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Enable empty list of values as attribute #5559

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merged 13 commits into from
Sep 26, 2023
Merged

Enable empty list of values as attribute #5559

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49c3dd8
Allow empty list of values in Constant op
gramalingam Aug 31, 2023
6f2668e
Update older versions of Constant
gramalingam Aug 31, 2023
70f477a
Run lint
gramalingam Sep 1, 2023
5715952
Add newlines
gramalingam Sep 5, 2023
afd9652
Merge branch 'main' into empty-list-check
gramalingam Sep 5, 2023
57aa1d5
Merge branch 'main' into empty-list-check
gramalingam Sep 7, 2023
77bdc35
Merge branch 'main' into empty-list-check
gramalingam Sep 12, 2023
520653f
Add test for value_floats
gramalingam Sep 12, 2023
0ae59bd
Fix type
gramalingam Sep 12, 2023
1f032d2
Merge branch 'main' into empty-list-check
gramalingam Sep 19, 2023
ea2ac02
Merge branch 'main' into empty-list-check
gramalingam Sep 21, 2023
2123ee0
Merge branch 'main' into empty-list-check
gramalingam Sep 23, 2023
e8e2363
Merge branch 'main' into empty-list-check
gramalingam Sep 26, 2023
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111 changes: 2 additions & 109 deletions onnx/defs/generator/defs.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
#include <cmath>

#include "onnx/defs/function.h"
#include "onnx/defs/generator/utils.h"
#include "onnx/defs/schema.h"

namespace ONNX_NAMESPACE {
Expand Down Expand Up @@ -57,115 +58,7 @@ 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_ir9(), "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(ConstantOpInference));

static const char* ConstantOfShape_ver20_doc = R"DOC(
Generate a tensor with given value and shape.
Expand Down
223 changes: 3 additions & 220 deletions onnx/defs/generator/old.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
#include <algorithm>
#include <functional>

#include "onnx/defs/generator/utils.h"
#include "onnx/defs/schema.h"

namespace ONNX_NAMESPACE {
Expand All @@ -13,116 +14,6 @@ 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 @@ -166,7 +57,7 @@ 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(ConstantInference));
.TypeAndShapeInferenceFunction(ConstantOpInference));

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 Expand Up @@ -216,115 +107,7 @@ 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(), "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(ConstantOpInference));

static const char* Constant_ver1_doc = R"DOC(A constant tensor.)DOC";

Expand Down