InvokeAI/tests/backend/patches/layers/test_set_parameter_layer.py

50 lines
1.7 KiB
Python

import pytest
import torch
from invokeai.backend.patches.layers.set_parameter_layer import SetParameterLayer
def test_set_parameter_layer_get_parameters():
orig_module = torch.nn.Linear(4, 8)
target_weight = torch.randn(8, 4)
layer = SetParameterLayer(param_name="weight", weight=target_weight)
params = layer.get_parameters(dict(orig_module.named_parameters(recurse=False)), weight=1.0)
assert len(params) == 1
new_weight = orig_module.weight + params["weight"]
assert torch.allclose(new_weight, target_weight)
@pytest.mark.parametrize(
["device"],
[
pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")),
pytest.param(
"mps", marks=pytest.mark.skipif(not torch.backends.mps.is_available(), reason="requires MPS device")
),
],
)
def test_set_parameter_layer_to(device: str):
"""Test moving SetParameterLayer to different device/dtype."""
target_weight = torch.randn(8, 4)
layer = SetParameterLayer(param_name="weight", weight=target_weight)
# SetParameterLayer should be initialized on CPU.
assert layer.weight.device.type == "cpu" # type: ignore
# Move to device.
layer.to(device=torch.device(device))
assert layer.weight.device.type == device # type: ignore
def test_set_parameter_layer_calc_size():
"""Test calculating parameter size of SetParameterLayer"""
param = torch.randn(4, 8)
layer = SetParameterLayer(param_name="weight", weight=param)
# Size should be number of elements * bytes per element
expected_size = param.nelement() * param.element_size()
assert layer.calc_size() == expected_size