mirror of
https://github.com/kohya-ss/sd-scripts.git
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217 lines
7.6 KiB
Python
217 lines
7.6 KiB
Python
import torch
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import pytest
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from library.lora_util import initialize_pissa
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from ..test_util import generate_synthetic_weights
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def test_initialize_pissa_basic():
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# Create a simple linear layer
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org_module = torch.nn.Linear(10, 5)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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torch.nn.init.xavier_uniform_(org_module.weight)
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torch.nn.init.zeros_(org_module.bias)
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# Create LoRA layers with matching shapes
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lora_down = torch.nn.Linear(10, 2)
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lora_up = torch.nn.Linear(2, 5)
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# Store original weight for comparison
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original_weight = org_module.weight.data.clone()
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# Call the initialization function
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=2)
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# Verify basic properties
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assert lora_down.weight.data is not None
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assert lora_up.weight.data is not None
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assert org_module.weight.data is not None
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# Check that the weights have been modified
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assert not torch.equal(original_weight, org_module.weight.data)
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def test_initialize_pissa_rank_constraints():
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# Test with different rank values
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org_module = torch.nn.Linear(20, 10)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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torch.nn.init.xavier_uniform_(org_module.weight)
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torch.nn.init.zeros_(org_module.bias)
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# Test with rank less than min dimension
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lora_down = torch.nn.Linear(20, 3)
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lora_up = torch.nn.Linear(3, 10)
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=3)
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# Test with rank equal to min dimension
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lora_down = torch.nn.Linear(20, 10)
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lora_up = torch.nn.Linear(10, 10)
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=10)
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def test_initialize_pissa_shape_mismatch():
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# Test with shape mismatch to ensure warning is printed
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org_module = torch.nn.Linear(20, 10)
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# Intentionally mismatched shapes to test warning mechanism
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lora_down = torch.nn.Linear(20, 5) # Different shape
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lora_up = torch.nn.Linear(3, 15) # Different shape
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with pytest.warns(UserWarning):
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=3)
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def test_initialize_pissa_scaling():
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# Test different scaling factors
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scales = [0.0, 0.1, 1.0]
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for scale in scales:
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org_module = torch.nn.Linear(10, 5)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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original_weight = org_module.weight.data.clone()
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lora_down = torch.nn.Linear(10, 2)
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lora_up = torch.nn.Linear(2, 5)
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initialize_pissa(org_module, lora_down, lora_up, scale=scale, rank=2)
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# Check that the weight modification follows the scaling
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weight_diff = original_weight - org_module.weight.data
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expected_diff = scale * (lora_up.weight.data @ lora_down.weight.data)
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torch.testing.assert_close(weight_diff, expected_diff, rtol=1e-4, atol=1e-4)
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def test_initialize_pissa_dtype():
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# Test with different data types
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dtypes = [torch.float16, torch.float32, torch.float64]
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for dtype in dtypes:
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org_module = torch.nn.Linear(10, 5).to(dtype=dtype)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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lora_down = torch.nn.Linear(10, 2)
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lora_up = torch.nn.Linear(2, 5)
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=2)
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# Verify output dtype matches input
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assert org_module.weight.dtype == dtype
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def test_initialize_pissa_svd_properties():
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# Verify SVD decomposition properties
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org_module = torch.nn.Linear(20, 10)
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lora_down = torch.nn.Linear(20, 3)
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lora_up = torch.nn.Linear(3, 10)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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original_weight = org_module.weight.data.clone()
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=3)
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# Reconstruct the weight
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reconstructed_weight = original_weight - 0.1 * (lora_up.weight.data @ lora_down.weight.data)
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# Check reconstruction is close to original
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torch.testing.assert_close(reconstructed_weight, org_module.weight.data, rtol=1e-4, atol=1e-4)
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def test_initialize_pissa_device_handling():
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# Test different device scenarios
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devices = [torch.device("cpu"), torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")]
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for device in devices:
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# Create modules on specific device
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org_module = torch.nn.Linear(10, 5).to(device)
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lora_down = torch.nn.Linear(10, 2).to(device)
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lora_up = torch.nn.Linear(2, 5).to(device)
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# Test initialization with explicit device
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=2, device=device)
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# Verify modules are on the correct device
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assert org_module.weight.data.device.type == device.type
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assert lora_down.weight.data.device.type == device.type
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assert lora_up.weight.data.device.type == device.type
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def test_initialize_pissa_dtype_preservation():
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# Test dtype preservation and conversion
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dtypes = [torch.float16, torch.float32, torch.float64]
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for dtype in dtypes:
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org_module = torch.nn.Linear(10, 5).to(dtype=dtype)
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lora_down = torch.nn.Linear(10, 2).to(dtype=dtype)
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lora_up = torch.nn.Linear(2, 5).to(dtype=dtype)
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=2)
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assert org_module.weight.dtype == dtype
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assert lora_down.weight.dtype == dtype
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assert lora_up.weight.dtype == dtype
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def test_initialize_pissa_rank_limits():
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# Test rank limits
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org_module = torch.nn.Linear(10, 5)
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# Test minimum rank (should work)
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lora_down_min = torch.nn.Linear(10, 1)
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lora_up_min = torch.nn.Linear(1, 5)
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initialize_pissa(org_module, lora_down_min, lora_up_min, scale=0.1, rank=1)
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# Test maximum rank (rank = min(input_dim, output_dim))
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max_rank = min(10, 5)
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lora_down_max = torch.nn.Linear(10, max_rank)
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lora_up_max = torch.nn.Linear(max_rank, 5)
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initialize_pissa(org_module, lora_down_max, lora_up_max, scale=0.1, rank=max_rank)
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def test_initialize_pissa_numerical_stability():
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# Test with numerically challenging scenarios
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scenarios = [
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torch.randn(20, 10) * 1e-10, # Very small values
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torch.randn(20, 10) * 1e10, # Very large values
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torch.zeros(20, 10), # Zero matrix
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]
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for i, weight_matrix in enumerate(scenarios):
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org_module = torch.nn.Linear(20, 10)
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org_module.weight.data = weight_matrix
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lora_down = torch.nn.Linear(10, 3)
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lora_up = torch.nn.Linear(3, 20)
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try:
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=3)
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except Exception as e:
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pytest.fail(f"Initialization failed for scenario ({i}): {e}")
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def test_initialize_pissa_scale_effects():
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# Test different scaling factors
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org_module = torch.nn.Linear(10, 5)
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original_weight = org_module.weight.data.clone()
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test_scales = [0.0, 0.1, 0.5, 1.0]
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for scale in test_scales:
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# Reset module for each test
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org_module.weight.data = original_weight.clone()
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lora_down = torch.nn.Linear(10, 2)
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lora_up = torch.nn.Linear(2, 5)
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initialize_pissa(org_module, lora_down, lora_up, scale=scale, rank=2)
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# Verify weight modification proportional to scale
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weight_diff = original_weight - org_module.weight.data
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# Approximate check of scaling effect
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if scale == 0.0:
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torch.testing.assert_close(weight_diff, torch.zeros_like(weight_diff), rtol=1e-4, atol=1e-6)
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else:
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assert not torch.allclose(weight_diff, torch.zeros_like(weight_diff), rtol=1e-4, atol=1e-6)
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