add testing

Author: SkafteNicki

tests/tests_pytorch/callbacks/test_pruning.py

@@ -262,13 +262,13 @@ def test_multiple_pruning_callbacks(tmp_path, caplog, make_pruning_permanent: bo
     actual = [m for m in actual if m.startswith("Applied")]
     percentage = r"\(\d+(?:\.\d+)?%\)"
     expected = [
-        rf"Applied `L1Unstructured`. Pruned: \d+\/1122 {percentage} -> \d+\/1122 {percentage}",
+        rf"Applied `L1Unstructured`. Pruned: \d+\/1088 {percentage} -> \d+\/1088 {percentage}",
         rf"Applied `L1Unstructured` to `Linear\(in_features=32, out_features=32, bias=True\).weight` with amount=0.5. Pruned: 0 \(0.00%\) -> \d+ {percentage}",  # noqa: E501
         rf"Applied `L1Unstructured` to `Linear\(in_features=32, out_features=2, bias=True\).weight` with amount=0.5. Pruned: 0 \(0.00%\) -> \d+ {percentage}",  # noqa: E501
-        rf"Applied `RandomUnstructured`. Pruned: \d+\/1122 {percentage} -> \d+\/1122 {percentage}",
+        rf"Applied `RandomUnstructured`. Pruned: \d+\/1088 {percentage} -> \d+\/1088 {percentage}",
         rf"Applied `RandomUnstructured` to `Linear\(in_features=32, out_features=32, bias=True\).weight` with amount=0.25. Pruned: \d+ {percentage} -> \d+ {percentage}",  # noqa: E501
         rf"Applied `RandomUnstructured` to `Linear\(in_features=32, out_features=2, bias=True\).weight` with amount=0.25. Pruned: \d+ {percentage} -> \d+ {percentage}",  # noqa: E501
-        rf"Applied `L1Unstructured`. Pruned: \d+\/1122 {percentage} -> \d+\/1122 {percentage}",
+        rf"Applied `L1Unstructured`. Pruned: \d+\/1088 {percentage} -> \d+\/1088 {percentage}",
         rf"Applied `L1Unstructured` to `Linear\(in_features=32, out_features=32, bias=True\).weight` with amount=0.5. Pruned: \d+ {percentage} -> \d+ {percentage}",  # noqa: E501
         rf"Applied `L1Unstructured` to `Linear\(in_features=32, out_features=2, bias=True\).weight` with amount=0.5. Pruned: \d+ {percentage} -> \d+ {percentage}",  # noqa: E501
     ]
@@ -329,9 +329,9 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint):
     actual = [m for m in actual if m.startswith("Applied")]
     percentage = r"\(\d+(?:\.\d+)?%\)"
     expected = [
-        rf"Applied `RandomUnstructured`. Pruned: \d+\/66 {percentage} -> \d+\/66 {percentage}",
-        rf"Applied `RandomUnstructured`. Pruned: \d+\/66 {percentage} -> \d+\/66 {percentage}",
-        rf"Applied `RandomUnstructured`. Pruned: \d+\/66 {percentage} -> \d+\/66 {percentage}",
+        rf"Applied `RandomUnstructured`. Pruned: \d+\/64 {percentage} -> \d+\/64 {percentage}",
+        rf"Applied `RandomUnstructured`. Pruned: \d+\/64 {percentage} -> \d+\/64 {percentage}",
+        rf"Applied `RandomUnstructured`. Pruned: \d+\/64 {percentage} -> \d+\/64 {percentage}",
     ]
     expected = [re.compile(s) for s in expected]
     assert all(regex.match(s) for s, regex in zip(actual, expected))
@@ -463,3 +463,91 @@ def __init__(self):
         f"Actual weight_orig: {weight_orig}\n"
         f"Max difference: {torch.max(torch.abs(weight_orig - original_weights))}"
     )
+
+
+@pytest.mark.parametrize("pruning_amount", [0.1, 0.2, 0.3, 0.5])
+@pytest.mark.parametrize("model_type", ["simple", "complex"])
+def test_sparsity_calculation(tmp_path, caplog, pruning_amount: float, model_type: str):
+    """Test that the sparsity calculation fix correctly reports percentages."""
+
+    class SimpleModel(BoringModel):
+        """Simple model with 66 parameters (64 weight + 2 bias)."""
+
+        def __init__(self):
+            super().__init__()
+            self.layer = nn.Linear(32, 2)  # 32*2 + 2 = 66 params
+
+    class ComplexModel(BoringModel):
+        """Complex model with multiple layers."""
+
+        def __init__(self):
+            super().__init__()
+            self.layer1 = nn.Linear(32, 64)  # 32*64 + 64 = 2112 params
+            self.layer2 = nn.Linear(64, 2)  # 64*2 + 2 = 130 params
+            # Total: 2112 + 130 = 2242 params (but only layer1 will be pruned)
+            # layer1 params: 2112
+
+        def forward(self, x):
+            x = torch.relu(self.layer1(x))
+            return self.layer2(x)
+
+    if model_type == "simple":
+        model = SimpleModel()
+        expected_total_params = 66
+        parameters_to_prune = None
+    else:
+        model = ComplexModel()
+        expected_total_params = 2112
+        parameters_to_prune = [(model.layer1, "weight"), (model.layer1, "bias")]
+
+    pruning = ModelPruning(
+        pruning_fn="l1_unstructured",
+        parameters_to_prune=parameters_to_prune,
+        amount=pruning_amount,
+        verbose=1,
+        use_global_unstructured=True,
+    )
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        enable_progress_bar=False,
+        enable_model_summary=False,
+        enable_checkpointing=False,
+        logger=False,
+        limit_train_batches=1,
+        max_epochs=1,
+        accelerator="cpu",
+        callbacks=[pruning],
+    )
+
+    with caplog.at_level(INFO):
+        trainer.fit(model)
+
+    sparsity_logs = [msg for msg in caplog.messages if "Applied `L1Unstructured`. Pruned:" in msg]
+    assert len(sparsity_logs) == 1, f"Expected 1 sparsity log, got {len(sparsity_logs)}"
+    sparsity_log = sparsity_logs[0]
+    pattern = r"Applied `L1Unstructured`\. Pruned: \d+/(\d+) \(\d+\.\d+%\) -> (\d+)/(\d+) \((\d+\.\d+)%\)"
+    match = re.search(pattern, sparsity_log)
+    assert match, f"Could not parse sparsity log: {sparsity_log}"
+
+    total_params_before = int(match.group(1))
+    pruned_count = int(match.group(2))
+    total_params_after = int(match.group(3))
+    sparsity_percentage = float(match.group(4))
+    assert total_params_before == expected_total_params, (
+        f"Total parameter count mismatch for {model_type} model. "
+        f"Expected {expected_total_params}, got {total_params_before}"
+    )
+    assert total_params_after == expected_total_params, (
+        f"Total parameter count should be consistent. Before: {total_params_before}, After: {total_params_after}"
+    )
+
+    # Verify sparsity percentage is approximately correct
+    expected_sparsity = pruning_amount * 100
+    tolerance = 5.0
+    assert abs(sparsity_percentage - expected_sparsity) <= tolerance
+
+    # Verify the number of pruned parameters is reasonable
+    expected_pruned_count = int(expected_total_params * pruning_amount)
+    pruned_tolerance = max(1, int(expected_total_params * 0.05))
+    assert abs(pruned_count - expected_pruned_count) <= pruned_tolerance