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First documentation of the light API#44

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sdpython merged 9 commits intomainfromdoc5
Nov 7, 2023
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@xadupre
xadupre committedNov 7, 2023
commit16e697e370db0ab83a41398fc957f76e41cb2934
2 changes: 1 addition & 1 deletionCHANGELOGS.rst
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Change Logs
===========

0.2.0
0.1.2
+++++

* :pr:`42`: first sketch for a very simple API to create onnx graph in one or two lines
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31 changes: 29 additions & 2 deletionsREADME.rst
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Expand Up@@ -2,7 +2,7 @@
.. image:: https://github.com/sdpython/onnx-array-api/raw/main/_doc/_static/logo.png
:width: 120

onnx-array-api:(Numpy) Array API forONNX
onnx-array-api:APIs to createONNX Graphs
==========================================

.. image:: https://dev.azure.com/xavierdupre3/onnx-array-api/_apis/build/status/sdpython.onnx-array-api
Expand All@@ -29,7 +29,9 @@ onnx-array-api: (Numpy) Array API for ONNX
.. image:: https://codecov.io/gh/sdpython/onnx-array-api/branch/main/graph/badge.svg?token=Wb9ZGDta8J
:target: https://codecov.io/gh/sdpython/onnx-array-api

**onnx-array-api** implements a numpy API for ONNX.
**onnx-array-api** implements APIs to create custom ONNX graphs.
The objective is to speed up the implementation of converter libraries.
The first one matches **numpy API**.
It gives the user the ability to convert functions written
following the numpy API to convert that function into ONNX as
well as to execute it.
Expand DownExpand Up@@ -111,6 +113,31 @@ It supports eager mode as well:
l2_loss=[0.002]
[0.042]

The second API ir **Light API** tends to do every thing in one line.
The euclidean distance looks like the following:

::

import numpy as np
from onnx_array_api.light_api import start
from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

model = (
start()
.vin("X")
.vin("Y")
.bring("X", "Y")
.Sub()
.rename("dxy")
.cst(np.array([2], dtype=np.int64), "two")
.bring("dxy", "two")
.Pow()
.ReduceSum()
.rename("Z")
.vout()
.to_onnx()
)

The library is released on
`pypi/onnx-array-api <https://pypi.org/project/onnx-array-api/>`_
and its documentation is published at
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41 changes: 35 additions & 6 deletions_doc/index.rst
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onnx-array-api:(Numpy) Array API forONNX
onnx-array-api:APIs to createONNX Graphs
==========================================

.. image:: https://dev.azure.com/xavierdupre3/onnx-array-api/_apis/build/status/sdpython.onnx-array-api
Expand All@@ -26,10 +26,8 @@ onnx-array-api: (Numpy) Array API for ONNX
.. image:: https://codecov.io/gh/sdpython/onnx-array-api/branch/main/graph/badge.svg?token=Wb9ZGDta8J
:target: https://codecov.io/gh/sdpython/onnx-array-api

**onnx-array-api** implements a numpy API for ONNX.
It gives the user the ability to convert functions written
following the numpy API to convert that function into ONNX as
well as to execute it.
**onnx-array-api** implements APIs to create custom ONNX graphs.
The objective is to speed up the implementation of converter libraries.

.. toctree::
:maxdepth: 1
Expand All@@ -47,6 +45,8 @@ well as to execute it.
CHANGELOGS
license

**Numpy API**

Sources available on
`github/onnx-array-api <https://github.com/sdpython/onnx-array-api>`_.

Expand All@@ -57,7 +57,7 @@ Sources available on

import numpy as np # A
from onnx_array_api.npx import absolute, jit_onnx
from onnx_array_api.plotting.dot_plot importto_dot
from onnx_array_api.plotting.text_plot importonnx_simple_text_plot

def l1_loss(x, y):
return absolute(x - y).sum()
Expand All@@ -78,6 +78,8 @@ Sources available on
res = jitted_myloss(x, y)
print(res)

print(onnx_simple_text_plot(jitted_myloss.get_onnx()))

.. gdot::
:script: DOT-SECTION
:process:
Expand DownExpand Up@@ -106,3 +108,30 @@ Sources available on
y = np.array([[0.11, 0.22], [0.33, 0.44]], dtype=np.float32)
res = jitted_myloss(x, y)
print(to_dot(jitted_myloss.get_onnx()))

**Light API**

.. runpython::
:showcode:

import numpy as np
from onnx_array_api.light_api import start
from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

model = (
start()
.vin("X")
.vin("Y")
.bring("X", "Y")
.Sub()
.rename("dxy")
.cst(np.array([2], dtype=np.int64), "two")
.bring("dxy", "two")
.Pow()
.ReduceSum()
.rename("Z")
.vout()
.to_onnx()
)

print(onnx_simple_text_plot(model))
60 changes: 60 additions & 0 deletions_doc/tutorial/onnx_api.rst
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Expand Up@@ -16,6 +16,66 @@ an existing onnx models or to merge models coming from different packages.
Sometimes, they are just not available, only onnx is.
Let's see how it looks like a very simply example.

Use torch or tensorflow
=======================

:epkg:`pytorch` offers the possibility to convert any function
implemented with pytorch function into onnx with :epkg:`torch.onnx`.
A couple of examples.

.. code-block:: python

import torch
import torch.nn


class MyModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(2, 2)

def forward(self, x, bias=None):
out = self.linear(x)
out = out + bias
return out

model = MyModel()
kwargs = {"bias": 3.}
args = (torch.randn(2, 2, 2),)

export_output = torch.onnx.dynamo_export(
model,
*args,
**kwargs).save("my_simple_model.onnx")

.. code-block:: python

from typing import Dict, Tuple
import torch
import torch.onnx


def func_with_nested_input_structure(
x_dict: Dict[str, torch.Tensor],
y_tuple: Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
):
if "a" in x_dict:
x = x_dict["a"]
elif "b" in x_dict:
x = x_dict["b"]
else:
x = torch.randn(3)

y1, (y2, y3) = y_tuple

return x + y1 + y2 + y3

x_dict = {"a": torch.tensor(1.)}
y_tuple = (torch.tensor(2.), (torch.tensor(3.), torch.tensor(4.)))
export_output = torch.onnx.dynamo_export(func_with_nested_input_structure, x_dict, y_tuple)

print(export_output.adapt_torch_inputs_to_onnx(x_dict, y_tuple))

Euclidian distance
==================

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2 changes: 1 addition & 1 deletiononnx_array_api/__init__.py
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# coding: utf-8
"""
(Numpy) Array API forONNX.
APIs to createONNX Graphs.
"""

__version__ = "0.1.2"
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