[Misc] Remove old now unsupported max_num_partial_prefills and max_long_partial_prefills (#49244)

Signed-off-by: NickLucche <nicolo.lucchesi@mistral.ai>

tests/lora/test_worker.py

@@ -66,7 +66,6 @@ def test_worker_apply_lora(qwen3_lora_files):
             runner_type="generate",
             max_num_batched_tokens=32,
             max_num_seqs=32,
-            max_num_partial_prefills=32,
         ),
         device_config=DeviceConfig(DEVICE_TYPE),
         cache_config=CacheConfig(

vllm/config/scheduler.py

@@ -67,16 +67,6 @@ class SchedulerConfig:
     In real usage, this should be set in `EngineArgs.create_engine_config`.
     """
 
-    max_num_partial_prefills: int = Field(default=1, ge=1)
-    """For chunked prefill, the maximum number of sequences that can be
-    partially prefilled concurrently."""
-
-    max_long_partial_prefills: int = Field(default=1, ge=1)
-    """For chunked prefill, the maximum number of prompts longer than
-    long_prefill_token_threshold that will be prefilled concurrently. Setting
-    this less than max_num_partial_prefills will allow shorter prompts to jump
-    the queue in front of longer prompts in some cases, improving latency."""
-
     long_prefill_token_threshold: int = Field(default=0, ge=0)
     """For chunked prefill, a request is considered long if the prompt is
     longer than this number of tokens. 0 disables the cap (default)."""
@@ -254,19 +244,6 @@ class SchedulerConfig:
                 self.max_num_batched_tokens,
             )
 
-        if self.max_num_partial_prefills > 1:
-            if self.long_prefill_token_threshold == 0:
-                self.long_prefill_token_threshold = int(max_model_len * 0.04)
-
-            logger.info(
-                "Concurrent partial prefills enabled with "
-                "max_num_partial_prefills=%d, max_long_partial_prefills=%d, "
-                "long_prefill_token_threshold=%d",
-                self.max_num_partial_prefills,
-                self.max_long_partial_prefills,
-                self.long_prefill_token_threshold,
-            )
-
         self.verify_max_model_len(max_model_len)
 
     def verify_max_model_len(self, max_model_len: int) -> Self:
@@ -298,24 +275,11 @@ class SchedulerConfig:
                 self.max_num_seqs * max_model_len,
             )
 
-        if self.max_num_partial_prefills > 1:
-            if not self.enable_chunked_prefill:
-                raise ValueError(
-                    "Chunked prefill must be enabled to set "
-                    "max_num_partial_prefills > 1."
-                )
-
-            if self.long_prefill_token_threshold > max_model_len:
-                raise ValueError(
-                    "long_prefill_token_threshold "
-                    f"({self.long_prefill_token_threshold}) cannot be greater "
-                    f"than the max_model_len ({max_model_len})."
-                )
-
-        if self.max_long_partial_prefills > self.max_num_partial_prefills:
+        if self.long_prefill_token_threshold > max_model_len:
             raise ValueError(
-                f"{self.max_long_partial_prefills=} must be less than or equal to "
-                f"{self.max_num_partial_prefills=}."
+                "long_prefill_token_threshold "
+                f"({self.long_prefill_token_threshold}) cannot be greater "
+                f"than the max_model_len ({max_model_len})."
             )
 
         return self

vllm/engine/arg_utils.py

@@ -527,8 +527,6 @@ class EngineArgs:
     kv_cache_memory_bytes: int | None = CacheConfig.kv_cache_memory_bytes
     max_num_batched_tokens: int | None = None
     max_num_scheduled_tokens: int | None = None
-    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
-    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
     long_prefill_token_threshold: int = SchedulerConfig.long_prefill_token_threshold
     max_num_seqs: int | None = None
     max_logprobs: int = ModelConfig.max_logprobs
@@ -1440,13 +1438,6 @@ class EngineArgs:
                 "default": None,
             },
         )
-        scheduler_group.add_argument(
-            "--max-num-partial-prefills", **scheduler_kwargs["max_num_partial_prefills"]
-        )
-        scheduler_group.add_argument(
-            "--max-long-partial-prefills",
-            **scheduler_kwargs["max_long_partial_prefills"],
-        )
         scheduler_group.add_argument(
             "--long-prefill-token-threshold",
             **scheduler_kwargs["long_prefill_token_threshold"],
@@ -2190,8 +2181,6 @@ class EngineArgs:
             is_encoder_decoder=model_config.is_encoder_decoder,
             policy=self.scheduling_policy,
             scheduler_cls=self.scheduler_cls,
-            max_num_partial_prefills=self.max_num_partial_prefills,
-            max_long_partial_prefills=self.max_long_partial_prefills,
             long_prefill_token_threshold=self.long_prefill_token_threshold,
             scheduler_reserve_full_isl=self.scheduler_reserve_full_isl,
             watermark=self.watermark,
@@ -2402,14 +2391,6 @@ class EngineArgs:
 
     def _check_feature_supported(self):
         """Raise an error if the feature is not supported."""
-        # No Concurrent Partial Prefills so far.
-        if (
-            self.max_num_partial_prefills != SchedulerConfig.max_num_partial_prefills
-            or self.max_long_partial_prefills
-            != SchedulerConfig.max_long_partial_prefills
-        ):
-            _raise_unsupported_error(feature_name="Concurrent Partial Prefill")
-
         if self.pipeline_parallel_size > 1:
             supports_pp = getattr(
                 self.distributed_executor_backend, "supports_pp", False