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Use default attribute values #4

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44ccd8a
Use default attribute values
gramalingam May 11, 2023
595c83d
Fix data propagation function scope
gramalingam May 11, 2023
818524c
Handle function call correctly for data propagation
gramalingam May 12, 2023
e47f256
Add test case
gramalingam May 12, 2023
0bb0c50
Run lint
gramalingam May 12, 2023
3a76dee
Run lint
gramalingam May 12, 2023
9489ccc
Merge branch 'main' into default_attr
gramalingam May 16, 2023
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220 changes: 111 additions & 109 deletions onnx/defs/generator/old.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,116 @@ This operator produces a constant tensor. Exactly one of the provided attributes
or value_* must be specified.
)DOC";

void ConstantInference(InferenceContext& ctx) {
auto* value = ctx.getAttribute("value");
auto* sparse_value = ctx.getAttribute("sparse_value");
auto* value_int = ctx.getAttribute("value_int");
auto* value_ints = ctx.getAttribute("value_ints");
auto* value_float = ctx.getAttribute("value_float");
auto* value_floats = ctx.getAttribute("value_floats");
auto* value_string = ctx.getAttribute("value_string");
auto* value_strings = ctx.getAttribute("value_strings");

std::vector<bool> non_null_attr = {
(nullptr != value),
(nullptr != sparse_value),
(nullptr != value_int),
(nullptr != value_ints),
(nullptr != value_float),
(nullptr != value_floats),
(nullptr != value_string),
(nullptr != value_strings)};
if (std::count(non_null_attr.begin(), non_null_attr.end(), true) != 1) {
fail_shape_inference(
"One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.");
}

if (nullptr != value) {
// OpSchema::Verify check ensures that the attribute value has_t():
const TensorProto& tensor_proto = value->t();
updateOutputElemType(ctx, 0, tensor_proto.data_type());
updateOutputShape(ctx, 0, tensor_proto);
return;
}

if (nullptr != value_int) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_int->has_i()) {
fail_shape_inference("Attribute 'value_int' expect an integer.")
}
updateOutputElemType(ctx, 0, TensorProto::INT64);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_ints) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_ints->ints_size() < 1) {
fail_shape_inference("Attribute 'value_ints' expect a list of integers.");
}
updateOutputElemType(ctx, 0, TensorProto::INT64);
appendDim(getOutputShape(ctx, 0), value_ints->ints_size());
return;
}

if (nullptr != value_float) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_float->has_f()) {
fail_shape_inference("Attribute 'value_float' expect a float.");
}
updateOutputElemType(ctx, 0, TensorProto::FLOAT);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_floats) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_floats->floats_size() < 1) {
fail_shape_inference("Attribute 'value_floats' expect a list of floats.");
}
updateOutputElemType(ctx, 0, TensorProto::FLOAT);
appendDim(getOutputShape(ctx, 0), value_floats->floats_size());
return;
}

if (nullptr != value_string) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_string->has_s()) {
fail_shape_inference("Attribute 'value_string' expect a string.");
}
updateOutputElemType(ctx, 0, TensorProto::STRING);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_strings) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_strings->strings_size() < 1) {
fail_shape_inference("Attribute 'value_strings' expect a list of strings.");
}
updateOutputElemType(ctx, 0, TensorProto::STRING);
appendDim(getOutputShape(ctx, 0), value_strings->strings_size());
return;
}

if (nullptr != sparse_value) {
// OpSchema::Verify check ensures that the attribute value
// has_sparse_tensor():
const SparseTensorProto& sparse = sparse_value->sparse_tensor();
// checker.cc::check_sparse_tensor checks that the sparse-value is
// well-formed
updateOutputElemType(ctx, 0, sparse.values().data_type());
auto* output_shape = getOutputShape(ctx, 0);
for (int i = 0; i < sparse.dims_size(); ++i)
appendDim(output_shape, sparse.dims(i));
return;
}

fail_shape_inference(
"TypeAndShapeInferenceFunction implementation incomplete: "
"this line should never be reached.");
}

ONNX_OPERATOR_SET_SCHEMA(
Constant,
13,
Expand Down Expand Up @@ -55,115 +165,7 @@ ONNX_OPERATOR_SET_SCHEMA(
false)
.Output(0, "output", "Output tensor containing the same value of the provided tensor.", "T")
.TypeConstraint("T", OpSchema::all_tensor_types_ir4(), "Constrain input and output types to all tensor types.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
auto* value = ctx.getAttribute("value");
auto* sparse_value = ctx.getAttribute("sparse_value");
auto* value_int = ctx.getAttribute("value_int");
auto* value_ints = ctx.getAttribute("value_ints");
auto* value_float = ctx.getAttribute("value_float");
auto* value_floats = ctx.getAttribute("value_floats");
auto* value_string = ctx.getAttribute("value_string");
auto* value_strings = ctx.getAttribute("value_strings");

std::vector<bool> non_null_attr = {
(nullptr != value),
(nullptr != sparse_value),
(nullptr != value_int),
(nullptr != value_ints),
(nullptr != value_float),
(nullptr != value_floats),
(nullptr != value_string),
(nullptr != value_strings)};
if (std::count(non_null_attr.begin(), non_null_attr.end(), true) != 1) {
fail_shape_inference(
"One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.");
}

if (nullptr != value) {
// OpSchema::Verify check ensures that the attribute value has_t():
const TensorProto& tensor_proto = value->t();
updateOutputElemType(ctx, 0, tensor_proto.data_type());
updateOutputShape(ctx, 0, tensor_proto);
return;
}

if (nullptr != value_int) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_int->has_i()) {
fail_shape_inference("Attribute 'value_int' expect an integer.")
}
updateOutputElemType(ctx, 0, TensorProto::INT64);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_ints) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_ints->ints_size() < 1) {
fail_shape_inference("Attribute 'value_ints' expect a list of integers.");
}
updateOutputElemType(ctx, 0, TensorProto::INT64);
appendDim(getOutputShape(ctx, 0), value_ints->ints_size());
return;
}

if (nullptr != value_float) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_float->has_f()) {
fail_shape_inference("Attribute 'value_float' expect a float.");
}
updateOutputElemType(ctx, 0, TensorProto::FLOAT);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_floats) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_floats->floats_size() < 1) {
fail_shape_inference("Attribute 'value_floats' expect a list of floats.");
}
updateOutputElemType(ctx, 0, TensorProto::FLOAT);
appendDim(getOutputShape(ctx, 0), value_floats->floats_size());
return;
}

if (nullptr != value_string) {
// OpSchema::Verify check ensures that the attribute value has_i():
if (!value_string->has_s()) {
fail_shape_inference("Attribute 'value_string' expect a string.");
}
updateOutputElemType(ctx, 0, TensorProto::STRING);
updateOutputShape(ctx, 0, TensorShapeProto());
return;
}

if (nullptr != value_strings) {
// OpSchema::Verify check ensures that the attribute value has ints.
if (value_strings->strings_size() < 1) {
fail_shape_inference("Attribute 'value_strings' expect a list of strings.");
}
updateOutputElemType(ctx, 0, TensorProto::STRING);
appendDim(getOutputShape(ctx, 0), value_strings->strings_size());
return;
}

if (nullptr != sparse_value) {
// OpSchema::Verify check ensures that the attribute value
// has_sparse_tensor():
const SparseTensorProto& sparse = sparse_value->sparse_tensor();
// checker.cc::check_sparse_tensor checks that the sparse-value is
// well-formed
updateOutputElemType(ctx, 0, sparse.values().data_type());
auto* output_shape = getOutputShape(ctx, 0);
for (int i = 0; i < sparse.dims_size(); ++i)
appendDim(output_shape, sparse.dims(i));
return;
}

fail_shape_inference(
"TypeAndShapeInferenceFunction implementation incomplete: "
"this line should never be reached.");
}));
.TypeAndShapeInferenceFunction(ConstantInference));

static const char* Constant_ver12_doc = R"DOC(
This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value,
Expand Down
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