refiners/tests/fluxion/layers/test_converter.py
2023-08-21 12:09:58 +02:00

63 lines
1.9 KiB
Python

import torch
import pytest
import refiners.fluxion.layers as fl
from refiners.fluxion.layers.chain import Distribute
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
def test_converter_device_single_tensor() -> None:
chain = fl.Chain(
fl.Converter(set_device=True, set_dtype=False),
fl.Linear(in_features=1, out_features=1, device="cuda:0"),
)
tensor = torch.randn(1, 1)
converted_tensor = chain(tensor)
assert converted_tensor.device == torch.device(device="cuda:0")
def test_converter_dtype_single_tensor() -> None:
chain = fl.Chain(
fl.Converter(set_device=False, set_dtype=True),
fl.Linear(in_features=1, out_features=1, dtype=torch.float64),
)
tensor = torch.randn(1, 1).to(dtype=torch.float32)
converted_tensor = chain(tensor)
assert converted_tensor.dtype == torch.float64
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
def test_converter_multiple_tensors() -> None:
chain = fl.Chain(
fl.Converter(set_device=True, set_dtype=True),
Distribute(
fl.Linear(in_features=1, out_features=1, device="cuda:0", dtype=torch.float64),
fl.Linear(in_features=1, out_features=1, device="cuda:0", dtype=torch.float64),
),
)
tensor1 = torch.randn(1, 1)
tensor2 = torch.randn(1, 1)
converted_tensor1, converted_tensor2 = chain(tensor1, tensor2)
assert converted_tensor1.device == torch.device(device="cuda:0")
assert converted_tensor1.dtype == torch.float64
assert converted_tensor2.device == torch.device(device="cuda:0")
assert converted_tensor2.dtype == torch.float64
def test_converter_no_parent_device_or_dtype() -> None:
chain = fl.Chain(
fl.Lambda(func=(lambda x: x)),
fl.Converter(set_device=True, set_dtype=False),
)
tensor = torch.randn(1, 1)
with pytest.raises(expected_exception=ValueError):
chain(tensor)