----------------------------------------------------------------------
Ran 11 tests in 26.689s

OK

import torch
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
from executorch.exir import to_edge_transform_and_lower
from executorch.extension.pybindings.portable_lib import _load_for_executorch_from_buffer

class Model(torch.nn.Module):
    def forward(self, x, y):
        return torch.add(x, y, alpha=10)

inputs = (
    torch.randn(10),
    torch.randn(10),
)
model = Model()

ep = torch.export.export(model, inputs)
lowered = to_edge_transform_and_lower(
    ep,
    partitioner=[XnnpackPartitioner()],
).to_executorch()

et_model = _load_for_executorch_from_buffer(lowered.buffer)

eager_output = model(*inputs)
et_output = et_model([*inputs])[0]

tolerance=1e-5
if torch.allclose(eager_output, et_output, atol=tolerance):
    print("Outputs are within the tolerance level.")
else:
    print("Outputs differ beyond the tolerance level.")

Outputs are within the tolerance level.