, j, k] maps to data_buffer[i*3*2 + j*2 + k].
+  buffer:uint;
+  name:string;  // For debugging and importing back into tensorflow.
+  quantization:QuantizationParameters;  // Optional.
+
+  is_variable:bool = false;
+}
+
+// A list of builtin operators. Builtin operators are slightly faster than custom
+// ones, but not by much. Moreover, while custom operators accept an opaque
+// object containing configuration parameters, builtins have a predetermined
+// set of acceptable options.
+enum BuiltinOperator : byte {
+  ADD = 0,
+  AVERAGE_POOL_2D = 1,
+  CONCATENATION = 2,
+  CONV_2D = 3,
+  DEPTHWISE_CONV_2D = 4,
+  // DEPTH_TO_SPACE = 5,
+  DEQUANTIZE = 6,
+  EMBEDDING_LOOKUP = 7,
+  FLOOR = 8,
+  FULLY_CONNECTED = 9,
+  HASHTABLE_LOOKUP = 10,
+  L2_NORMALIZATION = 11,
+  L2_POOL_2D = 12,
+  LOCAL_RESPONSE_NORMALIZATION = 13,
+  LOGISTIC = 14,
+  LSH_PROJECTION = 15,
+  LSTM = 16,
+  MAX_POOL_2D = 17,
+  MUL = 18,
+  RELU = 19,
+  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
+  // since different model developers use RELU1 in different ways. Never
+  // create another op called RELU1.
+  RELU_N1_TO_1 = 20,
+  RELU6 = 21,
+  RESHAPE = 22,
+  RESIZE_BILINEAR = 23,
+  RNN = 24,
+  SOFTMAX = 25,
+  SPACE_TO_DEPTH = 26,
+  SVDF = 27,
+  TANH = 28,
+  // TODO(aselle): Consider rename to CONCATENATE_EMBEDDINGS
+  CONCAT_EMBEDDINGS = 29,
+  SKIP_GRAM = 30,
+  CALL = 31,
+  CUSTOM = 32,
+  EMBEDDING_LOOKUP_SPARSE = 33,
+  PAD = 34,
+  UNIDIRECTIONAL_SEQUENCE_RNN = 35,
+  GATHER = 36,
+  BATCH_TO_SPACE_ND = 37,
+  SPACE_TO_BATCH_ND = 38,
+  TRANSPOSE = 39,
+  MEAN = 40,
+  SUB = 41,
+  DIV = 42,
+  SQUEEZE = 43,
+  UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
+  STRIDED_SLICE = 45,
+  BIDIRECTIONAL_SEQUENCE_RNN = 46,
+  EXP = 47,
+  TOPK_V2 = 48,
+  SPLIT = 49,
+  LOG_SOFTMAX = 50,
+  // DELEGATE is a special op type for the operations which are delegated to
+  // other backends.
+  // WARNING: Experimental interface, subject to change
+  DELEGATE = 51,
+  BIDIRECTIONAL_SEQUENCE_LSTM = 52,
+  CAST = 53,
+  PRELU = 54,
+  MAXIMUM = 55,
+  ARG_MAX = 56,
+  MINIMUM = 57,
+  LESS = 58,
+  NEG = 59,
+  PADV2 = 60,
+  GREATER = 61,
+  GREATER_EQUAL = 62,
+  LESS_EQUAL = 63,
+  SELECT = 64,
+  SLICE = 65,
+  SIN = 66,
+  TRANSPOSE_CONV = 67,
+  SPARSE_TO_DENSE = 68,
+  TILE = 69,
+  EXPAND_DIMS = 70,
+  EQUAL = 71,
+  NOT_EQUAL = 72,
+  LOG = 73,
+  SUM = 74,
+  SQRT = 75,
+  RSQRT = 76,
+  SHAPE = 77,
+  POW = 78,
+  ARG_MIN = 79,
+  FAKE_QUANT = 80,
+  REDUCE_PROD = 81,
+  REDUCE_MAX = 82,
+  PACK = 83,
+  LOGICAL_OR = 84,
+  ONE_HOT = 85,
+  LOGICAL_AND = 86,
+  LOGICAL_NOT = 87,
+  UNPACK = 88,
+  REDUCE_MIN = 89,
+  FLOOR_DIV = 90,
+  REDUCE_ANY = 91,
+  SQUARE = 92,
+  ZEROS_LIKE = 93,
+  FILL = 94,
+  FLOOR_MOD = 95,
+  RANGE = 96,
+  RESIZE_NEAREST_NEIGHBOR = 97,
+  LEAKY_RELU = 98,
+  SQUARED_DIFFERENCE = 99,
+  MIRROR_PAD = 100,
+  ABS = 101,
+  SPLIT_V = 102,
+}
+
+// Options for the builtin operators.
+union BuiltinOptions {
+  Conv2DOptions,
+  DepthwiseConv2DOptions,
+  ConcatEmbeddingsOptions,
+  LSHProjectionOptions,
+  Pool2DOptions,
+  SVDFOptions,
+  RNNOptions,
+  FullyConnectedOptions,
+  SoftmaxOptions,
+  ConcatenationOptions,
+  AddOptions,
+  L2NormOptions,
+  LocalResponseNormalizationOptions,
+  LSTMOptions,
+  ResizeBilinearOptions,
+  CallOptions,
+  ReshapeOptions,
+  SkipGramOptions,
+  SpaceToDepthOptions,
+  EmbeddingLookupSparseOptions,
+  MulOptions,
+  PadOptions,
+  GatherOptions,
+  BatchToSpaceNDOptions,
+  SpaceToBatchNDOptions,
+  TransposeOptions,
+  ReducerOptions,
+  SubOptions,
+  DivOptions,
+  SqueezeOptions,
+  SequenceRNNOptions,
+  StridedSliceOptions,
+  ExpOptions,
+  TopKV2Options,
+  SplitOptions,
+  LogSoftmaxOptions,
+  CastOptions,
+  DequantizeOptions,
+  MaximumMinimumOptions,
+  ArgMaxOptions,
+  LessOptions,
+  NegOptions,
+  PadV2Options,
+  GreaterOptions,
+  GreaterEqualOptions,
+  LessEqualOptions,
+  SelectOptions,
+  SliceOptions,
+  TransposeConvOptions,
+  SparseToDenseOptions,
+  TileOptions,
+  ExpandDimsOptions,
+  EqualOptions,
+  NotEqualOptions,
+  ShapeOptions,
+  PowOptions,
+  ArgMinOptions,
+  FakeQuantOptions,
+  PackOptions,
+  LogicalOrOptions,
+  OneHotOptions,
+  LogicalAndOptions,
+  LogicalNotOptions,
+  UnpackOptions,
+  FloorDivOptions,
+  SquareOptions,
+  ZerosLikeOptions,
+  FillOptions,
+  BidirectionalSequenceLSTMOptions,
+  BidirectionalSequenceRNNOptions,
+  UnidirectionalSequenceLSTMOptions,
+  FloorModOptions,
+  RangeOptions,
+  ResizeNearestNeighborOptions,
+  LeakyReluOptions,
+  SquaredDifferenceOptions,
+  MirrorPadOptions,
+  AbsOptions,
+  SplitVOptions,
+}
+
+enum Padding : byte { SAME, VALID }
+
+enum ActivationFunctionType : byte {
+  NONE = 0,
+  RELU = 1,
+  RELU_N1_TO_1 = 2,
+  RELU6 = 3,
+  TANH = 4,
+  SIGN_BIT = 5,
+}
+
+table Conv2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  fused_activation_function:ActivationFunctionType;
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table Pool2DOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  filter_width:int;
+  filter_height:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DepthwiseConv2DOptions {
+  // Parameters for DepthwiseConv version 1 or above.
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+  depth_multiplier:int;
+  fused_activation_function:ActivationFunctionType;
+  // Parameters for DepthwiseConv version 2 or above.
+  dilation_w_factor:int = 1;
+  dilation_h_factor:int = 1;
+}
+
+table ConcatEmbeddingsOptions {
+  num_channels:int;
+  num_columns_per_channel:[int];
+  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
+}
+
+enum LSHProjectionType: byte {
+  UNKNOWN = 0,
+  SPARSE = 1,
+  DENSE = 2,
+}
+
+table LSHProjectionOptions {
+  type: LSHProjectionType;
+}
+
+table SVDFOptions {
+  rank:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow RNNCell.
+table RNNOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow dynamic_rnn with RNNCell.
+table SequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+}
+
+// An implementation of TensorFlow bidrectional_dynamic_rnn with RNNCell.
+table BidirectionalSequenceRNNOptions {
+  time_major:bool;
+  fused_activation_function:ActivationFunctionType;
+  merge_outputs: bool;
+}
+
+enum FullyConnectedOptionsWeightsFormat: byte {
+  DEFAULT = 0,
+  SHUFFLED4x16INT8 = 1,
+}
+
+// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
+table FullyConnectedOptions {
+  // Parameters for FullyConnected version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+
+  // Parameters for FullyConnected version 2 or above.
+  weights_format:FullyConnectedOptionsWeightsFormat = DEFAULT;
+}
+
+table SoftmaxOptions {
+  beta: float;
+}
+
+// An implementation of TensorFlow concat.
+table ConcatenationOptions {
+  axis:int;
+  fused_activation_function:ActivationFunctionType;
+}
+
+table AddOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table MulOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table L2NormOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table LocalResponseNormalizationOptions {
+  radius:int;
+  bias:float;
+  alpha:float;
+  beta:float;
+}
+
+enum LSTMKernelType : byte {
+  // Full LSTM kernel which supports peephole and projection.
+  FULL = 0,
+  // Basic LSTM kernels. Equivalent to TensorFlow BasicLSTMCell.
+  BASIC = 1,
+}
+
+// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
+table LSTMOptions {
+  // Parameters for LSTM version 1 or above.
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // Parameters for LSTM version 2 or above.
+  // Basic kernel is only supported in version 2 or above.
+  kernel_type: LSTMKernelType = FULL;
+}
+
+// An implementation of TensorFlow dynamic_rnn with LSTMCell.
+table UnidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true then first dimension is sequence, otherwise batch.
+  time_major:bool;
+}
+
+table BidirectionalSequenceLSTMOptions {
+  fused_activation_function:ActivationFunctionType;
+  cell_clip: float; // Optional, 0.0 means no clipping
+  proj_clip: float; // Optional, 0.0 means no clipping
+
+  // If true, store the outputs of both directions into the first output.
+  merge_outputs: bool;
+}
+
+table ResizeBilinearOptions {
+  new_height: int (deprecated);
+  new_width: int (deprecated);
+  align_corners: bool;
+}
+
+table ResizeNearestNeighborOptions {
+  align_corners: bool;
+}
+
+// A call operation options
+table CallOptions {
+  // The subgraph index that needs to be called.
+  subgraph:uint;
+}
+
+table PadOptions {
+}
+
+table PadV2Options {
+}
+
+table ReshapeOptions {
+  new_shape:[int];
+}
+
+table SpaceToBatchNDOptions {
+}
+
+table BatchToSpaceNDOptions {
+}
+
+table SkipGramOptions {
+  ngram_size: int;
+  max_skip_size: int;
+  include_all_ngrams: bool;
+}
+
+table SpaceToDepthOptions {
+  block_size: int;
+}
+
+table SubOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table DivOptions {
+  fused_activation_function:ActivationFunctionType;
+}
+
+table TopKV2Options {
+}
+
+enum CombinerType : byte {
+  SUM = 0,
+  MEAN = 1,
+  SQRTN = 2,
+}
+
+table EmbeddingLookupSparseOptions {
+  combiner:CombinerType;
+}
+
+table GatherOptions {
+  axis: int;
+}
+
+table TransposeOptions {
+}
+
+table ExpOptions {
+}
+
+table ReducerOptions {
+  keep_dims: bool;
+}
+
+table SqueezeOptions {
+  squeeze_dims:[int];
+}
+
+table SplitOptions {
+  num_splits: int;
+}
+
+table SplitVOptions {
+  num_splits: int;
+}
+
+table StridedSliceOptions {
+  begin_mask: int;
+  end_mask: int;
+  ellipsis_mask: int;
+  new_axis_mask: int;
+  shrink_axis_mask: int;
+}
+
+table LogSoftmaxOptions {
+}
+
+table CastOptions {
+  in_data_type: TensorType;
+  out_data_type: TensorType;
+}
+
+table DequantizeOptions {
+}
+
+table MaximumMinimumOptions {
+}
+
+table TileOptions {
+}
+
+table ArgMaxOptions {
+  output_type : TensorType;
+}
+
+table ArgMinOptions {
+  output_type : TensorType;
+}
+
+table GreaterOptions {
+}
+
+table GreaterEqualOptions {
+}
+
+table LessOptions {
+}
+
+table LessEqualOptions {
+}
+
+table NegOptions {
+}
+
+table SelectOptions {
+}
+
+table SliceOptions {
+}
+
+table TransposeConvOptions {
+  padding:Padding;
+  stride_w:int;
+  stride_h:int;
+}
+
+table ExpandDimsOptions {
+}
+
+table SparseToDenseOptions {
+  validate_indices:bool;
+}
+
+table EqualOptions {
+}
+
+table NotEqualOptions {
+}
+
+table ShapeOptions {
+  // Optional output type of the operation (int32 or int64). Defaults to int32.
+  out_type : TensorType;
+}
+
+table PowOptions {
+}
+
+table FakeQuantOptions {
+  // Parameters supported by version 1:
+  min:float;
+  max:float;
+  num_bits:int;
+
+  // Parameters supported by version 2:
+  narrow_range:bool;
+}
+
+table PackOptions {
+  values_count:int;
+  axis:int;
+}
+
+table LogicalOrOptions {
+}
+
+table OneHotOptions {
+  axis:int;
+}
+
+table AbsOptions {
+}
+
+
+table LogicalAndOptions {
+}
+
+table LogicalNotOptions {
+}
+
+table UnpackOptions {
+  num:int;
+  axis:int;
+}
+
+table FloorDivOptions {
+}
+
+table SquareOptions {
+}
+
+table ZerosLikeOptions {
+}
+
+table FillOptions {
+}
+
+table FloorModOptions {
+}
+
+table RangeOptions {
+}
+
+table LeakyReluOptions {
+  alpha:float;
+}
+
+table SquaredDifferenceOptions {
+}
+
+enum MirrorPadMode : byte {
+  // Doesn't include borders.
+  REFLECT = 0,
+  // Includes borders.
+  SYMMETRIC = 1,
+}
+
+table MirrorPadOptions {
+  mode:MirrorPadMode;
+}
+
+// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
+// builtin, or a string if the operator is custom.
+table OperatorCode {
+  builtin_code:BuiltinOperator;
+  custom_code:string;
+
+  // The version of the operator. The version need to be bumped whenever new
+  // parameters are introduced into an op.
+  version:int = 1;
+}
+
+enum CustomOptionsFormat : byte {
+  FLEXBUFFERS = 0,
+}
+
+enum DataFormat : byte {
+  // For 2D data, NHWC(batch, height, width, channels)
+  // For 3D data, NDHWC(batch, depth, height, width, channels)
+  CHANNELS_LAST = 0,
+  // For 2D data, NCHW(batch, channels, height, width)
+  // For 3D data, NCDHW(batch, channels, depth, height, width)
+  CHANNELS_FIRST = 1,
+}
+
+// An operator takes tensors as inputs and outputs. The type of operation being
+// performed is determined by an index into the list of valid OperatorCodes,
+// while the specifics of each operations is configured using builtin_options
+// or custom_options.
+table Operator {
+  // Index into the operator_codes array. Using an integer here avoids
+  // complicate map lookups.
+  opcode_index:uint;
+
+  // Optional input and output tensors are indicated by -1.
+  inputs:[int];
+  outputs:[int];
+
+  builtin_options:BuiltinOptions;
+  custom_options:[ubyte];
+  custom_options_format:CustomOptionsFormat;
+
+  // A list of booleans indicating the input tensors which are being mutated by
+  // this operator.(e.g. used by RNN and LSTM).
+  // For example, if the "inputs" array refers to 5 tensors and the second and
+  // fifth are mutable variables, then this list will contain
+  // [false, true, false, false, true].
+  //
+  // If the list is empty, no variable is mutated in this operator.
+  // The list either has the same length as `inputs`, or is empty.
+  mutating_variable_inputs:[bool];
+}
+
+// The root type, defining a subgraph, which typically represents an entire
+// model.
+table SubGraph {
+  // A list of all tensors used in this subgraph.
+  tensors:[Tensor];
+
+  // Indices of the tensors that are inputs into this subgraph. Note this is
+  // the list of non-static tensors that feed into the subgraph for inference.
+  inputs:[int];
+
+  // Indices of the tensors that are outputs out of this subgraph. Note this is
+  // the list of output tensors that are considered the product of the
+  // subgraph's inference.
+  outputs:[int];
+
+  // All operators, in execution order.
+  operators:[Operator];
+
+  // Name of this subgraph (used for debugging).
+  name:string;
+
+  // Data format for input/output of SubGraph
+  data_format: DataFormat;
+}
+
+// Table of raw data buffers (used for constant tensors). Referenced by tensors
+// by index. The generous alignment accommodates mmap-friendly data structures.
+table Buffer {
+  data:[ubyte] (force_align: 16);
+}
+
+table Model {
+  // Version of the schema.
+  version:uint;
+
+  // A list of all operator codes used in this model. This is
+  // kept in order because operators carry an index into this
+  // vector.
+  operator_codes:[OperatorCode];
+
+  // All the subgraphs of the model. The 0th is assumed to be the main
+  // model.
+  subgraphs:[SubGraph];
+
+  // A description of the model.
+  description:string;
+
+  // Buffers of the model.
+  // Note the 0th entry of this array must be an empty buffer (sentinel).
+  // This is a convention so that tensors without a buffer can provide 0 as
+  // their buffer.
+  buffers:[Buffer];
+
+  // Metadata about the model.  Indirects into the existings buffers list.
+  metadata_buffer:[int];
+}
+
+root_type Model;
diff --git a/nnpackage/spec/00_requirement.md b/nnpackage/spec/00_requirement.md
new file mode 100644
index 000000000..035fc9856
--- /dev/null
+++ b/nnpackage/spec/00_requirement.md
@@ -0,0 +1,28 @@
+# Requirements (or Checkpoint)
+
+## Packaging
+
+### Packaging Format
+
+- [ ] PF1. support royalty free compression
+- [ ] PF2. compatible with low end devices
+
+### Manifest
+
+- [ ] MF1. human readable
+- [ ] MF2. easy to parse for several types of configuration variables.
+- [ ] MF3. small binary size for parsing (since the parser will be part of runtime)
+
+## Model
+
+- [ ] MD1. support multiple tensor layout (such as NHWC, NCHW, etc)
+  - define layout for model / submodel / other unit?
+  - use operator (such as loco)
+- [ ] MD2. describe operand?
+  - include in operator vs. independent field for operand
+  - support unspecified dimension value & unspecified rank?
+- [ ] MD3. describe operation type
+  - string vs. enum value?
+- [ ] MD4. support many quantization
+  - howto (ex. union type quantization parameter field, field handle quantization parameter table for quantization methodology)
+- [ ] MD5. backward-compatibility and maintainability
diff --git a/nnpackage/spec/10_packaging_and_manifest.md b/nnpackage/spec/10_packaging_and_manifest.md
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+# Packaging and Manifest
+
+## 1. Overview
+
+`nnpackage` is the input of nnruntime(`neurun`), and the output of nncompiler(`nncc`).
+
+`nnpackage` contains all data (such as model, `MANIFEST`, custom_op) that requires to run a given model.
+
+The document will cover packaging and `MANIFEST` only.
+
+For `model` and `custom_op`, see [20_model_and_operators.md](20_model_and_operators.md) and [30_custom_op.md](30_custom_op.md).
+
+## 2. Packaging Structure
+
+`nnpackage` is a Zip archive in the following structure:
+
+```
+nnpackage
+├── custom_op
+├── metadata
+│   └── MANIFEST
+└── mymodel.model
+```
+
+- `mymodel.model` is a model file that has computation graph and weights.
+- `metadata` is a directory that contains all metadata including `MANIFEST`.
+- `MANIFEST` is a collection of attributes about this package.
+- `custom_op` is a directory that contains implementation objects.
+
+## 3. Packaging Format
+
+`nnpackage` is contained in `Zip Archive`, which could be either `compressed` or `stored` (no compression).
+
+## 4. Manifest
+
+`MANIFEST` is a collection of attributes about `nnpacakge`.
+
+### Attributes
+
+#### version
+
+`version` is composed of 3 numbers in `MAJOR`.`MINOR`.`PATCH`.
+
+Given a version number MAJOR.MINOR.PATCH, increment the:
+
+MAJOR version when you make incompatible/breaking changes,
+MINOR version when you add functionality in a backwards-compatible manner, and
+PATCH version when you make backwards-compatible bug fixes.
+
+For detail, see [semantic versioning 2.0.0](https://semver.org/)
+
+##### major-version
+
+`major-version` is the major version of `nnpackage`.
+
+##### minor-version
+
+`minor-version` is the minor version of `nnpackage`.
+
+##### patch-version
+
+`patch-version` is the patch version of `nnpackage`.
+
+#### models
+
+`models` is an array of path to model files, which is relative path from top level directory of this package.
+The first element from the array will be the default model to be executed.
+
+#### model-types
+
+`model-types` is an array of strings that describes the type of each model in `models`.
+
+It can have the values (case-sensitive) in following table.
+
+| name   | description            |
+|--------|------------------------|
+| tflite | tensorflow lite schema |
+| circle | nnpackage schema       |
+
+### Example
+
+Here is an example of `MANIFEST`.
+
+```
+{
+    "major-version" : "1",
+    "minor-version" : "0",
+    "patch-version" : "0",
+    "models"      : [ "mymodel.model", "yourmodel.model" ],
+    "model-types" : [ "tflite", "circle" ]
+}
+```
diff --git a/nnpackage/spec/20_model_and_operators.md b/nnpackage/spec/20_model_and_operators.md
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+# Model
+
+## Serialization Format
+
+`nnpackage` uses flatbuffers to store model.
+
+Rationale:
+
+1. `flatbuffers` is:
+
+- space-efficient
+- explicit-schema based
+- royalty-free license open-source library
+- header-only solution (unless we use flatbuffer's reflection)
+- proven solution (used by TensorFlow-Lite)
+
+2. We've checked other solutions:
+- [`bjson (binary JSON)`](http://bjson.org/)
+- `protocol buffers`
+
+## Baseline Schema
+
+`nnpackage` schema is based on tensorflow-lite schema.
+
+Rationale:
+
+- Fundamentally, `nnpackage` and `TFLite` have same aim:
+Running pre-trained models on a device, which has relatively low computing power and memory.
+TFLite's solution is acceptable, we don't need to create same thing again.
+- We can use several infra-structures and tools from TFLite.
+
+## Extensions
+
+`nnpackage` model has some extensions that are different or missing from TFLite.
+
+### Multiple Layout
+
+`nnpackage` can support multiple layouts.
+
+1. The layout is presented using `DataFormat` enumeration.
+
+`DataFormat` must be one of the enumeration defined in `nnpackage_schema.fbs`.
+
+For example, `CHANNELS_FIRST` or `CHANNELS_LAST` can be used.
+
+```
+  // For 2D data, NHWC(batch, height, width, channels)
+  // For 3D data, NDHWC(batch, depth, height, width, channels)
+  CHANNELS_LAST = 0,
+  // For 2D data, NCHW(batch, channels, height, width)
+  // For 3D data, NCDHW(batch, channels, depth, height, width)
+  CHANNELS_FIRST = 1,
+```
+
+2. `DataFormat` must be same within a submodel.
+
+Rationale:
+
+- frequent switching between different layout degrades the performance
+
+Under this assumption, We expect to
+
+- simplify the runtime implementation
+- accelerate the performance
+- reduce the memory usage
+
+### Unspecified Dimension
+
+`nnpackage` represents unspecified dimension with `-1`.
+
+Rationale:
+
+1. It should be `int` since dimension is int type flatbuffer schema. Thus '?' cannot be used.
+2. `0` is also a candidate, which is used for Android NN API.
+However, we would like to reserve `0` because `0` could be a valid dimension for a certain
+operator (e.g. `tflite.slice`).
+
+## Operator Reference
+
+## Schema Source
+
+For detail, see the schema file [flatbuffers schema for nnpackage model](../schema/nnpackage_model.fbs).
diff --git a/nnpackage/spec/30_custom_op.md b/nnpackage/spec/30_custom_op.md
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+# Custom Operators
+
+To be filled