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sdpython · sdpython · Nov 7, 2023 · Nov 7, 2023 · Nov 7, 2023 · Nov 7, 2023
commit 16e697e370db0ab83a41398fc957f76e41cb2934
diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
@@ -1,7 +1,7 @@
 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

diff --git a/README.rst b/README.rst
@@ -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 for ONNX
+onnx-array-api: APIs to create ONNX Graphs
 ==========================================
 
 .. image:: https://dev.azure.com/xavierdupre3/onnx-array-api/_apis/build/status/sdpython.onnx-array-api
@@ -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.
@@ -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

diff --git a/_doc/index.rst b/_doc/index.rst
@@ -1,5 +1,5 @@
 
-onnx-array-api: (Numpy) Array API for ONNX
+onnx-array-api: APIs to create ONNX Graphs
 ==========================================
 
 .. image:: https://dev.azure.com/xavierdupre3/onnx-array-api/_apis/build/status/sdpython.onnx-array-api
@@ -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
@@ -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>`_.
 
@@ -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 import to_dot
+    from onnx_array_api.plotting.text_plot import onnx_simple_text_plot
 
     def l1_loss(x, y):
         return absolute(x - y).sum()
@@ -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:
@@ -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))
diff --git a/_doc/tutorial/onnx_api.rst b/_doc/tutorial/onnx_api.rst
@@ -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
 ==================
 

diff --git a/onnx_array_api/__init__.py b/onnx_array_api/__init__.py
@@ -1,6 +1,6 @@
 # coding: utf-8
 """
-(Numpy) Array API for ONNX.
+APIs to create ONNX Graphs.
 """
 
 __version__ = "0.1.2"