mirror of
https://github.com/kohya-ss/sd-scripts.git
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570 lines
23 KiB
Python
570 lines
23 KiB
Python
import pytest
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import torch
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from torch import Tensor
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from typing import Tuple
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from library.network_utils import convert_pissa_to_standard_lora, initialize_pissa
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def generate_synthetic_weights(org_weight, seed=42):
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generator = torch.manual_seed(seed)
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# Base random normal distribution
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weights = torch.randn_like(org_weight)
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# Add structured variance to mimic real-world weight matrices
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# Techniques to create more realistic weight distributions:
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# 1. Block-wise variation
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block_size = max(1, org_weight.shape[0] // 4)
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for i in range(0, org_weight.shape[0], block_size):
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block_end = min(i + block_size, org_weight.shape[0])
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block_variation = torch.randn(1, generator=generator) * 0.3 # Local scaling
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weights[i:block_end, :] *= 1 + block_variation
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# 2. Sparse connectivity simulation
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sparsity_mask = torch.rand(org_weight.shape, generator=generator) > 0.2 # 20% sparsity
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weights *= sparsity_mask.float()
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# 3. Magnitude decay
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magnitude_decay = torch.linspace(1.0, 0.5, org_weight.shape[0]).unsqueeze(1)
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weights *= magnitude_decay
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# 4. Add structured noise
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structural_noise = torch.randn_like(org_weight) * 0.1
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weights += structural_noise
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# Normalize to have similar statistical properties to trained weights
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weights = (weights - weights.mean()) / weights.std()
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return weights
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class TestPissa:
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"""Test suite for convert_pissa_to_standard_lora function."""
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@pytest.fixture
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def basic_matrices(self) -> Tuple[Tensor, Tensor, Tensor, Tensor, int]:
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"""Create basic test matrices with known properties."""
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torch.manual_seed(42)
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d_model, rank = 64, 8
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# Create original matrices
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orig_up = torch.randn(d_model, rank, dtype=torch.float32)
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orig_down = torch.randn(rank, d_model, dtype=torch.float32)
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# Create trained matrices (slightly different)
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noise_scale = 0.1
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trained_up = orig_up + noise_scale * torch.randn_like(orig_up)
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trained_down = orig_down + noise_scale * torch.randn_like(orig_down)
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return trained_up, trained_down, orig_up, orig_down, rank
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@pytest.fixture
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def small_matrices(self) -> Tuple[Tensor, Tensor, Tensor, Tensor, int]:
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"""Create small matrices for easier debugging."""
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torch.manual_seed(123)
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d_model, rank = 8, 2
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orig_up = torch.randn(d_model, rank, dtype=torch.float32)
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orig_down = torch.randn(rank, d_model, dtype=torch.float32)
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trained_up = orig_up + 0.1 * torch.randn_like(orig_up)
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trained_down = orig_down + 0.1 * torch.randn_like(orig_down)
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return trained_up, trained_down, orig_up, orig_down, rank
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def test_initialize_pissa_rank_constraints(self):
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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_rank_limits(self):
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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_basic(self):
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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_with_lowrank(self):
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# Test with low-rank SVD option
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org_module = torch.nn.Linear(50, 30)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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lora_down = torch.nn.Linear(50, 5)
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lora_up = torch.nn.Linear(5, 30)
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original_weight = org_module.weight.data.clone()
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# Call with low-rank SVD enabled
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=5, use_lowrank=True)
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# Verify weights are changed
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assert not torch.equal(original_weight, org_module.weight.data)
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def test_initialize_pissa_custom_lowrank_params(self):
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# Test with custom low-rank parameters
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org_module = torch.nn.Linear(30, 20)
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org_module.weight.data = generate_synthetic_weights(org_module.weight)
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lora_down = torch.nn.Linear(30, 5)
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lora_up = torch.nn.Linear(5, 20)
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# Test with custom q value and iterations
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=5, use_lowrank=True, lowrank_niter=6)
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# Check basic validity
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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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def test_initialize_pissa_device_handling(self):
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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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# Test with IPCA
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if device.type == "cpu": # IPCA might be slow on CPU for large matrices
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org_module_small = torch.nn.Linear(20, 10).to(device)
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lora_down_small = torch.nn.Linear(20, 3).to(device)
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lora_up_small = torch.nn.Linear(3, 10).to(device)
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initialize_pissa(org_module_small, lora_down_small, lora_up_small, scale=0.1, rank=3, device=device)
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assert org_module_small.weight.data.device.type == device.type
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def test_initialize_pissa_shape_mismatch(self):
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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(self):
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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(self):
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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(self):
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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_dtype_preservation(self):
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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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# Test with explicit dtype
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if dtype != torch.float16: # Skip float16 for computational stability in SVD
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org_module2 = torch.nn.Linear(10, 5).to(dtype=torch.float32)
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lora_down2 = torch.nn.Linear(10, 2).to(dtype=torch.float32)
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lora_up2 = torch.nn.Linear(2, 5).to(dtype=torch.float32)
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initialize_pissa(org_module2, lora_down2, lora_up2, scale=0.1, rank=2, dtype=dtype)
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# Original module should be converted to specified dtype
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assert org_module2.weight.dtype == torch.float32
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def test_initialize_pissa_numerical_stability(self):
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# Test with numerically challenging scenarios
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scenarios = [
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torch.randn(20, 10) * 1e-5, # Small values
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torch.randn(20, 10) * 1e5, # Large values
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torch.ones(20, 10), # Uniform values
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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(20, 3)
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lora_up = torch.nn.Linear(3, 10)
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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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# Test IPCA as well
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lora_down_ipca = torch.nn.Linear(20, 3)
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lora_up_ipca = torch.nn.Linear(3, 10)
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initialize_pissa(org_module, lora_down_ipca, lora_up_ipca, 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(self):
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# Test effect of different scaling factors
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org_module = torch.nn.Linear(15, 10)
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original_weight = torch.randn_like(org_module.weight.data)
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org_module.weight.data = original_weight.clone()
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# Try different scales
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scales = [0.0, 0.01, 0.1, 1.0]
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for scale in scales:
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# Reset to original weights
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org_module.weight.data = original_weight.clone()
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lora_down = torch.nn.Linear(15, 4)
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lora_up = torch.nn.Linear(4, 10)
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initialize_pissa(org_module, lora_down, lora_up, scale=scale, rank=4)
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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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# For non-zero scales, verify the magnitude of change is proportional to scale
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assert weight_diff.abs().sum() > 0
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# Do a second run with double the scale
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org_module2 = torch.nn.Linear(15, 10)
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org_module2.weight.data = original_weight.clone()
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lora_down2 = torch.nn.Linear(15, 4)
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lora_up2 = torch.nn.Linear(4, 10)
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initialize_pissa(org_module2, lora_down2, lora_up2, scale=scale * 2, rank=4)
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weight_diff2 = original_weight - org_module2.weight.data
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# The ratio of differences should be approximately 2
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# (allowing for numerical precision issues)
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ratio = weight_diff2.abs().sum() / (weight_diff.abs().sum() + 1e-10)
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assert 1.9 < ratio < 2.1
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def test_initialize_pissa_large_matrix_performance(self):
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# Test with a large matrix to ensure it works well
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# This is particularly relevant for IPCA mode
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# Skip if running on CPU to avoid long test times
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if not torch.cuda.is_available():
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pytest.skip("Skipping large matrix test on CPU")
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org_module = torch.nn.Linear(1000, 500)
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org_module.weight.data = torch.randn_like(org_module.weight.data) * 0.1
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lora_down = torch.nn.Linear(1000, 16)
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lora_up = torch.nn.Linear(16, 500)
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# Test standard approach
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try:
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=16)
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except Exception as e:
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pytest.fail(f"Standard SVD failed on large matrix: {e}")
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# Test IPCA approach
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lora_down_ipca = torch.nn.Linear(1000, 16)
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lora_up_ipca = torch.nn.Linear(16, 500)
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try:
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initialize_pissa(org_module, lora_down_ipca, lora_up_ipca, scale=0.1, rank=16)
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except Exception as e:
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pytest.fail(f"IPCA approach failed on large matrix: {e}")
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# Test IPCA with lowrank
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lora_down_both = torch.nn.Linear(1000, 16)
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lora_up_both = torch.nn.Linear(16, 500)
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try:
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initialize_pissa(org_module, lora_down_both, lora_up_both, scale=0.1, rank=16, use_lowrank=True)
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except Exception as e:
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pytest.fail(f"Combined IPCA+lowrank approach failed on large matrix: {e}")
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def test_initialize_pissa_requires_grad_preservation(self):
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# Test that requires_grad property is preserved
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org_module = torch.nn.Linear(20, 10)
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org_module.weight.requires_grad = False
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lora_down = torch.nn.Linear(20, 4)
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lora_up = torch.nn.Linear(4, 10)
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initialize_pissa(org_module, lora_down, lora_up, scale=0.1, rank=4)
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# Check requires_grad is preserved
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assert not org_module.weight.requires_grad
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# Test with requires_grad=True
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org_module2 = torch.nn.Linear(20, 10)
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org_module2.weight.requires_grad = True
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initialize_pissa(org_module2, lora_down, lora_up, scale=0.1, rank=4)
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# Check requires_grad is preserved
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assert org_module2.weight.requires_grad
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def test_basic_functionality(self, basic_matrices):
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"""Test that the function runs without errors and returns expected shapes."""
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trained_up, trained_down, orig_up, orig_down, rank = basic_matrices
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new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
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# Check output types
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assert isinstance(new_up, torch.Tensor)
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assert isinstance(new_down, torch.Tensor)
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# Check shapes - should be compatible for matrix multiplication
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d_model = trained_up.shape[0]
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expected_rank = min(rank * 2, min(d_model, trained_down.shape[1]))
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assert new_up.shape == torch.Size([d_model, expected_rank])
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assert new_down.shape == (expected_rank, trained_down.shape[1])
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def test_delta_preservation(self, basic_matrices):
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"""Test that the delta weight is preserved in the LoRA decomposition."""
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trained_up, trained_down, orig_up, orig_down, rank = basic_matrices
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# Calculate original delta
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original_delta = (trained_up @ trained_down) - (orig_up @ orig_down)
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# Convert to LoRA
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new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
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# Reconstruct delta from LoRA matrices
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reconstructed_delta = new_up @ new_down
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# Check that reconstruction approximates original delta
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# (Note: some information loss is expected due to rank reduction)
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relative_error = torch.norm(original_delta - reconstructed_delta) / torch.norm(original_delta)
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assert relative_error < 0.5 # Allow some approximation error
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def test_rank_handling(self, small_matrices):
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"""Test various rank scenarios."""
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trained_up, trained_down, orig_up, orig_down, base_rank = small_matrices
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d_model = trained_up.shape[0]
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|
|
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# Test with rank that would exceed matrix dimensions
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large_rank = d_model + 5
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new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, large_rank)
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|
|
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# Should not exceed available singular values
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max_possible_rank = min(d_model, trained_down.shape[1])
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assert new_up.shape[1] <= max_possible_rank
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assert new_down.shape[0] <= max_possible_rank
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|
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def test_zero_delta(self):
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"""Test behavior when trained and original matrices are identical."""
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torch.manual_seed(456)
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d_model, rank = 16, 4
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|
|
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# Create identical matrices
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|
orig_up = torch.randn(d_model, rank, dtype=torch.float32)
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orig_down = torch.randn(rank, d_model, dtype=torch.float32)
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trained_up = orig_up.clone()
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|
trained_down = orig_down.clone()
|
|
|
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new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# Reconstructed delta should be close to zero
|
|
reconstructed_delta = new_up @ new_down
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|
assert torch.allclose(reconstructed_delta, torch.zeros_like(reconstructed_delta), atol=1e-6)
|
|
|
|
def test_different_devices(self, basic_matrices):
|
|
"""Test that the function handles different device placement correctly."""
|
|
trained_up, trained_down, orig_up, orig_down, rank = basic_matrices
|
|
|
|
# Test with CPU tensors
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# Results should be on the same device as input
|
|
assert new_up.device == trained_up.device
|
|
assert new_down.device == trained_up.device
|
|
|
|
def test_gradient_disabled(self, basic_matrices):
|
|
"""Test that gradients are properly disabled."""
|
|
trained_up, trained_down, orig_up, orig_down, rank = basic_matrices
|
|
|
|
# Enable gradients on inputs
|
|
trained_up.requires_grad_(True)
|
|
trained_down.requires_grad_(True)
|
|
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# Outputs should not require gradients due to torch.no_grad()
|
|
assert not new_up.requires_grad
|
|
assert not new_down.requires_grad
|
|
|
|
def test_dtype_consistency(self, basic_matrices):
|
|
"""Test that output dtypes are consistent."""
|
|
trained_up, trained_down, orig_up, orig_down, rank = basic_matrices
|
|
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# Should maintain float32 dtype
|
|
assert new_up.dtype == torch.float32
|
|
assert new_down.dtype == torch.float32
|
|
|
|
def test_mathematical_properties(self, small_matrices):
|
|
"""Test mathematical properties of the SVD decomposition."""
|
|
trained_up, trained_down, orig_up, orig_down, rank = small_matrices
|
|
|
|
# Calculate delta manually
|
|
delta_w = (trained_up @ trained_down) - (orig_up @ orig_down)
|
|
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# The decomposition should satisfy: new_up @ new_down ≈ low-rank approximation of delta_w
|
|
reconstructed = new_up @ new_down
|
|
|
|
# Check that reconstruction has expected rank
|
|
actual_rank = torch.linalg.matrix_rank(reconstructed).item()
|
|
expected_max_rank = min(rank * 2, min(delta_w.shape))
|
|
assert actual_rank <= expected_max_rank
|
|
|
|
@pytest.mark.parametrize("rank", [1, 4, 8, 16])
|
|
def test_different_ranks(self, rank):
|
|
"""Test the function with different rank values."""
|
|
torch.manual_seed(789)
|
|
d_model = 32
|
|
|
|
orig_up = torch.randn(d_model, rank, dtype=torch.float32)
|
|
orig_down = torch.randn(rank, d_model, dtype=torch.float32)
|
|
trained_up = orig_up + 0.1 * torch.randn_like(orig_up)
|
|
trained_down = orig_down + 0.1 * torch.randn_like(orig_down)
|
|
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# Should handle all rank values gracefully
|
|
assert new_up.shape[0] == d_model
|
|
assert new_down.shape[1] == d_model
|
|
assert new_up.shape[1] == new_down.shape[0] # Compatible for multiplication
|
|
|
|
def test_edge_case_single_rank(self):
|
|
"""Test with minimal rank (rank=1)."""
|
|
torch.manual_seed(101)
|
|
d_model, rank = 8, 1
|
|
|
|
orig_up = torch.randn(d_model, rank, dtype=torch.float32)
|
|
orig_down = torch.randn(rank, d_model, dtype=torch.float32)
|
|
trained_up = orig_up + 0.2 * torch.randn_like(orig_up)
|
|
trained_down = orig_down + 0.2 * torch.randn_like(orig_down)
|
|
|
|
new_up, new_down = convert_pissa_to_standard_lora(trained_up, trained_down, orig_up, orig_down, rank)
|
|
|
|
# With rank=1, output rank should be 2 (rank * 2)
|
|
expected_rank = min(2, min(d_model, d_model))
|
|
assert new_up.shape[1] <= expected_rank
|
|
assert new_down.shape[0] <= expected_rank
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Run the tests
|
|
pytest.main([__file__, "-v"])
|