import functools
import torch
from torch.onnx import symbolic_helper
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import _beartype, jit_utils, registration
from torch.onnx import register_custom_op_symbolic
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return torch.asinh(x)
_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=14)
@_onnx_symbolic("aten::asinh")
# @symbolic_helper.parse_args("v")
# @_beartype.beartype
def asinh_symbolic(g: jit_utils.GraphContext, self, *, out=None):
return g.op("Asinh", self)
register_custom_op_symbolic('aten::asinh', asinh_symbolic, 14)
model = Model()
inputs = torch.rand(1, 3, 10, 10)
torch.onnx.export(model, inputs, 'asinh.onnx')
