ONNX ArrayFeatureExtractor operation
"Select elements of the input tensor based on the indices passed.
"
" The indices are applied to the last axes of the tensor."
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Y |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Z |
memref of any type values or tensor of any type values |
mlonnx.Binarizer (MLONNXBinarizerOp)
ONNX Binarizer operation
"Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value."
Attributes:
| Attribute |
MLIR Type |
Description |
threshold |
FloatAttr |
32-bit float attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
mlonnx.CastMap (MLONNXCastMapOp)
ONNX CastMap operation
"Converts a map to a tensor.
The map key must be an int64 and the values will be ordered"
" in ascending order based on this key.
The operator supports dense packing or sparse packing."
" If using sparse packing, the key cannot exceed the max_map-1 value."
Attributes:
| Attribute |
MLIR Type |
Description |
cast_to |
StringAttr |
string attribute |
map_form |
StringAttr |
string attribute |
max_map |
IntegerAttr |
64-bit signless integer attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.CategoryMapper (MLONNXCategoryMapperOp)
ONNX CategoryMapper operation
"Converts strings to integers and vice versa.
"
" Two sequences of equal length are used to map between integers and strings,"
" with strings and integers at the same index detailing the mapping.
"
" Each operator converts either integers to strings or strings to integers, depending "
" on which default value attribute is provided. Only one default value attribute"
" should be defined.
"
" If the string default value is set, it will convert integers to strings."
" If the int default value is set, it will convert strings to integers."
Attributes:
| Attribute |
MLIR Type |
Description |
cats_int64s |
ArrayAttr |
64-bit integer array attribute |
cats_strings |
ArrayAttr |
string array attribute |
default_int64 |
IntegerAttr |
64-bit signless integer attribute |
default_string |
StringAttr |
string attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.DictVectorizer (MLONNXDictVectorizerOp)
ONNX DictVectorizer operation
"Uses an index mapping to convert a dictionary to an array.
"
" Given a dictionary, each key is looked up in the vocabulary attribute corresponding to"
" the key type. The index into the vocabulary array at which the key is found is then"
" used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
"
" The key type of the input map must correspond to the element type of the defined vocabulary attribute."
" Therefore, the output array will be equal in length to the index mapping vector parameter."
" All keys in the input dictionary must be present in the index mapping vector."
" For each item in the input dictionary, insert its value in the output array."
" Any keys not present in the input dictionary, will be zero in the output array.
"
" For example: if the string_vocabulary parameter is set to [\"a\", \"c\", \"b\", \"z\"],"
" then an input of {\"a\": 4, \"c\": 8} will produce an output of [4, 8, 0, 0]."
" "
Attributes:
| Attribute |
MLIR Type |
Description |
int64_vocabulary |
ArrayAttr |
64-bit integer array attribute |
string_vocabulary |
ArrayAttr |
string array attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.FeatureVectorizer (MLONNXFeatureVectorizerOp)
ONNX FeatureVectorizer operation
"Concatenates input tensors into one continuous output.
"
" All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C]."
" Inputs are copied to the output maintaining the order of the input arguments.
"
" All inputs must be integers or floats, while the output will be all floating point values."
Attributes:
| Attribute |
MLIR Type |
Description |
inputdimensions |
ArrayAttr |
64-bit integer array attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit signless integer or 64-bit signless integer or 32-bit float or 64-bit float values or memref of 32-bit signless integer or 64-bit signless integer or 32-bit float or 64-bit float values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.Imputer (MLONNXImputerOp)
ONNX Imputer operation
"Replaces inputs that equal one value with another, leaving all other elements alone.
"
" This operator is typically used to replace missing values in situations where they have a canonical"
" representation, such as -1, 0, NaN, or some extreme value.
"
" One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor"
" holds floats, integers if the input tensor holds integers. The imputed values must all fit within the"
" width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,"
" which one depends on whether floats or integers are being processed.
"
" The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature."
Attributes:
| Attribute |
MLIR Type |
Description |
imputed_value_floats |
ArrayAttr |
32-bit float array attribute |
imputed_value_int64s |
ArrayAttr |
64-bit integer array attribute |
replaced_value_float |
FloatAttr |
32-bit float attribute |
replaced_value_int64 |
IntegerAttr |
64-bit signless integer attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
mlonnx.LabelEncoder (MLONNXLabelEncoderOp)
ONNX LabelEncoder operation
"Maps each element in the input tensor to another value.
"
" The mapping is determined by the two parallel attributes, 'keys_' and"
" 'values_' attribute. The i-th value in the specified 'keys_' attribute"
" would be mapped to the i-th value in the specified 'values_' attribute. It"
" implies that input's element type and the element type of the specified"
" 'keys_' should be identical while the output type is identical to the"
" specified 'values_' attribute. If an input element can not be found in the"
" specified 'keys_' attribute, the 'default_' that matches the specified"
" 'values_' attribute may be used as its output value.
"
" Let's consider an example which maps a string tensor to an integer tensor."
" Assume and 'keys_strings' is ["Amy", "Sally"], 'values_int64s' is [5, 6],"
" and 'default_int64' is '-1'. The input ["Dori", "Amy", "Amy", "Sally","
" "Sally"] would be mapped to [-1, 5, 5, 6, 6].
"
" Since this operator is an one-to-one mapping, its input and output shapes"
" are the same. Notice that only one of 'keys_'/'values_' can be set.
"
" For key look-up, bit-wise comparison is used so even a float NaN can be"
" mapped to a value in 'values_' attribute.
"
Attributes:
| Attribute |
MLIR Type |
Description |
default_float |
FloatAttr |
32-bit float attribute |
default_int64 |
IntegerAttr |
64-bit signless integer attribute |
default_string |
StringAttr |
string attribute |
keys_floats |
ArrayAttr |
32-bit float array attribute |
keys_int64s |
ArrayAttr |
64-bit integer array attribute |
keys_strings |
ArrayAttr |
string array attribute |
values_floats |
ArrayAttr |
32-bit float array attribute |
values_int64s |
ArrayAttr |
64-bit integer array attribute |
values_strings |
ArrayAttr |
string array attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.LinearClassifier (MLONNXLinearClassifierOp)
ONNX LinearClassifier operation
"Linear classifier"
Attributes:
| Attribute |
MLIR Type |
Description |
classlabels_ints |
ArrayAttr |
64-bit integer array attribute |
classlabels_strings |
ArrayAttr |
string array attribute |
coefficients |
ArrayAttr |
32-bit float array attribute |
intercepts |
ArrayAttr |
32-bit float array attribute |
multi_class |
IntegerAttr |
64-bit signless integer attribute |
post_transform |
StringAttr |
string attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
Z |
memref of any type values or tensor of any type values |
mlonnx.LinearRegressor (MLONNXLinearRegressorOp)
ONNX LinearRegressor operation
"Generalized linear regression evaluation.
"
" If targets is set to 1 (default) then univariate regression is performed.
"
" If targets is set to M then M sets of coefficients must be passed in as a sequence"
" and M results will be output for each input n in N.
"
" The coefficients array is of length n, and the coefficients for each target are contiguous."
" Intercepts are optional but if provided must match the number of targets."
Attributes:
| Attribute |
MLIR Type |
Description |
coefficients |
ArrayAttr |
32-bit float array attribute |
intercepts |
ArrayAttr |
32-bit float array attribute |
post_transform |
StringAttr |
string attribute |
targets |
IntegerAttr |
64-bit signless integer attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.Normalizer (MLONNXNormalizerOp)
ONNX Normalizer operation
"Normalize the input. There are three normalization modes, which have the corresponding formulas,"
" defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':
"
"
"
" Max: Y = X / max(X)
"
" L1: Y = X / sum(X)
"
" L2: Y = sqrt(X^2 / sum(X^2)}
"
" In all modes, if the divisor is zero, Y == X."
"
"
" For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row"
" of the batch is normalized independently."
Attributes:
| Attribute |
MLIR Type |
Description |
norm |
StringAttr |
string attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.OneHotEncoder (MLONNXOneHotEncoderOp)
ONNX OneHotEncoder operation
"Replace each input element with an array of ones and zeros, where a single"
" one is placed at the index of the category that was passed in. The total category count "
" will determine the size of the extra dimension of the output array Y.
"
" For example, if we pass a tensor with a single value of 4, and a category count of 8, "
" the output will be a tensor with [0,0,0,0,1,0,0,0].
"
" This operator assumes every input feature is from the same set of categories.
"
" If the input is a tensor of float, int32, or double, the data will be cast"
" to integers and the cats_int64s category list will be used for the lookups."
Attributes:
| Attribute |
MLIR Type |
Description |
cats_int64s |
ArrayAttr |
64-bit integer array attribute |
cats_strings |
ArrayAttr |
string array attribute |
zeros |
IntegerAttr |
64-bit signless integer attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.SVMClassifier (MLONNXSVMClassifierOp)
ONNX SVMClassifier operation
"Support Vector Machine classifier"
Attributes:
| Attribute |
MLIR Type |
Description |
classlabels_ints |
ArrayAttr |
64-bit integer array attribute |
classlabels_strings |
ArrayAttr |
string array attribute |
coefficients |
ArrayAttr |
32-bit float array attribute |
kernel_params |
ArrayAttr |
32-bit float array attribute |
kernel_type |
StringAttr |
string attribute |
post_transform |
StringAttr |
string attribute |
prob_a |
ArrayAttr |
32-bit float array attribute |
prob_b |
ArrayAttr |
32-bit float array attribute |
rho |
ArrayAttr |
32-bit float array attribute |
support_vectors |
ArrayAttr |
32-bit float array attribute |
vectors_per_class |
ArrayAttr |
64-bit integer array attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
Z |
memref of any type values or tensor of any type values |
mlonnx.SVMRegressor (MLONNXSVMRegressorOp)
ONNX SVMRegressor operation
"Support Vector Machine regression prediction and one-class SVM anomaly detection."
Attributes:
| Attribute |
MLIR Type |
Description |
coefficients |
ArrayAttr |
32-bit float array attribute |
kernel_params |
ArrayAttr |
32-bit float array attribute |
kernel_type |
StringAttr |
string attribute |
n_supports |
IntegerAttr |
64-bit signless integer attribute |
one_class |
IntegerAttr |
64-bit signless integer attribute |
post_transform |
StringAttr |
string attribute |
rho |
ArrayAttr |
32-bit float array attribute |
support_vectors |
ArrayAttr |
32-bit float array attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.Scaler (MLONNXScalerOp)
ONNX Scaler operation
"Rescale input data, for example to standardize features by removing the mean and scaling to unit variance."
Attributes:
| Attribute |
MLIR Type |
Description |
offset |
ArrayAttr |
32-bit float array attribute |
scale |
ArrayAttr |
32-bit float array attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.TreeEnsembleClassifier (MLONNXTreeEnsembleClassifierOp)
ONNX TreeEnsembleClassifier operation
"Tree Ensemble classifier. Returns the top class for each of N inputs.
"
" The attributes named 'nodes_X' form a sequence of tuples, associated by "
" index into the sequences, which must all be of equal length. These tuples"
" define the nodes.
"
" Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves."
" A leaf may have multiple votes, where each vote is weighted by"
" the associated class_weights index.
"
" One and only one of classlabels_strings or classlabels_int64s"
" will be defined. The class_ids are indices into this list."
Attributes:
| Attribute |
MLIR Type |
Description |
base_values |
ArrayAttr |
32-bit float array attribute |
class_ids |
ArrayAttr |
64-bit integer array attribute |
class_nodeids |
ArrayAttr |
64-bit integer array attribute |
class_treeids |
ArrayAttr |
64-bit integer array attribute |
class_weights |
ArrayAttr |
32-bit float array attribute |
classlabels_int64s |
ArrayAttr |
64-bit integer array attribute |
classlabels_strings |
ArrayAttr |
string array attribute |
nodes_falsenodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_featureids |
ArrayAttr |
64-bit integer array attribute |
nodes_hitrates |
ArrayAttr |
32-bit float array attribute |
nodes_missing_value_tracks_true |
ArrayAttr |
64-bit integer array attribute |
nodes_modes |
ArrayAttr |
string array attribute |
nodes_nodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_treeids |
ArrayAttr |
64-bit integer array attribute |
nodes_truenodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_values |
ArrayAttr |
32-bit float array attribute |
post_transform |
StringAttr |
string attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
Z |
memref of any type values or tensor of any type values |
mlonnx.TreeEnsembleRegressor (MLONNXTreeEnsembleRegressorOp)
ONNX TreeEnsembleRegressor operation
"Tree Ensemble regressor. Returns the regressed values for each input in N.
"
" All args with nodes_ are fields of a tuple of tree nodes, and"
" it is assumed they are the same length, and an index i will decode the"
" tuple across these inputs. Each node id can appear only once"
" for each tree id.
"
" All fields prefixed with target_ are tuples of votes at the leaves.
"
" A leaf may have multiple votes, where each vote is weighted by"
" the associated target_weights index.
"
" All trees must have their node ids start at 0 and increment by 1.
"
" Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF"
Attributes:
| Attribute |
MLIR Type |
Description |
aggregate_function |
StringAttr |
string attribute |
base_values |
ArrayAttr |
32-bit float array attribute |
n_targets |
IntegerAttr |
64-bit signless integer attribute |
nodes_falsenodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_featureids |
ArrayAttr |
64-bit integer array attribute |
nodes_hitrates |
ArrayAttr |
32-bit float array attribute |
nodes_missing_value_tracks_true |
ArrayAttr |
64-bit integer array attribute |
nodes_modes |
ArrayAttr |
string array attribute |
nodes_nodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_treeids |
ArrayAttr |
64-bit integer array attribute |
nodes_truenodeids |
ArrayAttr |
64-bit integer array attribute |
nodes_values |
ArrayAttr |
32-bit float array attribute |
post_transform |
StringAttr |
string attribute |
target_ids |
ArrayAttr |
64-bit integer array attribute |
target_nodeids |
ArrayAttr |
64-bit integer array attribute |
target_treeids |
ArrayAttr |
64-bit integer array attribute |
target_weights |
ArrayAttr |
32-bit float array attribute |
Operands:
| Operand |
Description |
X |
tensor of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values or memref of 32-bit float or 64-bit float or 64-bit signless integer or 32-bit signless integer values |
Results:
| Result |
Description |
Y |
memref of any type values or tensor of any type values |
mlonnx.ZipMap (MLONNXZipMapOp)
ONNX ZipMap operation
"Creates a map from the input and the attributes.
"
" The values are provided by the input tensor, while the keys are specified by the attributes."
" Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
"
" The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
"
Attributes:
| Attribute |
MLIR Type |
Description |
classlabels_int64s |
ArrayAttr |
64-bit integer array attribute |
classlabels_strings |
ArrayAttr |
string array attribute |
Operands:
| Operand |
Description |
X |
memref of any type values or tensor of any type values |
Results:
| Result |
Description |
Z |
memref of any type values or tensor of any type values |