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Update onnx submodule to 1.7.0 release candidate (#3405)

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Update onnx submodule to 1.7.0 release candidate.  This isn't a release tag,  but it will be released soon, in 1-2 weeks.
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Changming Sun 2020-04-04 16:23:42 -07:00 committed by GitHub
parent d4d19a75ba
commit 33006f48c0
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GPG key ID: 4AEE18F83AFDEB23
22 changed files with 697 additions and 150 deletions
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2
cgmanifest.json
View file

@ -49,7 +49,7 @@
"component": {
"type": "git",
"git": {
"commitHash": "423f1977d314f05df4be7edb44428c1c0211341c",
"commitHash": "8bee53756ba8b8a3aca47c5719e35fca150ab79e",
"repositoryUrl": "https://github.com/onnx/onnx.git"
}
}

2
cmake/external/onnx vendored

@ -1 +1 @@
Subproject commit 423f1977d314f05df4be7edb44428c1c0211341c
Subproject commit 8bee53756ba8b8a3aca47c5719e35fca150ab79e

12
onnxruntime/core/graph/contrib_ops/attn_lstm_schema_defs.cc
View file

@ -10,7 +10,7 @@ namespace onnxruntime {
namespace contrib {
using ::ONNX_NAMESPACE::AttributeProto;
using ::ONNX_NAMESPACE::OPTIONAL;
using ::ONNX_NAMESPACE::OPTIONAL_VALUE;
using ::ONNX_NAMESPACE::OpSchema;
// This Doc based on LSTM_ver7, and modification
@ -157,7 +157,7 @@ OpSchema& RegisterAttnLSTMContribOpSchema(OpSchema&& op_schema){
"be one of the activation functions specified above. Optional: See the equations "
"for default if not specified.",
AttributeProto::STRINGS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"activation_alpha",
"Optional scaling values used by some activation functions. The values are consumed "
@ -165,21 +165,21 @@ OpSchema& RegisterAttnLSTMContribOpSchema(OpSchema&& op_schema){
"are the same as of corresponding ONNX operators.For example with LeakyRelu, the "
"default alpha is 0.01.",
AttributeProto::FLOATS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"activation_beta",
"Optional scaling values used by some activation functions. The values are consumed in "
"the order of activation functions, for example (f, g, h) in LSTM. Default values are "
"the same as of corresponding ONNX operators.",
AttributeProto::FLOATS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"clip",
"Cell clip threshold. Clipping bounds the elements of a tensor in the range of "
"[-threshold, +threshold] and is applied to the input of activations. No clip if not "
"specified.",
AttributeProto::FLOAT,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"input_forget",
"Couple the input and forget gates if 1, default 0.",
@ -189,7 +189,7 @@ OpSchema& RegisterAttnLSTMContribOpSchema(OpSchema&& op_schema){
"hidden_size",
"Number of neurons in the hidden layer.",
AttributeProto::INT,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"direction",
"Specify if the RNN is forward, reverse, or bidirectional. Must be one of "

92
onnxruntime/core/graph/contrib_ops/contrib_defs.cc
View file

@ -164,7 +164,7 @@ namespace onnxruntime {
namespace contrib {
using ONNX_NAMESPACE::AttributeProto;
using ONNX_NAMESPACE::OpSchema;
using ONNX_NAMESPACE::OPTIONAL;
using ONNX_NAMESPACE::OPTIONAL_VALUE;
void ValidateTypeAndShapeForScaleAndZP(ONNX_NAMESPACE::InferenceContext& ctx, int index, ::google::protobuf::int32 expectedType, bool isScalar, int expectedTensorSize = 0) {
if (ctx.getNumInputs() > static_cast<size_t>(index)) {
@ -436,8 +436,8 @@ the tensor elementwise.
ONNX_CONTRIB_OPERATOR_SCHEMA(ParametricSoftplus)
.SinceVersion(1)
.SetDoc(ParametricSoftplus_ver1_doc)
.Attr("alpha", "Value of alpha", AttributeProto::FLOAT, OPTIONAL)
.Attr("beta", "Value of beta", AttributeProto::FLOAT, OPTIONAL)
.Attr("alpha", "Value of alpha", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Attr("beta", "Value of beta", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Input(0, "X", "1D input tensor", "T")
.Output(0, "Y", "1D input tensor", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.")
@ -450,7 +450,7 @@ the same ordering as the image pixel format.)DOC";
ONNX_CONTRIB_OPERATOR_SCHEMA(ImageScaler)
.SinceVersion(1)
.SetDoc(ImageScaler_ver1_doc)
.Attr("bias", "Bias applied to each channel, same size as C.", AttributeProto::FLOATS, OPTIONAL)
.Attr("bias", "Bias applied to each channel, same size as C.", AttributeProto::FLOATS, OPTIONAL_VALUE)
.Attr("scale", "The scale to apply.", AttributeProto::FLOAT, 1.0f)
.Input(0, "input", "Input tensor of shape [N,C,H,W]", "T")
.Output(0, "output", "Result, has same shape and type as input", "T")
@ -465,8 +465,8 @@ If scale is not provided, crop the borders as provided.)DOC";
ONNX_CONTRIB_OPERATOR_SCHEMA(Crop)
.SinceVersion(1)
.SetDoc(Crop_ver1_doc)
.Attr("border", "A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).", AttributeProto::INTS, OPTIONAL)
.Attr("scale", "A 1-D values of (height, width).", AttributeProto::INTS, OPTIONAL)
.Attr("border", "A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("scale", "A 1-D values of (height, width).", AttributeProto::INTS, OPTIONAL_VALUE)
.Input(0, "input", "Input tensor of shape [N,C,H,W]", "T")
.Output(0, "output", "Result, has same type as input, with H and W dimensions reduced.", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.");
@ -538,10 +538,10 @@ Example 2:
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to float tensors.")
.Attr("values", "", AttributeProto::FLOATS, OPTIONAL)
.Attr("shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("input_as_shape", "", AttributeProto::INT, OPTIONAL)
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("values", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
.Attr("shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("input_as_shape", "", AttributeProto::INT, OPTIONAL_VALUE)
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
ONNX_NAMESPACE::propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (ctx.getAttribute("shape") != nullptr) {
@ -598,7 +598,7 @@ value at X[t][n] >= seqLengths[n].
.Attr("drop_states",
"Bool to determine if hidden state is zeroes or passed "
"along for timesteps past the given sequence_length.",
AttributeProto::INT, OPTIONAL)
AttributeProto::INT, OPTIONAL_VALUE)
.Input(0, "hidden_prev", "The previous GRU hidden state.", "T")
.Input(
1,
@ -632,10 +632,10 @@ value at X[t][n] >= seqLengths[n].
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to float tensors.")
.Attr("values", "", AttributeProto::FLOATS, OPTIONAL)
.Attr("shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("input_as_shape", "", AttributeProto::INT, OPTIONAL)
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("values", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
.Attr("shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("input_as_shape", "", AttributeProto::INT, OPTIONAL_VALUE)
.Attr("extra_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
ONNX_NAMESPACE::propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (ctx.getAttribute("shape") != nullptr) {
@ -680,7 +680,7 @@ value at X[t][n] >= seqLengths[n].
.Attr("drop_states",
"Bool to determine if hidden state is zeroes or passed "
"along for timesteps past the given sequence_length.",
AttributeProto::INT, OPTIONAL)
AttributeProto::INT, OPTIONAL_VALUE)
.Input(0, "hidden_prev", "The previous GRU hidden state.", "T")
.Input(
1,
@ -720,8 +720,8 @@ value at X[t][n] >= seqLengths[n].
ONNX_OPERATOR_SCHEMA(ScaledTanh)
.SinceVersion(1)
.Attr("alpha", "Scaling value", AttributeProto::FLOAT, OPTIONAL)
.Attr("beta", "Scaling value", AttributeProto::FLOAT, OPTIONAL)
.Attr("alpha", "Scaling value", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Attr("beta", "Scaling value", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Input(0, "input", "Input tensor", "T")
.Output(
0,
@ -753,8 +753,8 @@ value at X[t][n] >= seqLengths[n].
.SinceVersion(10)
.Deprecate()
.SetDoc(ParametricSoftplus_ver1_doc)
.Attr("alpha", "Value of alpha", AttributeProto::FLOAT, OPTIONAL)
.Attr("beta", "Value of beta", AttributeProto::FLOAT, OPTIONAL)
.Attr("alpha", "Value of alpha", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Attr("beta", "Value of beta", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Input(0, "X", "1D input tensor", "T")
.Output(0, "Y", "1D input tensor", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.")
@ -764,7 +764,7 @@ value at X[t][n] >= seqLengths[n].
.SinceVersion(10)
.Deprecate()
.SetDoc(ImageScaler_ver1_doc)
.Attr("bias", "Bias applied to each channel, same size as C.", AttributeProto::FLOATS, OPTIONAL)
.Attr("bias", "Bias applied to each channel, same size as C.", AttributeProto::FLOATS, OPTIONAL_VALUE)
.Attr("scale", "The scale to apply.", AttributeProto::FLOAT, 1.0f)
.Input(0, "input", "Input tensor of shape [N,C,H,W]", "T")
.Output(0, "output", "Result, has same shape and type as input", "T")
@ -776,7 +776,7 @@ value at X[t][n] >= seqLengths[n].
.Deprecate()
.SetDoc(Crop_ver1_doc)
.Attr("border", "A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).", AttributeProto::INTS)
.Attr("scale", "A 1-D values of (height, width).", AttributeProto::INTS, OPTIONAL)
.Attr("scale", "A 1-D values of (height, width).", AttributeProto::INTS, OPTIONAL_VALUE)
.Input(0, "input", "Input tensor of shape [N,C,H,W]", "T")
.Output(0, "output", "Result, has same type as input, with H and W dimensions reduced.", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.")
@ -891,8 +891,8 @@ value at X[t][n] >= seqLengths[n].
ONNX_OPERATOR_SCHEMA(ScaledTanh)
.SinceVersion(10)
.Deprecate()
.Attr("alpha", "Scaling value", AttributeProto::FLOAT, OPTIONAL)
.Attr("beta", "Scaling value", AttributeProto::FLOAT, OPTIONAL)
.Attr("alpha", "Scaling value", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Attr("beta", "Scaling value", AttributeProto::FLOAT, OPTIONAL_VALUE)
.Input(0, "input", "Input tensor", "T")
.Output(
0,
@ -935,17 +935,17 @@ Sample echo operator.)DOC");
"kernel_shape",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr("pads",
"",
AttributeProto::INTS, OPTIONAL)
AttributeProto::INTS, OPTIONAL_VALUE)
.Attr(
"storage_order",
"",
AttributeProto::INT,
static_cast<int64_t>(0))
.Attr(
"strides", "", AttributeProto::INTS, OPTIONAL)
"strides", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Input(
0,
"X",
@ -971,21 +971,21 @@ Sample echo operator.)DOC");
"kernel_shape",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr("output_padding",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"dilations",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"strides",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"auto_pad",
"",
@ -1031,22 +1031,22 @@ activation.)DOC")
"kernel_shape",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"dilations",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"strides",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"pads",
"",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"group",
"",
@ -1056,12 +1056,12 @@ activation.)DOC")
"activation",
"",
AttributeProto::STRING,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"activation_params",
"",
AttributeProto::FLOATS,
OPTIONAL)
OPTIONAL_VALUE)
.Input(
0,
"X",
@ -1150,12 +1150,12 @@ activation and leaky_relu_alpha.)DOC")
"activation",
"",
AttributeProto::STRING,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"leaky_relu_alpha",
"",
AttributeProto::FLOAT,
OPTIONAL)
OPTIONAL_VALUE)
.TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (hasNInputShapes(ctx, 2)) {
@ -1395,7 +1395,7 @@ of [N, 0] then [N, 0].
" If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of"
" 'tokenexp' and 'separators' should be set.",
AttributeProto::STRING,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"separators",
"an optional list of strings attribute that contains a list of separators - regular expressions to match separators"
@ -1404,7 +1404,7 @@ of [N, 0] then [N, 0].
" the corresponding output would be [\"Hello\", \"World!\"]. To achieve character-level tokenization,"
" one should set the 'separators' to [\"\"], which contains an empty string.",
AttributeProto::STRINGS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"mincharnum",
"Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as \"A\" and \"B\" would be ignored",
@ -1728,13 +1728,13 @@ Output = Dequantize(Input) -> AveragePool on fp32 data -> Quantize(output)
"strides",
"Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.",
AttributeProto::INTS,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"auto_pad",
contrib_ops_auto_pad_doc,
AttributeProto::STRING,
std::string("NOTSET"))
.Attr("pads", contrib_ops_pads_doc, AttributeProto::INTS, OPTIONAL)
.Attr("pads", contrib_ops_pads_doc, AttributeProto::INTS, OPTIONAL_VALUE)
.Attr(
"ceil_mode",
"Whether to use ceil or floor (default) to compute the output shape.",
@ -1916,20 +1916,20 @@ Example 4:
"Integer representing the embedding vector size for each word."
"If not provide, use the fileter size of conv weight",
AttributeProto::INT,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"conv_window_size",
"This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1."
"Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]..."
"If not provide, use the first dimension of conv kernal shape.",
AttributeProto::INT,
OPTIONAL)
OPTIONAL_VALUE)
.Attr(
"char_embedding_size",
"Integer representing the embedding vector size for each char."
"If not provide, use the char embedding size of embedding vector.",
AttributeProto::INT,
OPTIONAL)
OPTIONAL_VALUE)
.Input(0, "Sequence", "Specify batchs of sequence words to embedding", "T")
.Input(1, "W", "Specify weights of conv", "T1")
.Input(2, "B", "Specify bias of conv", "T1")

22
onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc
View file

@ -21,7 +21,7 @@ namespace contrib {
using ONNX_NAMESPACE::AttributeProto;
using ONNX_NAMESPACE::InferenceContext;
using ONNX_NAMESPACE::OpSchema;
using ONNX_NAMESPACE::OPTIONAL;
using ONNX_NAMESPACE::OPTIONAL_VALUE;
void NchwcPoolOpSchemaGenerator(OpSchema& schema) {
schema.SetDomain(kMSNchwcDomain);
@ -29,9 +29,9 @@ void NchwcPoolOpSchemaGenerator(OpSchema& schema) {
schema.SetDoc(R"DOC(For internal use.)DOC");
schema.Attr("auto_pad", "", AttributeProto::STRING, std::string("NOTSET"));
schema.Attr("kernel_shape", "", AttributeProto::INTS);
schema.Attr("dilations", "", AttributeProto::INTS, OPTIONAL);
schema.Attr("strides", "", AttributeProto::INTS, OPTIONAL);
schema.Attr("pads", "", AttributeProto::INTS, OPTIONAL);
schema.Attr("dilations", "", AttributeProto::INTS, OPTIONAL_VALUE);
schema.Attr("strides", "", AttributeProto::INTS, OPTIONAL_VALUE);
schema.Attr("pads", "", AttributeProto::INTS, OPTIONAL_VALUE);
schema.Attr("ceil_mode", "", AttributeProto::INT, static_cast<int64_t>(0));
schema.Input(0, "X", "", "T");
schema.Output(0, "Y", "", "T");
@ -116,13 +116,13 @@ void RegisterNchwcSchemas() {
.SinceVersion(1)
.SetDoc(R"DOC(For internal use.)DOC")
.Attr("auto_pad", "", AttributeProto::STRING, std::string("NOTSET"))
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL)
.Attr("pads", "", AttributeProto::INTS, OPTIONAL)
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("pads", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("group", "", AttributeProto::INT, static_cast<int64_t>(1))
.Attr("activation", "", AttributeProto::STRING, OPTIONAL)
.Attr("activation_params", "", AttributeProto::FLOATS, OPTIONAL)
.Attr("activation", "", AttributeProto::STRING, OPTIONAL_VALUE)
.Attr("activation_params", "", AttributeProto::FLOATS, OPTIONAL_VALUE)
.Input(0, "X", "", "T")
.Input(1, "W", "", "T")
.Input(2, "B", "", "T", OpSchema::Optional)
@ -152,7 +152,7 @@ void RegisterNchwcSchemas() {
.SetDomain(kMSNchwcDomain)
.SinceVersion(1)
.SetDoc(R"DOC(For internal use.)DOC")
.Attr("scales", "", AttributeProto::INTS, OPTIONAL)
.Attr("scales", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Input(0, "X", "", "T")
.Output(0, "Y", "", "T")
.TypeConstraint("T", {"tensor(float)"}, "Constrain input and output types to float tensors")

2
onnxruntime/core/graph/contrib_ops/range_schema_defs.cc
View file

@ -12,7 +12,7 @@
namespace onnxruntime {
namespace contrib {
using ::ONNX_NAMESPACE::OPTIONAL;
using ::ONNX_NAMESPACE::OPTIONAL_VALUE;
using ::ONNX_NAMESPACE::OpSchema;
using ::ONNX_NAMESPACE::InferenceContext;
using ::ONNX_NAMESPACE::TensorShapeProto;

22
onnxruntime/core/graph/dml_ops/dml_defs.cc
View file

@ -21,7 +21,7 @@ namespace onnxruntime {
namespace dml {
using ONNX_NAMESPACE::AttributeProto;
using ONNX_NAMESPACE::OpSchema;
using ONNX_NAMESPACE::OPTIONAL;
using ONNX_NAMESPACE::OPTIONAL_VALUE;
void RegisterDmlSchemas() {
@ -34,11 +34,11 @@ void RegisterDmlSchemas() {
.Input(2, "B", "", "T", OpSchema::Optional)
.Output(0, "Y", "", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "")
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL)
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("auto_pad", "", AttributeProto::STRING, std::string("NOTSET"))
.Attr("pads", "", AttributeProto::INTS, OPTIONAL)
.Attr("pads", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("group", "", AttributeProto::INT, static_cast<int64_t>(1))
.Attr(AttrName::FusedActivation, "", onnx::AttributeProto::STRING)
.Attr(AttrName::FusedActivationDomain, "", onnx::AttributeProto::STRING)
@ -61,13 +61,13 @@ void RegisterDmlSchemas() {
.Input(2, "B", "", "T", OpSchema::Optional)
.Output(0, "Y", "", "T")
.TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "")
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("output_shape", "", AttributeProto::INTS, OPTIONAL)
.Attr("output_padding", "", AttributeProto::INTS, OPTIONAL)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL)
.Attr("kernel_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("output_shape", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("output_padding", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("dilations", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("strides", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("auto_pad", "", AttributeProto::STRING, std::string("NOTSET"))
.Attr("pads", "", AttributeProto::INTS, OPTIONAL)
.Attr("pads", "", AttributeProto::INTS, OPTIONAL_VALUE)
.Attr("group", "", AttributeProto::INT, static_cast<int64_t>(1))
.Attr(AttrName::FusedActivation, "", onnx::AttributeProto::STRING)
.Attr(AttrName::FusedActivationDomain, "", onnx::AttributeProto::STRING)

2
onnxruntime/core/graph/featurizers_ops/featurizers_defs.cc
View file

@ -29,7 +29,7 @@ namespace featurizers {
using ONNX_NAMESPACE::AttributeProto;
using ONNX_NAMESPACE::OpSchema;
using ONNX_NAMESPACE::OPTIONAL;
using ONNX_NAMESPACE::OPTIONAL_VALUE;
// Forward declarations
static void RegisterCatImputerFeaturizerVer1();

168
onnxruntime/core/protobuf/onnx-ml.proto
View file

@ -62,8 +62,8 @@ enum Version {
_START_VERSION = 0;
// The version field is always serialized and we will use it to store the
// version that the graph is generated from. This helps us set up version
// control.
// For the IR, we are using simple numbers starting with with 0x00000001,
// control.
// For the IR, we are using simple numbers starting with 0x00000001,
// which was the version we published on Oct 10, 2017.
IR_VERSION_2017_10_10 = 0x0000000000000001;
@ -92,7 +92,18 @@ enum Version {
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
IR_VERSION_2019_9_19 = 0x0000000000000006;
// IR VERSION 7 published on <TBD>
// - Add support to allow function body graph to rely on multiple external opreator sets.
// - Add a list to promote inference graph's initializers to global and
// mutable variables. Global variables are visible in all graphs of the
// stored models.
// - Add message TrainingInfoProto to store initialization
// method and training algorithm. The execution of TrainingInfoProto
// can modify the values of mutable variables.
// - Make inference graph callable from TrainingInfoProto via GraphCall operator.
IR_VERSION = 0x0000000000000007;
}
// Attributes
@ -136,10 +147,10 @@ message AttributeProto {
// The type field MUST be present for this version of the IR.
// For 0.0.1 versions of the IR, this field was not defined, and
// implementations needed to use has_field hueristics to determine
// implementations needed to use has_field heuristics to determine
// which value field was in use. For IR_VERSION 0.0.2 or later, this
// field MUST be set and match the f|i|s|t|... field in use. This
// change was made to accomodate proto3 implementations.
// change was made to accommodate proto3 implementations.
optional AttributeType type = 20; // discriminator that indicates which field below is in use
// Exactly ONE of the following fields must be present for this version of the IR
@ -199,12 +210,119 @@ message NodeProto {
optional string doc_string = 6;
}
// Training information
// TrainingInfoProto stores information for training a model.
// In particular, this defines two functionalities: an initialization-step
// and a training-algorithm-step. Initialization resets the model
// back to its original state as if no training has been consumed.
// Training algorithm improves the model based on input data.
//
// The semantics of the initialization-step is that the initializers
// in ModelProto.graph and in TrainingInfoProto.algorithm are first
// initialized as specified by the initializers in the graph, and then
// updated by the "initialization_binding" in every instance in
// ModelProto.training_info.
//
// The field "algorithm" defines a computation graph which represents a
// training algorithm's step. After the execution of a
// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
// may be immediately updated. If the targeted training algorithm contains
// consecutive update stages (such as block coordinate descent methods),
// the user needs to create a TrainingInfoProto for each stage.
message TrainingInfoProto {
// This field describes a graph to compute the initial tensors
// upon starting the training process. Initialization graph has no input
// and can have multiple outputs. Usually, trainable tensors in neural
// networks are randomly initialized. To achieve that, for each tensor,
// the user can put a random number operator such as RandomNormal or
// RandomUniform in TrainingInfoProto.initialization.node and assign its
// random output to the specific tensor using "initialization_binding".
// This graph can also set the initializers in "algorithm" in the same
// TrainingInfoProto; a use case is resetting the number of training
// iteration to zero.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
optional GraphProto initialization = 1;
// This field represents a training algorithm step. Given required inputs,
// it computes outputs to update initializers in its own or inference graph's
// initializer lists. In general, this graph contains loss node, gradient node,
// optimizer node, increment of iteration count, and some calls to the inference
// graph.
//
// The field algorithm.node is the only place the user can use GraphCall
// operator. The only callable graph is the one stored in ModelProto.graph.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
optional GraphProto algorithm = 2;
// This field specifies the bindings from the outputs of "initialization" to
// some initializers in "ModelProto.graph.initializer" and
// the "algorithm.initializer" in the same TrainingInfoProto.
// See "update_binding" below for details.
//
// By default, this field is empty and no initializer would be changed
// by the execution of "initialization".
repeated StringStringEntryProto initialization_binding = 3;
// Gradient-based training is usually an iterative procedure. In one gradient
// descent iteration, we apply
//
// x = x - r * g
//
// where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
// gradient of "x" with respect to a chosen loss. To avoid adding assignments
// into the training graph, we split the update equation into
//
// y = x - r * g
// x = y
//
// The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
// tell that "y" should be assigned to "x", the field "update_binding" may
// contain a key-value pair of strings, "x" (key of StringStringEntryProto)
// and "y" (value of StringStringEntryProto).
// For a neural network with multiple trainable (mutable) tensors, there can
// be multiple key-value pairs in "update_binding".
//
// The initializers appears as keys in "update_binding" are considered
// mutable and globally-visible variables. This implies some behaviors
// as described below.
//
// 1. We have only unique keys in all "update_binding"s so that two global
// variables may not have the same name. This ensures that one
// global variable is assigned up to once.
// 2. The keys must appear in names of "ModelProto.graph.initializer" or
// "TrainingInfoProto.algorithm.initializer".
// 3. The values must be output names of "algorithm".
// 4. If an optional input of a graph is omitted when using GraphCall, the
// global variable with the same name may be used.
// 5. When using GraphCall, the users always can pass values to optional
// inputs of the called graph even if the associated initializers appears
// as keys in "update_binding"s.
// 6. The graphs in TrainingInfoProto's can use global variables as
// their operator inputs.
// 7. Mutable variables are initialized to the value specified by the
// corresponding initializer, and then potentially updated by
// "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
//
// This field usually contains names of trainable tensors
// (in ModelProto.graph), optimizer states such as momentums in advanced
// stochastic gradient methods (in TrainingInfoProto.graph),
// and number of training iterations (in TrainingInfoProto.graph).
//
// By default, this field is empty and no initializer would be changed
// by the execution of "algorithm".
repeated StringStringEntryProto update_binding = 4;
}
// Models
//
// ModelProto is a top-level file/container format for bundling a ML model and
// associating its computation graph with metadata.
//
// The semantics of the model are described by the associated GraphProto.
// The semantics of the model are described by the associated GraphProto's.
message ModelProto {
// The version of the IR this model targets. See Version enum above.
// This field MUST be present.
@ -252,6 +370,17 @@ message ModelProto {
// Named metadata values; keys should be distinct.
repeated StringStringEntryProto metadata_props = 14;
// Training-specific information. Sequentially executing all stored
// `TrainingInfoProto.algorithm`s and assigning their outputs following
// the corresponding `TrainingInfoProto.update_binding`s is one training
// iteration. Similarly, to initialize the model
// (as if training hasn't happened), the user should sequentially execute
// all stored `TrainingInfoProto.initialization`s and assigns their outputs
// using `TrainingInfoProto.initialization_binding`s.
//
// If this field is empty, the training behavior of the model is undefined.
repeated TrainingInfoProto training_info = 20;
};
// StringStringEntryProto follows the pattern for cross-proto-version maps.
@ -376,7 +505,7 @@ message TensorProto {
// For float and complex64 values
// Complex64 tensors are encoded as a single array of floats,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
@ -448,7 +577,7 @@ message TensorProto {
// For double
// Complex128 tensors are encoded as a single array of doubles,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
@ -539,7 +668,7 @@ message TypeProto {
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
message Opaque {
// When missing, the domain is the same as the model's.
optional string domain = 1;
@ -568,10 +697,8 @@ message TypeProto {
SparseTensor sparse_tensor_type = 8;
Opaque opaque_type = 7;
}
// An optional denotation can be used to denote the whole
@ -605,7 +732,7 @@ enum OperatorStatus {
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
optional string name = 1;
// The first version of a function set which contains this function.
// When there's any breaking change for this function, the function set
// contains the function needs to bump its version, and since_version of
@ -628,9 +755,20 @@ message FunctionProto {
// The attributes of the function.
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
optional string doc_string = 8;
}
// The OperatorSets this function body (graph) relies on.
// A FunctionProto body (graph) may implicitly rely on the OperatorSet that
// this function belongs to. It can also explicitly rely on more OperatorSets
// with this field specified.
//
// All nodes in the function body (graph) will bind against the operator
// with the same-domain/same-op_type operator with the HIGHEST version
// in the referenced operator sets. This means at most one version can be relied
// for one domain.
repeated OperatorSetIdProto opset_import = 9;
}

169
onnxruntime/core/protobuf/onnx-ml.proto3
View file

@ -63,7 +63,7 @@ enum Version {
// The version field is always serialized and we will use it to store the
// version that the graph is generated from. This helps us set up version
// control.
// For the IR, we are using simple numbers starting with with 0x00000001,
// For the IR, we are using simple numbers starting with 0x00000001,
// which was the version we published on Oct 10, 2017.
IR_VERSION_2017_10_10 = 0x0000000000000001;
@ -92,7 +92,18 @@ enum Version {
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
IR_VERSION_2019_9_19 = 0x0000000000000006;
// IR VERSION 7 published on <TBD>
// - Add support to allow function body graph to rely on multiple external opreator sets.
// - Add a list to promote inference graph's initializers to global and
// mutable variables. Global variables are visible in all graphs of the
// stored models.
// - Add message TrainingInfoProto to store initialization
// method and training algorithm. The execution of TrainingInfoProto
// can modify the values of mutable variables.
// - Make inference graph callable from TrainingInfoProto via GraphCall operator.
IR_VERSION = 0x0000000000000007;
}
// Attributes
@ -136,10 +147,10 @@ message AttributeProto {
// The type field MUST be present for this version of the IR.
// For 0.0.1 versions of the IR, this field was not defined, and
// implementations needed to use has_field hueristics to determine
// implementations needed to use has_field heuristics to determine
// which value field was in use. For IR_VERSION 0.0.2 or later, this
// field MUST be set and match the f|i|s|t|... field in use. This
// change was made to accomodate proto3 implementations.
// change was made to accommodate proto3 implementations.
AttributeType type = 20; // discriminator that indicates which field below is in use
// Exactly ONE of the following fields must be present for this version of the IR
@ -199,12 +210,119 @@ message NodeProto {
string doc_string = 6;
}
// Training information
// TrainingInfoProto stores information for training a model.
// In particular, this defines two functionalities: an initialization-step
// and a training-algorithm-step. Initialization resets the model
// back to its original state as if no training has been consumed.
// Training algorithm improves the model based on input data.
//
// The semantics of the initialization-step is that the initializers
// in ModelProto.graph and in TrainingInfoProto.algorithm are first
// initialized as specified by the initializers in the graph, and then
// updated by the "initialization_binding" in every instance in
// ModelProto.training_info.
//
// The field "algorithm" defines a computation graph which represents a
// training algorithm's step. After the execution of a
// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
// may be immediately updated. If the targeted training algorithm contains
// consecutive update stages (such as block coordinate descent methods),
// the user needs to create a TrainingInfoProto for each stage.
message TrainingInfoProto {
// This field describes a graph to compute the initial tensors
// upon starting the training process. Initialization graph has no input
// and can have multiple outputs. Usually, trainable tensors in neural
// networks are randomly initialized. To achieve that, for each tensor,
// the user can put a random number operator such as RandomNormal or
// RandomUniform in TrainingInfoProto.initialization.node and assign its
// random output to the specific tensor using "initialization_binding".
// This graph can also set the initializers in "algorithm" in the same
// TrainingInfoProto; a use case is resetting the number of training
// iteration to zero.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
GraphProto initialization = 1;
// This field represents a training algorithm step. Given required inputs,
// it computes outputs to update initializers in its own or inference graph's
// initializer lists. In general, this graph contains loss node, gradient node,
// optimizer node, increment of iteration count, and some calls to the inference
// graph.
//
// The field algorithm.node is the only place the user can use GraphCall
// operator. The only callable graph is the one stored in ModelProto.graph.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
GraphProto algorithm = 2;
// This field specifies the bindings from the outputs of "initialization" to
// some initializers in "ModelProto.graph.initializer" and
// the "algorithm.initializer" in the same TrainingInfoProto.
// See "update_binding" below for details.
//
// By default, this field is empty and no initializer would be changed
// by the execution of "initialization".
repeated StringStringEntryProto initialization_binding = 3;
// Gradient-based training is usually an iterative procedure. In one gradient
// descent iteration, we apply
//
// x = x - r * g
//
// where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
// gradient of "x" with respect to a chosen loss. To avoid adding assignments
// into the training graph, we split the update equation into
//
// y = x - r * g
// x = y
//
// The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
// tell that "y" should be assigned to "x", the field "update_binding" may
// contain a key-value pair of strings, "x" (key of StringStringEntryProto)
// and "y" (value of StringStringEntryProto).
// For a neural network with multiple trainable (mutable) tensors, there can
// be multiple key-value pairs in "update_binding".
//
// The initializers appears as keys in "update_binding" are considered
// mutable and globally-visible variables. This implies some behaviors
// as described below.
//
// 1. We have only unique keys in all "update_binding"s so that two global
// variables may not have the same name. This ensures that one
// global variable is assigned up to once.
// 2. The keys must appear in names of "ModelProto.graph.initializer" or
// "TrainingInfoProto.algorithm.initializer".
// 3. The values must be output names of "algorithm".
// 4. If an optional input of a graph is omitted when using GraphCall, the
// global variable with the same name may be used.
// 5. When using GraphCall, the users always can pass values to optional
// inputs of the called graph even if the associated initializers appears
// as keys in "update_binding"s.
// 6. The graphs in TrainingInfoProto's can use global variables as
// their operator inputs.
// 7. Mutable variables are initialized to the value specified by the
// corresponding initializer, and then potentially updated by
// "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
//
// This field usually contains names of trainable tensors
// (in ModelProto.graph), optimizer states such as momentums in advanced
// stochastic gradient methods (in TrainingInfoProto.graph),
// and number of training iterations (in TrainingInfoProto.graph).
//
// By default, this field is empty and no initializer would be changed
// by the execution of "algorithm".
repeated StringStringEntryProto update_binding = 4;
}
// Models
//
// ModelProto is a top-level file/container format for bundling a ML model and
// associating its computation graph with metadata.
//
// The semantics of the model are described by the associated GraphProto.
// The semantics of the model are described by the associated GraphProto's.
message ModelProto {
// The version of the IR this model targets. See Version enum above.
// This field MUST be present.
@ -252,6 +370,17 @@ message ModelProto {
// Named metadata values; keys should be distinct.
repeated StringStringEntryProto metadata_props = 14;
// Training-specific information. Sequentially executing all stored
// `TrainingInfoProto.algorithm`s and assigning their outputs following
// the corresponding `TrainingInfoProto.update_binding`s is one training
// iteration. Similarly, to initialize the model
// (as if training hasn't happened), the user should sequentially execute
// all stored `TrainingInfoProto.initialization`s and assigns their outputs
// using `TrainingInfoProto.initialization_binding`s.
//
// If this field is empty, the training behavior of the model is undefined.
repeated TrainingInfoProto training_info = 20;
};
// StringStringEntryProto follows the pattern for cross-proto-version maps.
@ -270,6 +399,8 @@ message TensorAnnotation {
repeated StringStringEntryProto quant_parameter_tensor_names = 2;
}
// Graphs
//
// A graph defines the computational logic of a model and is comprised of a parameterized
@ -376,7 +507,7 @@ message TensorProto {
// For float and complex64 values
// Complex64 tensors are encoded as a single array of floats,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
@ -448,7 +579,7 @@ message TensorProto {
// For double
// Complex128 tensors are encoded as a single array of doubles,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
@ -539,7 +670,7 @@ message TypeProto {
int32 elem_type = 1;
TensorShapeProto shape = 2;
}
message Opaque {
// When missing, the domain is the same as the model's.
string domain = 1;
@ -568,9 +699,8 @@ message TypeProto {
SparseTensor sparse_tensor_type = 8;
Opaque opaque_type = 7;
Opaque opaque_type = 7;
}
@ -596,6 +726,7 @@ message OperatorSetIdProto {
int64 version = 2;
}
// Operator/function status.
enum OperatorStatus {
EXPERIMENTAL = 0;
@ -605,7 +736,7 @@ enum OperatorStatus {
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
string name = 1;
// The first version of a function set which contains this function.
// When there's any breaking change for this function, the function set
// contains the function needs to bump its version, and since_version of
@ -628,9 +759,21 @@ message FunctionProto {
// The attributes of the function.
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
string doc_string = 8;
}
// The OperatorSets this function body (graph) relies on.
// A FunctionProto body (graph) may implicitly rely on the OperatorSet that
// this function belongs to. It can also explicitly rely on more OperatorSets
// with this field specified.
//
// All nodes in the function body (graph) will bind against the operator
// with the same-domain/same-op_type operator with the HIGHEST version
// in the referenced operator sets. This means at most one version can be relied
// for one domain.
repeated OperatorSetIdProto opset_import = 9;
}

4
onnxruntime/core/protobuf/onnx-operators-ml.proto
View file

@ -9,7 +9,6 @@
syntax = "proto2";
package onnx;
import "onnx-ml.proto";
//
@ -47,7 +46,6 @@ import "onnx-ml.proto";
// this operator was initially declared in.
//
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
optional string op_type = 1;
@ -130,4 +128,4 @@ message OperatorSetProto {
// The functions specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
repeated FunctionProto functions = 9;
}
}

6
onnxruntime/core/protobuf/onnx-operators-ml.proto3
View file

@ -9,7 +9,6 @@
syntax = "proto3";
package onnx;
import "onnx-ml.proto3";
//
@ -47,7 +46,6 @@ import "onnx-ml.proto3";
// this operator was initially declared in.
//
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
string op_type = 1;
@ -130,4 +128,6 @@ message OperatorSetProto {
// The functions specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
repeated FunctionProto functions = 9;
}
}

2
onnxruntime/core/protobuf/onnx-operators.in.proto
View file

@ -4,7 +4,6 @@
syntax = "proto2";
package {PACKAGE_NAME};
// #if ONNX-ML
import "onnx-ml.proto";
// #else
@ -46,7 +45,6 @@ import "onnx.proto";
// this operator was initially declared in.
//
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
optional string op_type = 1;

167
onnxruntime/core/protobuf/onnx.in.proto
View file

@ -59,8 +59,8 @@ enum Version {
_START_VERSION = 0;
// The version field is always serialized and we will use it to store the
// version that the graph is generated from. This helps us set up version
// control.
// For the IR, we are using simple numbers starting with with 0x00000001,
// control.
// For the IR, we are using simple numbers starting with 0x00000001,
// which was the version we published on Oct 10, 2017.
IR_VERSION_2017_10_10 = 0x0000000000000001;
@ -89,7 +89,18 @@ enum Version {
// - Add support for sparse tensor constants stored in model.
// - Add message SparseTensorProto
// - Add sparse initializers
IR_VERSION = 0x0000000000000006;
IR_VERSION_2019_9_19 = 0x0000000000000006;
// IR VERSION 7 published on <TBD>
// - Add support to allow function body graph to rely on multiple external opreator sets.
// - Add a list to promote inference graph's initializers to global and
// mutable variables. Global variables are visible in all graphs of the
// stored models.
// - Add message TrainingInfoProto to store initialization
// method and training algorithm. The execution of TrainingInfoProto
// can modify the values of mutable variables.
// - Make inference graph callable from TrainingInfoProto via GraphCall operator.
IR_VERSION = 0x0000000000000007;
}
// Attributes
@ -133,10 +144,10 @@ message AttributeProto {
// The type field MUST be present for this version of the IR.
// For 0.0.1 versions of the IR, this field was not defined, and
// implementations needed to use has_field hueristics to determine
// implementations needed to use has_field heuristics to determine
// which value field was in use. For IR_VERSION 0.0.2 or later, this
// field MUST be set and match the f|i|s|t|... field in use. This
// change was made to accomodate proto3 implementations.
// change was made to accommodate proto3 implementations.
optional AttributeType type = 20; // discriminator that indicates which field below is in use
// Exactly ONE of the following fields must be present for this version of the IR
@ -196,12 +207,119 @@ message NodeProto {
optional string doc_string = 6;
}
// Training information
// TrainingInfoProto stores information for training a model.
// In particular, this defines two functionalities: an initialization-step
// and a training-algorithm-step. Initialization resets the model
// back to its original state as if no training has been consumed.
// Training algorithm improves the model based on input data.
//
// The semantics of the initialization-step is that the initializers
// in ModelProto.graph and in TrainingInfoProto.algorithm are first
// initialized as specified by the initializers in the graph, and then
// updated by the "initialization_binding" in every instance in
// ModelProto.training_info.
//
// The field "algorithm" defines a computation graph which represents a
// training algorithm's step. After the execution of a
// TrainingInfoProto.algorithm, the initializers specified by "update_binding"
// may be immediately updated. If the targeted training algorithm contains
// consecutive update stages (such as block coordinate descent methods),
// the user needs to create a TrainingInfoProto for each stage.
message TrainingInfoProto {
// This field describes a graph to compute the initial tensors
// upon starting the training process. Initialization graph has no input
// and can have multiple outputs. Usually, trainable tensors in neural
// networks are randomly initialized. To achieve that, for each tensor,
// the user can put a random number operator such as RandomNormal or
// RandomUniform in TrainingInfoProto.initialization.node and assign its
// random output to the specific tensor using "initialization_binding".
// This graph can also set the initializers in "algorithm" in the same
// TrainingInfoProto; a use case is resetting the number of training
// iteration to zero.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
optional GraphProto initialization = 1;
// This field represents a training algorithm step. Given required inputs,
// it computes outputs to update initializers in its own or inference graph's
// initializer lists. In general, this graph contains loss node, gradient node,
// optimizer node, increment of iteration count, and some calls to the inference
// graph.
//
// The field algorithm.node is the only place the user can use GraphCall
// operator. The only callable graph is the one stored in ModelProto.graph.
//
// By default, this field is an empty graph and its evaluation does not
// produce any output.
optional GraphProto algorithm = 2;
// This field specifies the bindings from the outputs of "initialization" to
// some initializers in "ModelProto.graph.initializer" and
// the "algorithm.initializer" in the same TrainingInfoProto.
// See "update_binding" below for details.
//
// By default, this field is empty and no initializer would be changed
// by the execution of "initialization".
repeated StringStringEntryProto initialization_binding = 3;
// Gradient-based training is usually an iterative procedure. In one gradient
// descent iteration, we apply
//
// x = x - r * g
//
// where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
// gradient of "x" with respect to a chosen loss. To avoid adding assignments
// into the training graph, we split the update equation into
//
// y = x - r * g
// x = y
//
// The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
// tell that "y" should be assigned to "x", the field "update_binding" may
// contain a key-value pair of strings, "x" (key of StringStringEntryProto)
// and "y" (value of StringStringEntryProto).
// For a neural network with multiple trainable (mutable) tensors, there can
// be multiple key-value pairs in "update_binding".
//
// The initializers appears as keys in "update_binding" are considered
// mutable and globally-visible variables. This implies some behaviors
// as described below.
//
// 1. We have only unique keys in all "update_binding"s so that two global
// variables may not have the same name. This ensures that one
// global variable is assigned up to once.
// 2. The keys must appear in names of "ModelProto.graph.initializer" or
// "TrainingInfoProto.algorithm.initializer".
// 3. The values must be output names of "algorithm".
// 4. If an optional input of a graph is omitted when using GraphCall, the
// global variable with the same name may be used.
// 5. When using GraphCall, the users always can pass values to optional
// inputs of the called graph even if the associated initializers appears
// as keys in "update_binding"s.
// 6. The graphs in TrainingInfoProto's can use global variables as
// their operator inputs.
// 7. Mutable variables are initialized to the value specified by the
// corresponding initializer, and then potentially updated by
// "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
//
// This field usually contains names of trainable tensors
// (in ModelProto.graph), optimizer states such as momentums in advanced
// stochastic gradient methods (in TrainingInfoProto.graph),
// and number of training iterations (in TrainingInfoProto.graph).
//
// By default, this field is empty and no initializer would be changed
// by the execution of "algorithm".
repeated StringStringEntryProto update_binding = 4;
}
// Models
//
// ModelProto is a top-level file/container format for bundling a ML model and
// associating its computation graph with metadata.
//
// The semantics of the model are described by the associated GraphProto.
// The semantics of the model are described by the associated GraphProto's.
message ModelProto {
// The version of the IR this model targets. See Version enum above.
// This field MUST be present.
@ -249,6 +367,17 @@ message ModelProto {
// Named metadata values; keys should be distinct.
repeated StringStringEntryProto metadata_props = 14;
// Training-specific information. Sequentially executing all stored
// `TrainingInfoProto.algorithm`s and assigning their outputs following
// the corresponding `TrainingInfoProto.update_binding`s is one training
// iteration. Similarly, to initialize the model
// (as if training hasn't happened), the user should sequentially execute
// all stored `TrainingInfoProto.initialization`s and assigns their outputs
// using `TrainingInfoProto.initialization_binding`s.
//
// If this field is empty, the training behavior of the model is undefined.
repeated TrainingInfoProto training_info = 20;
};
// StringStringEntryProto follows the pattern for cross-proto-version maps.
@ -373,7 +502,7 @@ message TensorProto {
// For float and complex64 values
// Complex64 tensors are encoded as a single array of floats,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
@ -445,7 +574,7 @@ message TensorProto {
// For double
// Complex128 tensors are encoded as a single array of doubles,
// with the real components appearing in odd numbered positions,
// and the corresponding imaginary component apparing in the
// and the corresponding imaginary component appearing in the
// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
// is encoded as [1.0, 2.0 ,3.0 ,4.0]
// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
@ -537,7 +666,7 @@ message TypeProto {
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
message Opaque {
// When missing, the domain is the same as the model's.
optional string domain = 1;
@ -568,11 +697,10 @@ message TypeProto {
// #if ONNX-ML
SparseTensor sparse_tensor_type = 8;
Opaque opaque_type = 7;
// #endif
// #endif
}
// An optional denotation can be used to denote the whole
@ -606,7 +734,7 @@ enum OperatorStatus {
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
optional string name = 1;
// The first version of a function set which contains this function.
// When there's any breaking change for this function, the function set
// contains the function needs to bump its version, and since_version of
@ -629,9 +757,20 @@ message FunctionProto {
// The attributes of the function.
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
optional string doc_string = 8;
// The OperatorSets this function body (graph) relies on.
// A FunctionProto body (graph) may implicitly rely on the OperatorSet that
// this function belongs to. It can also explicitly rely on more OperatorSets
// with this field specified.
//
// All nodes in the function body (graph) will bind against the operator
// with the same-domain/same-op_type operator with the HIGHEST version
// in the referenced operator sets. This means at most one version can be relied
// for one domain.
repeated OperatorSetIdProto opset_import = 9;
}

2
onnxruntime/core/session/environment.cc
View file

@ -7,6 +7,7 @@
#include "core/graph/op.h"
#include "onnx/defs/operator_sets.h"
#include "onnx/defs/operator_sets-ml.h"
#include "onnx/defs/operator_sets-training.h"
#ifndef DISABLE_CONTRIB_OPS
#include "core/graph/contrib_ops/contrib_defs.h"
#endif
@ -83,6 +84,7 @@ Status Environment::Initialize(std::unique_ptr<logging::LoggingManager> logging_
#endif
RegisterOnnxOperatorSetSchema();
RegisterOnnxMLOperatorSetSchema();
RegisterOnnxTrainingOperatorSetSchema();
});
// Register MemCpy schema;

2
onnxruntime/test/ir/op_test.cc
View file

@ -26,7 +26,9 @@ TEST(FormalParamTest, Success) {
OpSchema::FormalParameter p("input", "desc: integer input", "tensor(int32)");
EXPECT_EQ("input", p.GetName());
EXPECT_EQ("tensor(int32)", p.GetTypeStr());
#ifndef __ONNX_NO_DOC_STRINGS
EXPECT_EQ("desc: integer input", p.GetDescription());
#endif
// TODO: change onnx to make formal parameter construction self-contain.
//EXPECT_EQ(Utils::DataTypeUtils::ToType("tensor(int32)"), *p.GetTypes().begin());
}

21
onnxruntime/test/onnx/main.cc
View file

@ -400,7 +400,7 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
static const ORTCHAR_T* dnnl_disabled_tests[] = {ORT_TSTR("test_densenet121"), ORT_TSTR("test_resnet18v2"), ORT_TSTR("test_resnet34v2"), ORT_TSTR("test_resnet50v2"), ORT_TSTR("test_resnet101v2"),
ORT_TSTR("test_resnet101v2"), ORT_TSTR("test_vgg19"), ORT_TSTR("tf_inception_resnet_v2"), ORT_TSTR("tf_inception_v1"), ORT_TSTR("tf_inception_v3"), ORT_TSTR("tf_inception_v4"), ORT_TSTR("tf_mobilenet_v1_1.0_224"),
ORT_TSTR("tf_mobilenet_v2_1.0_224"), ORT_TSTR("tf_mobilenet_v2_1.4_224"), ORT_TSTR("tf_nasnet_large"), ORT_TSTR("tf_pnasnet_large"), ORT_TSTR("tf_resnet_v1_50"), ORT_TSTR("tf_resnet_v1_101"), ORT_TSTR("tf_resnet_v1_101"),
ORT_TSTR("tf_resnet_v2_101"), ORT_TSTR("tf_resnet_v2_152")};
ORT_TSTR("tf_resnet_v2_101"), ORT_TSTR("tf_resnet_v2_152"), ORT_TSTR("batchnorm_example_training_mode"), ORT_TSTR("batchnorm_epsilon_training_mode")};
std::unordered_set<std::basic_string<ORTCHAR_T> > all_disabled_tests(std::begin(immutable_broken_tests), std::end(immutable_broken_tests));
if (enable_cuda) {
@ -505,6 +505,7 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
}
if (enable_nuphar) {
broken_tests.insert({"cgan", "TVM exception during initialization"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_expanded", "TVM exception during initialization"});
}
if (enable_dnnl) {
broken_tests.insert({"tf_mobilenet_v2_1.0_224", "result mismatch"});
@ -529,6 +530,13 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
broken_tests.insert({"scan_sum", "disable temporarily"});
broken_tests.insert({"scan9_sum", "disable temporarily"});
broken_tests.insert({"convtranspose_1d", "1d convtranspose not supported yet"});
broken_tests.insert({"bvlc_alexnet", "disable temporarily"});
broken_tests.insert({"bvlc_googlenet", "disable temporarily"});
broken_tests.insert({"bvlc_reference_caffenet", "disable temporarily"});
broken_tests.insert({"bvlc_reference_rcnn_ilsvrc13", "disable temporarily"});
broken_tests.insert({"inception_v1", "disable temporarily"});
broken_tests.insert({"squeezenet", "disable temporarily"});
broken_tests.insert({"vgg19", "disable temporarily"});
#ifdef OPENVINO_CONFIG_GPU_FP32
broken_tests.insert({"tiny_yolov2", "accuracy mismatch"});
broken_tests.insert({"div", "will be fixed in the next release"});
@ -549,7 +557,15 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
broken_tests.insert({"range_float_type_positive_delta_expanded", "Temporarily disabled pending investigation"});
broken_tests.insert({"range_int32_type_negative_delta_expanded", "Temporarily disabled pending investigation"});
broken_tests.insert({"convtranspose_1d", "1d convtranspose not supported yet"});
broken_tests.insert({"maxpool_2d_uint8", "Does not work on DNNL, NNAPI"});
broken_tests.insert({"maxpool_2d_uint8", "result mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NC_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_mean_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_sum_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_mean_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_expanded", "shape mismatch"});
broken_tests.insert({"negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_expanded", "shape mismatch"});
}
if (enable_tensorrt) {
@ -577,6 +593,7 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
broken_tests.insert({"tf_inception_v1", "flaky test"}); //TODO: Investigate cause for flakiness
broken_tests.insert({"convtranspose_1d", "1d convtranspose not supported yet"});
broken_tests.insert({"faster_rcnn", "Linux: faster_rcnn:output=6383:shape mismatch, expect {77} got {57}"});
broken_tests.insert({"split_zero_size_splits", "alloc failed"});
}
if (enable_dml) {

8
onnxruntime/test/optimizer/graph_transform_test.cc
View file

@ -759,7 +759,11 @@ TEST(GraphTransformationTests, ReluClip6Fusion) {
// test handling of Clip 11
TEST(GraphTransformationTests, ReluClip11Fusion) {
Model model("ReluClip6Fusion", false, DefaultLoggingManager().DefaultLogger()); //, true, ModelMetaData(), IOnnxRuntimeOpSchemaRegistryList(), {{"", 11}}, {});
std::unordered_map<std::string, int> domain_to_version;
domain_to_version[kOnnxDomain] = 11;
Model model("ReluClip6Fusion", false, ModelMetaData(), PathString(),
IOnnxRuntimeOpSchemaRegistryList(),
domain_to_version, std::vector<ONNX_NAMESPACE::FunctionProto>(), DefaultLoggingManager().DefaultLogger()); //, true, ModelMetaData(), IOnnxRuntimeOpSchemaRegistryList(), {{"", 11}}, {});
auto& graph = model.MainGraph();
std::vector<NodeArg*> inputs;
@ -1666,4 +1670,4 @@ TEST(GraphTransformationTests, EmbedLayerNormFusionFormat5) {
#endif
} // namespace test
} // namespace onnxruntime
} // namespace onnxruntime

2
onnxruntime/test/providers/cpu/math/clip_test.cc
View file

@ -64,7 +64,7 @@ TEST(MathOpTest, Clip) {
TEST(MathOpTest, ClipDimWithZero) {
std::vector<int64_t> dims{3, 0}; // dim with value of zero should be handled
OpTester test("Clip", -1); // latest opset
OpTester test("Clip", 11); // latest opset
test.AddInput<float>("X", dims, {});
test.AddInput<float>("min", {}, {-5});
test.AddInput<float>("max", {}, {5});

123
onnxruntime/test/python/onnx_backend_test_series.py
View file

@ -84,16 +84,119 @@ def create_backend_test(testname=None):
backend_test.include(testname + '.*')
else:
# Tests that are failing temporarily and should be fixed
current_failing_tests = [ #'^test_cast_STRING_to_FLOAT_cpu', # old test data that is bad on Linux CI builds
'^test_unique_not_sorted_without_axis_cpu', # bad expected data. enable after https://github.com/onnx/onnx/pull/2381 is picked up
'^test_mod_float_mixed_sign_example_cpu', #onnxruntime::Mod::Compute fmod_ was false. fmod attribute must be true for float, float16 and double types
current_failing_tests = [
'^test_adagrad_cpu',
'^test_adagrad_multiple_cpu',
'^test_batchnorm_epsilon_old_cpu',
'^test_batchnorm_epsilon_training_mode_cpu',
'^test_batchnorm_example_old_cpu',
'^test_batchnorm_example_training_mode_cpu',
'^test_celu_cpu',
'^test_clip_cpu',
'^test_clip_default_inbounds_cpu',
'^test_clip_default_int8_inbounds_cpu',
'^test_clip_default_int8_max_cpu',
'^test_clip_default_int8_min_cpu',
'^test_clip_default_max_cpu',
'^test_clip_default_min_cpu',
'^test_clip_example_cpu',
'^test_clip_inbounds_cpu',
'^test_clip_outbounds_cpu',
'^test_clip_splitbounds_cpu',
'^test_dropout_default_cpu',
'^test_dropout_random_cpu',
'^test_einsum_batch_diagonal_cpu',
'^test_einsum_batch_matmul_cpu',
'^test_einsum_inner_prod_cpu',
'^test_einsum_sum_cpu',
'^test_einsum_transpose_cpu',
'^test_gathernd_example_int32_batch_dim1_cpu',
'^test_inverse_batched_cpu',
'^test_inverse_cpu',
'^test_max_float16_cpu',
'^test_max_float32_cpu',
'^test_max_float64_cpu',
'^test_max_int16_cpu',
'^test_max_int32_cpu',
'^test_max_int64_cpu',
'^test_max_int8_cpu',
'^test_max_uint16_cpu',
'^test_max_uint32_cpu',
'^test_max_uint64_cpu',
'^test_max_uint8_cpu',
'^test_mean_square_distance_mean_3d_cpu',
'^test_mean_square_distance_mean_3d_expanded_cpu',
'^test_mean_square_distance_mean_4d_cpu',
'^test_mean_square_distance_mean_4d_expanded_cpu',
'^test_mean_square_distance_mean_cpu',
'^test_mean_square_distance_mean_expanded_cpu',
'^test_mean_square_distance_none_cpu',
'^test_mean_square_distance_none_expanded_cpu',
'^test_mean_square_distance_none_weights_cpu',
'^test_mean_square_distance_none_weights_expanded_cpu',
'^test_mean_square_distance_sum_cpu',
'^test_mean_square_distance_sum_expanded_cpu',
'^test_min_float16_cpu',
'^test_min_float32_cpu',
'^test_min_float64_cpu',
'^test_min_int16_cpu',
'^test_min_int32_cpu',
'^test_min_int64_cpu',
'^test_min_int8_cpu',
'^test_min_uint16_cpu',
'^test_min_uint32_cpu',
'^test_min_uint64_cpu',
'^test_min_uint8_cpu',
'^test_momentum_cpu',
'^test_momentum_multiple_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NC_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_mean_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_reduction_sum_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_mean_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_cpu',
'^test_nesterov_momentum_cpu',
'^test_pow_bcast_array_cpu',
'^test_pow_bcast_scalar_cpu',
'^test_pow_cpu',
'^test_pow_example_cpu',
'^test_pow_types_float32_int32_cpu',
'^test_pow_types_float32_int64_cpu',
'^test_pow_types_float32_uint32_cpu',
'^test_pow_types_float32_uint64_cpu',
'^test_pow_types_float_cpu',
'^test_pow_types_int32_float32_cpu',
'^test_pow_types_int32_int32_cpu',
'^test_pow_types_int64_float32_cpu',
'^test_pow_types_int64_int64_cpu',
'^test_pow_types_int_cpu',
'^test_softmax_cross_entropy_mean_3d_cpu',
'^test_softmax_cross_entropy_mean_3d_expanded_cpu',
'^test_softmax_cross_entropy_mean_cpu',
'^test_softmax_cross_entropy_mean_expanded_cpu',
'^test_softmax_cross_entropy_mean_weight_cpu',
'^test_softmax_cross_entropy_mean_weight_expanded_cpu',
'^test_softmax_cross_entropy_mean_weight_ignore_index_cpu',
'^test_softmax_cross_entropy_mean_weight_ignore_index_expanded_cpu',
'^test_softmax_cross_entropy_none_cpu',
'^test_softmax_cross_entropy_none_expanded_cpu',
'^test_softmax_cross_entropy_none_weights_cpu',
'^test_softmax_cross_entropy_none_weights_expanded_cpu',
'^test_softmax_cross_entropy_sum_cpu',
'^test_softmax_cross_entropy_sum_expanded_cpu',
'^test_unfoldtodepth_with_padding_cpu',
'^test_unfoldtodepth_with_padding_stride_cpu',
'^test_unfoldtodepth_without_padding_cpu',
'^test_gradient_of_add_and_mul_cpu',
'^test_gradient_of_add_cpu',
'^test_negative_log_likelihood_loss_input_shape_is_NCd1d2_with_weight_reduction_sum_ignore_index_expanded_cpu',
'^test_batchnorm_example_training_mode_cpu',
'^test_batchnorm_epsilon_training_mode_cpu',
'^test_maxunpool_export_with_output_shape_cpu', #result mismatch
'^test_resize_downsample_scales_cubic_align_corners_cpu', # results mismatch with onnx tests
'^test_resize_downsample_scales_linear_align_corners_cpu', # results mismatch with onnx tests
'^test_resize_tf_crop_and_resize_cpu', # bad expected data, needs test fix
'^test_resize_upsample_sizes_nearest_ceil_half_pixel_cpu', # bad expected data, needs test fix
'^test_resize_upsample_sizes_nearest_floor_align_corners_cpu', # bad expected data, needs test fix
'^test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric_cpu', # bad expected data, needs test fix
'^test_maxunpool_export_with_output_shape_cpu', # Invalid output in ONNX test. See https://github.com/onnx/onnx/issues/2398'
'^test_resize_downsample_scales_linear_align_corners_cpu' # results mismatch with onnx tests
]
# Example of how to disable tests for a specific provider.
@ -105,7 +208,7 @@ def create_backend_test(testname=None):
'^test_argmin_negative_axis.*', '^test_hardmax_negative_axis.*', '^test_gemm_default_no_bias_cpu',
'^test_flatten_negative_axis.*', '^test_reduce_[a-z1-9_]*_negative_axes_.*',
'test_squeeze_negative_axes_cpu', 'test_unsqueeze_negative_axes_cpu', 'test_constant_pad_cpu',
'test_edge_pad_cpu', 'test_reflect_pad_cpu'
'test_edge_pad_cpu', 'test_reflect_pad_cpu', '^test_split_zero_size_splits_.*','^test_argmax_keepdims_example_select_last_index_.*', '^test_argmax_no_keepdims_example_select_last_index_.*','^test_argmin_no_keepdims_example_select_last_index_.*','^test_argmin_keepdims_example_select_last_index_.*'
]
if c2.supports_device('DNNL'):

14
tools/ci_build/build.py
View file

@ -615,7 +615,7 @@ def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs, enab
adb_push(source_dir, 'onnx_test_runner', '/data/local/tmp/', cwd=cwd)
adb_shell('cd /data/local/tmp && /data/local/tmp/onnxruntime_test_all')
if args.use_dnnlibrary:
adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner -e nnapi /data/local/tmp/test')
adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner -e nnapi -o 0 /data/local/tmp/test')
else:
adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner /data/local/tmp/test')
continue
@ -686,7 +686,8 @@ def run_onnx_tests(build_dir, configs, onnx_test_data_dir, provider, enable_mult
else:
exe = os.path.join(cwd, 'onnx_test_runner')
model_dir = os.path.join(build_dir, "models")
cmd = []
#Temporarily disable optimizers because some of them are failing
cmd = ["-o", "0"]
if provider:
cmd += ["-e", provider]
@ -723,7 +724,7 @@ def tensorrt_run_onnx_tests(args, build_dir, configs, onnx_test_data_dir, provid
exe = os.path.join(cwd, 'onnx_test_runner')
model_dir = os.path.join(build_dir, "models")
cmd_base = []
cmd_base = ['-o', '0']
if provider:
cmd_base += ["-e", provider]
@ -759,7 +760,7 @@ def dnnl_run_onnx_tests(build_dir, configs, onnx_test_data_dir):
else:
exe = os.path.join(cwd, 'onnx_test_runner')
model_dir = os.path.join(build_dir, "models")
cmd_base = ['-e', 'dnnl', '-c', '1', '-j', '1']
cmd_base = ['-o', '0', '-e', 'dnnl', '-c', '1', '-j', '1']
if os.path.exists(onnx_test_data_dir):
onnxdata_cmd = cmd_base + [onnx_test_data_dir]
# /data/onnx
@ -1047,8 +1048,9 @@ def main():
if args.use_cuda and not args.use_tensorrt:
run_onnx_tests(build_dir, configs, onnx_test_data_dir, 'cuda', args.enable_multi_device_test, False, 2)
if args.use_ngraph:
run_onnx_tests(build_dir, configs, onnx_test_data_dir, 'ngraph', args.enable_multi_device_test, True, 1)
#ngraph doesn't support opset12 yet.
#if args.use_ngraph:
# run_onnx_tests(build_dir, configs, onnx_test_data_dir, 'ngraph', args.enable_multi_device_test, True, 1)
if args.use_openvino:
run_onnx_tests(build_dir, configs, onnx_test_data_dir, 'openvino', args.enable_multi_device_test, False, 1, 1)

3
tools/ci_build/github/linux/docker/scripts/install_onnx.sh
View file

@ -30,7 +30,8 @@ version2tag=(5af210ca8a1c73aa6bae8754c9346ec54d0a756e-onnx123
bae6333e149a59a3faa9c4d9c44974373dcf5256-onnx130
9e55ace55aad1ada27516038dfbdc66a8a0763db-onnx141
7d7bc83d29a328233d3e8affa4c4ea8b3e3599ef-onnx150
1facb4c1bb9cc2107d4dbaf9fd647fefdbbeb0ab-onnxtip) #1.6.1
1facb4c1bb9cc2107d4dbaf9fd647fefdbbeb0ab-onnx161
8bee53756ba8b8a3aca47c5719e35fca150ab79e-onnxtip) #1.7.0
for v2t in ${version2tag[*]}; do
onnx_version="$(cut -d'-' -f1<<<${v2t})"
onnx_tag="$(cut -d'-' -f2<<<${v2t})"