[Bug fix][Core] fixup ngram not setup correctly (#4551)

Co-authored-by: Lei Wen <wenlei03@qiyi.com>
Co-authored-by: Cade Daniel <edacih@gmail.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

tests/spec_decode/e2e/conftest.py

@@ -55,7 +55,7 @@ class AsyncLLM:
     ) -> None:
         if "disable_log_stats" not in kwargs:
             kwargs["disable_log_stats"] = True
-        self.engine_args = AsyncEngineArgs(
+        engine_args = AsyncEngineArgs(
             model=model,
             tokenizer=tokenizer,
             tokenizer_mode=tokenizer_mode,
@@ -76,6 +76,8 @@ class AsyncLLM:
             **kwargs,
         )
         self.request_counter = Counter()
+        self.llm_engine = AsyncLLMEngine.from_engine_args(
+            engine_args, usage_context=UsageContext.LLM_CLASS)
 
     def generate(
         self,
@@ -88,9 +90,6 @@ class AsyncLLM:
         multi_modal_data: Optional[MultiModalData] = None,
     ) -> List[RequestOutput]:
 
-        llm_engine = AsyncLLMEngine.from_engine_args(
-            self.engine_args, usage_context=UsageContext.LLM_CLASS)
-
         if prompts is None:
             raise ValueError("prompts must be provided.")
         if isinstance(prompts, str):
@@ -111,8 +110,8 @@ class AsyncLLM:
 
         async def get_output(prompt, sampling_param) -> str:
             request_id = random_uuid()
-            results_generator = llm_engine.generate(prompt, sampling_param,
+            results_generator = self.llm_engine.generate(
-                                                    request_id)
+                prompt, sampling_param, request_id)
             final_output = None
             async for request_output in results_generator:
                 final_output = request_output
@@ -185,12 +184,25 @@ def create_llm_generator(baseline_or_test, request, common_llm_kwargs,
     return generator_outer
 
 
+def maybe_assert_ngram_worker(llm):
+    # Verify the proposer worker is ngram if ngram is specified.
+    if (not isinstance(llm, AsyncLLM)
+            and llm.llm_engine.speculative_config is not None
+            and llm.llm_engine.speculative_config.ngram_prompt_lookup_max > 0):
+        from vllm.spec_decode.ngram_worker import NGramWorker
+        assert isinstance(
+            llm.llm_engine.model_executor.driver_worker.proposer_worker,
+            NGramWorker)
+
+
 def get_output_from_llm_generator(
         llm_generator, prompts,
         sampling_params) -> Tuple[List[str], List[List[int]]]:
     tokens = []
     token_ids = []
     for llm in llm_generator():
+        maybe_assert_ngram_worker(llm)
+
         outputs = llm.generate(prompts, sampling_params, use_tqdm=True)
         token_ids = [output.outputs[0].token_ids for output in outputs]
         tokens = [output.outputs[0].text for output in outputs]

vllm/executor/gpu_executor.py

@@ -82,6 +82,10 @@ class GPUExecutor(ExecutorBase):
         draft_worker_kwargs.update(
             model_config=self.speculative_config.draft_model_config,
             parallel_config=self.speculative_config.draft_parallel_config,
+            ngram_prompt_lookup_max=self.speculative_config.
+            ngram_prompt_lookup_max,
+            ngram_prompt_lookup_min=self.speculative_config.
+            ngram_prompt_lookup_min,
             # TODO allow draft-model specific load config.
             #load_config=self.load_config,
         )

vllm/spec_decode/spec_decode_worker.py

@@ -57,13 +57,10 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         draft_worker_kwargs,
     ) -> "SpecDecodeWorker":
 
-        if "ngram_prompt_lookup_max" in draft_worker_kwargs:
         ngram_prompt_lookup_max = (
             draft_worker_kwargs.pop("ngram_prompt_lookup_max"))
         ngram_prompt_lookup_min = (
             draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
-        else:
-            ngram_prompt_lookup_max = 0
 
         if ngram_prompt_lookup_max > 0:
             proposer_worker = NGramWorker(**draft_worker_kwargs)
@@ -72,6 +69,9 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
         else:
             proposer_worker = MultiStepWorker(**draft_worker_kwargs)
 
+        logger.info("Configuring SpecDecodeWorker with proposer=%s",
+                    type(proposer_worker))
+
         return SpecDecodeWorker(
             proposer_worker,
             scorer_worker,