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* 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
90 lines
2.0 KiB
Python
90 lines
2.0 KiB
Python
# Generated content DO NOT EDIT
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
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from os import PathLike
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from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
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from candle import Tensor, DType, QTensor
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class ONNXModel:
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"""
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A wrapper around an ONNX model.
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"""
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def __init__(self, path: str):
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pass
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@property
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def doc_string(self) -> str:
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"""
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The doc string of the model.
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"""
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pass
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@property
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def domain(self) -> str:
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"""
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The domain of the operator set of the model.
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"""
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pass
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def initializers(self) -> Dict[str, Tensor]:
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"""
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Get the weights of the model.
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"""
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pass
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@property
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def inputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
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"""
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The inputs of the model.
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"""
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pass
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@property
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def ir_version(self) -> int:
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"""
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The version of the IR this model targets.
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"""
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pass
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@property
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def model_version(self) -> int:
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"""
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The version of the model.
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"""
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pass
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@property
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def outputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
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"""
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The outputs of the model.
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"""
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pass
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@property
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def producer_name(self) -> str:
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"""
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The producer of the model.
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"""
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pass
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@property
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def producer_version(self) -> str:
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"""
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The version of the producer of the model.
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"""
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pass
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def run(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
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"""
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Run the model on the given inputs.
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"""
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pass
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class ONNXTensorDescription:
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"""
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A wrapper around an ONNX tensor description.
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"""
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@property
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def dtype(self) -> DType:
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"""
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The data type of the tensor.
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"""
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pass
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@property
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def shape(self) -> Tuple[Union[int, str, Any]]:
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"""
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The shape of the tensor.
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"""
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pass
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