[Bugfix] Fix Positive Feature Layers in Llava Models (#13514)

Signed-off-by: Alex-Brooks <Alex.brooks@ibm.com>

tests/models/test_vision.py

@@ -0,0 +1,34 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+
+from vllm.model_executor.models.vision import resolve_visual_encoder_outputs
+
+
+@pytest.mark.parametrize(
+    ("feature_sample_layers", "num_layers_loaded", "max_possible_layers",
+     "expected_features"),
+    [
+        # All layers loaded
+        ([1, 10], 10, 10, [1, 10]),
+        ([-10, -1], 10, 10, [1, 10]),
+        # Some layers not loaded
+        ([1, 10], 10, 20, [1, 10]),
+        ([-20, -11], 10, 20, [1, 10]),
+    ])
+def test_resolve_visual_encoder_outputs(feature_sample_layers,
+                                        num_layers_loaded, max_possible_layers,
+                                        expected_features):
+    """
+    Test that offsets are correctly handled for vision feature layers.
+    """
+    encoder_outputs = [
+        torch.tensor([idx]) for idx in range(num_layers_loaded + 1)
+    ]
+    output_tensor = resolve_visual_encoder_outputs(
+        encoder_outputs=encoder_outputs,
+        feature_sample_layers=feature_sample_layers,
+        post_layer_norm=None,
+        max_possible_layers=max_possible_layers)
+    assert torch.equal(torch.tensor(expected_features), output_tensor)