fix(docs): restore operator examples (#8332)
### Motivation and Context Fixes #8314 The operator docs generator passed the default ONNX domain as an empty string, but the example lookup did not recognize it. It also assumed non-default examples lived in domain subpackages and depended on filenames matching operator names. This omitted existing examples from generated pages and placed GreaterOrEqual and LessOrEqual examples under Greater and Less in `docs/Operators.md`. This treats the empty string as the default domain, limits top-level fallback to the preview domains, aligns legacy module and exporter names where safe, and keeps Range's legacy module through an explicit alias. Example source is rendered directly inside trusted code fences so the Sphinx build can highlight it. Tested: - `python -m pytest -q tests/python/onnx_sphinx_test.py` (12 passed) - `python -m pytest -q tests/python/backend_test.py -k 'batchnorm or greater_equal or instancenorm or less_equal or test_range or softmaxcrossentropy'` (51 passed, 51 skipped) - `ONNX_ML=1 python onnx/defs/gen_doc.py` - `lintrunner -r HEAD` - `cd docs/docsgen && make html` --------- Signed-off-by: kiwigitops <kiwisclubco@gmail.com> Co-authored-by: Andreas Fehlner <fehlner@arcor.de>
This commit is contained in:
Kiwi
+160
-156
@@ -16281,7 +16281,86 @@ expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64")
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<details>
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<summary>greater</summary>
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<summary>greater_broadcast</summary>
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```python
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node = onnx.helper.make_node(
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"Greater",
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inputs=["x", "y"],
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outputs=["greater"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.greater(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
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```
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</details>
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### <a name="GreaterOrEqual"></a><a name="greaterorequal">**GreaterOrEqual**</a>
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Returns the tensor resulted from performing the `greater_equal` logical operation
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elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
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This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
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#### Version
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This version of the operator has been available since version 16 of the default ONNX operator set.
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Other versions of this operator: <a href="Changelog.md#GreaterOrEqual-12">12</a>
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#### Inputs
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<dl>
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<dt><tt>A</tt> (non-differentiable) : T</dt>
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<dd>First input operand for the logical operator.</dd>
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<dt><tt>B</tt> (non-differentiable) : T</dt>
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<dd>Second input operand for the logical operator.</dd>
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</dl>
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#### Outputs
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<dl>
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<dt><tt>C</tt> (non-differentiable) : T1</dt>
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<dd>Result tensor.</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
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<dd>Constrain input types to all numeric tensors.</dd>
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<dt><tt>T1</tt> : tensor(bool)</dt>
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<dd>Constrain output to boolean tensor.</dd>
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</dl>
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#### Examples
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<details>
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<summary>greater_broadcast</summary>
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```python
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node = onnx.helper.make_node(
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"GreaterOrEqual",
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inputs=["x", "y"],
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outputs=["greater_equal"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.greater_equal(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
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```
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</details>
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<details>
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<summary>greaterorequal</summary>
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```python
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node = onnx.helper.make_node(
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@@ -16329,83 +16408,6 @@ expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64")
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</details>
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<details>
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<summary>greater_broadcast</summary>
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```python
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node = onnx.helper.make_node(
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"Greater",
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inputs=["x", "y"],
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outputs=["greater"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.greater(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
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```
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|
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</details>
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<details>
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<summary>greater_broadcast</summary>
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|
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```python
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node = onnx.helper.make_node(
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"GreaterOrEqual",
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inputs=["x", "y"],
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outputs=["greater_equal"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.greater_equal(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
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```
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</details>
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### <a name="GreaterOrEqual"></a><a name="greaterorequal">**GreaterOrEqual**</a>
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Returns the tensor resulted from performing the `greater_equal` logical operation
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elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
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This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
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|
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#### Version
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This version of the operator has been available since version 16 of the default ONNX operator set.
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|
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Other versions of this operator: <a href="Changelog.md#GreaterOrEqual-12">12</a>
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#### Inputs
|
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|
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<dl>
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<dt><tt>A</tt> (non-differentiable) : T</dt>
|
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<dd>First input operand for the logical operator.</dd>
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<dt><tt>B</tt> (non-differentiable) : T</dt>
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<dd>Second input operand for the logical operator.</dd>
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</dl>
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#### Outputs
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|
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<dl>
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<dt><tt>C</tt> (non-differentiable) : T1</dt>
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<dd>Result tensor.</dd>
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</dl>
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|
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#### Type Constraints
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|
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<dl>
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<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
|
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<dd>Constrain input types to all numeric tensors.</dd>
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<dt><tt>T1</tt> : tensor(bool)</dt>
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<dd>Constrain output to boolean tensor.</dd>
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</dl>
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### <a name="GridSample"></a><a name="gridsample">**GridSample**</a>
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Given an input `X` and a flow-field `grid`, computes the output `Y` using `X` values and pixel locations from the `grid`.
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@@ -19536,7 +19538,86 @@ expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64")
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<details>
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<summary>less</summary>
|
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<summary>less_broadcast</summary>
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|
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```python
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node = onnx.helper.make_node(
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"Less",
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inputs=["x", "y"],
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outputs=["less"],
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)
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.less(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
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```
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|
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</details>
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### <a name="LessOrEqual"></a><a name="lessorequal">**LessOrEqual**</a>
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Returns the tensor resulted from performing the `less_equal` logical operation
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elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
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||||
|
||||
This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
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||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 16 of the default ONNX operator set.
|
||||
|
||||
Other versions of this operator: <a href="Changelog.md#LessOrEqual-12">12</a>
|
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|
||||
#### Inputs
|
||||
|
||||
<dl>
|
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<dt><tt>A</tt> (non-differentiable) : T</dt>
|
||||
<dd>First input operand for the logical operator.</dd>
|
||||
<dt><tt>B</tt> (non-differentiable) : T</dt>
|
||||
<dd>Second input operand for the logical operator.</dd>
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||||
</dl>
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||||
|
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#### Outputs
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||||
|
||||
<dl>
|
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<dt><tt>C</tt> (non-differentiable) : T1</dt>
|
||||
<dd>Result tensor.</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
|
||||
<dd>Constrain input types to all numeric tensors.</dd>
|
||||
<dt><tt>T1</tt> : tensor(bool)</dt>
|
||||
<dd>Constrain output to boolean tensor.</dd>
|
||||
</dl>
|
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|
||||
|
||||
#### Examples
|
||||
|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
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node = onnx.helper.make_node(
|
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"LessOrEqual",
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inputs=["x", "y"],
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outputs=["less_equal"],
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)
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|
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x = np.random.randn(3, 4, 5).astype(np.float32)
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y = np.random.randn(5).astype(np.float32)
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z = np.less_equal(x, y)
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expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
|
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```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary>lessorequal</summary>
|
||||
|
||||
```python
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node = onnx.helper.make_node(
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@@ -19584,83 +19665,6 @@ expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64")
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
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node = onnx.helper.make_node(
|
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"Less",
|
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inputs=["x", "y"],
|
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outputs=["less"],
|
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)
|
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|
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x = np.random.randn(3, 4, 5).astype(np.float32)
|
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y = np.random.randn(5).astype(np.float32)
|
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z = np.less(x, y)
|
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expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
|
||||
```
|
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|
||||
</details>
|
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|
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|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
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node = onnx.helper.make_node(
|
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"LessOrEqual",
|
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inputs=["x", "y"],
|
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outputs=["less_equal"],
|
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)
|
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|
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x = np.random.randn(3, 4, 5).astype(np.float32)
|
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y = np.random.randn(5).astype(np.float32)
|
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z = np.less_equal(x, y)
|
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expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
|
||||
```
|
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|
||||
</details>
|
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|
||||
|
||||
### <a name="LessOrEqual"></a><a name="lessorequal">**LessOrEqual**</a>
|
||||
|
||||
Returns the tensor resulted from performing the `less_equal` logical operation
|
||||
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
|
||||
|
||||
This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 16 of the default ONNX operator set.
|
||||
|
||||
Other versions of this operator: <a href="Changelog.md#LessOrEqual-12">12</a>
|
||||
|
||||
#### Inputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>A</tt> (non-differentiable) : T</dt>
|
||||
<dd>First input operand for the logical operator.</dd>
|
||||
<dt><tt>B</tt> (non-differentiable) : T</dt>
|
||||
<dd>Second input operand for the logical operator.</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>C</tt> (non-differentiable) : T1</dt>
|
||||
<dd>Result tensor.</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
|
||||
<dd>Constrain input types to all numeric tensors.</dd>
|
||||
<dt><tt>T1</tt> : tensor(bool)</dt>
|
||||
<dd>Constrain output to boolean tensor.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="LinearAttention"></a><a name="linearattention">**LinearAttention**</a>
|
||||
|
||||
Unified linear attention operator for autoregressive decoding (T=1) and prefill (T>1).
|
||||
|
||||
+81
-79
@@ -6,7 +6,7 @@
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||||
* [Overall Test Coverage](#overall-test-coverage)
|
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# Node Test Coverage
|
||||
## Summary
|
||||
Node tests have covered 190/202 (94.06%, 5 generators excluded) common operators.
|
||||
Node tests have covered 192/202 (95.05%, 5 generators excluded) common operators.
|
||||
|
||||
Node tests have covered 1/1 (100.00%, 0 generators excluded) experimental operators.
|
||||
|
||||
@@ -12058,7 +12058,7 @@ expect(
|
||||
|
||||
|
||||
### Greater
|
||||
There are 4 test cases, listed as following:
|
||||
There are 2 test cases, listed as following:
|
||||
<details>
|
||||
<summary>greater</summary>
|
||||
|
||||
@@ -12107,7 +12107,45 @@ expect(node, inputs=[x, y], outputs=[z], name="test_greater_uint64")
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>greater</summary>
|
||||
<summary>greater_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"Greater",
|
||||
inputs=["x", "y"],
|
||||
outputs=["greater"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.greater(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
### GreaterOrEqual
|
||||
There are 2 test cases, listed as following:
|
||||
<details>
|
||||
<summary>greater_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"GreaterOrEqual",
|
||||
inputs=["x", "y"],
|
||||
outputs=["greater_equal"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.greater_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>greaterorequal</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
@@ -12152,40 +12190,6 @@ z = np.greater_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_uint64")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>greater_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"Greater",
|
||||
inputs=["x", "y"],
|
||||
outputs=["greater"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.greater(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_greater_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>greater_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"GreaterOrEqual",
|
||||
inputs=["x", "y"],
|
||||
outputs=["greater_equal"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.greater_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_greater_equal_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
@@ -14255,7 +14259,7 @@ expect(node, inputs=[x], outputs=[y], name="test_leakyrelu_default")
|
||||
|
||||
|
||||
### Less
|
||||
There are 4 test cases, listed as following:
|
||||
There are 2 test cases, listed as following:
|
||||
<details>
|
||||
<summary>less</summary>
|
||||
|
||||
@@ -14304,7 +14308,45 @@ expect(node, inputs=[x, y], outputs=[z], name="test_less_uint64")
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>less</summary>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"Less",
|
||||
inputs=["x", "y"],
|
||||
outputs=["less"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.less(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
### LessOrEqual
|
||||
There are 2 test cases, listed as following:
|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"LessOrEqual",
|
||||
inputs=["x", "y"],
|
||||
outputs=["less_equal"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.less_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>lessorequal</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
@@ -14349,40 +14391,6 @@ z = np.less_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_uint64")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"Less",
|
||||
inputs=["x", "y"],
|
||||
outputs=["less"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.less(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_less_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
<details>
|
||||
<summary>less_broadcast</summary>
|
||||
|
||||
```python
|
||||
node = onnx.helper.make_node(
|
||||
"LessOrEqual",
|
||||
inputs=["x", "y"],
|
||||
outputs=["less_equal"],
|
||||
)
|
||||
|
||||
x = np.random.randn(3, 4, 5).astype(np.float32)
|
||||
y = np.random.randn(5).astype(np.float32)
|
||||
z = np.less_equal(x, y)
|
||||
expect(node, inputs=[x, y], outputs=[z], name="test_less_equal_bcast")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
@@ -31526,12 +31534,6 @@ expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d")
|
||||
### GlobalLpPool (call for test cases)
|
||||
|
||||
|
||||
### GreaterOrEqual (call for test cases)
|
||||
|
||||
|
||||
### LessOrEqual (call for test cases)
|
||||
|
||||
|
||||
### MaxRoiPool (call for test cases)
|
||||
|
||||
|
||||
|
||||
@@ -184,7 +184,7 @@ The function definition for this operator.
|
||||
#### {{ example }}
|
||||
|
||||
```python
|
||||
{{ format_example(code) }}
|
||||
{{ format_example(code) | safe }}
|
||||
```
|
||||
{% endfor %}
|
||||
{% endif %}
|
||||
@@ -644,6 +644,10 @@ def _process_example(code: str) -> str:
|
||||
return "\n".join(elements)
|
||||
|
||||
|
||||
_EXAMPLE_MODULE_ALIASES = {"Range": "rangeop"}
|
||||
_TOP_LEVEL_EXAMPLE_DOMAINS = {"ai.onnx.preview", "ai.onnx.preview.training"}
|
||||
|
||||
|
||||
def get_onnx_example(op_name, domain):
|
||||
"""Retrieves examples associated to one operator
|
||||
stored in onnx packages.
|
||||
@@ -652,17 +656,27 @@ def get_onnx_example(op_name, domain):
|
||||
:param fmt: rendering format
|
||||
:return: dictionary
|
||||
"""
|
||||
if domain in (None, "ai.onnx"):
|
||||
fallback_modules = []
|
||||
if domain in (None, "", "ai.onnx"):
|
||||
modules = [
|
||||
f"onnx.backend.test.case.node.{op_name.lower()}",
|
||||
f"onnx.backend.test.case.node.{pascal_to_snake_case(op_name)}",
|
||||
]
|
||||
if op_name in _EXAMPLE_MODULE_ALIASES:
|
||||
modules.append(
|
||||
f"onnx.backend.test.case.node.{_EXAMPLE_MODULE_ALIASES[op_name]}"
|
||||
)
|
||||
else:
|
||||
domain_ = domain.replace(".", "_")
|
||||
modules = [
|
||||
f"onnx.backend.test.case.node.{domain_}.{op_name.lower()}",
|
||||
f"onnx.backend.test.case.node.{domain_}.{pascal_to_snake_case(op_name)}",
|
||||
]
|
||||
if domain in _TOP_LEVEL_EXAMPLE_DOMAINS:
|
||||
fallback_modules = [
|
||||
f"onnx.backend.test.case.node.{op_name.lower()}",
|
||||
f"onnx.backend.test.case.node.{pascal_to_snake_case(op_name)}",
|
||||
]
|
||||
module = None
|
||||
for m in modules:
|
||||
try:
|
||||
@@ -670,6 +684,13 @@ def get_onnx_example(op_name, domain):
|
||||
module = m
|
||||
except ImportError: # noqa: PERF203
|
||||
continue
|
||||
if module is None:
|
||||
for m in fallback_modules:
|
||||
try:
|
||||
mod = importlib.import_module(m)
|
||||
module = m
|
||||
except ImportError: # noqa: PERF203
|
||||
continue
|
||||
if module is None:
|
||||
# Unable to find an example for 'op_name'.
|
||||
return {}
|
||||
|
||||
+1
-1
@@ -10,7 +10,7 @@ from onnx.backend.test.case.base import Base
|
||||
from onnx.backend.test.case.node import expect
|
||||
|
||||
|
||||
class Greater(Base):
|
||||
class GreaterOrEqual(Base):
|
||||
@staticmethod
|
||||
def export() -> None:
|
||||
node = onnx.helper.make_node(
|
||||
+1
-1
@@ -10,7 +10,7 @@ from onnx.backend.test.case.base import Base
|
||||
from onnx.backend.test.case.node import expect
|
||||
|
||||
|
||||
class Less(Base):
|
||||
class LessOrEqual(Base):
|
||||
@staticmethod
|
||||
def export() -> None:
|
||||
node = onnx.helper.make_node(
|
||||
@@ -0,0 +1,63 @@
|
||||
# Copyright (c) ONNX Project Contributors
|
||||
#
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
pytest.importorskip("sphinx")
|
||||
|
||||
from docs.docsgen.source import onnx_sphinx
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"op_name",
|
||||
[
|
||||
"BatchNormalization",
|
||||
"GreaterOrEqual",
|
||||
"InstanceNormalization",
|
||||
"LessOrEqual",
|
||||
"Range",
|
||||
"SoftmaxCrossEntropyLoss",
|
||||
],
|
||||
)
|
||||
def test_get_markdown_doc_includes_backend_examples(op_name: str) -> None:
|
||||
docs, _, example_count = onnx_sphinx.get_markdown_doc(
|
||||
".", op_name=op_name, domain="", example=True
|
||||
)
|
||||
|
||||
assert example_count > 0
|
||||
assert "### Examples" in docs
|
||||
|
||||
|
||||
def test_get_markdown_doc_keeps_example_source_unescaped() -> None:
|
||||
docs, _, example_count = onnx_sphinx.get_markdown_doc(
|
||||
".", op_name="Attention", domain="", example=True
|
||||
)
|
||||
|
||||
assert example_count > 0
|
||||
assert """ not in docs.split("### Examples", maxsplit=1)[1]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("op_name", "domain"),
|
||||
[
|
||||
("Adagrad", "ai.onnx.preview.training"),
|
||||
("Adam", "ai.onnx.preview.training"),
|
||||
("FlexAttention", "ai.onnx.preview"),
|
||||
("Momentum", "ai.onnx.preview.training"),
|
||||
],
|
||||
)
|
||||
def test_get_markdown_doc_finds_top_level_domain_examples(
|
||||
op_name: str, domain: str
|
||||
) -> None:
|
||||
docs, _, example_count = onnx_sphinx.get_markdown_doc(
|
||||
".", op_name=op_name, domain=domain, example=True
|
||||
)
|
||||
|
||||
assert example_count > 0
|
||||
assert "### Examples" in docs
|
||||
|
||||
|
||||
def test_get_onnx_example_does_not_fall_back_for_unrelated_domains() -> None:
|
||||
assert onnx_sphinx.get_onnx_example("Add", "ai.onnx.ml") == {}
|
||||
Reference in New Issue
Block a user