PyO3: Add optional `candle.onnx` module (#1282)

* Start onnx integration

* Merge remote-tracking branch 'upstream/main' into feat/pyo3-onnx

* Implement ONNXModel

* `fmt`

* add `onnx` flag to python ci

* Pin `protoc` to `25.0`

* Setup `protoc` in wheel builds

* Build wheels with `onnx`

* Install `protoc` in manylinux containers

* `apt` -> `yum`

* Download `protoc` via bash script

* Back to `manylinux: auto`

* Disable `onnx` builds for linux

2 files changed, 94 insertions, 0 deletions

diff --git a/candle-pyo3/py_src/candle/onnx/__init__.py b/candle-pyo3/py_src/candle/onnx/__init__.py
new file mode 100644
index 00000000..856ecd7d
--- /dev/null
+++ b/candle-pyo3/py_src/candle/onnx/__init__.py
@@ -0,0 +1,5 @@
+# Generated content DO NOT EDIT
+from .. import onnx
+
+ONNXModel = onnx.ONNXModel
+ONNXTensorDescription = onnx.ONNXTensorDescription
diff --git a/candle-pyo3/py_src/candle/onnx/__init__.pyi b/candle-pyo3/py_src/candle/onnx/__init__.pyi
new file mode 100644
index 00000000..8ce1b3aa
--- /dev/null
+++ b/candle-pyo3/py_src/candle/onnx/__init__.pyi
@@ -0,0 +1,89 @@
+# Generated content DO NOT EDIT
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
+from os import PathLike
+from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
+from candle import Tensor, DType, QTensor
+
+class ONNXModel:
+    """
+    A wrapper around an ONNX model.
+    """
+
+    def __init__(self, path: str):
+        pass
+    @property
+    def doc_string(self) -> str:
+        """
+        The doc string of the model.
+        """
+        pass
+    @property
+    def domain(self) -> str:
+        """
+        The domain of the operator set of the model.
+        """
+        pass
+    def initializers(self) -> Dict[str, Tensor]:
+        """
+        Get the weights of the model.
+        """
+        pass
+    @property
+    def inputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
+        """
+        The inputs of the model.
+        """
+        pass
+    @property
+    def ir_version(self) -> int:
+        """
+        The version of the IR this model targets.
+        """
+        pass
+    @property
+    def model_version(self) -> int:
+        """
+        The version of the model.
+        """
+        pass
+    @property
+    def outputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
+        """
+        The outputs of the model.
+        """
+        pass
+    @property
+    def producer_name(self) -> str:
+        """
+        The producer of the model.
+        """
+        pass
+    @property
+    def producer_version(self) -> str:
+        """
+        The version of the producer of the model.
+        """
+        pass
+    def run(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
+        """
+        Run the model on the given inputs.
+        """
+        pass
+
+class ONNXTensorDescription:
+    """
+    A wrapper around an ONNX tensor description.
+    """
+
+    @property
+    def dtype(self) -> DType:
+        """
+        The data type of the tensor.
+        """
+        pass
+    @property
+    def shape(self) -> Tuple[Union[int, str, Any]]:
+        """
+        The shape of the tensor.
+        """
+        pass