Bump up version to 0.1.1 (#204)

examples/llm_engine_example.py

@@ -30,7 +30,7 @@ def main(args: argparse.Namespace):
 
         request_outputs = engine.step()
         for request_output in request_outputs:
-            if request_output.finished():
+            if request_output.finished:
                 print(request_output)
 
         if not (engine.has_unfinished_requests() or test_prompts):

vllm/__init__.py

@@ -6,7 +6,7 @@ from vllm.entrypoints.llm import LLM
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 
 __all__ = [
     "LLM",

vllm/engine/async_llm_engine.py

@@ -154,7 +154,7 @@ class AsyncLLMEngine:
             yield request_output
 
             # Once finished, release the resources of the sequence group.
-            if request_output.finished():
+            if request_output.finished:
                 if self.log_requests:
                     logger.info(f"Finished request {request_id}.")
 

vllm/engine/llm_engine.py

@@ -87,7 +87,7 @@ class LLMEngine:
                 worker_cls = ray.remote(
                     num_cpus=0,
                     num_gpus=1,
-                    resources={node_resource: 1e-5},
+                    resources={node_resource: 1e-3},
                 )(worker_cls).remote
 
             worker = worker_cls(
@@ -127,6 +127,12 @@ class LLMEngine:
         # FIXME(woosuk): Change to debug log.
         logger.info(f'# GPU blocks: {num_gpu_blocks}, '
                     f'# CPU blocks: {num_cpu_blocks}')
+
+        if num_gpu_blocks <= 0 or num_cpu_blocks <= 0:
+            raise ValueError("No available memory for the cache blocks. "
+                             "Try increasing `gpu_memory_utilization` when "
+                             "initializing the engine.")
+
         self.cache_config.num_gpu_blocks = num_gpu_blocks
         self.cache_config.num_cpu_blocks = num_cpu_blocks
 

vllm/entrypoints/llm.py

@@ -133,7 +133,7 @@ class LLM:
         while self.llm_engine.has_unfinished_requests():
             step_outputs = self.llm_engine.step()
             for output in step_outputs:
-                if output.finished():
+                if output.finished:
                     outputs.append(output)
                     if use_tqdm:
                         pbar.update(1)

vllm/outputs.py

@@ -53,6 +53,7 @@ class RequestOutput:
         prompt: The prompt string of the request.
         prompt_token_ids: The token IDs of the prompt.
         outputs: The output sequences of the request.
+        finished: Whether the whole request is finished.
     """
     def __init__(
         self,
@@ -60,11 +61,13 @@ class RequestOutput:
         prompt: str,
         prompt_token_ids: List[int],
         outputs: List[CompletionOutput],
+        finished: bool,
     ) -> None:
         self.request_id = request_id
         self.prompt = prompt
         self.prompt_token_ids = prompt_token_ids
         self.outputs = outputs
+        self.finished = finished
 
     @classmethod
     def from_seq_group(cls, seq_group: SequenceGroup) -> "RequestOutput":
@@ -95,13 +98,13 @@ class RequestOutput:
         # Every sequence in the sequence group should have the same prompt.
         prompt = top_n_seqs[0].prompt
         prompt_token_ids = top_n_seqs[0].data.prompt_token_ids
-        return cls(seq_group.request_id, prompt, prompt_token_ids, outputs)
+        finished = seq_group.is_finished()
+        return cls(seq_group.request_id, prompt, prompt_token_ids, outputs,
+                   finished)
 
     def __repr__(self) -> str:
         return (f"RequestOutput(request_id={self.request_id}, "
                 f"prompt={self.prompt!r}, "
                 f"prompt_token_ids={self.prompt_token_ids}, "
-                f"outputs={self.outputs})")
-
-    def finished(self) -> bool:
-        return all(output.finished() for output in self.outputs)
+                f"outputs={self.outputs}, "
+                f"finished={self.finished})")