[Misc] unify variable for LLM instance (#20996)

Signed-off-by: Andy Xie <andy.xning@gmail.com>

tests/lora/test_llama_tp.py

@@ -186,7 +186,7 @@ def test_tp2_serialize_and_deserialize_lora(tmp_path, sql_lora_files,
     model_uri = tmp_path / "vllm" / model_ref / suffix / model_name
     tensorizer_config = TensorizerConfig(tensorizer_uri=str(model_uri))
 
-    loaded_vllm_model = LLM(model=model_ref,
+    loaded_llm = LLM(model=model_ref,
                      load_format="tensorizer",
                      enable_lora=True,
                      enforce_eager=True,
@@ -198,13 +198,13 @@ def test_tp2_serialize_and_deserialize_lora(tmp_path, sql_lora_files,
     tc_as_dict = tensorizer_config.to_serializable()
 
     print("lora adapter created")
-    assert do_sample(loaded_vllm_model,
+    assert do_sample(loaded_llm,
                      sql_lora_files,
                      tensorizer_config_dict=tc_as_dict,
                      lora_id=0) == EXPECTED_NO_LORA_OUTPUT
 
     print("lora 1")
-    assert do_sample(loaded_vllm_model,
+    assert do_sample(loaded_llm,
                      sql_lora_files,
                      tensorizer_config_dict=tc_as_dict,
                      lora_id=1) == EXPECTED_LORA_OUTPUT

tests/metrics/test_metrics.py

@@ -41,7 +41,7 @@ def test_metric_counter_prompt_tokens(
                      dtype=dtype,
                      disable_log_stats=False,
                      gpu_memory_utilization=0.4) as vllm_model:
-        tokenizer = vllm_model.model.get_tokenizer()
+        tokenizer = vllm_model.llm.get_tokenizer()
         prompt_token_counts = [
             len(tokenizer.encode(p)) for p in example_prompts
         ]
@@ -53,7 +53,7 @@ def test_metric_counter_prompt_tokens(
         vllm_prompt_token_count = sum(prompt_token_counts)
 
         _ = vllm_model.generate_greedy(example_prompts, max_tokens)
-        stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus']
+        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
         metric_count = stat_logger.metrics.counter_prompt_tokens.labels(
             **stat_logger.labels)._value.get()
 
@@ -77,8 +77,8 @@ def test_metric_counter_generation_tokens(
                      disable_log_stats=False,
                      gpu_memory_utilization=0.4) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-        tokenizer = vllm_model.model.get_tokenizer()
-        stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus']
+        tokenizer = vllm_model.llm.get_tokenizer()
+        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
         metric_count = stat_logger.metrics.counter_generation_tokens.labels(
             **stat_logger.labels)._value.get()
         vllm_generation_count = 0
@@ -113,8 +113,8 @@ def test_metric_counter_generation_tokens_multi_step(
             disable_async_output_proc=disable_async_output_proc,
     ) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-        tokenizer = vllm_model.model.get_tokenizer()
-        stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus']
+        tokenizer = vllm_model.llm.get_tokenizer()
+        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
         metric_count = stat_logger.metrics.counter_generation_tokens.labels(
             **stat_logger.labels)._value.get()
         vllm_generation_count = 0
@@ -145,7 +145,7 @@ def test_metric_set_tag_model_name(vllm_runner, model: str, dtype: str,
                      disable_log_stats=False,
                      gpu_memory_utilization=0.3,
                      served_model_name=served_model_name) as vllm_model:
-        stat_logger = vllm_model.model.llm_engine.stat_loggers['prometheus']
+        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
         metrics_tag_content = stat_logger.labels["model_name"]
 
     if envs.VLLM_CI_USE_S3:

tests/model_executor/test_model_load_with_params.py

@@ -32,8 +32,8 @@ def test_model_loading_with_params(vllm_runner):
         output = vllm_model.embed("Write a short story about a robot that"
                                   " dreams for the first time.\n")
 
-        model_config = vllm_model.model.llm_engine.model_config
-        model_tokenizer = vllm_model.model.llm_engine.tokenizer
+        model_config = vllm_model.llm.llm_engine.model_config
+        model_tokenizer = vllm_model.llm.llm_engine.tokenizer
 
         # asserts on the bert model config file
         assert model_config.encoder_config["max_seq_length"] == 512
@@ -70,8 +70,8 @@ def test_roberta_model_loading_with_params(vllm_runner):
         output = vllm_model.embed("Write a short story about a robot that"
                                   " dreams for the first time.\n")
 
-        model_config = vllm_model.model.llm_engine.model_config
-        model_tokenizer = vllm_model.model.llm_engine.tokenizer
+        model_config = vllm_model.llm.llm_engine.model_config
+        model_tokenizer = vllm_model.llm.llm_engine.tokenizer
 
         # asserts on the bert model config file
         assert model_config.encoder_config["max_seq_length"] == 512
@@ -108,7 +108,7 @@ def test_facebook_roberta_model_loading_with_params(vllm_runner):
         output = vllm_model.embed("Write a short story about a robot that"
                                   " dreams for the first time.\n")
 
-        model_tokenizer = vllm_model.model.llm_engine.tokenizer
+        model_tokenizer = vllm_model.llm.llm_engine.tokenizer
         assert model_tokenizer.tokenizer_id == model_name
 
         def check_model(model):

tests/models/language/generation/test_hybrid.py

@@ -274,7 +274,7 @@ def test_models_preemption_recompute(
     Tests that outputs are identical with and w/o preemptions (recompute).
     """
     with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
-        scheduler = vllm_model.model.llm_engine.scheduler[0]
+        scheduler = vllm_model.llm.llm_engine.scheduler[0]
         scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
         preempt_vllm_outputs = vllm_model.generate_greedy(
             example_prompts, max_tokens)

tests/models/language/generation/test_mistral.py

@@ -238,7 +238,7 @@ def test_mistral_symbolic_languages(vllm_runner, model: str,
                      load_format="mistral") as vllm_model:
         for prompt in SYMBOLIC_LANG_PROMPTS:
             msg = {"role": "user", "content": prompt}
-            outputs = vllm_model.model.chat([msg],
+            outputs = vllm_model.llm.chat([msg],
                                           sampling_params=SAMPLING_PARAMS)
             assert "�" not in outputs[0].outputs[0].text.strip()
 
@@ -253,11 +253,11 @@ def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
                      load_format="mistral") as vllm_model:
 
         msgs = copy.deepcopy(MSGS)
-        outputs = vllm_model.model.chat(msgs,
+        outputs = vllm_model.llm.chat(msgs,
                                       tools=TOOLS,
                                       sampling_params=SAMPLING_PARAMS)
 
-        tokenizer = vllm_model.model.get_tokenizer()
+        tokenizer = vllm_model.llm.get_tokenizer()
         tool_parser = MistralToolParser(tokenizer)
 
         model_output = outputs[0].outputs[0].text.strip()
@@ -308,7 +308,7 @@ def test_mistral_guided_decoding(
                 f"Give an example JSON for an employee profile that "
                 f"fits this schema: {SAMPLE_JSON_SCHEMA}"
             }]
-            outputs = vllm_model.model.chat(messages, sampling_params=params)
+            outputs = vllm_model.llm.chat(messages, sampling_params=params)
 
         generated_text = outputs[0].outputs[0].text
         json_response = json.loads(generated_text)

tests/models/language/pooling/mteb_utils.py

@@ -30,7 +30,7 @@ class VllmMtebEncoder(mteb.Encoder):
 
     def __init__(self, vllm_model):
         super().__init__()
-        self.model = vllm_model
+        self.llm = vllm_model
         self.rng = np.random.default_rng(seed=42)
 
     def encode(
@@ -43,7 +43,7 @@ class VllmMtebEncoder(mteb.Encoder):
         # issues by randomizing the order.
         r = self.rng.permutation(len(sentences))
         sentences = [sentences[i] for i in r]
-        outputs = self.model.embed(sentences, use_tqdm=False)
+        outputs = self.llm.embed(sentences, use_tqdm=False)
         embeds = np.array(outputs)
         embeds = embeds[np.argsort(r)]
         return embeds
@@ -61,7 +61,7 @@ class VllmMtebEncoder(mteb.Encoder):
         queries = [s[0] for s in sentences]
         corpus = [s[1] for s in sentences]
 
-        outputs = self.model.score(queries,
+        outputs = self.llm.score(queries,
                                  corpus,
                                  truncate_prompt_tokens=-1,
                                  use_tqdm=False)
@@ -178,11 +178,11 @@ def mteb_test_embed_models(hf_runner,
 
         if model_info.architecture:
             assert (model_info.architecture
-                    in vllm_model.model.llm_engine.model_config.architectures)
+                    in vllm_model.llm.llm_engine.model_config.architectures)
 
         vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
                                               MTEB_EMBED_TASKS)
-        vllm_dtype = vllm_model.model.llm_engine.model_config.dtype
+        vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
 
     with hf_runner(model_info.name,
                    is_sentence_transformer=True,
@@ -284,7 +284,7 @@ def mteb_test_rerank_models(hf_runner,
                      max_num_seqs=8,
                      **vllm_extra_kwargs) as vllm_model:
 
-        model_config = vllm_model.model.llm_engine.model_config
+        model_config = vllm_model.llm.llm_engine.model_config
 
         if model_info.architecture:
             assert (model_info.architecture in model_config.architectures)

tests/models/language/pooling/test_gritlm.py

@@ -120,7 +120,7 @@ def test_gritlm_offline_embedding(vllm_runner):
             task="embed",
             max_model_len=MAX_MODEL_LEN,
     ) as vllm_model:
-        llm = vllm_model.model
+        llm = vllm_model.llm
 
         d_rep = run_llm_encode(
             llm,
@@ -167,7 +167,7 @@ def test_gritlm_offline_generate(monkeypatch: pytest.MonkeyPatch, vllm_runner):
             task="generate",
             max_model_len=MAX_MODEL_LEN,
     ) as vllm_model:
-        llm = vllm_model.model
+        llm = vllm_model.llm
 
         sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
         outputs = llm.generate(input, sampling_params=sampling_params)

tests/models/language/pooling/test_jina.py

@@ -87,10 +87,10 @@ def test_matryoshka(
                      task="embed",
                      dtype=dtype,
                      max_model_len=None) as vllm_model:
-        assert vllm_model.model.llm_engine.model_config.is_matryoshka
+        assert vllm_model.llm.llm_engine.model_config.is_matryoshka
 
         matryoshka_dimensions = (
-            vllm_model.model.llm_engine.model_config.matryoshka_dimensions)
+            vllm_model.llm.llm_engine.model_config.matryoshka_dimensions)
         assert matryoshka_dimensions is not None
 
         if dimensions not in matryoshka_dimensions:

tests/models/language/pooling/test_nomic_max_model_len.py

@@ -23,7 +23,7 @@ max_model_len = int(original_max_position_embeddings * factor)
 def test_default(model_info, vllm_runner):
     with vllm_runner(model_info.name, task="embed",
                      max_model_len=None) as vllm_model:
-        model_config = vllm_model.model.llm_engine.model_config
+        model_config = vllm_model.llm.llm_engine.model_config
         if model_info.name == "nomic-ai/nomic-embed-text-v2-moe":
             # For nomic-embed-text-v2-moe the length is set to 512
             # by sentence_bert_config.json.
@@ -38,7 +38,7 @@ def test_set_max_model_len_legal(model_info, vllm_runner):
     # set max_model_len <= 512
     with vllm_runner(model_info.name, task="embed",
                      max_model_len=256) as vllm_model:
-        model_config = vllm_model.model.llm_engine.model_config
+        model_config = vllm_model.llm.llm_engine.model_config
         assert model_config.max_model_len == 256
 
     # set 512 < max_model_len <= 2048
@@ -52,7 +52,7 @@ def test_set_max_model_len_legal(model_info, vllm_runner):
     else:
         with vllm_runner(model_info.name, task="embed",
                          max_model_len=1024) as vllm_model:
-            model_config = vllm_model.model.llm_engine.model_config
+            model_config = vllm_model.llm.llm_engine.model_config
             assert model_config.max_model_len == 1024
 
 

tests/models/language/pooling/test_truncation_control.py

@@ -28,7 +28,7 @@ def test_smaller_truncation_size(vllm_runner,
 
     with vllm_runner(model_name, task="embed",
                      max_model_len=max_model_len) as vllm_model:
-        vllm_output = vllm_model.model.encode(
+        vllm_output = vllm_model.llm.encode(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
     prompt_tokens = vllm_output[0].prompt_token_ids
@@ -43,7 +43,7 @@ def test_max_truncation_size(vllm_runner,
 
     with vllm_runner(model_name, task="embed",
                      max_model_len=max_model_len) as vllm_model:
-        vllm_output = vllm_model.model.encode(
+        vllm_output = vllm_model.llm.encode(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
     prompt_tokens = vllm_output[0].prompt_token_ids
@@ -61,7 +61,7 @@ def test_bigger_truncation_size(vllm_runner,
             model_name, task="embed",
             max_model_len=max_model_len) as vllm_model:
 
-        llm_output = vllm_model.model.encode(
+        llm_output = vllm_model.llm.encode(
             input_str, truncate_prompt_tokens=truncate_prompt_tokens)
 
         assert llm_output == f"""truncate_prompt_tokens value 

tests/models/multimodal/generation/test_pixtral.py

@@ -180,8 +180,7 @@ def test_chat(
     ) as vllm_model:
         outputs = []
         for msg in MSGS:
-            output = vllm_model.model.chat(msg,
-                                           sampling_params=SAMPLING_PARAMS)
+            output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)
 
             outputs.extend(output)
 
@@ -217,7 +216,7 @@ def test_multi_modal_placeholders(vllm_runner, prompt,
             max_model_len=8192,
             limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
     ) as vllm_model:
-        outputs = vllm_model.model.generate(prompt)
+        outputs = vllm_model.llm.generate(prompt)
 
         assert len(outputs) == 1, f"{len(outputs)=}"
         output: RequestOutput = outputs[0]

tests/models/multimodal/generation/test_whisper.py

@@ -106,7 +106,7 @@ def run_test(
             tensor_parallel_size=tensor_parallel_size,
             distributed_executor_backend=distributed_executor_backend,
     ) as vllm_model:
-        llm = vllm_model.model
+        llm = vllm_model.llm
 
         sampling_params = SamplingParams(
             temperature=0,

tests/models/multimodal/generation/vlm_utils/core.py

@@ -85,7 +85,7 @@ def run_test(
                      enforce_eager=enforce_eager,
                      task=task,
                      **vllm_runner_kwargs_) as vllm_model:
-        tokenizer = vllm_model.model.get_tokenizer()
+        tokenizer = vllm_model.llm.get_tokenizer()
 
         vllm_kwargs: dict[str, Any] = {}
         if get_stop_token_ids is not None:

tests/models/multimodal/pooling/test_dse_qwen2_vl.py

@@ -96,7 +96,7 @@ def _run_test(
                      dtype=dtype,
                      enforce_eager=True,
                      max_model_len=8192) as vllm_model:
-        tokenizer = vllm_model.model.get_tokenizer()
+        tokenizer = vllm_model.llm.get_tokenizer()
         texts = [
             # this is necessary because vllm_model.embed will not apply any
             # templating to the prompt, and therefore lacks an image_pad

tests/models/multimodal/pooling/test_jinavl_reranker.py

@@ -56,7 +56,7 @@ def vllm_reranker(
             mm_processor_kwargs=mm_processor_kwargs,
             limit_mm_per_prompt=limit_mm_per_prompt,
     ) as vllm_model:
-        outputs = vllm_model.model.score(query, documents)
+        outputs = vllm_model.llm.score(query, documents)
 
     return [output.outputs.score for output in outputs]
 

tests/models/quantization/test_modelopt.py

@@ -45,7 +45,7 @@ EXPECTED_STRS_MAP = {
                     reason="fp8 is not supported on this GPU type.")
 @pytest.mark.parametrize("model_name", MODELS)
 def test_models(example_prompts, model_name) -> None:
-    model = LLM(
+    llm = LLM(
         model=model_name,
         max_model_len=MAX_MODEL_LEN,
         trust_remote_code=True,
@@ -68,9 +68,9 @@ def test_models(example_prompts, model_name) -> None:
     # Note: these need to be run 1 at a time due to numerical precision,
     # since the expected strs were generated this way.
     for prompt in formatted_prompts:
-        outputs = model.generate(prompt, params)
+        outputs = llm.generate(prompt, params)
         generations.append(outputs[0].outputs[0].text)
-    del model
+    del llm
 
     print(model_name, generations)
     expected_strs = EXPECTED_STRS_MAP[model_name]

tests/models/quantization/test_nvfp4.py

@@ -46,7 +46,7 @@ EXPECTED_STRS_MAP = {
                     reason="modelopt_fp4 is not supported on this GPU type.")
 @pytest.mark.parametrize("model_name", MODELS)
 def test_models(example_prompts, model_name) -> None:
-    model = LLM(
+    llm = LLM(
         model=model_name,
         max_model_len=MAX_MODEL_LEN,
         trust_remote_code=True,
@@ -69,9 +69,9 @@ def test_models(example_prompts, model_name) -> None:
     # Note: these need to be run 1 at a time due to numerical precision,
     # since the expected strs were generated this way.
     for prompt in formatted_prompts:
-        outputs = model.generate(prompt, params)
+        outputs = llm.generate(prompt, params)
         generations.append(outputs[0].outputs[0].text)
-    del model
+    del llm
 
     print(model_name, generations)
     expected_strs = EXPECTED_STRS_MAP[model_name]

tests/prefix_caching/test_disable_sliding_window.py

@@ -25,25 +25,25 @@ MODEL_LEN_LEN = [
 @pytest.mark.parametrize("model_len_len", MODEL_LEN_LEN)
 def test_disable_sliding_window(model_len_len, ):
     model, sliding_len, full_len = model_len_len
-    vllm_disabled_model = LLM(model, disable_sliding_window=True)
-    vllm_disabled_model.generate("Hi my name is")
-    model_config = vllm_disabled_model.llm_engine.model_config
+    disabled_llm = LLM(model, disable_sliding_window=True)
+    disabled_llm.generate("Hi my name is")
+    model_config = disabled_llm.llm_engine.model_config
     assert model_config.max_model_len == sliding_len, (
         "Max len expected to equal sliding_len of %s, but got %s", sliding_len,
         model_config.max_model_len)
 
-    del vllm_disabled_model
+    del disabled_llm
     cleanup_dist_env_and_memory()
 
-    vllm_enabled_model = LLM(model,
+    enabled_llm = LLM(model,
                       enforce_eager=True,
                       disable_sliding_window=False,
                       enable_prefix_caching=False)
-    vllm_enabled_model.generate("Hi my name is")
-    model_config = vllm_enabled_model.llm_engine.model_config
+    enabled_llm.generate("Hi my name is")
+    model_config = enabled_llm.llm_engine.model_config
     assert model_config.max_model_len == full_len, (
         "Max len expected to equal full_len of %s, but got %s", full_len,
         model_config.max_model_len)
 
-    del vllm_enabled_model
+    del enabled_llm
     cleanup_dist_env_and_memory()

tests/prefix_caching/test_prefix_caching.py

@@ -93,7 +93,7 @@ def test_mixed_requests(
             # Run all the promopts
             greedy_params = SamplingParams(temperature=0.0,
                                            max_tokens=max_tokens)
-            req_outputs = vllm_model.model.generate(example_prompts,
+            req_outputs = vllm_model.llm.generate(example_prompts,
                                                   greedy_params)
 
             # Verify number of cached tokens
@@ -161,7 +161,7 @@ def test_fully_cached_prefill_needs_uncached_token(model):
         max_num_batched_tokens=max_num_batched_tokens,
         max_num_seqs=max_num_batched_tokens,
     )
-    engine: LLMEngine = runner.model.llm_engine
+    engine: LLMEngine = runner.llm.llm_engine
 
     scheduler: Scheduler = SchedulerProxy(engine.scheduler[0])  # type: ignore
     engine.scheduler[0] = scheduler

tests/quantization/test_gptq_dynamic.py

@@ -39,7 +39,7 @@ def test_gptq_with_dynamic(vllm_runner, model_id: str, use_marlin_kernel: bool,
     linear_method_cls = GPTQMarlinLinearMethod if use_marlin_kernel else (
         GPTQLinearMethod)
 
-    for name, submodule in (vllm_model.model.llm_engine.model_executor.
+    for name, submodule in (vllm_model.llm.llm_engine.model_executor.
                             driver_worker.model_runner.model.named_modules()):
         if name == "lm_head":
             assert isinstance(submodule.quant_method, linear_method_cls)