[Core] Asynchronous Output Processor

benchmarks/benchmark_throughput.py

@@ -86,6 +86,7 @@ def run_vllm(
     use_v2_block_manager: bool = False,
     download_dir: Optional[str] = None,
     load_format: str = EngineArgs.load_format,
+    disable_async_output_proc: bool = False,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(
@@ -110,6 +111,7 @@ def run_vllm(
         load_format=load_format,
         num_scheduler_steps=num_scheduler_steps,
         use_v2_block_manager=use_v2_block_manager,
+        disable_async_output_proc=disable_async_output_proc,
     )
 
     # Add the requests to the engine.
@@ -237,7 +239,8 @@ def main(args: argparse.Namespace):
             args.enable_prefix_caching, args.enable_chunked_prefill,
             args.max_num_batched_tokens, args.distributed_executor_backend,
             args.gpu_memory_utilization, args.num_scheduler_steps,
-            args.use_v2_block_manager, args.download_dir, args.load_format)
+            args.use_v2_block_manager, args.download_dir, args.load_format,
+            args.disable_async_output_proc)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -418,6 +421,11 @@ def main(args: argparse.Namespace):
         'section for more information.\n'
         '* "bitsandbytes" will load the weights using bitsandbytes '
         'quantization.\n')
+    parser.add_argument(
+        "--disable-async-output-proc",
+        action='store_true',
+        default=False,
+        help="Disable async output processor for vLLM backend.")
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

tests/basic_correctness/test_chunked_prefill.py

@@ -88,6 +88,9 @@ def test_models(
 # NOTE: Increasing this in this suite will fail CI because we currently cannot
 # reset distributed env properly. Use a value > 1 just when you test.
 @pytest.mark.parametrize("tensor_parallel_size", [1])
+# Due to low-precision numerical divergence, this test is too sensitive to
+# the async postprocessor
+@pytest.mark.parametrize("disable_async_output_proc", [True])
 def test_models_with_fp8_kv_cache(
     vllm_runner,
     example_prompts,
@@ -97,6 +100,7 @@ def test_models_with_fp8_kv_cache(
     chunked_prefill_token_size: int,
     enforce_eager: bool,
     tensor_parallel_size: int,
+    disable_async_output_proc: bool,
 ) -> None:
     """
     Only checks log probs match between chunked-prefill and
@@ -126,6 +130,7 @@ def test_models_with_fp8_kv_cache(
             enforce_eager=enforce_eager,
             max_num_seqs=max_num_seqs,
             kv_cache_dtype=kv_cache_dtype,
+            disable_async_output_proc=disable_async_output_proc,
             **extra_kwargs,
     ) as vllm_model:
         no_chunked_prefill_outputs = vllm_model.generate_greedy_logprobs(
@@ -139,6 +144,7 @@ def test_models_with_fp8_kv_cache(
             enforce_eager=enforce_eager,
             max_num_seqs=max_num_seqs,
             kv_cache_dtype=kv_cache_dtype,
+            disable_async_output_proc=disable_async_output_proc,
             **extra_kwargs,
     ) as vllm_model:
         chunked_prefill_outputs = vllm_model.generate_greedy_logprobs(

tests/basic_correctness/test_preemption.py

@@ -209,7 +209,6 @@ def test_swap_infeasible(
     prefill_blocks = 2
     decode_blocks = max_tokens // BLOCK_SIZE
     example_prompts = example_prompts[:1]
-
     with vllm_runner(
             model,
             dtype=dtype,

tests/core/test_chunked_prefill_scheduler.py

@@ -21,7 +21,7 @@ def append_new_token(seq_group, token_id: int):
 
 
 def schedule_and_update_computed_tokens(scheduler):
-    metas, out = scheduler.schedule()
+    metas, out, _ = scheduler.schedule()
     for s, meta in zip(out.scheduled_seq_groups, metas):
         s.seq_group.update_num_computed_tokens(meta.token_chunk_size)
     return metas, out
@@ -180,7 +180,7 @@ def test_maximal_decoding():
     """Verify decoding requests are prioritized."""
     block_size = 4
     max_seqs = 2
-    max_model_len = 2
+    max_model_len = 8
     max_num_batched_tokens = 2
     scheduler_config = SchedulerConfig(max_num_batched_tokens,
                                        max_seqs,

tests/core/utils.py

@@ -199,7 +199,7 @@ def append_new_token(out, token_id: int):
 
 
 def schedule_and_update_computed_tokens(scheduler):
-    metas, out = scheduler.schedule()
+    metas, out, _ = scheduler.schedule()
     for s, meta in zip(out.scheduled_seq_groups, metas):
         s.seq_group.update_num_computed_tokens(meta.token_chunk_size)
     return metas, out

tests/distributed/test_pipeline_parallel.py

@@ -49,6 +49,8 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME,
         str(TP_SIZE),
         "--distributed-executor-backend",
         DIST_BACKEND,
+        # disable async output processor to test PP
+        "--disable-async-output-proc",
     ]
 
     # compare without pipeline parallelism

tests/distributed/test_pp_cudagraph.py

@@ -22,6 +22,8 @@ def test_pp_cudagraph(PP_SIZE, MODEL_NAME, ATTN_BACKEND):
         str(PP_SIZE),
         "--distributed-executor-backend",
         "mp",
+        # disable output proc callback to test PP
+        "--disable-async-output-proc",
     ]
     os.environ["VLLM_ATTENTION_BACKEND"] = ATTN_BACKEND
 

tests/engine/test_stop_strings.py

@@ -7,106 +7,157 @@
 MODEL = "meta-llama/llama-2-7b-hf"
 MAX_TOKENS = 200
 
+IS_ASYNC = False
+
 
 @pytest.fixture(scope="session")
 def vllm_model(vllm_runner):
     with vllm_runner(MODEL) as vllm_model:
         yield vllm_model
 
 
-@pytest.mark.skip_global_cleanup
-def test_stop_basic(vllm_model):
-    _test_stopping(vllm_model.model.llm_engine,
+def _test_stopping(llm_engine: LLMEngine,
+                   expected_output: str,
+                   expected_reason: Any,
+                   stop: Optional[List[str]] = None,
+                   stop_token_ids: Optional[List[int]] = None,
+                   include_in_output: bool = False,
+                   use_async_output_proc: bool = False) -> None:
+    llm_engine.add_request(
+        "id", "A story about vLLM:\n",
+        SamplingParams(
+            temperature=0.0,
+            max_tokens=MAX_TOKENS,
+            stop=stop,
+            stop_token_ids=stop_token_ids,
+            include_stop_str_in_output=include_in_output,
+        ), None)
+
+    output: Optional[CompletionOutput] = None
+    output_text = ""
+    stop_reason = None
+
+    if use_async_output_proc:
+        llm_engine.step()
+
+    while llm_engine.has_unfinished_requests():
+        (request_output, ) = llm_engine.step()
+        (output, ) = request_output.outputs
+
+        # Ensure we don't backtrack
+        assert output.text.startswith(output_text)
+        output_text = output.text
+        stop_reason = output.stop_reason
+
+    assert output is not None
+    assert output_text == expected_output
+    assert stop_reason == expected_reason
+
+
+def _set_async_mode(llm_engine, is_async):
+    llm_engine.scheduler[0].use_async_output_proc = is_async
+
+
+def _stop_basic(llm_engine, is_async):
+    _test_stopping(llm_engine,
                    stop=["."],
                    include_in_output=False,
                    expected_output="VLLM is a 100% volunteer organization",
-                   expected_reason=".")
+                   expected_reason=".",
+                   use_async_output_proc=is_async)
 
-    _test_stopping(vllm_model.model.llm_engine,
+    _test_stopping(llm_engine,
                    stop=["."],
                    include_in_output=True,
                    expected_output="VLLM is a 100% volunteer organization.",
-                   expected_reason=".")
+                   expected_reason=".",
+                   use_async_output_proc=is_async)
 
 
-@pytest.mark.skip_global_cleanup
-def test_stop_multi_tokens(vllm_model):
+def _stop_multi_tokens(llm_engine, is_async):
     _test_stopping(
-        vllm_model.model.llm_engine,
+        llm_engine,
         stop=["group of peo", "short"],
         include_in_output=False,
         expected_output="VLLM is a 100% volunteer organization. We are a ",
-        expected_reason="group of peo")
+        expected_reason="group of peo",
+        use_async_output_proc=is_async)
 
     _test_stopping(
-        vllm_model.model.llm_engine,
+        llm_engine,
         stop=["group of peo", "short"],
         include_in_output=True,
         expected_output=
         "VLLM is a 100% volunteer organization. We are a group of peo",
-        expected_reason="group of peo")
+        expected_reason="group of peo",
+        use_async_output_proc=is_async)
 
 
-@pytest.mark.skip_global_cleanup
-def test_stop_partial_token(vllm_model):
-    _test_stopping(vllm_model.model.llm_engine,
+def _stop_partial_token(llm_engine, is_async):
+    _test_stopping(llm_engine,
                    stop=["gani"],
                    include_in_output=False,
                    expected_output="VLLM is a 100% volunteer or",
-                   expected_reason="gani")
+                   expected_reason="gani",
+                   use_async_output_proc=is_async)
 
-    _test_stopping(vllm_model.model.llm_engine,
+    _test_stopping(llm_engine,
                    stop=["gani"],
                    include_in_output=True,
                    expected_output="VLLM is a 100% volunteer organi",
-                   expected_reason="gani")
+                   expected_reason="gani",
+                   use_async_output_proc=is_async)
 
 
-@pytest.mark.skip_global_cleanup
-def test_stop_token_id(vllm_model):
+def _stop_token_id(llm_engine, is_async):
     # token id 13013 => " organization"
 
-    _test_stopping(vllm_model.model.llm_engine,
+    _test_stopping(llm_engine,
                    stop_token_ids=[13013],
                    include_in_output=False,
                    expected_output="VLLM is a 100% volunteer",
-                   expected_reason=13013)
+                   expected_reason=13013,
+                   use_async_output_proc=is_async)
 
-    _test_stopping(vllm_model.model.llm_engine,
+    _test_stopping(llm_engine,
                    stop_token_ids=[13013],
                    include_in_output=True,
                    expected_output="VLLM is a 100% volunteer organization",
-                   expected_reason=13013)
+                   expected_reason=13013,
+                   use_async_output_proc=is_async)
 
 
-def _test_stopping(llm_engine: LLMEngine,
-                   expected_output: str,
-                   expected_reason: Any,
-                   stop: Optional[List[str]] = None,
-                   stop_token_ids: Optional[List[int]] = None,
-                   include_in_output: bool = False) -> None:
-    llm_engine.add_request(
-        "id", "A story about vLLM:\n",
-        SamplingParams(
-            temperature=0.0,
-            max_tokens=MAX_TOKENS,
-            stop=stop,
-            stop_token_ids=stop_token_ids,
-            include_stop_str_in_output=include_in_output,
-        ), None)
+@pytest.mark.skip_global_cleanup
+def test_stop_basic(vllm_model):
+    _set_async_mode(vllm_model.model.llm_engine, True)
+    _stop_basic(vllm_model.model.llm_engine, is_async=True)
 
-    output: Optional[CompletionOutput] = None
-    output_text = ""
-    stop_reason = None
-    while llm_engine.has_unfinished_requests():
-        (request_output, ) = llm_engine.step()
-        (output, ) = request_output.outputs
+    _set_async_mode(vllm_model.model.llm_engine, False)
+    _stop_basic(vllm_model.model.llm_engine, is_async=False)
 
-        # Ensure we don't backtrack
-        assert output.text.startswith(output_text)
-        output_text = output.text
-        stop_reason = output.stop_reason
 
-    assert output is not None
-    assert output_text == expected_output
-    assert stop_reason == expected_reason
+@pytest.mark.skip_global_cleanup
+def test_stop_multi_tokens(vllm_model):
+    _set_async_mode(vllm_model.model.llm_engine, True)
+    _stop_multi_tokens(vllm_model.model.llm_engine, is_async=True)
+
+    _set_async_mode(vllm_model.model.llm_engine, False)
+    _stop_multi_tokens(vllm_model.model.llm_engine, is_async=False)
+
+
+@pytest.mark.skip_global_cleanup
+def test_stop_partial_token(vllm_model):
+    _set_async_mode(vllm_model.model.llm_engine, True)
+    _stop_partial_token(vllm_model.model.llm_engine, is_async=True)
+
+    _set_async_mode(vllm_model.model.llm_engine, False)
+    _stop_partial_token(vllm_model.model.llm_engine, is_async=False)
+
+
+@pytest.mark.skip_global_cleanup
+def test_stop_token_id(vllm_model):
+    _set_async_mode(vllm_model.model.llm_engine, True)
+    _stop_token_id(vllm_model.model.llm_engine, is_async=True)
+
+    _set_async_mode(vllm_model.model.llm_engine, False)
+    _stop_token_id(vllm_model.model.llm_engine, is_async=False)

tests/multi_step/test_correctness_async_llm.py

@@ -62,6 +62,9 @@ async def test_multi_step(example_prompts, model: str, tp_size: int,
     ms_server_args = DEFAULT_SERVER_ARGS + \
         ["--num-scheduler-steps", f"{num_scheduler_steps}"]
 
+    # Disable output proc callback as its not supported
+    # with multi-step right now
+    ms_server_args += ["--disable-async-output-proc"]
     if eager_mode:
         ms_server_args.append("--enforce-eager")