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