[Dynamic Spec Decoding] Minor fix for disabling speculative decoding (#5000)

tests/spec_decode/e2e/test_ngram_correctness.py

@@ -170,3 +170,44 @@ def test_ngram_different_k(baseline_llm_generator, test_llm_generator,
                                          batch_size,
                                          max_output_len=output_len,
                                          force_output_len=True)
+
+
+@pytest.mark.parametrize(
+    "common_llm_kwargs",
+    [{
+        "model": "JackFram/llama-68m",
+
+        # Skip cuda graph recording for fast test.
+        "enforce_eager": True,
+
+        # Required for spec decode.
+        "use_v2_block_manager": True
+    }])
+@pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
+@pytest.mark.parametrize("baseline_llm_kwargs", [{}])
+@pytest.mark.parametrize("test_llm_kwargs",
+                         [{
+                             "speculative_model": "[ngram]",
+                             "num_speculative_tokens": 5,
+                             "ngram_prompt_lookup_max": 3,
+                             "speculative_disable_by_batch_size": 4
+                         }])
+@pytest.mark.parametrize("batch_size", [1, 5])
+@pytest.mark.parametrize(
+    "output_len",
+    [
+        # Use smaller output len for fast test.
+        32,
+    ])
+@pytest.mark.parametrize("seed", [1])
+def test_ngram_disable_queue(baseline_llm_generator, test_llm_generator,
+                             batch_size: int, output_len: int):
+    """Verify that ngram speculative decoding produces exact equality
+    to without spec decode with many different values of k and
+    different ngram_prompt_lookup_max.
+    """
+    run_greedy_equality_correctness_test(baseline_llm_generator,
+                                         test_llm_generator,
+                                         batch_size,
+                                         max_output_len=output_len,
+                                         force_output_len=True)

tests/spec_decode/test_dynamic_spec_decode.py

@@ -1,4 +1,4 @@
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
@@ -13,9 +13,9 @@ from vllm.spec_decode.top1_proposer import Top1Proposer
 from .utils import create_batch, mock_worker
 
 
-@pytest.mark.parametrize('queue_size', [2, 4])
+@pytest.mark.parametrize('queue_size', [4])
-@pytest.mark.parametrize('batch_size', [1, 2, 3, 6])
+@pytest.mark.parametrize('batch_size', [1])
-@pytest.mark.parametrize('k', [1, 2, 5, 7, 10])
+@pytest.mark.parametrize('k', [1])
 @torch.inference_mode()
 def test_disable_spec_tokens(queue_size: int, batch_size: int, k: int):
     """Verify that speculative tokens are disabled when the batch size
@@ -42,7 +42,11 @@ def test_disable_spec_tokens(queue_size: int, batch_size: int, k: int):
         num_lookahead_slots=k,
         running_queue_size=queue_size)
 
-    with pytest.raises(ValueError, match=exception_secret):
+    if queue_size > disable_by_batch_size:
+        with patch.object(worker,
+                          '_run_no_spec',
+                          side_effect=ValueError(exception_secret)), \
+            pytest.raises(ValueError, match=exception_secret):
             worker.execute_model(execute_model_req=execute_model_req)
 
     # When the batch size is larger than the threshold,

vllm/spec_decode/spec_decode_worker.py

@@ -273,10 +273,17 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         self._maybe_disable_speculative_tokens(
             disable_all_speculation, execute_model_req.seq_group_metadata_list)
 
-        # If no spec tokens, call the proposer and scorer workers normally.
+        # Speculative decoding is disabled in the following cases:
-        # Used for prefill.
+        # 1. Prefill phase: Speculative decoding is not
+        #    used during the prefill phase.
+        # 2. Auto-disable enabled: The running queue size exceeds
+        #    the specified threshold.
+        # 3. No request: There are no requests in the batch.
+        # In any of these cases, the proposer and scorer workers
+        # are called normally.
         if num_lookahead_slots == 0 or len(
-                execute_model_req.seq_group_metadata_list) == 0:
+                execute_model_req.seq_group_metadata_list
+        ) == 0 or disable_all_speculation:
             return self._run_no_spec(execute_model_req,
                                      skip_proposer=disable_all_speculation)
 
@@ -316,8 +323,8 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
     @nvtx_range("spec_decode_worker._run_no_spec")
     def _run_no_spec(self, execute_model_req: ExecuteModelRequest,
                      skip_proposer: bool) -> List[SamplerOutput]:
-        """Run a prefill step, without any speculation. The input is sent to
+        """Run a single generation step without any speculation. The input is
-        the proposer and scorer model so that the KV cache is consistent
+        sent to the proposer and scorer model so that the KV cache is consistent
         between the two. When skip_proposer is True, the proposer model is
         not called, meaning that the kv-cache in proposer for requests is not
         updated, so they cannot enable spec decode in the rest decoding.