Raise error for long prompt (#273)

vllm/config.py

@@ -186,14 +186,18 @@ class SchedulerConfig:
             a single iteration.
         max_num_seqs: Maximum number of sequences to be processed in a single
             iteration.
+        max_seq_len: Maximum length of a sequence (including prompt
+            and generated text).
     """
     def __init__(
         self,
         max_num_batched_tokens: int,
         max_num_seqs: int,
+        max_seq_len: int
     ) -> None:
         self.max_num_batched_tokens = max_num_batched_tokens
         self.max_num_seqs = max_num_seqs
+        self.max_seq_len = max_seq_len
 
 
 _STR_DTYPE_TO_TORCH_DTYPE = {

vllm/core/scheduler.py

@@ -102,11 +102,12 @@ class Scheduler:
     def get_num_unfinished_seq_groups(self) -> int:
         return len(self.waiting) + len(self.running) + len(self.swapped)
 
-    def _schedule(self) -> Tuple[SchedulerOutputs, List[str]]:
+    def _schedule(self) -> Tuple[SchedulerOutputs, List[str], List[SequenceGroup]]:
         # Blocks that need to be swaped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}
         blocks_to_swap_out: Dict[int, int] = {}
         blocks_to_copy: Dict[int, List[int]] = {}
+        ignored_seq_groups: List[SequenceGroup] = []
 
         # Fix the current time.
         now = time.time()
@@ -187,12 +188,24 @@ class Scheduler:
                 # If the sequence group has been preempted in this step, stop.
                 if seq_group in preempted:
                     break
+
+                num_prompt_tokens = seq_group.get_seqs()[0].get_len()
+                if num_prompt_tokens >= self.scheduler_config.max_seq_len:
+                    logger.warn(
+                        f"Input prompt ({num_prompt_tokens} tokens) is too long"
+                        " and exceeds limit of "
+                        f"{self.scheduler_config.max_seq_len}")
+                    for seq in seq_group.get_seqs():
+                        seq.status = SequenceStatus.FINISHED_IGNORED
+                    ignored_seq_groups.append(seq_group)
+                    self.waiting.pop(0)
+                    break
+
                 # If the sequence group cannot be allocated, stop.
                 if not self.block_manager.can_allocate(seq_group):
                     break
 
                 # If the number of batched tokens exceeds the limit, stop.
-                num_prompt_tokens = seq_group.get_seqs()[0].get_len()
                 if (num_batched_tokens + num_prompt_tokens
                     > self.scheduler_config.max_num_batched_tokens):
                     break
@@ -218,7 +231,7 @@ class Scheduler:
             blocks_to_copy=blocks_to_copy,
         )
         if not self.log_stats:
-            return scheduler_outputs, prompt_group_ids
+            return scheduler_outputs, prompt_group_ids, ignored_seq_groups
 
         # TODO(woosuk): Move the below code to the engine.
         now = time.time()
@@ -258,13 +271,13 @@ class Scheduler:
                 f"Pending: {len(self.waiting)} reqs, "
                 f"GPU KV cache usage: {gpu_cache_usage * 100:.1f}%, "
                 f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
-        return scheduler_outputs, prompt_group_ids
+        return scheduler_outputs, prompt_group_ids, ignored_seq_groups
 
-    def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
+    def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs, List[SequenceGroup]]:
         # Schedule sequence groups.
         # This function call changes the internal states of the scheduler
         # such as self.running, self.swapped, and self.waiting.
-        scheduler_outputs, prompt_group_ids = self._schedule()
+        scheduler_outputs, prompt_group_ids, ignored_seq_groups = self._schedule()
 
         # Create input data structures.
         seq_group_metadata_list: List[SequenceGroupMetadata] = []
@@ -286,7 +299,7 @@ class Scheduler:
                 block_tables=block_tables,
             )
             seq_group_metadata_list.append(seq_group_metadata)
-        return seq_group_metadata_list, scheduler_outputs
+        return seq_group_metadata_list, scheduler_outputs, ignored_seq_groups
 
     def update(
         self,

vllm/engine/arg_utils.py

@@ -123,8 +123,12 @@ class EngineArgs:
         parallel_config = ParallelConfig(self.pipeline_parallel_size,
                                          self.tensor_parallel_size,
                                          self.worker_use_ray)
+        max_seq_len = min(
+            self.max_num_batched_tokens, 
+            getattr(model_config.hf_config, "max_position_embeddings", 
+                    float("inf")))
         scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
-                                           self.max_num_seqs)
+                                           self.max_num_seqs, max_seq_len)
         return model_config, cache_config, parallel_config, scheduler_config
 
 

vllm/engine/llm_engine.py

@@ -226,8 +226,8 @@ class LLMEngine:
         and updates the scheduler with the model outputs. Finally, it decodes
         the sequences and returns the newly generated results.
         """
-        seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
-        if (not seq_group_metadata_list) and scheduler_outputs.is_empty():
+        seq_group_metadata_list, scheduler_outputs, ignored_seq_groups = self.scheduler.schedule()
+        if (not seq_group_metadata_list) and scheduler_outputs.is_empty() and (not ignored_seq_groups):
             # Nothing to do.
             return []
 
@@ -251,7 +251,7 @@ class LLMEngine:
 
         # Create the outputs.
         request_outputs: List[RequestOutput] = []
-        for seq_group in seq_groups:
+        for seq_group in seq_groups + ignored_seq_groups:
             request_output = RequestOutput.from_seq_group(seq_group)
             request_outputs.append(request_output)
         return request_outputs
@@ -288,6 +288,12 @@ class LLMEngine:
                 if stopped:
                     continue
 
+                # Check if the sequence has reached max_seq_len.
+                if (seq.get_len() >=
+                    self.scheduler.scheduler_config.max_seq_len):
+                    self.scheduler.free_seq(
+                        seq, SequenceStatus.FINISHED_LENGTH_CAPPED)
+                    continue
                 # Check if the sequence has reached max_tokens.
                 if seq.get_output_len() == sampling_params.max_tokens:
                     self.scheduler.free_seq(

vllm/sequence.py

@@ -13,6 +13,7 @@ class SequenceStatus(enum.Enum):
     FINISHED_STOPPED = enum.auto()
     FINISHED_LENGTH_CAPPED = enum.auto()
     FINISHED_ABORTED = enum.auto()
+    FINISHED_IGNORED = enum.auto()
 
     @staticmethod
     def is_finished(status: "SequenceStatus") -> bool:
@@ -20,6 +21,7 @@ class SequenceStatus(enum.Enum):
             SequenceStatus.FINISHED_STOPPED,
             SequenceStatus.FINISHED_LENGTH_CAPPED,
             SequenceStatus.FINISHED_ABORTED,
+            SequenceStatus.FINISHED_IGNORED
         ]
 
     @staticmethod
@@ -30,6 +32,8 @@ class SequenceStatus(enum.Enum):
             finish_reason = "length"
         elif status == SequenceStatus.FINISHED_ABORTED:
             finish_reason = "abort"
+        elif status == SequenceStatus.FINISHED_IGNORED:
+            finish_reason = "length"
         else:
             finish_reason = None
         return finish_reason