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Revert "Implement Op Annotation's for ONNX (#1648)" (#1812)
This reverts commit 0628582.
1 parent 61f1e9e commit b92eee8

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13 files changed

+75
-442
lines changed

13 files changed

+75
-442
lines changed

‎docs/OpSchema.md‎

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‎onnx/common/ir.h‎

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@@ -21,7 +21,6 @@
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#include "onnx/common/interned_strings.h"
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#include "onnx/common/graph_node_list.h"
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#include "onnx/common/tensor.h"
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#include "onnx/defs/schema.h"
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#define ONNX_DISALLOW_COPY_AND_ASSIGN(TypeName) \
@@ -465,13 +464,6 @@ struct Node : public Attributes<Node> {
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stage_ = s;
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return this;
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}
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inline bool containsOpAnnotation(OpAnnotationFlag flag) const{
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auto op_schema = OpSchemaRegistry::Instance()->Schema(this->kind().toString());
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if (nullptr != op_schema) {
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return op_schema->ContainsOpAnnotation(flag);
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}
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return false;
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}
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// NB: This returns an ArrayRef; that means that it will
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// get invalidated if you resize inputs (e.g., using addInput)
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// We can't return a std::vector<Node*>& because there's no

‎onnx/defs/math/defs.cc‎

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@@ -81,9 +81,6 @@ will throw errors.
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.");
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schema.AddOpAnnotation(OpAnnotationFlag::ElementwiseDependent);
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schema.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing);
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schema.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput);
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};
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}
@@ -226,10 +223,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Relu_ver6_doc = R"DOC(
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Relu takes one input data (Tensor<T>) and produces one output data
@@ -248,9 +242,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseWeakMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* LeakyRelu_ver6_doc = R"DOC(
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LeakyRelu takes input data (Tensor<T>) and an argument alpha, and produces one
@@ -270,9 +262,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseWeakMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Selu_ver6_doc = R"DOC(
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Selu takes one input data (Tensor<T>) and produces one output data
@@ -304,10 +294,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Elu_ver6_doc = R"DOC(
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Elu takes one input data (Tensor<T>) and produces one output data
@@ -328,10 +315,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Exp_ver6_doc = R"DOC(
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Calculates the exponential of the given input tensor, element-wise.
@@ -353,10 +337,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Log_ver6_doc = R"DOC(
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Calculates the natural log of the given input tensor, element-wise.
@@ -378,10 +359,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Tanh_ver6_doc = R"DOC(
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Calculates the hyperbolic tangent of the given input tensor element-wise.
@@ -403,10 +381,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* Pow_ver7_doc = R"DOC(
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Pow takes input data (Tensor<T>) and exponent Tensor, and
@@ -485,10 +460,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
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.AddOpAnnotation(
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OpAnnotationFlag::ElementwiseStrictMonotonicIncreasing));
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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static const char* HardSigmoid_ver6_doc = R"DOC(
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HardSigmoid takes one input data (Tensor<T>) and produces one output data
@@ -509,9 +481,7 @@ ONNX_OPERATOR_SET_SCHEMA(
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"T",
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{"tensor(float16)", "tensor(float)", "tensor(double)"},
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"Constrain input and output types to float tensors.")
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.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
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.AddOpAnnotation(OpAnnotationFlag::ElementwiseIndependent)
514-
.AddOpAnnotation(OpAnnotationFlag::ElementwiseWeakMonotonicIncreasing));
484+
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));
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516486
std::function<void(OpSchema&)> ElementwiseMultiOpDocGenerator(
517487
const char* name) {

‎onnx/defs/op_annotation.cc‎

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