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op function body not to allow default opset version #5908

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op function body not to allow default opset version #5908

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7cbd43d
op function body not to allow default opset version
liqunfu Feb 6, 2024
229c200
Merge branch 'main' into liqun/op-function-body-not-to-allow-default-…
linkerzhang Mar 7, 2024
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2 changes: 1 addition & 1 deletion onnx/cpp2py_export.cc
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -395,7 +395,7 @@ PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
}
FunctionBodyBuildContextImpl ctx(proto, input_types);
FunctionProto func_proto;
op->BuildContextDependentFunction(ctx, func_proto);
op->BuildContextDependentFunction(ctx, func_proto, op->SinceVersion());
func_proto.SerializeToString(&func_bytes);
}
return py::bytes(func_bytes);
Expand Down
9 changes: 6 additions & 3 deletions onnx/defs/generator/defs.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -479,7 +479,8 @@ ONNX_OPERATOR_SET_SCHEMA(
"T",
{"tensor(float)", "tensor(double)", "tensor(int16)", "tensor(int32)", "tensor(int64)"},
"Constrain input types to common numeric type tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
sub_result = Sub (limit, start)
sub_result_casted = Cast <to = 1> (sub_result)
Expand All @@ -496,7 +497,8 @@ ONNX_OPERATOR_SET_SCHEMA(
range = Identity (prev)
}>
}
)ONNX")
)ONNX",
11)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
// Type inference
propagateElemTypeFromInputToOutput(ctx, 0, 0);
Expand Down Expand Up @@ -618,5 +620,6 @@ ONNX_OPERATOR_SET_SCHEMA(
.Add("output = Cast (X_greater)", "to", int64_t(dtype));
schema.BuildFunction(functionProto);
return true;
}));
},
15));
} // namespace ONNX_NAMESPACE
12 changes: 8 additions & 4 deletions onnx/defs/logical/defs.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -196,13 +196,15 @@ ONNX_OPERATOR_SET_SCHEMA(
.TypeConstraint("T", OpSchema::all_numeric_types_ir4(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
O1 = Less (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
)ONNX",
16));

ONNX_OPERATOR_SET_SCHEMA(
GreaterOrEqual,
Expand All @@ -212,13 +214,15 @@ ONNX_OPERATOR_SET_SCHEMA(
.TypeConstraint("T", OpSchema::all_numeric_types_ir4(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
O1 = Greater (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
)ONNX",
16));

static const char* BitwiseNot_ver18_doc = R"DOC(
Returns the bitwise not of the input tensor element-wise.
Expand Down
12 changes: 8 additions & 4 deletions onnx/defs/logical/old.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -224,13 +224,15 @@ ONNX_OPERATOR_SET_SCHEMA(
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
O1 = Less (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
)ONNX",
12));

ONNX_OPERATOR_SET_SCHEMA(
GreaterOrEqual,
Expand All @@ -240,13 +242,15 @@ ONNX_OPERATOR_SET_SCHEMA(
.TypeConstraint("T", OpSchema::all_numeric_types(), "Constrain input types to all numeric tensors.")
.TypeConstraint("T1", {"tensor(bool)"}, "Constrain output to boolean tensor.")
.TypeAndShapeInferenceFunction(InferenceFunction())
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
O1 = Greater (A, B)
O2 = Equal (A, B)
C = Or (O1, O2)
}
)ONNX"));
)ONNX",
12));

ONNX_OPERATOR_SET_SCHEMA(
Equal,
Expand Down
55 changes: 35 additions & 20 deletions onnx/defs/math/defs.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -329,7 +329,8 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(bfloat16)", "tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to float tensors.")
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
Alpha = Constant <value_float: float = @alpha>()
AlphaCast = CastLike (Alpha, X)
Expand All @@ -339,7 +340,8 @@ ONNX_OPERATOR_SET_SCHEMA(
AlphaMulX = Mul (AlphaCast, X)
Y = Where (XLessThanZero, AlphaMulX, X)
}
)ONNX"));
)ONNX",
16));

static const char* ThresholdedRelu_ver10_doc = R"DOC(
ThresholdedRelu takes one input data (Tensor<T>) and produces one output data
Expand Down Expand Up @@ -483,13 +485,15 @@ ONNX_OPERATOR_SET_SCHEMA(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input X and output types to float tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
Softplus_X = Softplus (X)
TanHSoftplusX = Tanh (Softplus_X)
Y = Mul (X, TanHSoftplusX)
}
)ONNX")
)ONNX",
18)
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* celu_ver12_doc = R"DOC(
Expand Down Expand Up @@ -557,7 +561,7 @@ ONNX_OPERATOR_SET_SCHEMA(
AttributeProto::FLOAT,
celu_default_alpha)
.TypeConstraint("T", {"tensor(float)"}, "Constrain input and output types to float32 tensors.")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyCelu)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyCelu, 12)
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* gelu_ver20_doc = R"DOC(
Expand Down Expand Up @@ -641,7 +645,7 @@ ONNX_OPERATOR_SET_SCHEMA(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
"Constrain input and output types to float tensors.")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyGelu)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyGelu, 20)
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

static const char* Exp_ver13_doc = R"DOC(
Expand Down Expand Up @@ -813,15 +817,17 @@ ONNX_OPERATOR_SET_SCHEMA(
"tensor(int64)"},
"Constrain input and output types to float/int tensors.")
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
Zero = Constant <value = float {0.0}>()
ZeroCast = CastLike(Zero, X)
XLessThanZero = Less (X, ZeroCast)
SlopeMulX = Mul (slope, X)
Y = Where(XLessThanZero, SlopeMulX, X)
}
)ONNX"));
)ONNX",
16));

static const char* Sigmoid_ver13_doc = R"DOC(
Sigmoid takes one input data (Tensor<T>) and produces one output data
Expand Down Expand Up @@ -899,12 +905,14 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to float tensors.")
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput)
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
HS_X = HardSigmoid<alpha = 0.16666667163372, beta = 0.5>(X)
Y = Mul (X, HS_X)
}
)ONNX"));
)ONNX",
14));

// Generate opschema for element-wise ops. Leaves type constraint "T"
// unspecified.
Expand Down Expand Up @@ -1067,7 +1075,7 @@ ONNX_OPERATOR_SET_SCHEMA(
"T",
OpSchema::all_numeric_types_ir4(),
"Constrain input and output types to all numeric tensors.")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyClip)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodyClip, 13)
.TypeAndShapeInferenceFunction(propagateShapeAndTypeFromFirstInput));

std::function<void(OpSchema&)>
Expand Down Expand Up @@ -1158,7 +1166,8 @@ ONNX_OPERATOR_SET_SCHEMA(

schema.BuildFunction(functionProto);
return true;
})
},
13)
// function body builder for opset version 18.
// ReduceSum is updated in opset 18 to have axes as the second input.
// Therefore function body for opset version 18
Expand Down Expand Up @@ -2505,7 +2514,7 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input, weight, and output types to floating-point tensors.")
.TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain target to integer types")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBody)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBody, 13)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
// Type inference
propagateElemTypeFromInputToOutput(ctx, 0, 0);
Expand Down Expand Up @@ -2862,7 +2871,7 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
"Constrain input and output types to float tensors.")
.TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain target to integer types")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodySCE)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodySCE, 13)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
std::string reduction = getAttribute(ctx, "reduction", "mean");
Expand Down Expand Up @@ -3164,7 +3173,8 @@ ONNX_OPERATOR_SET_SCHEMA(
.FillUsing(CosineSumWindowOpDocGenerator("Hann"))
.TypeConstraint("T1", {"tensor(int32)", "tensor(int64)"}, "Constrain the input size to int64_t.")
.TypeConstraint("T2", OpSchema::all_numeric_types_ir4(), "Constrain output types to numeric tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
A0 = Constant <value = float {0.5}>()
A1 = Constant <value = float {0.5}>()
Expand Down Expand Up @@ -3193,7 +3203,8 @@ ONNX_OPERATOR_SET_SCHEMA(
Temp1 = Add (Temp0, A2_Component)
output = Cast <to : int = @output_datatype> (Temp1)
}
)ONNX"));
)ONNX",
17));

ONNX_OPERATOR_SET_SCHEMA(
HammingWindow,
Expand All @@ -3202,7 +3213,8 @@ ONNX_OPERATOR_SET_SCHEMA(
.FillUsing(CosineSumWindowOpDocGenerator("Hamming"))
.TypeConstraint("T1", {"tensor(int32)", "tensor(int64)"}, "Constrain the input size to int64_t.")
.TypeConstraint("T2", OpSchema::all_numeric_types_ir4(), "Constrain output types to numeric tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
A0 = Constant <value = float {0.54347826087}>()
A1 = Constant <value = float {0.45652173913}>()
Expand Down Expand Up @@ -3231,7 +3243,8 @@ ONNX_OPERATOR_SET_SCHEMA(
Temp1 = Add (Temp0, A2_Component)
output = Cast <to : int = @output_datatype> (Temp1)
}
)ONNX"));
)ONNX",
17));

ONNX_OPERATOR_SET_SCHEMA(
BlackmanWindow,
Expand All @@ -3240,7 +3253,8 @@ ONNX_OPERATOR_SET_SCHEMA(
.FillUsing(CosineSumWindowOpDocGenerator("Blackman"))
.TypeConstraint("T1", {"tensor(int32)", "tensor(int64)"}, "Constrain the input size to int64_t.")
.TypeConstraint("T2", OpSchema::all_numeric_types_ir4(), "Constrain output types to numeric tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
A0 = Constant <value = float {0.42}>()
A1 = Constant <value = float {0.5}>()
Expand Down Expand Up @@ -3269,7 +3283,8 @@ ONNX_OPERATOR_SET_SCHEMA(
Temp1 = Add (Temp0, A2_Component)
output = Cast <to : int = @output_datatype> (Temp1)
}
)ONNX"));
)ONNX",
17));

static const char* MelWeightMatrix_ver17_doc = R"DOC(
Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale.
Expand Down
4 changes: 2 additions & 2 deletions onnx/defs/math/old.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1280,7 +1280,7 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input, weight, and output types to floating-point tensors.")
.TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain target to integer types")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBody_opset12)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBody_opset12, 12)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
// Type inference
propagateElemTypeFromInputToOutput(ctx, 0, 0);
Expand Down Expand Up @@ -1477,7 +1477,7 @@ ONNX_OPERATOR_SET_SCHEMA(
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to float tensors.")
.TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain target to integer types")
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodySCE_opset12)
.SetContextDependentFunctionBodyBuilder(BuildContextDependentFunctionBodySCE_opset12, 12)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
std::string reduction = getAttribute(ctx, "reduction", "mean");
Expand Down
9 changes: 6 additions & 3 deletions onnx/defs/nn/defs.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2259,7 +2259,8 @@ ONNX_OPERATOR_SET_SCHEMA(
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
"Constrain input and output types to all numeric tensors.")
.FunctionBody(R"ONNX(
.FunctionBody(
R"ONNX(
{
Exponent = Constant <value = float {2.0}>()
Epsilon = Constant <value = float {1e-9}>()
Expand All @@ -2273,7 +2274,8 @@ ONNX_OPERATOR_SET_SCHEMA(
Processed_STD = Add (STD, Epsilon)
Y = Div (X_variance, Processed_STD)
}
)ONNX")
)ONNX",
13)
.FunctionBody(
R"ONNX(
{
Expand Down Expand Up @@ -2849,5 +2851,6 @@ ONNX_OPERATOR_SET_SCHEMA(

schema.BuildFunction(functionProto);
return true;
}));
},
21));
} // namespace ONNX_NAMESPACE
31 changes: 17 additions & 14 deletions onnx/defs/nn/old.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -255,19 +255,21 @@
"T",
{"tensor(float16)", "tensor(float)", "tensor(double)"},
"Constrain input and output types to all numeric tensors.")
.FunctionBody(FunctionBodyHelper::BuildNodes(
{// nodes: {outputs, op, inputs, attributes}
FunctionBodyHelper::Const<float>("Exponent", 2.0f),
FunctionBodyHelper::Const<float>("Epsilon", float(1e-9)),
{{"X_RM"}, "ReduceMean", {"X"}, {MakeRefAttribute("axes", AttributeProto::INTS)}},
{{"EX_squared"}, "Pow", {"X_RM", "Exponent"}},
{{"X_squared"}, "Pow", {"X", "Exponent"}},
{{"E_Xsquared"}, "ReduceMean", {"X_squared"}, {MakeRefAttribute("axes", AttributeProto::INTS)}},
{{"Variance"}, "Sub", {"E_Xsquared", "EX_squared"}},
{{"STD"}, "Sqrt", {"Variance"}},
{{"X_variance"}, "Sub", {"X", "X_RM"}},
{{"Processed_STD"}, "Add", {"STD", "Epsilon"}},
{{"Y"}, "Div", {"X_variance", "Processed_STD"}}})));
.FunctionBody(
FunctionBodyHelper::BuildNodes(
{// nodes: {outputs, op, inputs, attributes}
FunctionBodyHelper::Const<float>("Exponent", 2.0f),
FunctionBodyHelper::Const<float>("Epsilon", float(1e-9)),

Check warning on line 262 in onnx/defs/nn/old.cc

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[cpplint] reported by reviewdog 🐶
 Using deprecated casting style.  Use static_cast<float>(...) instead  [readability/casting] [4]

Raw Output:
onnx/defs/nn/old.cc:262:  Using deprecated casting style.  Use static_cast<float>(...) instead  [readability/casting] [4]
{{"X_RM"}, "ReduceMean", {"X"}, {MakeRefAttribute("axes", AttributeProto::INTS)}},
{{"EX_squared"}, "Pow", {"X_RM", "Exponent"}},
{{"X_squared"}, "Pow", {"X", "Exponent"}},
{{"E_Xsquared"}, "ReduceMean", {"X_squared"}, {MakeRefAttribute("axes", AttributeProto::INTS)}},
{{"Variance"}, "Sub", {"E_Xsquared", "EX_squared"}},
{{"STD"}, "Sqrt", {"Variance"}},
{{"X_variance"}, "Sub", {"X", "X_RM"}},
{{"Processed_STD"}, "Add", {"STD", "Epsilon"}},
{{"Y"}, "Div", {"X_variance", "Processed_STD"}}}),
9));

const char* pads_doc2 =
"Padding for the beginning and ending along each spatial axis, it can take any value greater "
Expand Down Expand Up @@ -2546,5 +2548,6 @@

schema.BuildFunction(functionProto);
return true;
}));
},
18));
} // namespace ONNX_NAMESPACE