mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
5a3195f757
- Replace AnyModelLoader with ModelLoaderRegistry - Fix type check errors in multiple files - Remove apparently unneeded `get_model_config_enum()` method from model manager - Remove last vestiges of old model manager - Updated tests and documentation resolve conflict with seamless.py
103 lines
4.2 KiB
Python
103 lines
4.2 KiB
Python
# test that if the model's device changes while the lora is applied, the weights can still be restored
|
|
|
|
# test that LoRA patching works on both CPU and CUDA
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from invokeai.backend.lora import LoRALayer, LoRAModelRaw
|
|
from invokeai.backend.model_patcher import ModelPatcher
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"device",
|
|
[
|
|
"cpu",
|
|
pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")),
|
|
],
|
|
)
|
|
@torch.no_grad()
|
|
def test_apply_lora(device):
|
|
"""Test the basic behavior of ModelPatcher.apply_lora(...). Check that patching and unpatching produce the correct
|
|
result, and that model/LoRA tensors are moved between devices as expected.
|
|
"""
|
|
|
|
linear_in_features = 4
|
|
linear_out_features = 8
|
|
lora_dim = 2
|
|
model = torch.nn.ModuleDict(
|
|
{"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device=device, dtype=torch.float16)}
|
|
)
|
|
|
|
lora_layers = {
|
|
"linear_layer_1": LoRALayer(
|
|
layer_key="linear_layer_1",
|
|
values={
|
|
"lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
|
|
"lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
|
|
},
|
|
)
|
|
}
|
|
lora = LoRAModelRaw("lora_name", lora_layers)
|
|
|
|
lora_weight = 0.5
|
|
orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
|
|
expected_patched_linear_weight = orig_linear_weight + (lora_dim * lora_weight)
|
|
|
|
with ModelPatcher.apply_lora(model, [(lora, lora_weight)], prefix=""):
|
|
# After patching, all LoRA layer weights should have been moved back to the cpu.
|
|
assert lora_layers["linear_layer_1"].up.device.type == "cpu"
|
|
assert lora_layers["linear_layer_1"].down.device.type == "cpu"
|
|
|
|
# After patching, the patched model should still be on its original device.
|
|
assert model["linear_layer_1"].weight.data.device.type == device
|
|
|
|
torch.testing.assert_close(model["linear_layer_1"].weight.data, expected_patched_linear_weight)
|
|
|
|
# After unpatching, the original model weights should have been restored on the original device.
|
|
assert model["linear_layer_1"].weight.data.device.type == device
|
|
torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight)
|
|
|
|
|
|
@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")
|
|
@torch.no_grad()
|
|
def test_apply_lora_change_device():
|
|
"""Test that if LoRA patching is applied on the CPU, and then the patched model is moved to the GPU, unpatching
|
|
still behaves correctly.
|
|
"""
|
|
linear_in_features = 4
|
|
linear_out_features = 8
|
|
lora_dim = 2
|
|
# Initialize the model on the CPU.
|
|
model = torch.nn.ModuleDict(
|
|
{"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device="cpu", dtype=torch.float16)}
|
|
)
|
|
|
|
lora_layers = {
|
|
"linear_layer_1": LoRALayer(
|
|
layer_key="linear_layer_1",
|
|
values={
|
|
"lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
|
|
"lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
|
|
},
|
|
)
|
|
}
|
|
lora = LoRAModelRaw("lora_name", lora_layers)
|
|
|
|
orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
|
|
|
|
with ModelPatcher.apply_lora(model, [(lora, 0.5)], prefix=""):
|
|
# After patching, all LoRA layer weights should have been moved back to the cpu.
|
|
assert lora_layers["linear_layer_1"].up.device.type == "cpu"
|
|
assert lora_layers["linear_layer_1"].down.device.type == "cpu"
|
|
|
|
# After patching, the patched model should still be on the CPU.
|
|
assert model["linear_layer_1"].weight.data.device.type == "cpu"
|
|
|
|
# Move the model to the GPU.
|
|
assert model.to("cuda")
|
|
|
|
# After unpatching, the original model weights should have been restored on the GPU.
|
|
assert model["linear_layer_1"].weight.data.device.type == "cuda"
|
|
torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight, check_device=False)
|