[Hybrid Allocator] Better layer padding strategy for gpt-oss eagle (#29303)

Signed-off-by: Chen Zhang <zhangch99@outlook.com>

vllm/v1/core/kv_cache_utils.py

@@ -971,7 +971,16 @@ def _get_kv_cache_groups_uniform_page_size(
     # is the minimum number of layers among all attention types. Need a better
     # strategy if we want to support more complex patterns (e.g., 20 full + 30
     # sw, where the group size should be 10).
-    group_size = min([len(layers) for layers in same_type_layers.values()])
+    min_num_layers = min([len(layers) for layers in same_type_layers.values()])
+    group_size = min_num_layers
+    max_num_layers = max([len(layers) for layers in same_type_layers.values()])
+    if max_num_layers < min_num_layers * 1.25:
+        # If the number of layers is not much larger than the minimum number of layers,
+        # use the maximum number of layers as the group size to avoid too many padding
+        # layers. A typical example is gpt-oss-20b + eagle, with 12 sw + 13 full. We
+        # pad it to (13 sw, 13 full) instead of (12 sw, 24 full). 1.25 is just a
+        # magic number to avoid too many padding layers.
+        group_size = max_num_layers
     grouped_layers = []
     for layers in same_type_layers.values():
         num_padding_layers = group_size - len(layers) % group_size