diff --git a/CMakeLists.txt b/CMakeLists.txt
index e3b474d..9084b78 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -31,7 +31,16 @@ add_subdirectory(third_party/pybind11)
add_subdirectory(third_party/variant)
set(CMAKE_CXX_STANDARD 14)
+
+if ($ENV{EXCLUDE_ONNX_ML})
+ set(INCLUDE_ONNX_ML FALSE)
+else()
+ set(INCLUDE_ONNX_ML TRUE)
+endif()
+
+message(STATUS "INCLUDE_ONNX_ML Dialect " ${INCLUDE_ONNX_ML})
+
add_subdirectory(utils)
add_subdirectory(src)
add_subdirectory(docs)
-add_subdirectory(test)
\ No newline at end of file
+add_subdirectory(test)
diff --git a/docs/Dialects/mlonnx.md b/docs/Dialects/mlonnx.md
new file mode 100644
index 0000000..d7ab1e0
--- /dev/null
+++ b/docs/Dialects/mlonnx.md
@@ -0,0 +1,597 @@
+
+### `mlonnx.ArrayFeatureExtractor` (MLONNXArrayFeatureExtractorOp)
+
+ONNX ArrayFeatureExtractor operation
+
+"Select elements of the input tensor based on the indices passed.
"
+" The indices are applied to the last axes of the tensor."
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+`Y` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Z` | memref of any type values or tensor of any type values
+
+### `mlonnx.Binarizer` (MLONNXBinarizerOp)
+
+ONNX Binarizer operation
+
+"Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`threshold` | FloatAttr | 32-bit float attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
The map key must be an int64 and the values will be ordered"
+" in ascending order based on this key.
The operator supports dense packing or sparse packing."
+" If using sparse packing, the key cannot exceed the max_map-1 value."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`cast_to` | StringAttr | string attribute
+`map_form` | StringAttr | string attribute
+`max_map` | IntegerAttr | 64-bit signless integer attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Y` | memref of any type values or tensor of any type values
+
+### `mlonnx.CategoryMapper` (MLONNXCategoryMapperOp)
+
+ONNX CategoryMapper operation
+
+"Converts strings to integers and vice versa.
"
+" Two sequences of equal length are used to map between integers and strings,"
+" with strings and integers at the same index detailing the mapping.
"
+" Each operator converts either integers to strings or strings to integers, depending "
+" on which default value attribute is provided. Only one default value attribute"
+" should be defined.
"
+" If the string default value is set, it will convert integers to strings."
+" If the int default value is set, it will convert strings to integers."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`cats_int64s` | ArrayAttr | 64-bit integer array attribute
+`cats_strings` | ArrayAttr | string array attribute
+`default_int64` | IntegerAttr | 64-bit signless integer attribute
+`default_string` | StringAttr | string attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Y` | memref of any type values or tensor of any type values
+
+### `mlonnx.DictVectorizer` (MLONNXDictVectorizerOp)
+
+ONNX DictVectorizer operation
+
+"Uses an index mapping to convert a dictionary to an array.
"
+" Given a dictionary, each key is looked up in the vocabulary attribute corresponding to"
+" the key type. The index into the vocabulary array at which the key is found is then"
+" used to index the output 1-D tensor 'Y' and insert into it the value found in the dictionary 'X'.
"
+" The key type of the input map must correspond to the element type of the defined vocabulary attribute."
+" Therefore, the output array will be equal in length to the index mapping vector parameter."
+" All keys in the input dictionary must be present in the index mapping vector."
+" For each item in the input dictionary, insert its value in the output array."
+" Any keys not present in the input dictionary, will be zero in the output array.
"
+" For example: if the ``string_vocabulary`` parameter is set to ``[\"a\", \"c\", \"b\", \"z\"]``,"
+" then an input of ``{\"a\": 4, \"c\": 8}`` will produce an output of ``[4, 8, 0, 0]``."
+" "
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`int64_vocabulary` | ArrayAttr | 64-bit integer array attribute
+`string_vocabulary` | ArrayAttr | string array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Y` | memref of any type values or tensor of any type values
+
+### `mlonnx.FeatureVectorizer` (MLONNXFeatureVectorizerOp)
+
+ONNX FeatureVectorizer operation
+
+"Concatenates input tensors into one continuous output.
"
+" All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C]."
+" Inputs are copied to the output maintaining the order of the input arguments.
"
+" All inputs must be integers or floats, while the output will be all floating point values."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`inputdimensions` | ArrayAttr | 64-bit integer array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" This operator is typically used to replace missing values in situations where they have a canonical"
+" representation, such as -1, 0, NaN, or some extreme value.
"
+" One and only one of imputed_value_floats or imputed_value_int64s should be defined -- floats if the input tensor"
+" holds floats, integers if the input tensor holds integers. The imputed values must all fit within the"
+" width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,"
+" which one depends on whether floats or integers are being processed.
"
+" The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`imputed_value_floats` | ArrayAttr | 32-bit float array attribute
+`imputed_value_int64s` | ArrayAttr | 64-bit integer array attribute
+`replaced_value_float` | FloatAttr | 32-bit float attribute
+`replaced_value_int64` | IntegerAttr | 64-bit signless integer attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" The mapping is determined by the two parallel attributes, 'keys_*' and"
+" 'values_*' attribute. The i-th value in the specified 'keys_*' attribute"
+" would be mapped to the i-th value in the specified 'values_*' attribute. It"
+" implies that input's element type and the element type of the specified"
+" 'keys_*' should be identical while the output type is identical to the"
+" specified 'values_*' attribute. If an input element can not be found in the"
+" specified 'keys_*' attribute, the 'default_*' that matches the specified"
+" 'values_*' attribute may be used as its output value.
"
+" Let's consider an example which maps a string tensor to an integer tensor."
+" Assume and 'keys_strings' is [\"Amy\", \"Sally\"], 'values_int64s' is [5, 6],"
+" and 'default_int64' is '-1'. The input [\"Dori\", \"Amy\", \"Amy\", \"Sally\","
+" \"Sally\"] would be mapped to [-1, 5, 5, 6, 6].
"
+" Since this operator is an one-to-one mapping, its input and output shapes"
+" are the same. Notice that only one of 'keys_*'/'values_*' can be set.
"
+" For key look-up, bit-wise comparison is used so even a float NaN can be"
+" mapped to a value in 'values_*' attribute.
"
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`default_float` | FloatAttr | 32-bit float attribute
+`default_int64` | IntegerAttr | 64-bit signless integer attribute
+`default_string` | StringAttr | string attribute
+`keys_floats` | ArrayAttr | 32-bit float array attribute
+`keys_int64s` | ArrayAttr | 64-bit integer array attribute
+`keys_strings` | ArrayAttr | string array attribute
+`values_floats` | ArrayAttr | 32-bit float array attribute
+`values_int64s` | ArrayAttr | 64-bit integer array attribute
+`values_strings` | ArrayAttr | string array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Y` | memref of any type values or tensor of any type values
+
+### `mlonnx.LinearClassifier` (MLONNXLinearClassifierOp)
+
+ONNX LinearClassifier operation
+
+"Linear classifier"
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`classlabels_ints` | ArrayAttr | 64-bit integer array attribute
+`classlabels_strings` | ArrayAttr | string array attribute
+`coefficients` | ArrayAttr | 32-bit float array attribute
+`intercepts` | ArrayAttr | 32-bit float array attribute
+`multi_class` | IntegerAttr | 64-bit signless integer attribute
+`post_transform` | StringAttr | string attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" If targets is set to 1 (default) then univariate regression is performed.
"
+" If targets is set to M then M sets of coefficients must be passed in as a sequence"
+" and M results will be output for each input n in N.
"
+" The coefficients array is of length n, and the coefficients for each target are contiguous."
+" Intercepts are optional but if provided must match the number of targets."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`coefficients` | ArrayAttr | 32-bit float array attribute
+`intercepts` | ArrayAttr | 32-bit float array attribute
+`post_transform` | StringAttr | string attribute
+`targets` | IntegerAttr | 64-bit signless integer attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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':
"
+"
"
+" Max: Y = X / max(X)
"
+" L1: Y = X / sum(X)
"
+" L2: Y = sqrt(X^2 / sum(X^2)}
"
+" In all modes, if the divisor is zero, Y == X."
+"
"
+" For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row"
+" of the batch is normalized independently."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`norm` | StringAttr | string attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" For example, if we pass a tensor with a single value of 4, and a category count of 8, "
+" the output will be a tensor with ``[0,0,0,0,1,0,0,0]``.
"
+" This operator assumes every input feature is from the same set of categories.
"
+" If the input is a tensor of float, int32, or double, the data will be cast"
+" to integers and the cats_int64s category list will be used for the lookups."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`cats_int64s` | ArrayAttr | 64-bit integer array attribute
+`cats_strings` | ArrayAttr | string array attribute
+`zeros` | IntegerAttr | 64-bit signless integer attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Y` | memref of any type values or tensor of any type values
+
+### `mlonnx.SVMClassifier` (MLONNXSVMClassifierOp)
+
+ONNX SVMClassifier operation
+
+"Support Vector Machine classifier"
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`classlabels_ints` | ArrayAttr | 64-bit integer array attribute
+`classlabels_strings` | ArrayAttr | string array attribute
+`coefficients` | ArrayAttr | 32-bit float array attribute
+`kernel_params` | ArrayAttr | 32-bit float array attribute
+`kernel_type` | StringAttr | string attribute
+`post_transform` | StringAttr | string attribute
+`prob_a` | ArrayAttr | 32-bit float array attribute
+`prob_b` | ArrayAttr | 32-bit float array attribute
+`rho` | ArrayAttr | 32-bit float array attribute
+`support_vectors` | ArrayAttr | 32-bit float array attribute
+`vectors_per_class` | ArrayAttr | 64-bit integer array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" The attributes named 'nodes_X' form a sequence of tuples, associated by "
+" index into the sequences, which must all be of equal length. These tuples"
+" define the nodes.
"
+" Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves."
+" A leaf may have multiple votes, where each vote is weighted by"
+" the associated class_weights index.
"
+" One and only one of classlabels_strings or classlabels_int64s"
+" will be defined. The class_ids are indices into this list."
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`base_values` | ArrayAttr | 32-bit float array attribute
+`class_ids` | ArrayAttr | 64-bit integer array attribute
+`class_nodeids` | ArrayAttr | 64-bit integer array attribute
+`class_treeids` | ArrayAttr | 64-bit integer array attribute
+`class_weights` | ArrayAttr | 32-bit float array attribute
+`classlabels_int64s` | ArrayAttr | 64-bit integer array attribute
+`classlabels_strings` | ArrayAttr | string array attribute
+`nodes_falsenodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_featureids` | ArrayAttr | 64-bit integer array attribute
+`nodes_hitrates` | ArrayAttr | 32-bit float array attribute
+`nodes_missing_value_tracks_true` | ArrayAttr | 64-bit integer array attribute
+`nodes_modes` | ArrayAttr | string array attribute
+`nodes_nodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_treeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_truenodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_values` | ArrayAttr | 32-bit float array attribute
+`post_transform` | StringAttr | string attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" All args with nodes_ are fields of a tuple of tree nodes, and"
+" it is assumed they are the same length, and an index i will decode the"
+" tuple across these inputs. Each node id can appear only once"
+" for each tree id.
"
+" All fields prefixed with target_ are tuples of votes at the leaves.
"
+" A leaf may have multiple votes, where each vote is weighted by"
+" the associated target_weights index.
"
+" All trees must have their node ids start at 0 and increment by 1.
"
+" Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF"
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`aggregate_function` | StringAttr | string attribute
+`base_values` | ArrayAttr | 32-bit float array attribute
+`n_targets` | IntegerAttr | 64-bit signless integer attribute
+`nodes_falsenodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_featureids` | ArrayAttr | 64-bit integer array attribute
+`nodes_hitrates` | ArrayAttr | 32-bit float array attribute
+`nodes_missing_value_tracks_true` | ArrayAttr | 64-bit integer array attribute
+`nodes_modes` | ArrayAttr | string array attribute
+`nodes_nodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_treeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_truenodeids` | ArrayAttr | 64-bit integer array attribute
+`nodes_values` | ArrayAttr | 32-bit float array attribute
+`post_transform` | StringAttr | string attribute
+`target_ids` | ArrayAttr | 64-bit integer array attribute
+`target_nodeids` | ArrayAttr | 64-bit integer array attribute
+`target_treeids` | ArrayAttr | 64-bit integer array attribute
+`target_weights` | ArrayAttr | 32-bit float array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | 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.
"
+" The values are provided by the input tensor, while the keys are specified by the attributes."
+" Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
"
+" The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
"
+
+#### Attributes:
+
+| Attribute | MLIR Type | Description |
+| :-------: | :-------: | ----------- |
+`classlabels_int64s` | ArrayAttr | 64-bit integer array attribute
+`classlabels_strings` | ArrayAttr | string array attribute
+
+#### Operands:
+
+| Operand | Description |
+| :-----: | ----------- |
+`X` | memref of any type values or tensor of any type values
+
+#### Results:
+
+| Result | Description |
+| :----: | ----------- |
+`Z` | memref of any type values or tensor of any type values
+
diff --git a/src/Builder/CMakeLists.txt b/src/Builder/CMakeLists.txt
index 3e331a1..1e87f7e 100644
--- a/src/Builder/CMakeLists.txt
+++ b/src/Builder/CMakeLists.txt
@@ -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()
diff --git a/src/Builder/FrontendDialectHelper.hpp b/src/Builder/FrontendDialectHelper.hpp
index e920cd6..82d0c21 100644
--- a/src/Builder/FrontendDialectHelper.hpp
+++ b/src/Builder/FrontendDialectHelper.hpp
@@ -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 {
@@ -98,4 +102,4 @@ private:
std::map nameToInitializedTensor;
};
-} // namespace onnx_mlir
\ No newline at end of file
+} // namespace onnx_mlir
diff --git a/src/Builder/FrontendDialectTransformer.cpp b/src/Builder/FrontendDialectTransformer.cpp
index f094761..22a8b57 100644
--- a/src/Builder/FrontendDialectTransformer.cpp
+++ b/src/Builder/FrontendDialectTransformer.cpp
@@ -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
+
}
/*!
diff --git a/src/Builder/MLOpBuildTable.inc b/src/Builder/MLOpBuildTable.inc
new file mode 100644
index 0000000..582497b
--- /dev/null
+++ b/src/Builder/MLOpBuildTable.inc
@@ -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(node, /* expected_num_operands = */ 2, /* expected_num_results = */ 1);
+if (opName == "Binarizer")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "CastMap")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "CategoryMapper")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "DictVectorizer")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "FeatureVectorizer")
+ return buildOperation(node, /* expected_num_operands = */ -1, /* expected_num_results = */ 1);
+if (opName == "Imputer")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "LabelEncoder")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "LinearClassifier")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
+if (opName == "LinearRegressor")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "Normalizer")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "OneHotEncoder")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "SVMClassifier")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
+if (opName == "SVMRegressor")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "Scaler")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "TreeEnsembleClassifier")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 2);
+if (opName == "TreeEnsembleRegressor")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
+if (opName == "ZipMap")
+ return buildOperation(node, /* expected_num_operands = */ 1, /* expected_num_results = */ 1);
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 7f67912..aa0a02f 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -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 ${ONNX_MLIR_BIN_ROOT})
diff --git a/src/Dialect/CMakeLists.txt b/src/Dialect/CMakeLists.txt
index 52599c3..9454959 100644
--- a/src/Dialect/CMakeLists.txt
+++ b/src/Dialect/CMakeLists.txt
@@ -1,2 +1,5 @@
add_subdirectory(Krnl)
-add_subdirectory(ONNX)
\ No newline at end of file
+add_subdirectory(ONNX)
+if (INCLUDE_ONNX_ML)
+ add_subdirectory(MLONNX)
+endif()
diff --git a/src/Dialect/MLONNX/CMakeLists.txt b/src/Dialect/MLONNX/CMakeLists.txt
new file mode 100644
index 0000000..da4e977
--- /dev/null
+++ b/src/Dialect/MLONNX/CMakeLists.txt
@@ -0,0 +1,22 @@
+set(LLVM_TARGET_DEFINITIONS MLONNXOps.td)
+onnx_mlir_tablegen(MLONNXOps.hpp.inc -gen-op-decls "-I${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)
diff --git a/src/Dialect/MLONNX/MLONNXOps.cpp b/src/Dialect/MLONNX/MLONNXOps.cpp
new file mode 100644
index 0000000..02d5ef1
--- /dev/null
+++ b/src/Dialect/MLONNX/MLONNXOps.cpp
@@ -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"
diff --git a/src/Dialect/MLONNX/MLONNXOps.hpp b/src/Dialect/MLONNX/MLONNXOps.hpp
new file mode 100644
index 0000000..380a86a
--- /dev/null
+++ b/src/Dialect/MLONNX/MLONNXOps.hpp
@@ -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