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onnx-array-api implements APIs to create custom ONNX graphs.The objective is to speed up the implementation of converter libraries.The first one matchesnumpy API.It gives the user the ability to convert functions writtenfollowing the numpy API to convert that function into ONNX aswell as to execute it.

importnumpyasnpfromonnx_array_api.npximportabsolute,jit_onnxfromonnx_array_api.plotting.text_plotimportonnx_simple_text_plotdefl1_loss(x,y):returnabsolute(x-y).sum()defl2_loss(x,y):return ((x-y)**2).sum()defmyloss(x,y):returnl1_loss(x[:,0],y[:,0])+l2_loss(x[:,1],y[:,1])jitted_myloss=jit_onnx(myloss)x=np.array([[0.1,0.2], [0.3,0.4]],dtype=np.float32)y=np.array([[0.11,0.22], [0.33,0.44]],dtype=np.float32)res=jitted_myloss(x,y)print(res)print(onnx_simple_text_plot(jitted_myloss.get_onnx()))

[0.042]opset: domain='' version=18input: name='x0' type=dtype('float32') shape=['', '']input: name='x1' type=dtype('float32') shape=['', '']Sub(x0, x1) -> r__0  Abs(r__0) -> r__1    ReduceSum(r__1, keepdims=0) -> r__2output: name='r__2' type=dtype('float32') shape=None

It supports eager mode as well:

importnumpyasnpfromonnx_array_api.npximportabsolute,eager_onnxdefl1_loss(x,y):err=absolute(x-y).sum()print(f"l1_loss={err.numpy()}")returnerrdefl2_loss(x,y):err= ((x-y)**2).sum()print(f"l2_loss={err.numpy()}")returnerrdefmyloss(x,y):returnl1_loss(x[:,0],y[:,0])+l2_loss(x[:,1],y[:,1])eager_myloss=eager_onnx(myloss)x=np.array([[0.1,0.2], [0.3,0.4]],dtype=np.float32)y=np.array([[0.11,0.22], [0.33,0.44]],dtype=np.float32)res=eager_myloss(x,y)print(res)

l1_loss=[0.04]l2_loss=[0.002][0.042]

The second API orLight API tends to do every thing in one line.The euclidean distance looks like the following:

import numpy as npfrom onnx_array_api.light_api import startfrom onnx_array_api.plotting.text_plot import onnx_simple_text_plotmodel = (    start()    .vin("X")    .vin("Y")    .bring("X", "Y")    .Sub()    .rename("dxy")    .cst(np.array([2], dtype=np.int64), "two")    .bring("dxy", "two")    .Pow()    .ReduceSum()    .rename("Z")    .vout()    .to_onnx())

The library is released onpypi/onnx-array-apiand its documentation is published atAPIs to create ONNX Graphs.