Raise error for long prompt (#273)

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(