ONNX Script enables developers to naturally author ONNX functions and models using a subset of Python. It is intended to be:
Expressive: enables the authoring of all ONNX functions.
Simple and concise: function code is natural and simple.
Debuggable: allows for eager-mode evaluation that enables debugging the code using standard python debuggers.
Note however that ONNX Script does not intend to support the entirety of the Python language.
ONNX Script provides a few major capabilities for authoring and debugging ONNX models and functions:
A converter which translates a Python ONNX Script function into an ONNX graph, accomplished by traversing the Python Abstract Syntax Tree to build an ONNX graph equivalent of the function.
A runtime shim that allows such functions to be evaluated (in an “eager mode”). This functionality currently relies on ONNX Runtime for executing ONNX ops and there is a Python-only reference runtime for ONNX underway that will also be supported.
A converter that translates ONNX models and functions into ONNX Script. This capability can be used to fully round-trip ONNX Script ↔ ONNX graph.
Note that the runtime is intended to help understand and debug function definitions. Performance is not a goal here.
The following toy example illustrates how to use onnxscript.
from onnxscript import script # We use ONNX opset 15 to define the function below. from onnxscript import opset15 as op # We use the script decorator to indicate that the following function is meant # to be translated to ONNX. @script() def MatmulAdd(X, Wt, Bias): return op.MatMul(X, Wt) + Bias
The decorator parses the code of the function and converts it into an intermediate representation. If it fails, it produces an error message indicating the error detected. If it succeeds, the corresponding ONNX representation of the function (a value of type FunctionProto) can be generated as shown below:
fp = MatmulAdd.to_function_proto() # returns an onnx.FunctionProto
One can similarly generate an ONNX Model. There are a few differences between ONNX models and ONNX functions. For example, ONNX models must specify the type of inputs and outputs (unlike ONNX functions). The following example illustrates how we can generate an ONNX Model:
from onnxscript import script from onnxscript import opset15 as op from onnxscript import FLOAT @script() def MatmulAddModel(X : FLOAT[64, 128] , Wt: FLOAT[128, 10], Bias: FLOAT) -> FLOAT[64, 10]: return op.MatMul(X, Wt) + Bias model = MatmulAddModel.to_model_proto() # returns an onnx.ModelProto
Eager evaluation mode is mostly use to debug and check intermediate results are as expected. The function defined earlier can be called as below, and this executes in an eager-evaluation mode.
import numpy as np x = np.array([[0, 1], [2, 3]], dtype=np.float32) wt = np.array([[0, 1], [2, 3]], dtype=np.float32) bias = np.array([0, 1], dtype=np.float32) result = MatmulAdd(x, wt, bias)
onnxscript comes with a MIT license.