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Improves documentation#66

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sdpython merged 1 commit intomainfromdoc
Jan 23, 2024
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15 changes: 15 additions & 0 deletionsCODE_OF_CONDUCT.md
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@@ -0,0 +1,15 @@
# Code of Conduct

We are a community based on openness, as well as friendly and didactic discussions.

We aspire to treat everybody equally, and value their contributions.

Decisions are made based on technical merit and consensus.

Code is not the only way to help the project. Reviewing pull requests,
answering questions to help others on mailing lists or issues, organizing and
teaching tutorials, working on the website, improving the documentation, are
all priceless contributions.

We abide by the principles of openness, respect, and consideration of others of
the Python Software Foundation: https://www.python.org/psf/codeofconduct/
38 changes: 37 additions & 1 deletionREADME.rst
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Expand Up@@ -31,6 +31,10 @@ onnx-array-api: APIs to create ONNX Graphs

**onnx-array-api** implements APIs to create custom ONNX graphs.
The objective is to speed up the implementation of converter libraries.

Numpy API
+++++++++

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
Expand DownExpand Up@@ -113,10 +117,15 @@ It supports eager mode as well:
l2_loss=[0.002]
[0.042]

Light API
+++++++++

The second API or **Light API** tends to do every thing in one line.
It is inspired from the `Reverse Polish Notation
<https://en.wikipedia.org/wiki/Reverse_Polish_notation>`_.
The euclidean distance looks like the following:

::
.. code-block:: python

import numpy as np
from onnx_array_api.light_api import start
Expand All@@ -142,3 +151,30 @@ The library is released on
`pypi/onnx-array-api <https://pypi.org/project/onnx-array-api/>`_
and its documentation is published at
`APIs to create ONNX Graphs <https://sdpython.github.io/doc/onnx-array-api/dev/>`_.

GraphBuilder API
++++++++++++++++

Almost every converting library (converting a machine learned model to ONNX) is implementing
its own graph builder and customizes it for its needs.
It handles some frequent tasks such as giving names to intermediate
results, loading, saving onnx models. It can be used as well to extend an existing graph.

.. code-block:: python

import numpy as np
from onnx_array_api.graph_api import GraphBuilder

g = GraphBuilder()
g.make_tensor_input("X", np.float32, (None, None))
g.make_tensor_input("Y", np.float32, (None, None))
r1 = g.make_node("Sub", ["X", "Y"]) # the name given to the output is given by the class,
# it ensures the name is unique
init = g.make_initializer(np.array([2], dtype=np.int64)) # the class automatically
# converts the array to a tensor
r2 = g.make_node("Pow", [r1, init])
g.make_node("ReduceSum", [r2], outputs=["Z"]) # the output name is given because
# the user wants to choose the name
g.make_tensor_output("Z", np.float32, (None, None))

onx = g.to_onnx() # final conversion to onnx
105 changes: 74 additions & 31 deletions_doc/index.rst
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Expand Up@@ -45,11 +45,83 @@ The objective is to speed up the implementation of converter libraries.
CHANGELOGS
license

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

GraphBuilder API
++++++++++++++++

Almost every converting library (converting a machine learned model to ONNX) is implementing
its own graph builder and customizes it for its needs.
It handles some frequent tasks such as giving names to intermediate
results, loading, saving onnx models. It can be used as well to extend an existing graph.
See :ref:`l-graph-api`.

.. runpython::
:showcode:

import numpy as np
from onnx_array_api.graph_api import GraphBuilder
from onnx_array_api.plotting.text_plot import onnx_simple_text_plot

g = GraphBuilder()
g.make_tensor_input("X", np.float32, (None, None))
g.make_tensor_input("Y", np.float32, (None, None))
r1 = g.make_node("Sub", ["X", "Y"]) # the name given to the output is given by the class,
# it ensures the name is unique
init = g.make_initializer(np.array([2], dtype=np.int64)) # the class automatically
# converts the array to a tensor
r2 = g.make_node("Pow", [r1, init])
g.make_node("ReduceSum", [r2], outputs=["Z"]) # the output name is given because
# the user wants to choose the name
g.make_tensor_output("Z", np.float32, (None, None))

onx = g.to_onnx() # final conversion to onnx

print(onnx_simple_text_plot(onx))

Light API
+++++++++

The syntax is inspired from the
`Reverse Polish Notation <https://en.wikipedia.org/wiki/Reverse_Polish_notation>`_.
This kind of API is easy to use to build new graphs,
less easy to extend an existing graph. See :ref:`l-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))

Numpy API
+++++++++

Sources available on
`github/onnx-array-api <https://github.com/sdpython/onnx-array-api>`_.
Writing ONNX graphs requires to know ONNX syntax unless
it is possible to reuse an existing syntax such as :epkg:`numpy`.
This is what this API is doing.
This kind of API is easy to use to build new graphs,
almost impossible to use to extend new graphs as it usually requires
to know onnx for that. See :ref:`l-numpy-api-onnx`.

.. runpython::
:showcode:
Expand DownExpand Up@@ -110,35 +182,6 @@ Sources available on
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))


Older versions
++++++++++++++

Expand Down
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