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73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
import pytest
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import torch
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from torch.testing import assert_close
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from peft import OSFConfig, get_peft_model
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from peft.tuners.osf.layer import OSFLayer
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from peft.tuners.osf.utils import (
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decompose_weight_matrix,
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reconstruct_weight_matrix,
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)
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def test_osf_roundtrip():
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w = torch.randn(10, 8)
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svd = decompose_weight_matrix(w, top_k=4)
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w_rec = reconstruct_weight_matrix(svd)
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assert_close(w_rec, w, atol=1e-5, rtol=1e-5)
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class DummyConfig(dict):
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pass
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class DummyModel(torch.nn.Module):
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def __init__(self, config=None):
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super().__init__()
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self.config = config
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self.linear = torch.nn.Linear(8, 4)
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def forward(self, x):
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return self.linear(x)
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def test_osf_gradient_projection_hook():
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torch.manual_seed(0)
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model = DummyModel(DummyConfig())
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# Specify target module explicitly for DummyModel
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cfg = OSFConfig(target_modules=["linear"], effective_rank=2)
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wrapped = get_peft_model(model, cfg)
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x = torch.randn(3, 8)
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wrapped(x).sum().backward()
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# Access the injected OSF layer
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osf_linear = wrapped.base_model.model.linear
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adapter = wrapped.base_model.active_adapters[0]
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U_high = osf_linear._osf_U_high[adapter]
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V_high = osf_linear._osf_V_high[adapter]
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svd_params = osf_linear.osf_svd_params[adapter]
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# Check orthogonality of gradients after projection
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proj_u = U_high.T @ svd_params["U_low"].grad
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proj_v = svd_params["V_low"].grad @ V_high.T
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assert_close(proj_u, torch.zeros_like(proj_u), atol=1e-6, rtol=1e-6)
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assert_close(proj_v, torch.zeros_like(proj_v), atol=1e-6, rtol=1e-6)
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def test_osf_merge_and_unload_and_unmerge_behavior():
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model = DummyModel(DummyConfig())
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cfg = OSFConfig(target_modules=["linear"], effective_rank=2)
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wrapped = get_peft_model(model, cfg)
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# merge_adapter should work via BaseTuner and OSFLayer.merge
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osf_linear = wrapped.base_model.model.linear
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assert isinstance(osf_linear, OSFLayer)
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wrapped.merge_adapter()
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assert osf_linear.merged, "OSF layer should be marked as merged after merge_adapter()"
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# unmerge_adapter is not supported for OSF
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with pytest.raises(NotImplementedError):
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wrapped.unmerge_adapter()
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# merge_and_unload should return the base model (no OSF wrappers)
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merged_model = wrapped.merge_and_unload()
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assert isinstance(merged_model.linear, torch.nn.Linear)
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