[Misc] Remove deprecated code (vllm-project#12383)

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

tests/async_engine/test_api_server.py

@@ -25,27 +25,32 @@ def _query_server_long(prompt: str) -> dict:
 
 
 @pytest.fixture
-def api_server(tokenizer_pool_size: int, worker_use_ray: bool):
+def api_server(tokenizer_pool_size: int, distributed_executor_backend: str):
     script_path = Path(__file__).parent.joinpath(
         "api_server_async_engine.py").absolute()
     commands = [
-        sys.executable, "-u",
-        str(script_path), "--model", "facebook/opt-125m", "--host",
-        "127.0.0.1", "--tokenizer-pool-size",
-        str(tokenizer_pool_size)
+        sys.executable,
+        "-u",
+        str(script_path),
+        "--model",
+        "facebook/opt-125m",
+        "--host",
+        "127.0.0.1",
+        "--tokenizer-pool-size",
+        str(tokenizer_pool_size),
+        "--distributed-executor-backend",
+        distributed_executor_backend,
     ]
 
-    if worker_use_ray:
-        commands.append("--worker-use-ray")
     uvicorn_process = subprocess.Popen(commands)
     yield
     uvicorn_process.terminate()
 
 
 @pytest.mark.parametrize("tokenizer_pool_size", [0, 2])
-@pytest.mark.parametrize("worker_use_ray", [False, True])
+@pytest.mark.parametrize("distributed_executor_backend", ["mp", "ray"])
 def test_api_server(api_server, tokenizer_pool_size: int,
-                    worker_use_ray: bool):
+                    distributed_executor_backend: str):
     """
     Run the API server and test it.
 

tests/basic_correctness/test_preemption.py

@@ -29,10 +29,10 @@ def check_settings():
 
 
 @pytest.fixture
-def worker_use_ray() -> bool:
-    # When SPMD worker is used, use ray_use_worker=True
+def distributed_executor_backend() -> str:
+    # When SPMD worker is used, use distributed_executor_backend="ray"
     # to test delta input optimization works with preemption.
-    return envs.VLLM_USE_RAY_SPMD_WORKER
+    return "ray" if envs.VLLM_USE_RAY_SPMD_WORKER else "mp"
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -47,7 +47,7 @@ def test_chunked_prefill_recompute(
     dtype: str,
     max_tokens: int,
     chunked_prefill_token_size: int,
-    worker_use_ray: bool,
+    distributed_executor_backend: str,
 ) -> None:
     """Ensure that chunked prefill works with preemption."""
     max_num_seqs = min(chunked_prefill_token_size, 256)
@@ -66,7 +66,7 @@ def test_chunked_prefill_recompute(
             max_num_batched_tokens=max_num_batched_tokens,
             enable_chunked_prefill=enable_chunked_prefill,
             max_num_seqs=max_num_seqs,
-            worker_use_ray=worker_use_ray,
+            distributed_executor_backend=distributed_executor_backend,
             disable_log_stats=False,
     ) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
@@ -93,7 +93,7 @@ def test_preemption(
     model: str,
     dtype: str,
     max_tokens: int,
-    worker_use_ray: bool,
+    distributed_executor_backend: str,
 ) -> None:
     """By default, recompute preemption is enabled"""
 
@@ -104,7 +104,7 @@ def test_preemption(
             model,
             dtype=dtype,
             disable_log_stats=False,
-            worker_use_ray=worker_use_ray,
+            distributed_executor_backend=distributed_executor_backend,
     ) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
         assert (vllm_model.model.llm_engine.scheduler[0].artificial_preempt_cnt
@@ -144,7 +144,7 @@ def test_preemption_infeasible(
     model: str,
     dtype: str,
     max_tokens: int,
-    worker_use_ray: bool,
+    distributed_executor_backend: str,
 ) -> None:
     """Verify infeasible preemption request will be ignored."""
     BLOCK_SIZE = 16
@@ -159,7 +159,7 @@ def test_preemption_infeasible(
             # ignored instead of hanging forever.
             num_gpu_blocks_override=prefill_blocks + decode_blocks // 2,
             max_model_len=((prefill_blocks + decode_blocks // 2) * BLOCK_SIZE),
-            worker_use_ray=worker_use_ray,
+            distributed_executor_backend=distributed_executor_backend,
     ) as vllm_model:
         sampling_params = SamplingParams(max_tokens=max_tokens,
                                          ignore_eos=True)

tests/multi_step/test_correctness_async_llm.py

@@ -16,7 +16,8 @@
 NUM_PROMPTS = [10]
 
 DEFAULT_SERVER_ARGS: List[str] = [
-    "--worker-use-ray",
+    "--distributed-executor-backend",
+    "ray",
     "--gpu-memory-utilization",
     "0.85",
     "--swap-space",

vllm/config.py

@@ -1227,9 +1227,6 @@ class ParallelConfig:
     pipeline_parallel_size: int = 1  # Number of pipeline parallel groups.
     tensor_parallel_size: int = 1  # Number of tensor parallel groups.
 
-    # Deprecated, use distributed_executor_backend instead.
-    worker_use_ray: Optional[bool] = None
-
     # Maximum number of multiple batches
     # when load model sequentially. To avoid RAM OOM when using tensor
     # parallel and large models.
@@ -1283,13 +1280,6 @@ def __post_init__(self) -> None:
         self.world_size = self.pipeline_parallel_size * \
             self.tensor_parallel_size
 
-        if self.worker_use_ray:
-            if self.distributed_executor_backend is None:
-                self.distributed_executor_backend = "ray"
-            elif not self.use_ray:
-                raise ValueError(f"worker-use-ray can't be used with "
-                                 f"distributed executor backend "
-                                 f"'{self.distributed_executor_backend}'.")
         ray_only_devices = ["tpu"]
         from vllm.platforms import current_platform
         if (current_platform.device_type in ray_only_devices

vllm/engine/arg_utils.py

@@ -100,7 +100,6 @@ class EngineArgs:
     kv_cache_dtype: str = 'auto'
     seed: int = 0
     max_model_len: Optional[int] = None
-    worker_use_ray: bool = False
     # Note: Specifying a custom executor backend by passing a class
     # is intended for expert use only. The API may change without
     # notice.
@@ -389,10 +388,6 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             'to "ray" if Ray is installed and fail otherwise. Note that tpu '
             'only supports Ray for distributed inference.')
 
-        parser.add_argument(
-            '--worker-use-ray',
-            action='store_true',
-            help='Deprecated, use ``--distributed-executor-backend=ray``.')
         parser.add_argument('--pipeline-parallel-size',
                             '-pp',
                             type=int,
@@ -1071,7 +1066,6 @@ def create_engine_config(self,
         parallel_config = ParallelConfig(
             pipeline_parallel_size=self.pipeline_parallel_size,
             tensor_parallel_size=self.tensor_parallel_size,
-            worker_use_ray=self.worker_use_ray,
             max_parallel_loading_workers=self.max_parallel_loading_workers,
             disable_custom_all_reduce=self.disable_custom_all_reduce,
             tokenizer_pool_config=TokenizerPoolConfig.create_config(

vllm/engine/metrics.py

@@ -259,21 +259,6 @@ def __init__(self, labelnames: List[str], vllm_config: VllmConfig):
             documentation="Number of emitted tokens.",
             labelnames=labelnames))
 
-        # Deprecated in favor of vllm:prompt_tokens_total
-        self.gauge_avg_prompt_throughput = self._gauge_cls(
-            name="vllm:avg_prompt_throughput_toks_per_s",
-            documentation="Average prefill throughput in tokens/s.",
-            labelnames=labelnames,
-            multiprocess_mode="sum",
-        )
-        # Deprecated in favor of vllm:generation_tokens_total
-        self.gauge_avg_generation_throughput = self._gauge_cls(
-            name="vllm:avg_generation_throughput_toks_per_s",
-            documentation="Average generation throughput in tokens/s.",
-            labelnames=labelnames,
-            multiprocess_mode="sum",
-        )
-
 
 # end-metrics-definitions
 
@@ -635,20 +620,6 @@ def _log_prometheus(self, stats: Stats) -> None:
         self._log_histogram(self.metrics.histogram_max_tokens_request,
                             stats.max_tokens_requests)
 
-    def _log_prometheus_interval(self, prompt_throughput: float,
-                                 generation_throughput: float) -> None:
-        # Logs metrics to prometheus that are computed every logging_interval.
-        # Support legacy gauge metrics that make throughput calculations on
-        # the vLLM side. Moving forward, we should use counters like
-        # counter_prompt_tokens, counter_generation_tokens
-        # Which log raw data and calculate summaries using rate() on the
-        # grafana/prometheus side. See
-        # https://github.com/vllm-project/vllm/pull/2316#discussion_r1464204666
-        self.metrics.gauge_avg_prompt_throughput.labels(
-            **self.labels).set(prompt_throughput)
-        self.metrics.gauge_avg_generation_throughput.labels(
-            **self.labels).set(generation_throughput)
-
     def log(self, stats: Stats):
         """Logs to prometheus and tracked stats every iteration."""
         # Log to prometheus.
@@ -664,20 +635,6 @@ def log(self, stats: Stats):
         # Log locally every local_interval seconds.
         if local_interval_elapsed(stats.now, self.last_local_log,
                                   self.local_interval):
-            # Compute summary metrics for tracked stats (and log them
-            # to promethus if applicable).
-            prompt_throughput = get_throughput(self.num_prompt_tokens,
-                                               now=stats.now,
-                                               last_log=self.last_local_log)
-            generation_throughput = get_throughput(
-                self.num_generation_tokens,
-                now=stats.now,
-                last_log=self.last_local_log)
-
-            self._log_prometheus_interval(
-                prompt_throughput=prompt_throughput,
-                generation_throughput=generation_throughput)
-
             if self.spec_decode_metrics is not None:
                 self._log_gauge(
                     self.metrics.gauge_spec_decode_draft_acceptance_rate,