[Attention] Make split_decodes_and_prefills(..., require_uniform=True) support padding

tests/v1/attention/test_attention_splitting.py

@@ -154,7 +154,10 @@ def test_split_attn_metadata_decode_batch(large_decode_metadata):
 
 
 def apply_split_decodes_and_prefills(
-    query_lens: list[int], decode_threshold: int, require_uniform: bool
+    query_lens: list[int],
+    decode_threshold: int,
+    require_uniform: bool,
+    padded_num_tokens: int | None = None,
 ):
     """Helper function to apply split_decodes_and_prefills and return
     the results."""
@@ -165,6 +168,10 @@ def apply_split_decodes_and_prefills(
         block_size=16,
         device=device,
     )
+
+    if padded_num_tokens is not None:
+        common_metadata.num_actual_tokens = padded_num_tokens
+
     return split_decodes_and_prefills(
         common_metadata,
         decode_threshold=decode_threshold,
@@ -271,6 +278,22 @@ def test_split_decodes_and_prefills_uniform_mixed_batch_non_uniform_decodes():
     assert num_prefill_tokens == (sum(query_lens) - 2)  # rest of the tokens
 
 
+def test_split_decodes_and_prefills_uniform_padded_batch_all_same():
+    """uniform batch where all query lengths are identical with 0 length padded reqs."""
+    # All query lengths are 2, with decode_threshold=3 (so 2 <= 3)
+    # This triggers the padded uniform path at line 891
+    query_lens = [2, 2, 2, 0]
+    padded_num_tokens = 8
+    num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
+        apply_split_decodes_and_prefills(query_lens, 3, True, padded_num_tokens)
+    )
+    # With uniform batch, all requests are treated as decodes
+    assert num_decodes == 4
+    assert num_prefills == 0
+    assert num_decode_tokens == padded_num_tokens
+    assert num_prefill_tokens == 0
+
+
 @pytest.mark.parametrize(
     "seq_lens,query_lens,split_point,expected_first_reqs,expected_second_reqs",
     [

vllm/v1/attention/backends/utils.py

@@ -883,11 +883,15 @@ def split_decodes_and_prefills(
         return 0, num_reqs, 0, num_tokens
 
     if require_uniform:
+        # check if we are in a padded uniform batch; this is used for full-CGs, some
+        # requests may have a query length of 0 but since they are padding its fine
+        # to treat them as decodes (ensures num_decodes matches the captured size)
+        if torch.all((query_lens == query_lens[0]) | (query_lens == 0)):
+            assert num_reqs * query_lens[0] == num_tokens, "tokens not padded correctly"
+            return num_reqs, 0, num_tokens, 0  # all decodes
         is_prefill = query_lens != query_lens[0]
     else:
-        # 0-query len indicates a padded request; leave this at the back
-        # of the batch with the prefills
-        is_prefill = (query_lens > decode_threshold) | (query_lens == 0)
+        is_prefill = query_lens > decode_threshold
 
     if not torch.any(is_prefill):
         return num_reqs, 0, num_tokens, 0