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import onnx ml operations as a separate dialect (#97) * generate MLONNX dialect * Add MLONNX to Builder * add MLONNX dialect operation document * fix the format issues Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>
2020-05-13 06:43:44 +08:00
<!-- Autogenerated by mlir-tblgen; don't manually edit -->
### `mlonnx.ArrayFeatureExtractor` (MLONNXArrayFeatureExtractorOp)
ONNX ArrayFeatureExtractor operation
"Select elements of the input tensor based on the indices passed.<br>"
" 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` | 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.CastMap` (MLONNXCastMapOp)
ONNX CastMap operation
"Converts a map to a tensor.<br>The map key must be an int64 and the values will be ordered"
" in ascending order based on this key.<br>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.<br>"
" Two sequences of equal length are used to map between integers and strings,"
" with strings and integers at the same index detailing the mapping.<br>"
" 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.<br>"
" 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.<br>"
" 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'.<br>"
" 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.<br>"
" 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.<br>"
" 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.<br>"
" 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` | 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.Imputer` (MLONNXImputerOp)
ONNX Imputer operation
"Replaces inputs that equal one value with another, leaving all other elements alone.<br>"
" 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.<br>"
" 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.<br>"
" 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."
#### 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` | 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.LabelEncoder` (MLONNXLabelEncoderOp)
ONNX LabelEncoder operation
"Maps each element in the input tensor to another value.<br>"
" 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.<br>"
" 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].<br>"
" 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.<br>"
" For key look-up, bit-wise comparison is used so even a float NaN can be"
" mapped to a value in 'values_*' attribute.<br>"
#### 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` | 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
`Z` | memref of any type values or tensor of any type values
### `mlonnx.LinearRegressor` (MLONNXLinearRegressorOp)
ONNX LinearRegressor operation
"Generalized linear regression evaluation.<br>"
" If targets is set to 1 (default) then univariate regression is performed.<br>"
" 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.<br>"
" 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` | 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.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':<br>"
"<br>"
" Max: Y = X / max(X)<br>"
" L1: Y = X / sum(X)<br>"
" L2: Y = sqrt(X^2 / sum(X^2)}<br>"
" In all modes, if the divisor is zero, Y == X."
"<br>"
" 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` | 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.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.<br>"
" 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]``.<br>"
" This operator assumes every input feature is from the same set of categories.<br>"
" 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` | 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
`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` | 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.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` | 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.TreeEnsembleClassifier` (MLONNXTreeEnsembleClassifierOp)
ONNX TreeEnsembleClassifier operation
"Tree Ensemble classifier. Returns the top class for each of N inputs.<br>"
" 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.<br>"
" 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.<br>"
" 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` | 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
`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.<br>"
" 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.<br>"
" All fields prefixed with target_ are tuples of votes at the leaves.<br>"
" A leaf may have multiple votes, where each vote is weighted by"
" the associated target_weights index.<br>"
" All trees must have their node ids start at 0 and increment by 1.<br>"
" 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` | 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.ZipMap` (MLONNXZipMapOp)
ONNX ZipMap operation
"Creates a map from the input and the attributes.<br>"
" 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).<br>"
" 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>"
#### 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