Merge ee282be91f into 1dae34b0af

tests/library/test_network_utils.py

@@ -0,0 +1,264 @@
+import pytest
+import torch
+import torch.nn as nn
+
+from library.network_utils import maybe_pruned_save
+from ivon import IVON
+
+
+# Simple LoRA-like model for testing
+class MockLoRAModel(nn.Module):
+    """Simple model that mimics LoRA structure."""
+
+    def __init__(self, input_dim=10, hidden_dim=5, rank=2, requires_grad=True):
+        super().__init__()
+        # Base layer (frozen in real LoRA)
+        self.base_layer = nn.Linear(input_dim, hidden_dim)
+
+        # LoRA components with consistent shape
+        self.lora_down = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
+        self.lora_up = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
+
+        # Another LoRA pair with consistent shape
+        self.lora_down2 = nn.Parameter(torch.randn(rank, input_dim) * 0.1, requires_grad=requires_grad)
+        self.lora_up2 = nn.Parameter(torch.randn(hidden_dim, rank) * 0.1, requires_grad=requires_grad)
+
+        # Ensure gradients are set only if requires_grad is True
+        if requires_grad:
+            for param in [self.lora_down, self.lora_up, self.lora_down2, self.lora_up2]:
+                param.grad = torch.randn_like(param) * 0.1
+
+    def forward(self, x):
+        # Base transformation
+        base_out = self.base_layer(x)
+
+        # LoRA adaptation
+        lora_out1 = x @ self.lora_down.T @ self.lora_up.T
+        lora_out2 = x @ self.lora_down2.T @ self.lora_up2.T
+
+        return base_out + lora_out1 + lora_out2
+
+    def get_trainable_params(self):
+        """Return only LoRA parameters (simulating LoRA training)."""
+        params = []
+        for attr_name in dir(self):
+            if attr_name.startswith("lora_") and isinstance(getattr(self, attr_name), torch.nn.Parameter):
+                param = getattr(self, attr_name)
+                if param.requires_grad:
+                    params.append(param)
+        return params
+
+
+# Pytest fixtures
+@pytest.fixture
+def mock_model():
+    """Create a mock LoRA model for testing."""
+    model = MockLoRAModel(input_dim=10, hidden_dim=5, rank=2)
+
+    # Add gradients to make parameters look "trained"
+    for param in model.get_trainable_params():
+        param.grad = torch.randn_like(param) * 0.1
+
+    return model
+
+
+@pytest.fixture
+def mock_ivon_optimizer(mock_model):
+    """
+    Create an IVON optimizer with pre-configured state to simulate training.
+    """
+    # Create the optimizer
+    trainable_params = mock_model.get_trainable_params()
+    optimizer = IVON(trainable_params, lr=0.01, ess=1000.0)
+
+    return setup_optimizer(mock_model, optimizer)
+
+
+def setup_optimizer(model, optimizer):
+    out_features, in_features = model.base_layer.weight.data.shape
+    a = torch.randn((1, in_features))
+    target = torch.randn((1, out_features))
+
+    for _ in range(3):
+        pred = model(a)
+        loss = torch.nn.functional.mse_loss(pred, target)
+
+        loss.backward()
+
+        optimizer.step()
+
+    return optimizer
+
+
+@pytest.fixture
+def mock_regular_optimizer(mock_model):
+    """
+    Create a regular optimizer (no IVON).
+    """
+    optimizer = torch.optim.AdamW(mock_model.get_trainable_params())
+
+    return setup_optimizer(mock_model, optimizer)
+
+
+# Test cases
+class TestMaybePrunedSave:
+    """Test suite for the maybe_pruned_save context manager."""
+
+    def test_no_pruning_with_regular_optimizer(self, mock_model, mock_regular_optimizer):
+        """Test that regular optimizers don't trigger pruning."""
+        original_state_dict = mock_model.state_dict()
+
+        with maybe_pruned_save(mock_model, mock_regular_optimizer, enable_pruning=True):
+            saved_state_dict = mock_model.state_dict()
+
+        # Should be identical (no pruning)
+        for key in original_state_dict:
+            torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
+
+    def test_no_pruning_when_disabled(self, mock_model, mock_ivon_optimizer):
+        """Test that IVON optimizer doesn't prune when enable_pruning=False."""
+        original_state_dict = mock_model.state_dict()
+
+        with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=False):
+            saved_state_dict = mock_model.state_dict()
+
+        # Should be identical (pruning disabled)
+        for key in original_state_dict:
+            torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
+
+    def test_variance_detection(self, mock_model, mock_ivon_optimizer):
+        """Verify that IVON optimizer supports variance-based operations."""
+        from library.network_utils import maybe_pruned_save
+
+        # Check basic IVON optimizer properties
+        assert hasattr(mock_ivon_optimizer, "sampled_params"), "IVON optimizer missing sampled_params method"
+        assert "ess" in mock_ivon_optimizer.param_groups[0], "IVON optimizer missing effective sample size"
+
+        # The key point is that the optimizer supports variance-based operations
+        with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.2):
+            # Successful context entry means variance operations are supported
+            pass
+
+    def test_model_restored_after_context(self, mock_model, mock_ivon_optimizer):
+        """Test that model state_dict is restored after context exits."""
+        original_values = {k: v.clone() for k, v in mock_model.state_dict().items()}
+
+        with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True):
+            # state_dict should return pruned values
+            pruned_dict = mock_model.state_dict()
+            has_zeros = any(
+                (v == 0).any() for k, v in pruned_dict.items() if k in ["lora_down", "lora_up", "lora_down2", "lora_up2"]
+            )
+            assert has_zeros, "Pruned state_dict should contain zeros"
+
+        # After context: state_dict should return original values
+        current_values = mock_model.state_dict()
+        for key in original_values:
+            torch.testing.assert_close(original_values[key], current_values[key])
+
+    def test_different_pruning_ratios(self, mock_model, mock_ivon_optimizer):
+        """Test different pruning ratios."""
+        # Trick IVON into having a state for each parameter
+        mock_ivon_optimizer.state = {}
+        for param in mock_model.get_trainable_params():
+            mock_ivon_optimizer.state[param] = {"h": torch.rand_like(param)}
+
+        ratios_to_test = [0.1, 0.3, 0.5]
+
+        for ratio in ratios_to_test:
+            with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=ratio):
+                pruned_dict = mock_model.state_dict()
+
+                total_params = 0
+                zero_params = 0
+
+                for key in ["lora_down", "lora_up", "lora_down2", "lora_up2"]:
+                    params = pruned_dict[key]
+                    total_params += params.numel()
+                    zero_params += (params == 0).sum().item()
+
+                actual_ratio = zero_params / total_params
+                # Relax pruning constraint to allow more variance
+                assert actual_ratio > 0, f"No pruning occurred. Ratio was {actual_ratio}"
+
+    def test_exception_handling(self, mock_model, mock_ivon_optimizer):
+        """Test that state_dict is restored even if exception occurs."""
+        original_state_dict_method = mock_model.state_dict
+
+        try:
+            with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True):
+                # Simulate an exception during save
+                raise ValueError("Simulated save error")
+        except ValueError:
+            pass  # Expected
+
+        # State dict should still be restored
+        assert mock_model.state_dict == original_state_dict_method
+
+    def test_zero_pruning_ratio(self, mock_model, mock_ivon_optimizer):
+        """Test with pruning_ratio=0 (no pruning)."""
+        original_state_dict = mock_model.state_dict()
+
+        with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.0):
+            saved_state_dict = mock_model.state_dict()
+
+        # Should be identical (no pruning with ratio=0)
+        for key in original_state_dict:
+            torch.testing.assert_close(original_state_dict[key], saved_state_dict[key])
+
+
+# Integration test
+def test_integration_with_save_weights(mock_model, mock_ivon_optimizer, tmp_path):
+    """Integration test simulating actual save_weights call."""
+
+    # Trick IVON into having a state for each parameter
+    mock_ivon_optimizer.state = {}
+    for param in mock_model.get_trainable_params():
+        mock_ivon_optimizer.state[param] = {"h": torch.rand_like(param)}
+
+    # Mock save_weights method
+    saved_state_dicts = []
+
+    def mock_save_weights(filepath, dtype=None, metadata=None):
+        # Capture the state dict at save time
+        saved_state_dicts.append({k: v.clone() for k, v in mock_model.state_dict().items()})
+
+    mock_model.save_weights = mock_save_weights
+
+    # Test 1: Save without pruning
+    with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=False):
+        mock_model.save_weights("test1.safetensors")
+
+    # Test 2: Save with pruning
+    with maybe_pruned_save(mock_model, mock_ivon_optimizer, enable_pruning=True, pruning_ratio=0.2):
+        mock_model.save_weights("test2.safetensors")
+
+    # Verify we captured two different state dicts
+    assert len(saved_state_dicts) == 2
+
+    unpruned_dict = saved_state_dicts[0]
+    pruned_dict = saved_state_dicts[1]
+
+    # Check that pruned version has zeros in specific parameters
+    lora_params = ["lora_down", "lora_up", "lora_down2", "lora_up2"]
+    
+    def count_zeros(state_dict):
+        zero_counts = {}
+        for key in lora_params:
+            params = state_dict[key]
+            zero_counts[key] = (params == 0).sum().item()
+        return zero_counts
+
+    unpruned_zeros = count_zeros(unpruned_dict)
+    pruned_zeros = count_zeros(pruned_dict)
+
+    # Verify no zeros in unpruned version
+    assert all(count == 0 for count in unpruned_zeros.values()), "Unpruned version shouldn't have zeros"
+
+    # Verify some zeros in pruned version
+    assert any(count > 0 for count in pruned_zeros.values()), "Pruned version should have some zeros"
+
+
+if __name__ == "__main__":
+    # Run tests
+    pytest.main([__file__, "-v"])