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>
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chentong319
GitHub
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@ -31,6 +31,15 @@ add_subdirectory(third_party/pybind11)
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add_subdirectory(third_party/variant)
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set(CMAKE_CXX_STANDARD 14)
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if ($ENV{EXCLUDE_ONNX_ML})
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set(INCLUDE_ONNX_ML FALSE)
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else()
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set(INCLUDE_ONNX_ML TRUE)
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endif()
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message(STATUS "INCLUDE_ONNX_ML Dialect " ${INCLUDE_ONNX_ML})
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add_subdirectory(utils)
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add_subdirectory(src)
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add_subdirectory(docs)
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@ -0,0 +1,597 @@
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<!-- 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` | 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.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` | 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.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` | 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.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` | 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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`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` | 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.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` | 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.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` | 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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`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` | 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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| :----: | ----------- |
|
||||
`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
|
||||
|
||||
|
|
@ -8,6 +8,10 @@ target_include_directories(OMBuilder PRIVATE ${ONNX_MLIR_SRC_ROOT})
|
|||
target_include_directories(OMBuilder PRIVATE ${CMAKE_BINARY_DIR})
|
||||
target_include_directories(OMBuilder PRIVATE ${ONNX_MLIR_BIN_ROOT})
|
||||
|
||||
if (INCLUDE_ONNX_ML)
|
||||
add_definitions(-DINCLUDE_ONNX__ML=1)
|
||||
endif()
|
||||
|
||||
# This will cause onnx to be built. More importantly, some variable definitions
|
||||
# when building onnx such as -DONNX_ML=1 -DONNX_NAMESPACE=onnx will be carried over
|
||||
# when compiling FrontendDialectHelper.cpp, etc.
|
||||
|
|
@ -24,3 +28,7 @@ target_include_directories(OMBuilder
|
|||
# will NOT be carried over when compiling FrontendDialectHelper.cpp, etc. so
|
||||
# the compilation will fail.
|
||||
add_dependencies(OMBuilder OMONNXOps)
|
||||
|
||||
if (INCLUDE_ONNX_ML)
|
||||
add_dependencies(OMBuilder OMMLONNXOps)
|
||||
endif()
|
||||
|
|
|
|||
|
|
@ -32,6 +32,10 @@
|
|||
#include "llvm/Support/raw_ostream.h"
|
||||
|
||||
#include "src/Dialect/ONNX/ONNXOps.hpp"
|
||||
#if INCLUDE_ONNX_ML == 1
|
||||
#include "src/Dialect/MLONNX/MLONNXOps.hpp"
|
||||
#endif
|
||||
|
||||
#include "onnx/onnx_pb.h"
|
||||
|
||||
namespace onnx_mlir {
|
||||
|
|
|
|||
|
|
@ -369,6 +369,10 @@ private:
|
|||
// one known reeason is the optional input
|
||||
|
||||
#include "src/Builder/OpBuildTable.inc"
|
||||
#if INCLUDE_ONNX_ML == 1
|
||||
#include "src/Builder/MLOpBuildTable.inc"
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
/*!
|
||||
|
|
|
|||
|
|
@ -0,0 +1,42 @@
|
|||
//********************************************************
|
||||
// Do not modify this file directly.
|
||||
// This file is automatically generated via script.
|
||||
// Details can be found in docs/readonnxdefs.md .
|
||||
//********************************************************
|
||||
|
||||
if (opName == "ArrayFeatureExtractor")
|
||||
return buildOperation<mlir::MLONNXArrayFeatureExtractorOp>(node, /* expected_num_operands = */ 2, /* expected_num_results = */ 1);
|
||||
if (opName == "Binarizer")
|
||||
return buildOperation<mlir::MLONNXBinarizerOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "CastMap")
|
||||
return buildOperation<mlir::MLONNXCastMapOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "CategoryMapper")
|
||||
return buildOperation<mlir::MLONNXCategoryMapperOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "DictVectorizer")
|
||||
return buildOperation<mlir::MLONNXDictVectorizerOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "FeatureVectorizer")
|
||||
return buildOperation<mlir::MLONNXFeatureVectorizerOp>(node, /* expected_num_operands = */ -1, /* expected_num_results = */ 1);
|
||||
if (opName == "Imputer")
|
||||
return buildOperation<mlir::MLONNXImputerOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "LabelEncoder")
|
||||
return buildOperation<mlir::MLONNXLabelEncoderOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "LinearClassifier")
|
||||
return buildOperation<mlir::MLONNXLinearClassifierOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
|
||||
if (opName == "LinearRegressor")
|
||||
return buildOperation<mlir::MLONNXLinearRegressorOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "Normalizer")
|
||||
return buildOperation<mlir::MLONNXNormalizerOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "OneHotEncoder")
|
||||
return buildOperation<mlir::MLONNXOneHotEncoderOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "SVMClassifier")
|
||||
return buildOperation<mlir::MLONNXSVMClassifierOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
|
||||
if (opName == "SVMRegressor")
|
||||
return buildOperation<mlir::MLONNXSVMRegressorOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "Scaler")
|
||||
return buildOperation<mlir::MLONNXScalerOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "TreeEnsembleClassifier")
|
||||
return buildOperation<mlir::MLONNXTreeEnsembleClassifierOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
|
||||
if (opName == "TreeEnsembleRegressor")
|
||||
return buildOperation<mlir::MLONNXTreeEnsembleRegressorOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
if (opName == "ZipMap")
|
||||
return buildOperation<mlir::MLONNXZipMapOp>(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
|
||||
|
|
@ -43,6 +43,11 @@ target_link_libraries(onnx-mlir
|
|||
${MLIRLibs}
|
||||
${CMAKE_DL_LIBS})
|
||||
|
||||
if (INCLUDE_ONNX_ML)
|
||||
target_link_libraries(onnx-mlir OMMLONNXOps)
|
||||
endif()
|
||||
|
||||
|
||||
target_include_directories(onnx-mlir PRIVATE ${ONNX_MLIR_SRC_ROOT})
|
||||
target_include_directories(onnx-mlir PRIVATE ${CMAKE_BINARY_DIR})
|
||||
target_include_directories(onnx-mlir PRIVATE ${ONNX_MLIR_BIN_ROOT})
|
||||
|
|
|
|||
|
|
@ -1,2 +1,5 @@
|
|||
add_subdirectory(Krnl)
|
||||
add_subdirectory(ONNX)
|
||||
if (INCLUDE_ONNX_ML)
|
||||
add_subdirectory(MLONNX)
|
||||
endif()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,22 @@
|
|||
set(LLVM_TARGET_DEFINITIONS MLONNXOps.td)
|
||||
onnx_mlir_tablegen(MLONNXOps.hpp.inc -gen-op-decls "-I${ONNX_MLIR_SRC_ROOT}/compiler/pass")
|
||||
onnx_mlir_tablegen(MLONNXOps.cpp.inc -gen-op-defs "-I${ONNX_MLIR_SRC_ROOT}/compiler/pass")
|
||||
|
||||
set(GEN_DOC_FILE ${CMAKE_BINARY_DIR}/docs/Dialects/mlonnx.md)
|
||||
add_public_tablegen_target(OMMLONNXOpsIncGen)
|
||||
|
||||
add_library(OMMLONNXOps
|
||||
MLONNXOps.cpp
|
||||
MLONNXOps.hpp)
|
||||
target_include_directories(OMMLONNXOps
|
||||
PRIVATE
|
||||
${ONNX_MLIR_SRC_ROOT}
|
||||
${ONNX_MLIR_BIN_ROOT}
|
||||
${ONNX_MLIR_SRC_ROOT})
|
||||
add_dependencies(OMMLONNXOps OMMLONNXOpsIncGen)
|
||||
# Linking dependencies:
|
||||
add_dependencies(OMMLONNXOps
|
||||
OMPromotableConstOperandsOpInterface
|
||||
OMShapeInferenceOpInterface)
|
||||
|
||||
add_onnx_mlir_dialect_doc(mlonnx MLONNXOps.td)
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
//===------------------ MLONNXOps.cpp - ONNX ML Operations ----------------===//
|
||||
//
|
||||
// Copyright 2019-2020 The IBM Research Authors.
|
||||
//
|
||||
// =============================================================================
|
||||
//
|
||||
// This file provides definition of ONNX ML dialect operations.
|
||||
//
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#include "mlir/Dialect/Traits.h"
|
||||
#include "mlir/IR/Block.h"
|
||||
#include "mlir/IR/Builders.h"
|
||||
#include "mlir/IR/Function.h"
|
||||
#include "mlir/IR/IntegerSet.h"
|
||||
#include "mlir/IR/Matchers.h"
|
||||
#include "mlir/IR/Module.h"
|
||||
#include "mlir/IR/OpImplementation.h"
|
||||
#include "mlir/IR/PatternMatch.h"
|
||||
#include "llvm/ADT/SetVector.h"
|
||||
#include "llvm/ADT/SmallBitVector.h"
|
||||
|
||||
#include "MLONNXOps.hpp"
|
||||
|
||||
using namespace mlir;
|
||||
using namespace mlir::OpTrait::util;
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// MLONNXOpsDialect
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
/// Dialect creation, the instance will be owned by the context. This is the
|
||||
/// point of registration of custom types and operations for the dialect.
|
||||
MLONNXOpsDialect::MLONNXOpsDialect(mlir::MLIRContext *ctx)
|
||||
: mlir::Dialect(getDialectNamespace(), ctx) {
|
||||
addOperations<
|
||||
#define GET_OP_LIST
|
||||
#include "src/Dialect/MLONNX/MLONNXOps.cpp.inc"
|
||||
>();
|
||||
}
|
||||
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// TableGen'd op method definitions
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#define GET_OP_CLASSES
|
||||
#include "src/Dialect/MLONNX/MLONNXOps.cpp.inc"
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
//===----------------- MLONNXOps.hpp - ONNX ML Operations ----_------------===//
|
||||
//
|
||||
// Copyright 2019 The IBM Research Authors.
|
||||
//
|
||||
// =============================================================================
|
||||
//
|
||||
// This file defines ONNX ML operations in the MLIR operation set.
|
||||
//
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <map>
|
||||
#include <string>
|
||||
|
||||
#include "mlir/Dialect/StandardOps/IR/Ops.h"
|
||||
#include "mlir/IR/Builders.h"
|
||||
#include "mlir/IR/Dialect.h"
|
||||
#include "mlir/IR/OpDefinition.h"
|
||||
#include "mlir/IR/StandardTypes.h"
|
||||
|
||||
#include "src/Interface/ShapeInferenceInterface.hpp"
|
||||
#include "src/Interface/PromotableConstOperandsOpInterface.hpp"
|
||||
|
||||
namespace mlir {
|
||||
|
||||
class MLONNXOpsDialect : public Dialect {
|
||||
public:
|
||||
MLONNXOpsDialect(MLIRContext* context);
|
||||
|
||||
/// Provide a utility accessor to the dialect namespace. This is used by
|
||||
/// several utilities for casting between dialects.
|
||||
static StringRef getDialectNamespace() { return "onnx"; }
|
||||
};
|
||||
|
||||
/// Include the auto-generated header file containing the declarations of the
|
||||
/// ONNX operations.
|
||||
#define GET_OP_CLASSES
|
||||
#include "src/Dialect/MLONNX/MLONNXOps.hpp.inc"
|
||||
|
||||
} // end namespace mlir
|
||||
|
||||
namespace onnx_mlir {}
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
//===-- MLONNXOps.td -- ONNX ML Dialect Operation Definitions -*- tablegen -==//
|
||||
//
|
||||
// Copyright 2019-2020 The IBM Research Authors
|
||||
//
|
||||
// =============================================================================
|
||||
//
|
||||
// Defines ONNX ML Dialect operations.
|
||||
//
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
#ifdef MLONNX_OPS
|
||||
#else
|
||||
#define MLONNX_OPS
|
||||
|
||||
#ifdef OP_BASE
|
||||
#else
|
||||
include "mlir/IR/OpBase.td"
|
||||
#endif // OP_BASE
|
||||
|
||||
#ifdef SHAPE_INFERENCE_INTERFACE
|
||||
#else
|
||||
include "src/Interface/ShapeInferenceInterface.td"
|
||||
#endif // SHAPE_INFERENCE_INTERFACE
|
||||
|
||||
#ifdef PROMOTABLE_CONST_OPERANDS_OP_INTERFACE
|
||||
#else
|
||||
include "src/Interface/PromotableConstOperandsOpInterface.td"
|
||||
#endif // PROMOTABLE_CONST_OPERANDS_OP_INTERFACE
|
||||
|
||||
def MLONNX_Dialect : Dialect {
|
||||
let name = "mlonnx";
|
||||
let cppNamespace = "";
|
||||
}
|
||||
|
||||
// Base class for ONNX dialect operations. This operation inherits from the base
|
||||
// `Op` class in OpBase.td, and provides:
|
||||
// * The parent dialect of the operation.
|
||||
// * The mnemonic for the operation, or the name without the dialect prefix.
|
||||
// * A list of traits for the operation.
|
||||
class MLONNX_Op<string mnemonic, list<OpTrait> traits = []> :
|
||||
Op<MLONNX_Dialect, mnemonic, traits>;
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// MLONNX Operations
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
//the tablegen code onnxop.in is generated with gen_doc.py
|
||||
//clone and install onnx
|
||||
// git clone --recursive https://github.com/onnx/onnx.git
|
||||
// set up env for anaconda3 and for ONNX MLIR (BOOSTROOT, cmake, gcc ...)
|
||||
// cd onnx
|
||||
//install onnx
|
||||
// CC=gcc CXX=g++ pip install -e .
|
||||
//run the script
|
||||
// python onnx/defs/gen_doc.py
|
||||
//result is in docs/onnx_ops.td.inc
|
||||
//current limitations:
|
||||
// 1. Attributes are not processed
|
||||
// 2. output type inference not implemented except Add
|
||||
// 3. Type Attribute: 'optional' and 'Variadic hetergeneous' are ignored
|
||||
// 4. type of string, complex64 and complex128 for input/output are ignored
|
||||
// 5. unsigned int are treated as signed one
|
||||
|
||||
include "mlir/Interfaces/SideEffects.td"
|
||||
include "src/Dialect/MLONNX/MLONNXOps.td.inc"
|
||||
|
||||
#endif // MLONNX_OPS
|
||||
|
|
@ -0,0 +1,373 @@
|
|||
//********************************************************
|
||||
// Do not modify this file directly.
|
||||
// This file is automatically generated via script.
|
||||
// Details can be found in docs/readonnxdefs.md .
|
||||
//********************************************************
|
||||
|
||||
def MLONNXArrayFeatureExtractorOp:MLONNX_Op<"ArrayFeatureExtractor",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX ArrayFeatureExtractor operation";
|
||||
let description = [{
|
||||
"Select elements of the input tensor based on the indices passed.<br>"
|
||||
" The indices are applied to the last axes of the tensor."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Z);
|
||||
}
|
||||
|
||||
def MLONNXBinarizerOp:MLONNX_Op<"Binarizer",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX Binarizer operation";
|
||||
let description = [{
|
||||
"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."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<F32Attr, "0.0">:$threshold);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXCastMapOp:MLONNX_Op<"CastMap",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX CastMap operation";
|
||||
let description = [{
|
||||
"Converts a map to a tensor.<br>The map key must be an int64 and the values will be ordered"
|
||||
" in ascending order based on this key.<br>The operator supports dense packing or sparse packing."
|
||||
" If using sparse packing, the key cannot exceed the max_map-1 value."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<StrAttr, "TO_FLOAT">:$cast_to,
|
||||
DefaultValuedAttr<StrAttr, "DENSE">:$map_form,
|
||||
DefaultValuedAttr<I64Attr, "1">:$max_map);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXCategoryMapperOp:MLONNX_Op<"CategoryMapper",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX CategoryMapper operation";
|
||||
let description = [{
|
||||
"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."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$cats_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$cats_strings,
|
||||
DefaultValuedAttr<I64Attr, "-1">:$default_int64,
|
||||
DefaultValuedAttr<StrAttr, "_Unused">:$default_string);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXDictVectorizerOp:MLONNX_Op<"DictVectorizer",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX DictVectorizer operation";
|
||||
let description = [{
|
||||
"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]``."
|
||||
" "
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$int64_vocabulary,
|
||||
OptionalAttr<StrArrayAttr>:$string_vocabulary);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXFeatureVectorizerOp:MLONNX_Op<"FeatureVectorizer",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX FeatureVectorizer operation";
|
||||
let description = [{
|
||||
"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."
|
||||
}];
|
||||
let arguments = (ins Variadic<AnyTypeOf<[AnyMemRef, AnyTensor]>>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$inputdimensions);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXImputerOp:MLONNX_Op<"Imputer",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX Imputer operation";
|
||||
let description = [{
|
||||
"Replaces inputs that equal one value with another, leaving all other elements alone.<br>"
|
||||
" This operator is typically used to replace missing values in situations where they have a canonical"
|
||||
" representation, such as -1, 0, NaN, or some extreme value.<br>"
|
||||
" One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor"
|
||||
" holds floats, integers if the input tensor holds integers. The imputed values must all fit within the"
|
||||
" width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,"
|
||||
" which one depends on whether floats or integers are being processed.<br>"
|
||||
" The imputed_value attribute length can be 1 element, or it can have one element per input feature.<br>In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<F32ArrayAttr>:$imputed_value_floats,
|
||||
OptionalAttr<I64ArrayAttr>:$imputed_value_int64s,
|
||||
DefaultValuedAttr<F32Attr, "0.0">:$replaced_value_float,
|
||||
DefaultValuedAttr<I64Attr, "0">:$replaced_value_int64);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXLabelEncoderOp:MLONNX_Op<"LabelEncoder",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX LabelEncoder operation";
|
||||
let description = [{
|
||||
"Maps each element in the input tensor to another value.<br>"
|
||||
" The mapping is determined by the two parallel attributes, 'keys_*' and"
|
||||
" 'values_*' attribute. The i-th value in the specified 'keys_*' attribute"
|
||||
" would be mapped to the i-th value in the specified 'values_*' attribute. It"
|
||||
" implies that input's element type and the element type of the specified"
|
||||
" 'keys_*' should be identical while the output type is identical to the"
|
||||
" specified 'values_*' attribute. If an input element can not be found in the"
|
||||
" specified 'keys_*' attribute, the 'default_*' that matches the specified"
|
||||
" 'values_*' attribute may be used as its output value.<br>"
|
||||
" Let's consider an example which maps a string tensor to an integer tensor."
|
||||
" Assume and 'keys_strings' is [\"Amy\", \"Sally\"], 'values_int64s' is [5, 6],"
|
||||
" and 'default_int64' is '-1'. The input [\"Dori\", \"Amy\", \"Amy\", \"Sally\","
|
||||
" \"Sally\"] would be mapped to [-1, 5, 5, 6, 6].<br>"
|
||||
" Since this operator is an one-to-one mapping, its input and output shapes"
|
||||
" are the same. Notice that only one of 'keys_*'/'values_*' can be set.<br>"
|
||||
" For key look-up, bit-wise comparison is used so even a float NaN can be"
|
||||
" mapped to a value in 'values_*' attribute.<br>"
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<F32Attr, "-0.0">:$default_float,
|
||||
DefaultValuedAttr<I64Attr, "-1">:$default_int64,
|
||||
DefaultValuedAttr<StrAttr, "_Unused">:$default_string,
|
||||
OptionalAttr<F32ArrayAttr>:$keys_floats,
|
||||
OptionalAttr<I64ArrayAttr>:$keys_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$keys_strings,
|
||||
OptionalAttr<F32ArrayAttr>:$values_floats,
|
||||
OptionalAttr<I64ArrayAttr>:$values_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$values_strings);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXLinearClassifierOp:MLONNX_Op<"LinearClassifier",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX LinearClassifier operation";
|
||||
let description = [{
|
||||
"Linear classifier"
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$classlabels_ints,
|
||||
OptionalAttr<StrArrayAttr>:$classlabels_strings,
|
||||
F32ArrayAttr:$coefficients,
|
||||
OptionalAttr<F32ArrayAttr>:$intercepts,
|
||||
DefaultValuedAttr<I64Attr, "0">:$multi_class,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$Z);
|
||||
}
|
||||
|
||||
def MLONNXLinearRegressorOp:MLONNX_Op<"LinearRegressor",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX LinearRegressor operation";
|
||||
let description = [{
|
||||
"Generalized linear regression evaluation.<br>"
|
||||
" If targets is set to 1 (default) then univariate regression is performed.<br>"
|
||||
" If targets is set to M then M sets of coefficients must be passed in as a sequence"
|
||||
" and M results will be output for each input n in N.<br>"
|
||||
" The coefficients array is of length n, and the coefficients for each target are contiguous."
|
||||
" Intercepts are optional but if provided must match the number of targets."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<F32ArrayAttr>:$coefficients,
|
||||
OptionalAttr<F32ArrayAttr>:$intercepts,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform,
|
||||
DefaultValuedAttr<I64Attr, "1">:$targets);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXNormalizerOp:MLONNX_Op<"Normalizer",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX Normalizer operation";
|
||||
let description = [{
|
||||
"Normalize the input. There are three normalization modes, which have the corresponding formulas,"
|
||||
" defined using element-wise infix operators '/' and '^' and tensor-wide functions 'max' and 'sum':<br>"
|
||||
"<br>"
|
||||
" Max: Y = X / max(X)<br>"
|
||||
" L1: Y = X / sum(X)<br>"
|
||||
" L2: Y = sqrt(X^2 / sum(X^2)}<br>"
|
||||
" In all modes, if the divisor is zero, Y == X."
|
||||
"<br>"
|
||||
" For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row"
|
||||
" of the batch is normalized independently."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<StrAttr, "MAX">:$norm);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXOneHotEncoderOp:MLONNX_Op<"OneHotEncoder",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX OneHotEncoder operation";
|
||||
let description = [{
|
||||
"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."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$cats_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$cats_strings,
|
||||
DefaultValuedAttr<I64Attr, "1">:$zeros);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXSVMClassifierOp:MLONNX_Op<"SVMClassifier",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX SVMClassifier operation";
|
||||
let description = [{
|
||||
"Support Vector Machine classifier"
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$classlabels_ints,
|
||||
OptionalAttr<StrArrayAttr>:$classlabels_strings,
|
||||
OptionalAttr<F32ArrayAttr>:$coefficients,
|
||||
OptionalAttr<F32ArrayAttr>:$kernel_params,
|
||||
DefaultValuedAttr<StrAttr, "LINEAR">:$kernel_type,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform,
|
||||
OptionalAttr<F32ArrayAttr>:$prob_a,
|
||||
OptionalAttr<F32ArrayAttr>:$prob_b,
|
||||
OptionalAttr<F32ArrayAttr>:$rho,
|
||||
OptionalAttr<F32ArrayAttr>:$support_vectors,
|
||||
OptionalAttr<I64ArrayAttr>:$vectors_per_class);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$Z);
|
||||
}
|
||||
|
||||
def MLONNXSVMRegressorOp:MLONNX_Op<"SVMRegressor",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX SVMRegressor operation";
|
||||
let description = [{
|
||||
"Support Vector Machine regression prediction and one-class SVM anomaly detection."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<F32ArrayAttr>:$coefficients,
|
||||
OptionalAttr<F32ArrayAttr>:$kernel_params,
|
||||
DefaultValuedAttr<StrAttr, "LINEAR">:$kernel_type,
|
||||
DefaultValuedAttr<I64Attr, "0">:$n_supports,
|
||||
DefaultValuedAttr<I64Attr, "0">:$one_class,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform,
|
||||
OptionalAttr<F32ArrayAttr>:$rho,
|
||||
OptionalAttr<F32ArrayAttr>:$support_vectors);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXScalerOp:MLONNX_Op<"Scaler",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX Scaler operation";
|
||||
let description = [{
|
||||
"Rescale input data, for example to standardize features by removing the mean and scaling to unit variance."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<F32ArrayAttr>:$offset,
|
||||
OptionalAttr<F32ArrayAttr>:$scale);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXTreeEnsembleClassifierOp:MLONNX_Op<"TreeEnsembleClassifier",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX TreeEnsembleClassifier operation";
|
||||
let description = [{
|
||||
"Tree Ensemble classifier. Returns the top class for each of N inputs.<br>"
|
||||
" The attributes named 'nodes_X' form a sequence of tuples, associated by "
|
||||
" index into the sequences, which must all be of equal length. These tuples"
|
||||
" define the nodes.<br>"
|
||||
" Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves."
|
||||
" A leaf may have multiple votes, where each vote is weighted by"
|
||||
" the associated class_weights index.<br>"
|
||||
" One and only one of classlabels_strings or classlabels_int64s"
|
||||
" will be defined. The class_ids are indices into this list."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<F32ArrayAttr>:$base_values,
|
||||
OptionalAttr<I64ArrayAttr>:$class_ids,
|
||||
OptionalAttr<I64ArrayAttr>:$class_nodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$class_treeids,
|
||||
OptionalAttr<F32ArrayAttr>:$class_weights,
|
||||
OptionalAttr<I64ArrayAttr>:$classlabels_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$classlabels_strings,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_falsenodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_featureids,
|
||||
OptionalAttr<F32ArrayAttr>:$nodes_hitrates,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_missing_value_tracks_true,
|
||||
OptionalAttr<StrArrayAttr>:$nodes_modes,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_nodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_treeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_truenodeids,
|
||||
OptionalAttr<F32ArrayAttr>:$nodes_values,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$Z);
|
||||
}
|
||||
|
||||
def MLONNXTreeEnsembleRegressorOp:MLONNX_Op<"TreeEnsembleRegressor",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX TreeEnsembleRegressor operation";
|
||||
let description = [{
|
||||
"Tree Ensemble regressor. Returns the regressed values for each input in N.<br>"
|
||||
" All args with nodes_ are fields of a tuple of tree nodes, and"
|
||||
" it is assumed they are the same length, and an index i will decode the"
|
||||
" tuple across these inputs. Each node id can appear only once"
|
||||
" for each tree id.<br>"
|
||||
" All fields prefixed with target_ are tuples of votes at the leaves.<br>"
|
||||
" A leaf may have multiple votes, where each vote is weighted by"
|
||||
" the associated target_weights index.<br>"
|
||||
" All trees must have their node ids start at 0 and increment by 1.<br>"
|
||||
" Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF"
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<StrAttr, "SUM">:$aggregate_function,
|
||||
OptionalAttr<F32ArrayAttr>:$base_values,
|
||||
OptionalAttr<I64Attr>:$n_targets,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_falsenodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_featureids,
|
||||
OptionalAttr<F32ArrayAttr>:$nodes_hitrates,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_missing_value_tracks_true,
|
||||
OptionalAttr<StrArrayAttr>:$nodes_modes,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_nodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_treeids,
|
||||
OptionalAttr<I64ArrayAttr>:$nodes_truenodeids,
|
||||
OptionalAttr<F32ArrayAttr>:$nodes_values,
|
||||
DefaultValuedAttr<StrAttr, "NONE">:$post_transform,
|
||||
OptionalAttr<I64ArrayAttr>:$target_ids,
|
||||
OptionalAttr<I64ArrayAttr>:$target_nodeids,
|
||||
OptionalAttr<I64ArrayAttr>:$target_treeids,
|
||||
OptionalAttr<F32ArrayAttr>:$target_weights);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Y);
|
||||
}
|
||||
|
||||
def MLONNXZipMapOp:MLONNX_Op<"ZipMap",
|
||||
[NoSideEffect]> {
|
||||
let summary = "ONNX ZipMap operation";
|
||||
let description = [{
|
||||
"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>"
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
OptionalAttr<I64ArrayAttr>:$classlabels_int64s,
|
||||
OptionalAttr<StrArrayAttr>:$classlabels_strings);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$Z);
|
||||
}
|
||||
|
||||
|
|
@ -22,3 +22,28 @@ add_custom_target(OMONNXOpsBuildTableIncGen
|
|||
add_custom_target(OMONNXOpsIncTranslation
|
||||
DEPENDS OMONNXOpsTableGenIncGen
|
||||
OMONNXOpsBuildTableIncGen)
|
||||
|
||||
# Invoke gen_doc.py to obtain ONNXOps.td.inc, OpBuildTable.inc.
|
||||
add_custom_command(OUTPUT ${CMAKE_CURRENT_SOURCE_DIR}/MLONNXOps.td.inc
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/MLOpBuildTable.inc
|
||||
COMMAND python ${CMAKE_CURRENT_SOURCE_DIR}/gen_doc.py --domain="ONNX_ML"
|
||||
DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/gen_doc.py)
|
||||
|
||||
# Copy the generated files to respective destinations:
|
||||
# ONNXOps.td.inc -> src/Dialect/ONNX/ONNXOps.td.inc
|
||||
add_custom_target(OMMLONNXOpsTableGenIncGen
|
||||
COMMAND ${CMAKE_COMMAND} -E copy
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/MLONNXOps.td.inc
|
||||
${ONNX_MLIR_SRC_ROOT}/src/Dialect/MLONNX/MLONNXOps.td.inc
|
||||
DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/MLONNXOps.td.inc)
|
||||
|
||||
# OpBuildTable.inc -> src/Builder/OpBuildTable.inc
|
||||
add_custom_target(OMMLONNXOpsBuildTableIncGen
|
||||
COMMAND ${CMAKE_COMMAND} -E copy
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/MLOpBuildTable.inc
|
||||
${ONNX_MLIR_SRC_ROOT}/src/Builder/MLOpBuildTable.inc
|
||||
DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/MLOpBuildTable.inc)
|
||||
|
||||
add_custom_target(OMMLONNXOpsIncTranslation
|
||||
DEPENDS OMMLONNXOpsTableGenIncGen
|
||||
OMMLONNXOpsBuildTableIncGen)
|
||||
|
|
|
|||
|
|
@ -29,6 +29,10 @@ parser.add_argument("--dry-run-op-build-table",
|
|||
help="Output OpBuildTable.inc content to stdout.",
|
||||
action="store_true",
|
||||
default=False)
|
||||
parser.add_argument("--domain",
|
||||
help="specify domain, ONNX or ONNX_ML",
|
||||
default = "ONNX")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Manual specification of attribute defaults.
|
||||
|
|
@ -86,17 +90,10 @@ custom_builder_ops_list = ['Abs', 'Mul', 'Exp', 'ReduceSum', 'ReduceSumSquare']
|
|||
|
||||
SNIPPETS = collect_snippets()
|
||||
SAMPLE_IMPLEMENTATIONS = collect_sample_implementations()
|
||||
ONNX_ML = not bool(os.getenv('ONNX_ML') == '0')
|
||||
ONNX_ML = bool(args.domain == "ONNX_ML")
|
||||
|
||||
ONNX_ML = False
|
||||
sys.stderr.write("ONNX_ML {}\n".format(ONNX_ML))
|
||||
|
||||
if ONNX_ML:
|
||||
ext = '-ml.md'
|
||||
else:
|
||||
ext = '.md'
|
||||
|
||||
|
||||
def should_render_domain(domain): # type: (Text) -> bool
|
||||
if domain == ONNX_ML_DOMAIN and not ONNX_ML:
|
||||
return False
|
||||
|
|
@ -360,6 +357,9 @@ def get_promotable_const_operands_func(s, indent, const_operands_name_to_idx):
|
|||
|
||||
def gen_op_def(schema):
|
||||
indent = inc_indent()
|
||||
if (ONNX_ML) :
|
||||
s = 'def MLONNX{0}Op:MLONNX_Op<"{0}",\n'.format(schema.name)
|
||||
else :
|
||||
s = 'def ONNX{0}Op:ONNX_Op<"{0}",\n'.format(schema.name)
|
||||
|
||||
# Generate decl for op traits.
|
||||
|
|
@ -476,6 +476,10 @@ def gen_op_importer(schema, file):
|
|||
if OpSchema.FormalParameterOption.Variadic == output.option:
|
||||
expected_num_results = -1
|
||||
|
||||
if ONNX_ML:
|
||||
handler_func = special_op_handler.get(
|
||||
schema.name, "buildOperation<mlir::MLONNX{}Op>".format(schema.name))
|
||||
else:
|
||||
handler_func = special_op_handler.get(
|
||||
schema.name, "buildOperation<mlir::ONNX{}Op>".format(schema.name))
|
||||
|
||||
|
|
@ -556,6 +560,9 @@ if __name__ == '__main__':
|
|||
class Args(object):
|
||||
if args.dry_run_onnx_ops:
|
||||
op_def = StringIO()
|
||||
else:
|
||||
if args.domain == 'ONNX_ML':
|
||||
op_def_file_path = os.path.join(curr_dir, 'MLONNXOps.td.inc')
|
||||
else:
|
||||
op_def_file_path = os.path.join(curr_dir, 'ONNXOps.td.inc')
|
||||
op_def = io.open(op_def_file_path, 'w', newline='')
|
||||
|
|
@ -563,6 +570,9 @@ if __name__ == '__main__':
|
|||
if args.dry_run_op_build_table:
|
||||
op_importer = StringIO()
|
||||
else:
|
||||
if args.domain == 'ONNX_ML':
|
||||
op_importer_file_path = os.path.join(curr_dir, 'MLOpBuildTable.inc')
|
||||
else :
|
||||
op_importer_file_path = os.path.join(curr_dir, 'OpBuildTable.inc')
|
||||
op_importer = io.open(op_importer_file_path, 'w', newline='')
|
||||
main(Args)
|
||||
|
|
|
|||
Loading…
Reference in New Issue