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defs.cc
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/
defs.cc
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/*
* SPDX-License-Identifier: Apache-2.0
*/
#include "onnx/defs/data_propagators.h"
#include "onnx/defs/tensor/utils.h"
#include "onnx/defs/function.h"
#include "onnx/defs/tensor_proto_util.h"
#include <algorithm>
#include <cmath>
#include <numeric>
namespace ONNX_NAMESPACE {
static const char* Cast_ver13_doc = R"DOC(
The operator casts the elements of a given input tensor to a data type
specified by the 'to' argument and returns an output tensor of the same size in
the converted type. The 'to' argument must be one of the data types specified
in the 'DataType' enum field in the TensorProto message.
Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
(e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
result 100. There are some string literals reserved for special floating-point values;
"+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
to string tensors, plain floating-point representation (such as "314.15926") would be used.
Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
Conversion from a numerical type to any numerical type is always allowed.
User must be aware of precision loss and value change caused by range difference between two types.
For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
)DOC";
ONNX_OPERATOR_SET_SCHEMA(
Cast,
13,
OpSchema()
.SetDoc(Cast_ver13_doc)
.Attr(
"to",
"The data type to which the elements of the input tensor are cast. "
"Strictly must be one of the types from DataType enum in TensorProto",
AttributeProto::INT)
.Input(
0,
"input",
"Input tensor to be cast.",
"T1",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.Output(
0,
"output",
"Output tensor with the same shape as input with type "
"specified by the 'to' argument",
"T2",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.TypeConstraint(
"T1",
{"tensor(float16)",
"tensor(float)",
"tensor(double)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(bool)",
"tensor(string)",
"tensor(bfloat16)"},
"Constrain input types. Casting from complex is not supported.")
.TypeConstraint(
"T2",
{"tensor(float16)",
"tensor(float)",
"tensor(double)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(bool)",
"tensor(string)",
"tensor(bfloat16)"},
"Constrain output types. Casting to complex is not supported.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
propagateElemTypeFromAttributeToOutput(ctx, "to", 0);
if (hasNInputShapes(ctx, 1)) {
propagateShapeFromInputToOutput(ctx, 0, 0);
}
})
.PartialDataPropagationFunction([](DataPropagationContext& ctx) {
PropagateShapeDataFromInputToOutput(ctx, 0);
}));
static const char* CastLike_ver15_doc = R"DOC(
The operator casts the elements of a given input tensor (the first input) to
the same data type as the elements of the second input tensor.
See documentation of the Cast operator for further details.
)DOC";
ONNX_OPERATOR_SET_SCHEMA(
CastLike,
15,
OpSchema()
.SetDoc(CastLike_ver15_doc)
.Input(
0,
"input",
"Input tensor to be cast.",
"T1",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.Input(
1,
"target_type",
"The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.",
"T2",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.Output(
0,
"output",
"Output tensor produced by casting the first input tensor to have the same type as the second input tensor.",
"T2",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.TypeConstraint(
"T1",
{ "tensor(float16)",
"tensor(float)",
"tensor(double)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(bool)",
"tensor(string)",
"tensor(bfloat16)" },
"Constrain input types. Casting from complex is not supported.")
.TypeConstraint(
"T2",
{ "tensor(float16)",
"tensor(float)",
"tensor(double)",
"tensor(int8)",
"tensor(int16)",
"tensor(int32)",
"tensor(int64)",
"tensor(uint8)",
"tensor(uint16)",
"tensor(uint32)",
"tensor(uint64)",
"tensor(bool)",
"tensor(string)",
"tensor(bfloat16)" },
"Constrain output types. Casting to complex is not supported.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 1, 0);
if (hasNInputShapes(ctx, 1)) {
propagateShapeFromInputToOutput(ctx, 0, 0);
}
})
.SetContextDependentFunctionBodyBuilder(
[](const FunctionBodyBuildContext& ctx,
const OpSchema& schema,
FunctionProto& functionProto) -> bool {
auto target_type = ctx.getInputType(1);
if ((target_type == nullptr) || (!target_type->has_tensor_type())) {
// we cannot create a correct function body without knowing the target element type
return false;
}
auto target_elt_type = target_type->tensor_type().elem_type();
std::vector<FunctionBodyHelper::NodeDef> body{
// nodes: {outputs, op, inputs, attributes}
{ {"output"}, "Cast", {"input"}, {MakeAttribute("to", (int64_t)(target_elt_type))} }
};
return FunctionBodyHelper::BuildFunctionProto(functionProto, schema, body, {});
}));
static const char* Reshape_ver14_doc = R"DOC(
Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
dimension will be set explicitly to zero (i.e. not taken from input tensor))DOC";
ONNX_OPERATOR_SET_SCHEMA(
Reshape,
14,
OpSchema()
.SetDoc(Reshape_ver14_doc)
.Attr(
"allowzero",
"(Optional) By default, when any value in the 'shape' input is equal to zero "
"the corresponding dimension value is copied from the input tensor dynamically. "
"allowzero=1 indicates that if any value in the 'shape' input is set to zero, "
"the zero value is honored, similar to NumPy.",
AttributeProto::INT,
static_cast<int64_t>(0))
.Input(0, "data", "An input tensor.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable)
.Input(
1,
"shape",
"Specified shape for output.",
"tensor(int64)",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.Output(0, "reshaped", "Reshaped data.", "T", OpSchema::Single, true, 1, OpSchema::Differentiable)
.TypeConstraint(
"T",
OpSchema::all_tensor_types_with_bfloat(),
"Constrain input and output types to all tensor types.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
// Type inference
propagateElemTypeFromInputToOutput(ctx, 0, 0);
// Shape Inference if 2nd input data (the target shape) is available
// or the target shape is generated via partial data propagation
const TensorProto* targetShapeInitializer = ctx.getInputData(1);
const auto* shapeInput = ctx.getSymbolicInput(1);
// The targetShapeProto represents the specified shape for output.
TensorShapeProto targetShapeProto;
if (targetShapeInitializer) {
auto targetShape = ParseData<int64_t>(targetShapeInitializer);
for (auto val : targetShape) {
targetShapeProto.add_dim()->set_dim_value(val);
}
} else if (shapeInput) {
targetShapeProto.CopyFrom(*shapeInput);
} else {
return;
}
int allowzero = static_cast<int>(getAttribute(ctx, "allowzero", 0));
// Iterate through targetShape, adding dimensions in the outputShape
// TensorProto. If the targetShape dimension is -1, we do not set the
// dimension value in this iteration, but we record the Dimension. If
// targetShape dimension is 0, we attempt to propagate the dimension
// value/param. If the value cannot be inferred, we set the flag in
// the unresolveZeros vector. If targetShape dimension is positive, we
// set the dimension value in the outputShape. We track the product of
// the dimensions we are setting outputShape in the outputProduct
// variable. The outputProduct will potentially be used for inferring
// a dimension marked -1.
auto* outputShape = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
TensorShapeProto::Dimension* negativeOneDim = nullptr;
const auto& dataInputTensorType = ctx.getInputType(0)->tensor_type();
std::vector<bool> unresolvedZeros(targetShapeProto.dim_size(), false);
int64_t outputProduct = 1;
bool outputProductValid = true;
for (int i = 0; i < static_cast<int>(targetShapeProto.dim_size()); ++i) {
// Add a new dimension to outputShape
auto* new_dim = outputShape->add_dim();
if (targetShapeProto.dim(i).has_dim_param()) {
// There is a tricky edge case here. It is possible that the value of
// symbolic dim can be -1 or 0 at runtime. In that case simply propgating this
// symbol can be erroneous. This should be a very rare scenario and in such a
// case an option is to turn off data propagation during shape inference.
new_dim->set_dim_param(targetShapeProto.dim(i).dim_param());
outputProductValid = false;
} else {
if (!targetShapeProto.dim(i).has_dim_value()) {
outputProductValid = false;
// treat this dim as unknown dim
continue;
}
const auto dim_value = targetShapeProto.dim(i).dim_value();
if (dim_value == -1) {
// Check if multiple -1's. If not, set negativeOneDim, marking
// this dimension to potentially be filled in later.
if (negativeOneDim) {
fail_shape_inference("Target shape may not have multiple -1 dimensions.");
}
negativeOneDim = new_dim;
} else if (dim_value == 0) {
// Check if data input has a shape and if the index i is within
// its bounds. If these conditions are satisfied, any dimension
// value/param should be propogated. If dimension value cannot be
// inferred, set the corresponding unresolvedZeros flag to true.
// If allowzero is set however, do not propagate values, since output
// dimension is explicitly zero.
if (allowzero == 0) {
unresolvedZeros[i] = true;
if (dataInputTensorType.has_shape()) {
if (i >= dataInputTensorType.shape().dim_size()) {
fail_shape_inference("Invalid position of 0.");
}
if (dataInputTensorType.shape().dim(i).has_dim_value()) {
const auto& input_dim_value = dataInputTensorType.shape().dim(i).dim_value();
new_dim->set_dim_value(input_dim_value);
outputProduct *= input_dim_value;
unresolvedZeros[i] = false;
} else if (dataInputTensorType.shape().dim(i).has_dim_param()) {
new_dim->set_dim_param(dataInputTensorType.shape().dim(i).dim_param());
}
}
} else {
new_dim->set_dim_value(dim_value);
outputProduct *= dim_value;
}
} else if (dim_value > 0) {
// Set the dimension value to dim_value
new_dim->set_dim_value(dim_value);
outputProduct *= dim_value;
} else {
// Check if value is less than -1; fail if so
fail_shape_inference("Invalid dimension value: ", dim_value);
}
}
}
// If negativeOneDim has been set, we attempt to infer its value. This
// can be done if all dimension values for the data input tensor shape
// are known other than the ones corresponding to unresolvedZeros
// flags.
if (negativeOneDim && outputProductValid) {
// First, attempt to compute product of data input shape dimensions
// that are not marked by unresolvedZeros. If not possible, set the
// inputProductValid flag to false.
if (!outputProduct) {
fail_shape_inference("Invalid Target shape product of 0. Product cannot be 0 in combination with -1");
}
int64_t inputProduct = 1;
bool inputProductValid = true;
if (!dataInputTensorType.has_shape()) {
inputProductValid = false;
} else {
for (int i = 0; i < dataInputTensorType.shape().dim_size(); ++i) {
if (dataInputTensorType.shape().dim(i).has_dim_value()) {
inputProduct *= dataInputTensorType.shape().dim(i).dim_value();
} else if (i >= static_cast<int>(unresolvedZeros.size()) || !unresolvedZeros[i]) {
inputProductValid = false;
break;
}
}
}
if (inputProductValid) {
if (inputProduct % outputProduct != 0) {
fail_shape_inference("Dimension could not be inferred: incompatible shapes");
}
negativeOneDim->set_dim_value(inputProduct / outputProduct);
}
}
}));
static const char* Shape_ver15_doc = R"DOC(
Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor.
Optional attributes start and end can be used to compute a slice of the input tensor's shape.
If start axis is omitted, the slice starts from axis 0.
The end axis, if specified, is exclusive (and the returned value will not include the size of that axis).
If the end axis is omitted, the axes upto the last one will be included.
Negative axes indicate counting back from the last axis.
Note that axes will be clipped to the range [0, r-1], where r is the
rank of the input tensor if they are out-of-range (after adding r in the case of
negative axis). Thus, specifying any end value > r is equivalent to specifying an end
value of r, and specifying any start value < -r is equivalent to specifying a start
value of 0.
For example:
Input tensor with shape: [2, 3, 4]
No attributes specified.
Output: [2, 3, 4]
Input tensor with shape: [2, 3, 4]
start: -1
Output: [4]
Input tensor with shape: [2, 3, 4]
end: -1
Output: [2, 3]
Input tensor with shape: [2, 3, 4]
start: 1
end: 2
Output: [3]
)DOC";
ONNX_OPERATOR_SET_SCHEMA(
Shape,
15,
OpSchema()
.SetDoc(Shape_ver15_doc)
.Input(0, "data", "An input tensor.", "T", OpSchema::Single, true, 1, OpSchema::NonDifferentiable)
.Output(0, "shape", "Shape of the input tensor", "T1", OpSchema::Single, true, 1, OpSchema::NonDifferentiable)
.Attr(
"start",
"(Optional) Starting axis for slicing the shape. Default value is 0."
"Negative value means counting dimensions from the back.",
AttributeProto::INT,
static_cast<int64_t>(0))
.Attr(
"end",
"(Optional) Ending axis for slicing the shape. "
"Negative value means counting dimensions from the back. "
"If omitted, sizes of all axes upto (including) the last one will be included.",
AttributeProto::INT,
OPTIONAL_VALUE)
.TypeConstraint("T", OpSchema::all_tensor_types_with_bfloat(), "Input tensor can be of arbitrary type.")
.TypeConstraint("T1", {"tensor(int64)"}, "Constrain output to int64 tensor.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(TensorProto::INT64);
auto* output_shape = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
auto* output_length = output_shape->add_dim();
if (!hasNInputShapes(ctx, 1)) {
return;
}
int64_t rank = static_cast<int64_t>(ctx.getInputType(0)->tensor_type().shape().dim_size());
int64_t start = getAttribute(ctx, "start", 0);
if (start < 0)
start += rank;
start = (start < 0) ? 0 : (start > rank) ? rank : start;
int64_t end = getAttribute(ctx, "end", rank);
if (end < 0)
end += rank;
end = (end < 0) ? 0 : (end > rank) ? rank : end;
output_length->set_dim_value((end - start) < 0 ? 0 : (end - start));
})
.PartialDataPropagationFunction([](DataPropagationContext& ctx) {
if (ctx.getInputType(0)->tensor_type().has_shape()) {
auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
int64_t rank = static_cast<int64_t>(input_shape.dim_size());
int64_t start = getAttribute(ctx, "start", 0);
if (start < 0)
start += rank;
start = (start < 0) ? 0 : (start > rank) ? rank : start;
int64_t end = getAttribute(ctx, "end", rank);
if (end < 0)
end += rank;
end = (end < 0) ? 0 : (end > rank) ? rank : end;
TensorShapeProto output_shape;
for (int64_t d = start; d < end; ++d) {
*output_shape.add_dim() = input_shape.dim(static_cast<int>(d));
}
ctx.addOutputData(0, std::move(output_shape));
}
}));
static const char* Size_ver13_doc = R"DOC(
Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.
)DOC";
ONNX_OPERATOR_SET_SCHEMA(
Size,
13,
OpSchema()
.SetDoc(Size_ver13_doc)
.Input(0,
"data",
"An input tensor.",
"T",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.Output(0,
"size",
"Total number of elements of the input tensor",
"T1",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.TypeConstraint(
"T",
OpSchema::all_tensor_types_with_bfloat(),
"Input tensor can be of arbitrary type.")
.TypeConstraint(
"T1",
{"tensor(int64)"},
"Constrain output to int64 tensor, which should be a scalar though.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
ctx.getOutputType(0)->mutable_tensor_type()->set_elem_type(
TensorProto::INT64);
ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
})
.PartialDataPropagationFunction([](DataPropagationContext& ctx) {
const auto input_data = ctx.getInputData(0);
if (input_data != nullptr) {
TensorShapeProto tsp;
tsp.mutable_dim()->Add()->set_dim_value(input_data->dim_size());
ctx.addOutputData(0, std::move(tsp));
}
}));
ONNX_OPERATOR_SET_SCHEMA(
Concat,
13,
OpSchema()
.Attr(
"axis",
"Which axis to concat on. A negative value means counting dimensions from the back. "
"Accepted range is [-r, r-1] where r = rank(inputs)..",
AttributeProto::INT)
.SetDoc(
"Concatenate a list of tensors into a single tensor. "
"All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.")
.Input(
0,
"inputs",
"List of tensors for concatenation",
"T",
OpSchema::Variadic,
true,
1,
OpSchema::Differentiable)
.Output(
0,
"concat_result",
"Concatenated tensor",
"T",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.TypeConstraint(
"T",
OpSchema::all_tensor_types_with_bfloat(),
"Constrain output types to any tensor type.")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
propagateElemTypeFromInputToOutput(ctx, 0, 0);
auto numInputs = ctx.getNumInputs();
if (numInputs < 1 ||
!hasNInputShapes(ctx, static_cast<int>(numInputs))) {
return;
}
auto rank = ctx.getInputType(0)->tensor_type().shape().dim_size();
auto axisAttr = ctx.getAttribute("axis");
if (!axisAttr) {
fail_shape_inference("Required attribute axis is missing");
}
int axis = static_cast<int>(axisAttr->i());
if (axis < -rank || axis >= rank) {
fail_shape_inference("axis must be in [-rank, rank-1].");
}
if (axis < 0) {
axis += rank;
}
if (numInputs == 1) {
propagateShapeFromInputToOutput(ctx, 0, 0);
return;
}
bool all_lengths_known = true;
int total_length = 0;
auto* output_shape =
ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape();
for (int64_t i = 0; i < rank; ++i) {
output_shape->add_dim();
}
for (size_t i = 0; i < numInputs; i++) {
const auto& shape = ctx.getInputType(i)->tensor_type().shape();
if (shape.dim_size() != rank) {
fail_shape_inference("All inputs to Concat must have same rank");
}
for (int j = 0; j < rank; j++) {
if (j == axis) {
if (shape.dim(j).has_dim_value()) {
total_length += static_cast<int>(shape.dim(j).dim_value());
} else {
all_lengths_known = false;
}
} else {
auto& output_dim = *output_shape->mutable_dim(j);
const auto& input_dim = shape.dim(j);
mergeInDimensionInfo(input_dim, output_dim, j);
}
}
}
if (all_lengths_known) {
output_shape->mutable_dim(axis)->set_dim_value(total_length);
}
})
.PartialDataPropagationFunction([](DataPropagationContext& ctx) {
if (!axisIsZero(ctx)) {
return;
}
TensorShapeProto tsp;
for (size_t i = 0; i < ctx.getNumInputs(); ++i) {
const auto input_data = ctx.getInputData(i);
if (input_data == nullptr) {
return;
}
for (int j = 0; j < input_data->dim_size(); ++j) {
appendDimToTensorShapeProto(tsp, input_data->dim(j));
}
}
if (tsp.dim_size() > 0) {
ctx.addOutputData(0, std::move(tsp));
}
}));
static const char* Split_ver13_doc =
R"DOC(Split a tensor into a list of tensors, along the specified
'axis'. Lengths of the parts can be specified using input 'split'.
Otherwise, the tensor is split to equal sized parts.
)DOC";
ONNX_OPERATOR_SET_SCHEMA(
Split,
13,
OpSchema()
.Input(
0,
"input",
"The tensor to split",
"T",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.Input(
1,
"split",
"Optional length of each output. Values should be >= 0."
"Sum of the values must be equal to the dim value at 'axis' specified.",
"tensor(int64)",
OpSchema::Optional,
true,
1,
OpSchema::NonDifferentiable)
.Output(
0,
"outputs",
"One or more outputs forming list of tensors after splitting",
"T",
OpSchema::Variadic,
true,
1,
OpSchema::Differentiable)
.TypeConstraint(
"T",
OpSchema::all_tensor_types_with_bfloat(),
"Constrain input and output types to all tensor types.")
.Attr(
"axis",
"Which axis to split on. "
"A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] "
"where r = rank(input).",
AttributeProto::INT,
static_cast<int64_t>(0))
.SetDoc(Split_ver13_doc)
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); ++i) {
propagateElemTypeFromInputToOutput(ctx, 0, i);
}
if (!hasNInputShapes(ctx, 1)) {
return;
}
const auto& shape = ctx.getInputType(0)->tensor_type().shape();
int rank = shape.dim_size();
int axis = static_cast<int>(getAttribute(ctx, "axis", 0));
if (axis < -rank || axis >= rank) {
fail_type_inference(
"Invalid value of attribute 'axis'. Rank=",
rank,
" Value=",
axis);
}
if (axis < 0) {
axis += rank;
}
const auto& split_dim = shape.dim(axis);
if (!split_dim.has_dim_value()) {
for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
*ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
shape;
ctx.getOutputType(i)
->mutable_tensor_type()
->mutable_shape()
->mutable_dim(axis)
->Clear();
}
return;
}
int split_dim_value = static_cast<int>(split_dim.dim_value());
std::vector<int64_t> split;
size_t num_inputs = ctx.getNumInputs();
if ((num_inputs == 2) && ctx.getInputType(1)) { //'split' is input
auto split_proto = ctx.getInputData(1);
if (split_proto == nullptr) {
// skip if split is not an initializer
return;
}
split = ParseData<int64_t>(split_proto);
if (split.size() != ctx.getNumOutputs()) {
fail_shape_inference(
"Mismatch between number of splits (",
split.size(),
") and outputs (",
ctx.getNumOutputs(),
")");
}
int64_t total_dim = 0;
for (int64_t d : split) {
total_dim += d;
}
if (total_dim != split_dim_value) {
fail_shape_inference(
"Mismatch between the sum of 'split' (",
total_dim,
") and the split dimension of the input (",
split_dim_value,
")");
}
} else { // no value available for 'split'
int num_outputs = static_cast<int>(ctx.getNumOutputs());
if (split_dim_value % num_outputs != 0) {
fail_shape_inference("The input is not evenly splittable");
}
int chunk_size = split_dim_value / num_outputs;
split.reserve(ctx.getNumOutputs());
for (int i = 0; i < static_cast<int>(ctx.getNumOutputs()); i++) {
split.push_back(chunk_size);
}
}
for (size_t i = 0; i < ctx.getNumOutputs(); i++) {
*ctx.getOutputType(i)->mutable_tensor_type()->mutable_shape() =
shape;
ctx.getOutputType(i)
->mutable_tensor_type()
->mutable_shape()
->mutable_dim(axis)
->set_dim_value(split[i]);
}
}));
static const char* Slice_ver13_doc = R"DOC(
Produces a slice of the input tensor along multiple axes. Similar to numpy:
https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
dimension and step for each axis in the list of axes, it uses this information to
slice the input `data` tensor. If a negative value is passed for any of the
start or end indices, it represents number of elements before the end of that
dimension. If the value passed to start or end is larger than the `n` (the
number of elements in this dimension), it represents `n`. For slicing to the
end of a dimension with unknown size, it is recommended to pass in `INT_MAX`
when sclicing forward and 'INT_MIN' when slicing backward.
If a negative value is passed for step, it represents slicing backward.
However step value cannot be 0.
If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
If `steps` are omitted, they are set to `[1, ..., 1]` of length `len(starts)`
Example 1:
data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
steps = [1, 2]
result = [
[5, 7],
]
Example 2:
data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
starts = [0, 1]
ends = [-1, 1000]
result = [
[2, 3, 4],
]
)DOC";
inline void processSliceInputs(const int64_t input_rank,
int64_t& start, int64_t& end, int64_t& step) {
auto clamp = [](int64_t val, int64_t min, int64_t max) -> int64_t {
return (val < min) ? min : (val > max) ? max : val;
};
// process step
if (step == 0) {
fail_shape_inference("'step' cannot be 0 for Slice");
}
// process start
if (start < 0)
start += input_rank;
if (step < 0)
start = clamp(start, 0, input_rank - 1);
else
start = clamp(start, 0, input_rank);
// process end
if (end < 0)
end += input_rank;
if (step < 0)
end = clamp(end, -1, input_rank);
else
end = clamp(end, 0, input_rank);
}
ONNX_OPERATOR_SET_SCHEMA(
Slice,
13,
OpSchema()
.SetDoc(Slice_ver13_doc)
.Input(
0,
"data",
"Tensor of data to extract slices from.",
"T",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.Input(
1,
"starts",
"1-D tensor of starting indices of corresponding axis in `axes`",
"Tind",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.Input(
2,
"ends",
"1-D tensor of ending indices (exclusive) of corresponding axis in `axes`",
"Tind",
OpSchema::Single,
true,
1,
OpSchema::NonDifferentiable)
.Input(
3,
"axes",
"1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions "
"from the back. Accepted range is [-r, r-1] where r = rank(data).",
"Tind",
OpSchema::Optional,
true,
1,
OpSchema::NonDifferentiable)
.Input(
4,
"steps",
"1-D tensor of slice step of corresponding axis in `axes`. "
"Negative value means slicing backward. 'steps' cannot be 0. "
"Defaults to 1.",
"Tind",
OpSchema::Optional,
true,
1,
OpSchema::NonDifferentiable)
.Output(
0,
"output",
"Sliced data tensor.",
"T",
OpSchema::Single,
true,
1,
OpSchema::Differentiable)
.TypeConstraint(
"T",
OpSchema::all_tensor_types_with_bfloat(),
"Constrain input and output types to all tensor types.")
.TypeConstraint(
"Tind",
{"tensor(int32)", "tensor(int64)"},
"Constrain indices to integer types")
.TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
size_t num_inputs = ctx.getNumInputs();
if (num_inputs != 3 && num_inputs != 4 && num_inputs != 5) {
fail_type_inference(
"Slice op must have either three, four or five inputs.");
}
propagateElemTypeFromInputToOutput(ctx, 0, 0);
if (!hasNInputShapes(ctx, 1)) {
return;
}
// Shape Inference if
// 1. 2nd and 3rd input data (starts, ends) are available.
// and 2. 4th and 5th optional input (axes, steps) are either not set,
// or set and is initializer.
const TensorProto* startsInitializer = ctx.getInputData(1);
const TensorProto* endsInitializer = ctx.getInputData(2);
const TensorProto* axesInitializer =
hasInputShape(ctx, 3) ? ctx.getInputData(3) : nullptr;
const TensorProto* stepsInitializer =
hasInputShape(ctx, 4) ? ctx.getInputData(4) : nullptr;
if (!startsInitializer || !endsInitializer ||
(hasInputShape(ctx, 3) && !ctx.getInputData(3)) ||
(hasInputShape(ctx, 4) && !ctx.getInputData(4))) {
return;
}
// don't know data_type- can't proceed
if (!startsInitializer->has_data_type())
return;
auto get_initializer_data =
[](const TensorProto* initializer) -> std::vector<int64_t> {
std::vector<int64_t> vec;
if (initializer->data_type() == TensorProto::INT64) {
const auto& data = ParseData<int64_t>(initializer);
vec.insert(vec.end(), data.begin(), data.end());
} else if (initializer->data_type() == TensorProto::INT32) {
const auto& data = ParseData<int32_t>(initializer);
vec.insert(vec.end(), data.begin(), data.end());
} else {
// unaccepted data type
fail_shape_inference(
"Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps");
}
return vec;
};
std::vector<int64_t> starts = get_initializer_data(startsInitializer);
std::vector<int64_t> ends = get_initializer_data(endsInitializer);
if (starts.size() != ends.size()) {
fail_shape_inference(
"Incorrect or missing input value for starts and ends");
}
const auto& input_shape = ctx.getInputType(0)->tensor_type().shape();
const auto input_rank = input_shape.dim_size();
std::vector<int64_t> axes(starts.size());
if (!axesInitializer) {
std::iota(axes.begin(), axes.end(), 0);
} else {
axes = get_initializer_data(axesInitializer);
if (axes.size() != starts.size()) {
fail_shape_inference("Input axes has incorrect length");
}
}
std::vector<int64_t> steps;
if (!stepsInitializer) {
steps = std::vector<int64_t>(starts.size(), 1);
} else {
steps = get_initializer_data(stepsInitializer);
if (steps.size() != axes.size()) {
fail_shape_inference("Input steps has incorrect length");
}
}
for (size_t i = 0; (int64_t)i < input_rank; ++i) {
// first update rank of output dim
auto* output_dim = ctx.getOutputType(0)
->mutable_tensor_type()
->mutable_shape()
->add_dim();
const auto& input_dim = input_shape.dim((int)i);
if (input_dim.has_dim_value()) {
output_dim->set_dim_value(input_dim.dim_value());
} else if (input_dim.has_dim_param()) {
output_dim->set_dim_param(input_dim.dim_param());
}
}
std::unordered_set<int64_t> unique_axes;
size_t axes_size = axes.size();
for (size_t axis_index = 0; axis_index < axes_size; ++axis_index) {
auto axis = axes[axis_index] < 0
? axes[axis_index] + static_cast<int64_t>(input_rank)
: axes[axis_index];