Complete Numpy API for ONNX#

The numpy API is meant to simplofy the creation of ONNX graphs by using functions very similar to what numpy implements. This page only makes a list of the available functions. A tutorial is available at Numpy to ONNX: Create ONNX graphs with an API similar to numpy. This API was first added to mlprodict in version 0.6.

Introduction #

Converting custom code into ONNX is not necessarily easy. One big obstacle is ONNX does not represent all numpy functions with a single operator. One possible option is to provide a numpy API to ONNX. That’s the purpose of wrapper onnxnumpy. It takes a function written with functions following the same signature as numpy and provides a way to execute them with an ONNX runtime. In the below example, custom_fct creates an ONNX graph, the wrapper loads it in a runtime and runs it everytime the function is called.

<<<

import numpy
from typing import Any
from mlprodict.npy import onnxnumpy_default, NDArray
import mlprodict.npy.numpy_onnx_impl as nxnp


@onnxnumpy_default
def custom_fct(x: NDArray[Any, numpy.float32],
               ) -> NDArray[Any, numpy.float32]:
    "onnx numpy abs"
    return nxnp.abs(x) + numpy.float32(1)


x = numpy.array([[6.1, -5], [3.5, -7.8]], dtype=numpy.float32)
y = custom_fct(x)
print(y)

>>>

    [[7.1 6. ]
     [4.5 8.8]]

Annotations are mandatory to indicate inputs and outputs type. The decorator returns a function which is strict about types as opposed to numpy. This approach is similar to what tensorflow with autograph.

Signatures #

mlprodict.npy.NDArray (self, args, kwargs)

Used to annotation ONNX numpy functions.

ShapeType (params)

Stores shape information.

mlprodict.npy.NDArraySameType (self, dtypes = None)

Shortcut to simplify signature description.

mlprodict.npy.NDArraySameTypeSameShape (self, dtypes = None)

Shortcut to simplify signature description.

mlprodict.npy.NDArrayType (self, dtypes = None, dtypes_out = None, n_optional = None, nvars = False)

Shortcut to simplify signature description.

mlprodict.npy.onnx_numpy_annotation.NDArrayTypeSameShape (self, dtypes = None, dtypes_out = None, n_optional = None, nvars = False)

Shortcut to simplify signature description.

Decorators #

mlprodict.npy.onnxnumpy (op_version = None, runtime = None, signature = None)

Decorator to declare a function implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxnumpy_default (fct)

Decorator with options to declare a function implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxnumpy_np (op_version = None, runtime = None, signature = None)

Decorator to declare a function implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxsklearn_class (method_name, op_version = None, runtime = None, signature = None, method_names = None, overwrite = True)

Decorator to declare a converter for a class derivated from scikit-learn, implementing inference method and using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxsklearn_classifier (op_version = None, runtime = None, signature = None, register_class = None, overwrite = True)

Decorator to declare a converter for a classifier implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxsklearn_cluster (op_version = None, runtime = None, signature = None, register_class = None, overwrite = True)

Decorator to declare a converter for a cluster implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxsklearn_regressor (op_version = None, runtime = None, signature = None, register_class = None, overwrite = True)

Decorator to declare a converter for a regressor implemented using numpy syntax but executed with ONNX operators.

mlprodict.npy.onnxsklearn_transformer (op_version = None, runtime = None, signature = None, register_class = None, overwrite = True)

Decorator to declare a converter for a transformer implemented using numpy syntax but executed with ONNX operators.

OnnxNumpyCompiler #

mlprodict.npy.OnnxNumpyCompiler (self, fct, op_version = None, runtime = None, signature = None, version = None, fctsig = None)

Implements a class which runs onnx graph.

to_onnx (self, kwargs)

Returns the ONNX graph for the wrapped function. It takes additional arguments to distinguish between multiple graphs. This happens when a function needs to support multiple type.

mlprodict.npy.FctVersion (self, args, kwargs)

Identifies a version of a function based on its arguments and its parameters.

as_string (self)

Returns a single string identifier.

as_tuple (self)

Returns a single tuple for the version.

as_tuple_with_sep (self, sep)

Returns a single tuple for the version.

OnnxVar #

mlprodict.npy.onnx_variable.OnnxVar (self, inputs, op = None, select_output = None, dtype = None, kwargs)

Variables used into onnx computation.

astype (self, dtype)

Cast

copy (self)

Returns a copy of self (use of Identity node).

flatten (self, axis = 0)

Flattens a matrix (see numpy.ndarray.flatten).

not_ (self)

Not.

reshape (self, shape)

Reshape

set_onnx_name (self, name_type)

Forces this variable to get this name during

to_algebra (self, op_version = None)

Converts the variable into an operator.

mlprodict.npy.onnx_variable.MultiOnnxVar (self, inputs, op = None, dtype = None, kwargs)

Class used to return multiple OnnxVar at the same time.

to_algebra (self, op_version = None)

Converts the variable into an operator.

mlprodict.npy.onnx_variable.TupleOnnxAny

Registration #

mlprodict.npy.update_registered_converter_npy (model, alias, convert_fct, shape_fct = None, overwrite = True, parser = None, options = None)

Registers or updates a converter for a new model so that it can be converted when inserted in a scikit-learn pipeline. This function assumes the converter is written as a function decoarated with onnxsklearn_transformer.

Available functions implemented with ONNX operators #

All functions are implemented in two submodules:

numpy function: module npy.numpy_onnx_impl
machine learned models: module npy.numpy_onnx_impl_skl

ONNX functions executed python ONNX runtime #

Same function as above, the import goes from from mlprodict.npy.numpy_onnx_impl import <function-name> to from mlprodict.npy.numpy_onnx_pyrt import <function-name>. These function are usually not used except in unit test or as reference for more complex functions. See the source on github, numpy_onnx_pyrt.py and numpy_onnx_pyrt_skl.py.