module `onnx_tools.onnx_grammar.onnx_translation`#

Short summary#

module mlprodict.onnx_tools.onnx_grammar.onnx_translation

One class which visits a syntax tree.

Functions#

function	truncated documentation
`get_default_context`	Returns a default context useful for most of the conversion from a function using numpy into ONNX.
`get_default_context_cpl`	Returns a default useful context to compile the converter returned by `translate_fct2onnx()`.
`py_make_float_array`	Creates an array with a single element from a constant.
`py_mul`	Function for python operator `*`.
`py_opp`	Function for python unary operator `-`.
`py_pow`	Function for python operator `**`.
`squareform_pdist`	Replacements for squareform …
`translate_fct2onnx`	Translates a function into ONNX. The code it produces is using classes OnnxAbs, OnnxAdd, …

Documentation#

One class which visits a syntax tree.

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.get_default_context()#

Returns a default context useful for most of the conversion from a function using numpy into ONNX.

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.get_default_context_cpl()#

Returns a default useful context to compile the converter returned by translate_fct2onnx.

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.py_make_float_array(cst, op_version=None)#

Creates an array with a single element from a constant.

Parameters:

cst – constant
op_version – unused

Returns:

array

<<<

from mlprodict.onnx_tools.onnx_grammar.onnx_translation import py_make_float_array
print(py_make_float_array(5.5))

>>>

    [5.5]

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.py_mul(*x, op_version=None)#

Function for python operator *.

Parameters:

x – floats
op_version – unused

Returns:

x*y

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.py_opp(x, op_version=None)#

Function for python unary operator -.

Parameters:

x – floats
op_version – unused

Returns:

-x

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.py_pow(x, p, op_version=None)#

Function for python operator **.

Parameters:

x – float
p – power
op_version – unused

Returns:

$x^p$

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.squareform_pdist(X, metric='sqeuclidean', op_version=None)#

Replacements for squareform and pdist.

source on GitHub

mlprodict.onnx_tools.onnx_grammar.onnx_translation.translate_fct2onnx(fct, context=None, cpl=False, context_cpl=None, output_names=None, dtype=<class 'numpy.float32'>, verbose=0, fLOG=None)#

Translates a function into ONNX. The code it produces is using classes OnnxAbs, OnnxAdd, …

Parameters:

fct – function to convert
context – context of the function to convert something like {'numpy.transpose': numpy.transpose}, if context is None, it receives a default value returnd by get_default_context
cpl – compile the function after it was created
context_cpl – context used at compiling time if context_cpl is None, it receives a default value returnd by get_default_context_cpl
output_names – names of the output in the ONNX graph
dtype – numpy float type used to produce the model
verbose – integer, display more information
fLOG – logging function

Returns:

code or compiled code

Convert a function into ONNX code

The following code parses a python function and returns another python function which produces an ONNX graph if executed.

<<<

import numpy
from mlprodict.onnx_tools.onnx_grammar import translate_fct2onnx


def trs(x, y):
    z = x + numpy.transpose(y, axes=[1, 0])
    return x * z


onnx_code = translate_fct2onnx(
    trs, context={'numpy.transpose': numpy.transpose})
print(onnx_code)

>>>

    def trs(x, y, dtype=numpy.float32, op_version=None):
        z = (
            OnnxAdd(
                x,
                OnnxTranspose(
                    y,
                    perm=[1, 0],
                    op_version=op_version
                ),
                op_version=op_version
            )
        )
        return (
            OnnxMul(
                x,
                z,
                op_version=op_version
            )
        )

Next example goes further and compile the outcome.

Convert a function into ONNX code and run

The following code parses a python function and returns another python function which produces an ONNX graph if executed. The example executes the function, creates an ONNX then uses OnnxInference to compute predictions. Finally it compares them to the original.

<<<

import numpy
from mlprodict.onnx_tools.onnx_grammar import translate_fct2onnx
from mlprodict.plotting.text_plot import onnx_simple_text_plot
from mlprodict.onnxrt import OnnxInference
from mlprodict.npy.xop import loadop


OnnxAdd, OnnxTranspose, OnnxMul, OnnxIdentity = loadop(
    'Add', 'Transpose', 'Mul', 'Identity')


ctx = {'OnnxAdd': OnnxAdd,
       'OnnxTranspose': OnnxTranspose,
       'OnnxMul': OnnxMul,
       'OnnxIdentity': OnnxIdentity}


def trs(x, y):
    z = x + numpy.transpose(y, axes=[1, 0])
    return x * z


inputs = {'x': numpy.array([[1, 2]], dtype=numpy.float32),
          'y': numpy.array([[-0.3, 0.4]], dtype=numpy.float32).T}

original = trs(inputs['x'], inputs['y'])

print('original output:', original)

onnx_fct = translate_fct2onnx(
    trs, context={'numpy.transpose': numpy.transpose},
    cpl=True, context_cpl=ctx, output_names=['Z'])

onnx_code = onnx_fct('x', 'y', op_version=12)

onnx_g = onnx_code.to_onnx(inputs, target_opset=12)
print("ONNX model")
print(onnx_simple_text_plot(onnx_g))

oinf = OnnxInference(onnx_g)
res = oinf.run(inputs)

print('-----------')
print("ONNX inference:", res['Z'])

>>>

    original output: [[0.7 4.8]]
    ONNX model
    opset: domain='' version=12
    input: name='x' type=dtype('float32') shape=[1, 2]
    input: name='y' type=dtype('float32') shape=[2, 1]
    Transpose(y, perm=[1,0]) -> out_tra_0
      Add(x, out_tra_0) -> out_add_0
        Mul(x, out_add_0) -> Z
    output: name='Z' type=dtype('float32') shape=[1, 2]
    -----------
    ONNX inference: [[0.7 4.8]]

The function to be converted may include python functions which must not be converted. In that case, their name must be prefixed by py_. The execution of the function this one builds produces the following error:

TypeError: Parameter to MergeFrom() must be instance of same class:
expected onnx.TensorProto got onnx.AttributeProto.

It indicates that constants in the code marges multiple types, usually floats and tensor of floats. Floats should be converted using the following function:

def py_make_float_array(cst):
    return numpy.array([cst], dtype=numpy.float32)

The function replaces empty contexts by default values which covers many numpy functions. The tutorial Execute ONNX graphs gives an example of how it can be used on a more complex function.

source on GitHub

module onnx_tools.onnx_grammar.onnx_translation#

Short summary#

Functions#

Documentation#

module `onnx_tools.onnx_grammar.onnx_translation`#