Fix KV sharing fast prefill with cudagraph enabled (vllm-project#28537)

Signed-off-by: Yong Hoon Shin <[email protected]>
Co-authored-by: Cyrus Leung <[email protected]>

Author: sarckk

tests/v1/e2e/test_kv_sharing_fast_prefill.py

@@ -4,13 +4,11 @@
 import random
 
 import pytest
-import torch
 
 from vllm import LLM, SamplingParams
 from vllm.config import CompilationConfig, CompilationMode
-from vllm.distributed import cleanup_dist_env_and_memory
 
-from ...utils import fork_new_process_for_each_test
+from ...utils import check_answers, fork_new_process_for_each_test, prep_prompts
 
 # global seed
 SEED = 42
@@ -45,28 +43,12 @@ def test_prompts():
     return prompts
 
 
-def cleanup(llm: LLM, compilation_config: CompilationConfig):
-    # hacky: below lines are required to free up memory for the next test
-    # when setting VLLM_ENABLE_V1_MULTIPROCESSING=0, del llm is not sufficient
-    # TODO(sarckk): when enforce_eager=False, memory is not freed:
-    # find out why and re-enable test for enforce_eager=False case
-    llm_engine = llm.llm_engine.engine_core.engine_core
-    model_runner = llm_engine.model_executor.driver_worker.worker.model_runner
-    del model_runner.model
-    del model_runner.kv_caches
-    del compilation_config.static_forward_context
-    compilation_config.static_forward_context = {}
-
-    del llm
-    torch.cuda.empty_cache()
-    cleanup_dist_env_and_memory()
-
-
 @fork_new_process_for_each_test
-@pytest.mark.parametrize("enforce_eager", [True])
-@pytest.mark.skip(reason="Disable until Gemma3n supports fast prefill")
+@pytest.mark.parametrize("kv_sharing_fast_prefill", [False, True])
+@pytest.mark.parametrize("enforce_eager", [True, False])
 def test_kv_sharing_fast_prefill(
     monkeypatch: pytest.MonkeyPatch,
+    kv_sharing_fast_prefill: bool,
     enforce_eager: bool,
     test_prompts: list[str],
 ):
@@ -79,36 +61,25 @@ def test_kv_sharing_fast_prefill(
         if not enforce_eager
         else CompilationMode.NONE,
     )
+    batch_size = 10
 
     with monkeypatch.context() as m:
         # Make scheduling deterministic for reproducibility
         m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 
-        llm = LLM(
-            model="google/gemma-3n-E2B-it",
-            enforce_eager=enforce_eager,
-            compilation_config=compilation_config,
-            seed=SEED,
-        )
-        ref_responses = llm.generate(test_prompts, sampling_params)
-
-        cleanup(llm, compilation_config)
+        prompts, answer, indices = prep_prompts(batch_size)
 
         llm = LLM(
             model="google/gemma-3n-E2B-it",
             enforce_eager=enforce_eager,
             compilation_config=compilation_config,
             seed=SEED,
-            kv_sharing_fast_prefill=True,
+            kv_sharing_fast_prefill=kv_sharing_fast_prefill,
+        )
+        responses = llm.generate(prompts, sampling_params)
+        check_answers(
+            indices,
+            answer,
+            [response.outputs[0].text for response in responses],
+            accept_rate=1.0,
         )
-        optimized_responses = llm.generate(test_prompts, sampling_params)
-
-        cleanup(llm, compilation_config)
-
-        misses = 0
-
-        for ref_response, optimized_response in zip(ref_responses, optimized_responses):
-            if ref_response.outputs[0].text != optimized_response.outputs[0].text:
-                misses += 1
-
-        assert misses == 0

vllm/v1/attention/backends/utils.py

@@ -965,12 +965,6 @@ def reshape_attn_output_for_spec_decode(attn_output: torch.Tensor) -> torch.Tens
     return attn_output.view(total_tokens, attn_output.shape[2], attn_output.shape[3])
 
 
-KV_SHARING_FAST_PREFILL_METADATA_FIELDS = [
-    ("logits_indices_padded", torch.Tensor | None, None),
-    ("num_logits_indices", int, 0),
-]
-
-
 def subclass_attention_metadata(
     name_prefix: str,
     metadata_cls: Any,
@@ -986,8 +980,8 @@ def subclass_attention_metadata(
 
 @runtime_checkable
 class KVSharingFastPrefillMetadata(Protocol):
-    logits_indices_padded: torch.Tensor
-    num_logits_indices: int
+    logits_indices_padded: torch.Tensor | None = None
+    num_logits_indices: int | None = None
 
 
 def create_fast_prefill_custom_backend(
@@ -1019,11 +1013,6 @@ def __init__(self, metadata, common_attn_metadata):
                     for _field in fields(metadata.__class__):
                         setattr(self, _field.name, getattr(metadata, _field.name))
 
-                    # Set additional fields that will be used in model code
-                    assert (
-                        common_attn_metadata.logits_indices_padded is not None
-                        and common_attn_metadata.num_logits_indices is not None
-                    )
                     self.logits_indices_padded = (
                         common_attn_metadata.logits_indices_padded
                     )

vllm/v1/worker/gpu_model_runner.py

@@ -1314,7 +1314,7 @@ def _build_attention_metadata(
         :return: tuple[attn_metadata, spec_decode_common_attn_metadata]
         """
         logits_indices_padded = None
-        num_logits_indices = 0
+        num_logits_indices = None
         if logits_indices is not None:
             num_logits_indices = logits_indices.size(0)
             if self.cache_config.kv_sharing_fast_prefill: