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linkerzhangebarsoum
linkerzhang
authored and
ebarsoum
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move map and sequence types to onnx domain, (#2244)
Move map and sequence types to onnx domain, this is the first step of merging onnx-ml and onnx types.
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6 files changed

+94
-54
lines changed

‎docs/IR.md‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -308,8 +308,8 @@ The following types are used to define the types of graph and node inputs and ou
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|Variant | Type | Description |
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|---|---|---|
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ONNX|dense tensors|Tensors are a generalization of vectors and matrices; whereas vectors have one dimension, and matrices two, tensors can have any number of dimensions, including zero. A zero-dimensional tensor is logically equivalent to a scalar value.
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ONNX-ML|sequence|Sequences represent dense, ordered, collections of elements that are of homogeneous types.
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ONNX-ML|map|Maps represent associative tables, defined by a key type and a value type.
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ONNX|sequence|Sequences represent dense, ordered, collections of elements that are of homogeneous types.
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ONNX|map|Maps represent associative tables, defined by a key type and a value type.
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ONNX currently does not define a sparse tensor type.
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‎onnx/onnx-ml.proto‎

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -512,15 +512,6 @@ message TypeProto {
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optional TensorShapeProto shape = 2;
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}
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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
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optional int32 elem_type = 1;
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optional TensorShapeProto shape = 2;
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}
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// repeated T
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message Sequence {
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// The type and optional shape of each element of the sequence.
@@ -538,6 +529,15 @@ message TypeProto {
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optional TypeProto value_type = 2;
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};
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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
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optional int32 elem_type = 1;
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optional TensorShapeProto shape = 2;
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}
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message Opaque {
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// When missing, the domain is the same as the model's.
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optional string domain = 1;
@@ -553,7 +553,6 @@ message TypeProto {
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// The type of a tensor.
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Tensor tensor_type = 1;
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// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
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// as input and output to graphs and nodes. These types are needed to naturally
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// support classical ML operators. DNN operators SHOULD restrict their input
@@ -565,10 +564,11 @@ message TypeProto {
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// The type of a map.
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Map map_type = 5;
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Opaque opaque_type = 7;
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SparseTensor sparse_tensor_type = 8;
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Opaque opaque_type = 7;
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}
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// An optional denotation can be used to denote the whole

‎onnx/onnx-ml.proto3‎

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -512,15 +512,6 @@ message TypeProto {
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TensorShapeProto shape = 2;
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}
514514

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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
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int32 elem_type = 1;
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TensorShapeProto shape = 2;
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}
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// repeated T
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message Sequence {
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// The type and optional shape of each element of the sequence.
@@ -538,6 +529,15 @@ message TypeProto {
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TypeProto value_type = 2;
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};
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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
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int32 elem_type = 1;
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TensorShapeProto shape = 2;
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}
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message Opaque {
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// When missing, the domain is the same as the model's.
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string domain = 1;
@@ -553,7 +553,6 @@ message TypeProto {
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// The type of a tensor.
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Tensor tensor_type = 1;
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// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
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// as input and output to graphs and nodes. These types are needed to naturally
559558
// support classical ML operators. DNN operators SHOULD restrict their input
@@ -565,10 +564,11 @@ message TypeProto {
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// The type of a map.
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Map map_type = 5;
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Opaque opaque_type = 7;
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SparseTensor sparse_tensor_type = 8;
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Opaque opaque_type = 7;
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}
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// An optional denotation can be used to denote the whole

‎onnx/onnx.in.proto‎

Lines changed: 14 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -509,16 +509,6 @@ message TypeProto {
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optional TensorShapeProto shape = 2;
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}
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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
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optional int32 elem_type = 1;
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optional TensorShapeProto shape = 2;
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}
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// #if ONNX-ML
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// repeated T
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message Sequence {
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// The type and optional shape of each element of the sequence.
@@ -536,6 +526,16 @@ message TypeProto {
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optional TypeProto value_type = 2;
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};
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// #if ONNX-ML
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message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
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// This field MUST have a valid TensorProto.DataType value
534+
// This field MUST be present for this version of the IR.
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optional int32 elem_type = 1;
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optional TensorShapeProto shape = 2;
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}
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539539
message Opaque {
540540
// When missing, the domain is the same as the model's.
541541
optional string domain = 1;
@@ -552,8 +552,6 @@ message TypeProto {
552552
// The type of a tensor.
553553
Tensor tensor_type = 1;
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555-
// #if ONNX-ML
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557555
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
558556
// as input and output to graphs and nodes. These types are needed to naturally
559557
// support classical ML operators. DNN operators SHOULD restrict their input
@@ -565,10 +563,12 @@ message TypeProto {
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// The type of a map.
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Map map_type = 5;
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568-
Opaque opaque_type = 7;
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// #if ONNX-ML
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570568
SparseTensor sparse_tensor_type = 8;
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Opaque opaque_type = 7;
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// #endif
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}
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‎onnx/onnx.proto‎

Lines changed: 27 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -510,19 +510,39 @@ message TypeProto {
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optional TensorShapeProto shape = 2;
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}
512512

513-
message SparseTensor {
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// This field MUST NOT have the value of UNDEFINED
513+
// repeated T
514+
message Sequence {
515+
// The type and optional shape of each element of the sequence.
516+
// This field MUST be present for this version of the IR.
517+
optional TypeProto elem_type = 1;
518+
};
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// map<K,V>
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message Map {
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// This field MUST have a valid TensorProto.DataType value
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// This field MUST be present for this version of the IR.
517-
optional int32 elem_type = 1;
518-
optional TensorShapeProto shape = 2;
519-
}
520-
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// This field MUST be present for this version of the IR.
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// This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
525+
optional int32 key_type = 1;
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// This field MUST be present for this version of the IR.
527+
optional TypeProto value_type = 2;
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};
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522531
oneof value {
523532
// The type of a tensor.
524533
Tensor tensor_type = 1;
525534

535+
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
536+
// as input and output to graphs and nodes. These types are needed to naturally
537+
// support classical ML operators. DNN operators SHOULD restrict their input
538+
// and output types to tensors.
539+
540+
// The type of a sequence.
541+
Sequence sequence_type = 4;
542+
543+
// The type of a map.
544+
Map map_type = 5;
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}
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// An optional denotation can be used to denote the whole

‎onnx/onnx.proto3‎

Lines changed: 27 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -510,19 +510,39 @@ message TypeProto {
510510
TensorShapeProto shape = 2;
511511
}
512512

513-
message SparseTensor {
514-
// This field MUST NOT have the value of UNDEFINED
513+
// repeated T
514+
message Sequence {
515+
// The type and optional shape of each element of the sequence.
516+
// This field MUST be present for this version of the IR.
517+
TypeProto elem_type = 1;
518+
};
519+
520+
// map<K,V>
521+
message Map {
515522
// This field MUST have a valid TensorProto.DataType value
516-
// This field MUST be present for this version of the IR.
517-
int32 elem_type = 1;
518-
TensorShapeProto shape = 2;
519-
}
520-
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// This field MUST be present for this version of the IR.
524+
// This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
525+
int32 key_type = 1;
526+
// This field MUST be present for this version of the IR.
527+
TypeProto value_type = 2;
528+
};
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522531
oneof value {
523532
// The type of a tensor.
524533
Tensor tensor_type = 1;
525534

535+
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values
536+
// as input and output to graphs and nodes. These types are needed to naturally
537+
// support classical ML operators. DNN operators SHOULD restrict their input
538+
// and output types to tensors.
539+
540+
// The type of a sequence.
541+
Sequence sequence_type = 4;
542+
543+
// The type of a map.
544+
Map map_type = 5;
545+
526546
}
527547

528548
// An optional denotation can be used to denote the whole

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