mirror of
https://github.com/finegrain-ai/refiners.git
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71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import torch
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import pytest
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from warnings import warn
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import refiners.fluxion.layers as fl
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from refiners.fluxion.layers.chain import Distribute
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def test_converter_device_single_tensor(test_device: torch.device) -> None:
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if test_device.type != "cuda":
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warn("only running on CUDA, skipping")
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pytest.skip()
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chain = fl.Chain(
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fl.Converter(set_device=True, set_dtype=False),
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fl.Linear(in_features=1, out_features=1, device=test_device),
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)
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tensor = torch.randn(1, 1)
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converted_tensor = chain(tensor)
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assert converted_tensor.device == torch.device(device=test_device)
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def test_converter_dtype_single_tensor() -> None:
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chain = fl.Chain(
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fl.Converter(set_device=False, set_dtype=True),
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fl.Linear(in_features=1, out_features=1, dtype=torch.float64),
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)
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tensor = torch.randn(1, 1).to(dtype=torch.float32)
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converted_tensor = chain(tensor)
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assert converted_tensor.dtype == torch.float64
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def test_converter_multiple_tensors(test_device: torch.device) -> None:
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if test_device.type != "cuda":
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warn("only running on CUDA, skipping")
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pytest.skip()
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chain = fl.Chain(
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fl.Converter(set_device=True, set_dtype=True),
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Distribute(
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fl.Linear(in_features=1, out_features=1, device=test_device, dtype=torch.float64),
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fl.Linear(in_features=1, out_features=1, device=test_device, dtype=torch.float64),
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),
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)
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tensor1 = torch.randn(1, 1)
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tensor2 = torch.randn(1, 1)
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converted_tensor1, converted_tensor2 = chain(tensor1, tensor2)
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assert converted_tensor1.device == torch.device(device=test_device)
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assert converted_tensor1.dtype == torch.float64
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assert converted_tensor2.device == torch.device(device=test_device)
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assert converted_tensor2.dtype == torch.float64
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def test_converter_no_parent_device_or_dtype() -> None:
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chain = fl.Chain(
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fl.Lambda(func=(lambda x: x)),
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fl.Converter(set_device=True, set_dtype=False),
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)
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tensor = torch.randn(1, 1)
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with pytest.raises(expected_exception=ValueError):
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chain(tensor)
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