documentation (#13)

* improve ci

* fix README

* change version

* spelling

* change style

* fix a few things

* rename

Author: xadupre

.github/workflows/spell-check.yml

@@ -0,0 +1,24 @@
+name: Spell Check
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  spell-check:
+    runs-on: ubuntu-latest
+
+    steps:
+      # Checkout the repository
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      # Install codespell
+      - name: Install codespell
+        run: pip install codespell
+
+      # Run codespell
+      - name: Run codespell
+        run: codespell --skip="*.png,*.jpg,*.jpeg,*.gif,*.svg,*.ico,*.pdf,*.js" --ignore-words-list="nd,te,OT" --check-filenames

.github/workflows/wheels-any.yml

@@ -22,7 +22,13 @@ jobs:
           python-version: '3.12'
 
       - name: build wheel
-        run: python -m pip wheel .
+        run: python -m pip wheel . -v
+
+      - name: install twine
+        run: python -m pip install twine
+
+      - name: check wheel
+        run: python -m twine check ./onnx_diagnostic*.whl
 
       - uses: actions/upload-artifact@v4
         with:

README.rst

@@ -26,7 +26,7 @@ onnx-diagnostic: investigate onnx models
     :target: https://codecov.io/gh/sdpython/onnx-diagnostic
 
 Helps investigating onnx models, exporting modes into onnx.
-See :epkg:`documentation of onnx-diagnostic <https://sdpython.github.io/doc/onnx-diagnostic/dev/>`_.
+See `documentation of onnx-diagnostic <https://sdpython.github.io/doc/onnx-diagnostic/dev/>`_.
 
 Getting started
 +++++++++++++++
@@ -43,18 +43,26 @@ or
 
     pip install onnx-diagnostic
 
-**Enlightening Examples**
+Enlightening Examples
++++++++++++++++++++++
+
+**Torch Export**
 
 * `Use DYNAMIC or AUTO when exporting if dynamic shapes has constraints
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_with_dynamic_shapes_auto.html>`_
 * `Export with DynamicCache and dynamic shapes
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_with_dynamic_cache.html>`_
 * `Steel method forward to guess the dynamic shapes
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_export_tiny_llm.html>`_
-* `Running ReferenceEvaluator on a failing model
-  <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_failing_reference_evaluator.html>`_
+
+**Investigate ONNX models**
+
 * `Find where a model is failing by running submodels
   <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_failing_model_extract.html>`_
+* `Intermediate results with (ONNX) ReferenceEvaluator
+  <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_failing_reference_evaluator.html>`_
+* `Intermediate results with onnxruntime
+  <https://sdpython.github.io/doc/onnx-diagnostic/dev/auto_examples/plot_failing_onnxruntime_evaluator.html>`_
 
 Snapshot of usefuls tools
 +++++++++++++++++++++++++
@@ -99,4 +107,4 @@ Snapshot of usefuls tools
 
 **max_diff**
 
-Returns the maximum discrancies accross nested containers containing tensors.
+Returns the maximum discrancies across nested containers containing tensors.

_doc/_static/custom.css

@@ -0,0 +1,13 @@
+.output {
+    background-color: #f8f9fa;
+    border-left: 4px solid #0078d4;
+    padding: 10px;
+    font-family: monospace;
+    font-size: 0.9em;
+}
+.sphx-glr-script-out {
+    padding: 10px;
+    font-family: monospace;
+    font-size: 0.9em;
+    overflow-x: auto;
+}

_doc/conf.py

@@ -50,6 +50,7 @@
 html_theme_path = ["_static"]
 html_theme_options = {}
 html_static_path = ["_static"]
+html_css_files = ["custom.css"]
 html_sourcelink_suffix = ""
 
 issues_github_path = "sdpython/onnx-diagnostic"

_doc/examples/plot_export_tiny_llm.py

@@ -4,7 +4,7 @@
 Steel method forward to guess the dynamic shapes
 ================================================
 
-Inputs are always dynamic with LLMs that is why dyanmic shapes
+Inputs are always dynamic with LLMs that is why dynamic shapes
 needs to be specified when a LLM is exported with:func:`torch.export.export`.
 Most of the examples on :epkg:`HuggingFace` use method
 :meth:`transformers.GenerationMixin.generate` but we only want to
@@ -15,7 +15,7 @@
 
 We focus on the model
 `Tiny-LLM <https://huggingface.co/arnir0/Tiny-LLM>`_.
-To avoid downloading any weigths, we write a function creating a
+To avoid downloading any weights, we write a function creating a
 random model based on the same architecture.
 
 Steel the forward method

_doc/examples/plot_export_with_dynamic_cache.py

@@ -54,9 +54,9 @@ def forward(self, x, y):
 pprint.pprint(ds)
 
 # %%
-# The function returns a tuple with two objets.
+# The function returns a tuple with two objects.
 # The first one for the positional arguments, the other one
-# for the named arguments. There is no named argements. We
+# for the named arguments. There is no named arguments. We
 # we used the first result to export.
 
 ep = torch.export.export(model, (x, y), dynamic_shapes=ds[0])
@@ -66,7 +66,7 @@ def forward(self, x, y):
 # kwargs
 # ++++++
 #
-# We do the same with named argments.
+# We do the same with named arguments.
 
 
 class Model(torch.nn.Module):

_doc/examples/plot_export_with_dynamic_shapes_auto.py

@@ -4,7 +4,7 @@
 Use DYNAMIC or AUTO when exporting if dynamic shapes has constraints
 ====================================================================
 
-Settings the dynamic shapes is not always easy.
+Setting the dynamic shapes is not always easy.
 Here are a few tricks to make it work.
 
 dx + dy not allowed?

_doc/examples/plot_failing_model_extract.py

@@ -39,7 +39,7 @@
             oh.make_node("Cast", ["C"], ["X999"], to=999, name="failing"),
             oh.make_node("CastLike", ["X999", "Y"], ["Z"], name="n4"),
         ],
-        "nd",
+        "-nd-",
         [
             oh.make_tensor_value_info("X", TFLOAT, ["a", "b", "c"]),
             oh.make_tensor_value_info("Y", TFLOAT, ["a", "b", "c"]),

_doc/examples/plot_failing_onnxruntime_evaluator.py

@@ -1,14 +1,14 @@
 """
 .. _l-plot-failing-onnxruntime-evaluator:
 
-Running OnnxruntimeEvaluator on a failing model
-===============================================
+Intermediate results with onnxruntime
+=====================================
 
 Example :ref:`l-plot-failing-reference-evaluator` demonstrated
 how to run a python runtime on a model but it may very slow sometimes
 and it could show some discrepancies if the only provider is not CPU.
 Let's use :class:`OnnxruntimeEvaluator <onnx_diagnostic.reference.OnnxruntimeEvaluator>`.
-It splits the model into node and runs them independantly until it succeeds
+It splits the model into node and runs them independently until it succeeds
 or fails. This class converts every node into model based on the types
 discovered during the execution. It relies on :class:`InferenceSessionForTorch
 <onnx_diagnostic.ort_session.InferenceSessionForTorch>` or
@@ -43,7 +43,7 @@
             oh.make_node("Cast", ["C"], ["X999"], to=999, name="failing"),
             oh.make_node("CastLike", ["X999", "Y"], ["Z"], name="n4"),
         ],
-        "nd",
+        "-nd-",
         [
             oh.make_tensor_value_info("X", TBFLOAT16, ["a", "b", "c"]),
             oh.make_tensor_value_info("Y", TBFLOAT16, ["a", "b", "c"]),
@@ -100,7 +100,7 @@
 # %%
 # We can see it run until it reaches `Cast` and stops.
 # The error message is not always obvious to interpret.
-# It gets improved everytime from time to time.
+# It gets improved every time from time to time.
 # This runtime is useful when it fails for a numerical reason.
 # It is possible to insert prints in the python code to print
 # more information or debug if needed.