[refactor][MiniCPM-o] Consolidate MiniCPM-o 4.5 2-GPU deploy configs and E2E tests (#5458)

Signed-off-by: y_null <y_null@qq.com>
Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>
Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>

examples/online_serving/minicpmo/README.md

@@ -25,15 +25,14 @@ The deploy config auto-loads via `--omni`.
 The default `vllm_omni/deploy/minicpmo_4_5.yaml` keeps all three stages on
 logical device 0 with memory budgets of 55%, 22%, and 22%. The profile admits
 at most four sequences per stage and bounds startup video profiling to 32
-frames per video. For throughput,`minicpmo_4_5_batching.yaml` gives the Thinker
+frames per video. For throughput,`minicpmo_4_5_2gpu.yaml` gives the Thinker
 GPU 0 (90%) and colocates the Talker (55%) and Code2Wav (35%) on GPU 1. That
 profile admits at most four concurrent sequences per stage.
 
 | deploy config | GPUs | Notes |
 |---|---|---|
 | `minicpmo_4_5.yaml` (default) | 1 | Memory-constrained compatibility layout. |
-| `minicpmo_4_5_batching.yaml` | 2 | Recommended continuous-batching layout; Talker and Code2Wav share GPU 1. |
-| `minicpmo_4_5_2gpu.yaml` | 2 | Backward-compatible alias for `minicpmo_4_5_batching.yaml`. |
+| `minicpmo_4_5_2gpu.yaml` | 2 | Recommended continuous-batching layout; Talker and Code2Wav share GPU 1. |
 | `minicpmo_4_5_3gpu.yaml` | 3 | One GPU per stage. |
 | `minicpmo_4_5_8x4090.yaml` | 8 | Full 8x4090 layout. |
 | `minicpmo_4_5_duplex.yaml` | 1 | Experimental native full-duplex overlay. |

recipes/OpenBMB/MiniCPM-o-4_5.md

@@ -30,8 +30,6 @@ also provided.
   `hf_config.version="4.5"`):
   - Default single-GPU compatibility layout (auto-loaded):
     [`vllm_omni/deploy/minicpmo_4_5.yaml`](../../vllm_omni/deploy/minicpmo_4_5.yaml)
-  - Recommended 2-GPU continuous-batching layout:
-    [`vllm_omni/deploy/minicpmo_4_5_batching.yaml`](../../vllm_omni/deploy/minicpmo_4_5_batching.yaml)
   - 2-GPU and 3-GPU layouts:
     [`vllm_omni/deploy/minicpmo_4_5_2gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_2gpu.yaml),
     [`vllm_omni/deploy/minicpmo_4_5_3gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_3gpu.yaml)
@@ -68,7 +66,7 @@ Code2Wav consumes them through a shared-memory async connector.
 MiniCPM-o 4.5 now requires the three-stage topology: the Talker owns
 request-local codec generation and Code2Wav owns waveform state and
 reference-voice prompt features. `minicpmo_4_5.yaml` remains the stable
-single-GPU entry point; `minicpmo_4_5_batching.yaml` is the recommended
+single-GPU entry point; `minicpmo_4_5_2gpu.yaml` is the recommended
 two-GPU profile. The removed fused two-stage implementation is not retained as
 a fallback because it would duplicate state machines and correctness paths.
 
@@ -105,7 +103,7 @@ The deploy config is auto-loaded by the model registry — no
 For the recommended two-GPU layout, add:
 
 ```bash
---deploy-config vllm_omni/deploy/minicpmo_4_5_batching.yaml
+--deploy-config vllm_omni/deploy/minicpmo_4_5_2gpu.yaml
 ```
 
 #### Performance comparison

tests/dfx/reliability/invalid_param_test/test_invalid_minicpmo_4_5_omni.py

@@ -14,15 +14,17 @@ from tests.helpers.stage_config import get_deploy_config_path
 
 pytestmark = [pytest.mark.slow, pytest.mark.omni, pytest.mark.full_model]
 
-_MINICPMO_DEPLOY = get_deploy_config_path("minicpmo_4_5_batching.yaml")
+_MINICPMO_DEPLOY = get_deploy_config_path("minicpmo_4_5.yaml")
 
 _MINICPMO_SERVER = [
     pytest.param(
         OmniServerParams(
             model="openbmb/MiniCPM-o-4_5",
             stage_config_path=_MINICPMO_DEPLOY,
-            use_stage_cli=True,
-            server_args=["--trust-remote-code"],
+            use_stage_cli=False,
+            server_args=[
+                "--trust-remote-code",
+            ],
         ),
         id="minicpmo_4_5",
     )
@@ -48,7 +50,7 @@ def _prompt() -> str:
     return "What is the capital of China? Answer in 20 words."
 
 
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", _MINICPMO_SERVER, indirect=True)
 def test_invalid_audio_format_rejected(omni_server: OmniServerParams, openai_client: OpenAIClientHandler) -> None:
     """

tests/e2e/offline_inference/test_minicpmo_4_5.py

@@ -14,7 +14,7 @@ from tests.helpers.stage_config import get_deploy_config_path
 
 models = ["openbmb/MiniCPM-o-4_5"]
 
-_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5_batching.yaml")
+_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5.yaml")
 
 
 test_params = [(model, None, {"deploy_config": _CI_DEPLOY, "trust_remote_code": True}) for model in models]
@@ -31,11 +31,10 @@ def get_question(prompt_type: str = "text") -> str:
     return prompts.get(prompt_type, prompts["text"])
 
 
-@pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/5437")
 @pytest.mark.core_model
 @pytest.mark.advanced_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_text_to_text(omni_runner, omni_runner_handler) -> None:
     """Test processing text, generating text output."""
@@ -45,7 +44,7 @@ def test_text_to_text(omni_runner, omni_runner_handler) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_audio_to_text(omni_runner, omni_runner_handler) -> None:
     """Test processing audio, generating text output."""
@@ -58,7 +57,7 @@ def test_audio_to_text(omni_runner, omni_runner_handler) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_image_to_text(omni_runner, omni_runner_handler) -> None:
     """Test processing image, generating text output."""
@@ -69,7 +68,7 @@ def test_image_to_text(omni_runner, omni_runner_handler) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_video_to_text(omni_runner, omni_runner_handler) -> None:
     """Test processing video, generating text output."""
@@ -80,7 +79,7 @@ def test_video_to_text(omni_runner, omni_runner_handler) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_text_to_audio(omni_runner, omni_runner_handler) -> None:
     """Test processing text and generating audio through Talker and Code2Wav."""
@@ -88,11 +87,10 @@ def test_text_to_audio(omni_runner, omni_runner_handler) -> None:
     omni_runner_handler.send_omni_request(request_config)
 
 
-@pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/5437")
 @pytest.mark.core_model
 @pytest.mark.advanced_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_mix_to_audio(omni_runner, omni_runner_handler) -> None:
     """Test processing mixed modalities (image + audio), generating audio output."""
@@ -111,7 +109,7 @@ def test_mix_to_audio(omni_runner, omni_runner_handler) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_runner", test_params, indirect=True)
 def test_video_to_audio(omni_runner, omni_runner_handler) -> None:
     """Test processing video, generating audio output."""

tests/e2e/online_serving/test_minicpmo_4_5.py

@@ -1,7 +1,10 @@
-"""E2E online tests for MiniCPM-o 4.5 multimodal input and audio/text output.
+"""
+E2E Online tests for MiniCPM-o 4.5 model with multimodal input and audio / text output.
 
-The batching deployment uses async chunk transfer across separate Thinker,
-Talker, and Code2Wav stages.
+MiniCPM-o 4.5 is a three-stage pipeline: Stage 0 Thinker (text), Stage 1 Talker
+(TTS codec), Stage 2 Code2Wav (vocoder). The talker -> Code2Wav bridge supports
+both async_chunk (streaming) and full-payload (``--no-async-chunk``) modes, so the
+tests below exercise the sync path as well.
 """
 
 import os
@@ -16,15 +19,17 @@ from tests.helpers.stage_config import get_deploy_config_path
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 _MODEL = "openbmb/MiniCPM-o-4_5"
-_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5_batching.yaml")
+_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5.yaml")
 
 test_params = [
     pytest.param(
         OmniServerParams(
             model=_MODEL,
             stage_config_path=_CI_DEPLOY,
-            use_stage_cli=True,
-            server_args=["--trust-remote-code"],
+            use_stage_cli=False,
+            server_args=[
+                "--trust-remote-code",
+            ],
         ),
         id="default",
     )
@@ -60,11 +65,10 @@ def get_max_batch_size(size_type="few"):
     return batch_sizes.get(size_type, 5)
 
 
-@pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/5437")
 @pytest.mark.core_model
 @pytest.mark.advanced_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_text_to_text_001(omni_server, openai_client) -> None:
     """
@@ -89,12 +93,12 @@ def test_text_to_text_001(omni_server, openai_client) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_text_to_audio_001(omni_server, openai_client) -> None:
     """
     Test text-only input generating text + audio output via OpenAI API.
-    This exercises Talker TTS region detection and the Code2Wav stage.
+    This exercises the talker TTS region detection and in-process token2wav vocoder.
     Deploy Setting: default 2GPU
     Input Modal: text
     Output Modal: text + audio
@@ -114,7 +118,7 @@ def test_text_to_audio_001(omni_server, openai_client) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_audio_to_text_audio_001(omni_server, openai_client) -> None:
     """
@@ -128,14 +132,13 @@ def test_audio_to_text_audio_001(omni_server, openai_client) -> None:
     messages = dummy_messages_from_mix_data(
         system_prompt=get_system_prompt(),
         audio_data_url=audio_data_url,
-        content_text=get_prompt(),
+        content_text=get_prompt("mix"),
     )
 
     request_config = {
         "model": omni_server.model,
         "messages": messages,
         "stream": True,
-        "key_words": {"text": ["Beijing"]},
     }
 
     openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
@@ -143,7 +146,7 @@ def test_audio_to_text_audio_001(omni_server, openai_client) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_image_to_text_audio_001(omni_server, openai_client) -> None:
     """
@@ -171,7 +174,7 @@ def test_image_to_text_audio_001(omni_server, openai_client) -> None:
 
 @pytest.mark.full_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_video_to_text_audio_001(omni_server, openai_client) -> None:
     """
@@ -197,11 +200,10 @@ def test_video_to_text_audio_001(omni_server, openai_client) -> None:
     openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
 
 
-@pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/5437")
 @pytest.mark.core_model
 @pytest.mark.advanced_model
 @pytest.mark.omni
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_mix_to_text_audio_001(omni_server, openai_client) -> None:
     """

tests/e2e/online_serving/test_minicpmo_4_5_expansion.py

@@ -18,14 +18,14 @@ pytestmark = [pytest.mark.full_model, pytest.mark.omni]
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 _MODEL = "openbmb/MiniCPM-o-4_5"
-_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5_batching.yaml")
+_CI_DEPLOY = get_deploy_config_path("minicpmo_4_5.yaml")
 
 test_params = [
     pytest.param(
         OmniServerParams(
             model=_MODEL,
             stage_config_path=_CI_DEPLOY,
-            use_stage_cli=True,
+            use_stage_cli=False,
             server_args=[
                 "--trust-remote-code",
             ],
@@ -63,7 +63,7 @@ def get_max_batch_size(size_type="few"):
     return batch_sizes.get(size_type, 5)
 
 
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_text_video_to_text_001(omni_server, openai_client) -> None:
     """
@@ -90,7 +90,7 @@ def test_text_video_to_text_001(omni_server, openai_client) -> None:
     openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
 
 
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_sequential_requests_independent(omni_server, openai_client) -> None:
     """
@@ -131,7 +131,7 @@ def test_sequential_requests_independent(omni_server, openai_client) -> None:
     )
 
 
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_text_to_audio_long_output_001(omni_server, openai_client) -> None:
     """
@@ -157,7 +157,7 @@ def test_text_to_audio_long_output_001(omni_server, openai_client) -> None:
     openai_client.send_omni_request(request_config, request_num=get_max_batch_size())
 
 
-@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=2)
+@hardware_test(res={"cuda": "H100", "npu": "A2"}, num_cards=1)
 @pytest.mark.parametrize("omni_server", test_params, indirect=True)
 def test_chinese_text_to_audio(omni_server, openai_client) -> None:
     """

tests/model_executor/models/minicpmo_4_5/test_pipeline.py

@@ -36,7 +36,6 @@ _PIPELINE_KEY = "minicpmo_4_5"
 _DEPLOY_DIR = Path(__file__).resolve().parents[4] / "vllm_omni" / "deploy"
 _DEPLOY_LAYOUTS = {
     "minicpmo_4_5.yaml": ["0", "0", "0"],
-    "minicpmo_4_5_batching.yaml": ["0", "1", "1"],
     "minicpmo_4_5_2gpu.yaml": ["0", "1", "1"],
     "minicpmo_4_5_3gpu.yaml": ["0", "1", "2"],
     "minicpmo_4_5_8x4090.yaml": ["0,1,2,3", "4", "5"],
@@ -170,7 +169,7 @@ class TestDeployTopology:
                 0.22,
             ]
             assert stages[0].yaml_engine_args["limit_mm_per_prompt"] == {"video": {"count": 1, "num_frames": 32}}
-        elif filename in {"minicpmo_4_5_batching.yaml", "minicpmo_4_5_2gpu.yaml"}:
+        elif filename in {"minicpmo_4_5_2gpu.yaml"}:
             assert [stage.yaml_engine_args["gpu_memory_utilization"] for stage in stages] == [
                 0.9,
                 0.55,

vllm_omni/deploy/minicpmo_4_5_2gpu.yaml

@@ -1,2 +1,88 @@
-# Backward-compatible name for the canonical two-GPU batching profile.
-base_config: minicpmo_4_5_batching.yaml
+# MiniCPM-o 4.5 two-GPU continuous-batching layout.
+#
+# Thinker owns GPU 0. Talker and Code2Wav share GPU 1, keeping text decoding
+# isolated from codec/vocoder contention while preserving stage-2 batching.
+pipeline: minicpmo_4_5
+async_chunk: true
+
+trust_remote_code: true
+enable_prefix_caching: false
+
+connectors:
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
+    extra:
+      connector_get_sleep_s: 0.01
+      connector_get_max_wait_first_chunk: 3000
+      connector_get_max_wait: 300
+      codec_chunk_frames: 25
+      codec_left_context_frames: 3
+
+stages:
+  - stage_id: 0
+    max_num_seqs: 4
+    gpu_memory_utilization: 0.9
+    enforce_eager: false
+    tensor_parallel_size: 1
+    max_num_batched_tokens: 16384
+    devices: "0"
+    limit_mm_per_prompt:
+      video: 1
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 2048
+      seed: 42
+      detokenize: true
+      repetition_penalty: 1.1
+
+  - stage_id: 1
+    max_num_seqs: 4
+    gpu_memory_utilization: 0.55
+    enforce_eager: false
+    max_num_batched_tokens: 8192
+    devices: "1"
+    output_connectors:
+      to_stage_2: connector_of_shared_memory
+    # Codec sampling is model-owned by the checkpoint's tts_config (defaults:
+    # T=0.8, top-k=25, top-p=0.85, repetition penalty=1.05, seed=42).
+    # These parameters only govern vLLM's binary continue/stop token.
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      min_tokens: 50
+      max_tokens: 2048
+      stop_token_ids: [1]
+      seed: 42
+      detokenize: false
+
+  - stage_id: 2
+    max_num_seqs: 4
+    gpu_memory_utilization: 0.35
+    enforce_eager: true
+    enable_chunked_prefill: false
+    max_num_batched_tokens: 65536
+    max_model_len: 65536
+    devices: "1"
+    input_connectors:
+      from_stage_1: connector_of_shared_memory
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 65536
+      detokenize: true
+
+platforms:
+  npu:
+    stages:
+      - stage_id: 0
+        max_num_batched_tokens: 8192
+        compilation_config:
+          cudagraph_mode: PIECEWISE
+      - stage_id: 1
+        max_num_batched_tokens: 8192
+        compilation_config:
+          cudagraph_mode: PIECEWISE

vllm_omni/deploy/minicpmo_4_5_batching.yaml

@@ -1,89 +0,0 @@
-# MiniCPM-o 4.5 two-GPU continuous-batching layout.
-#
-# Thinker owns GPU 0. Talker and Code2Wav share GPU 1, keeping text decoding
-# isolated from codec/vocoder contention while preserving stage-2 batching.
-pipeline: minicpmo_4_5
-async_chunk: true
-
-trust_remote_code: true
-enable_prefix_caching: false
-
-connectors:
-  connector_of_shared_memory:
-    name: SharedMemoryConnector
-    extra:
-      connector_get_sleep_s: 0.01
-      connector_get_max_wait_first_chunk: 3000
-      connector_get_max_wait: 300
-      codec_chunk_frames: 25
-      codec_left_context_frames: 3
-
-stages:
-  - stage_id: 0
-    max_num_seqs: 4
-    gpu_memory_utilization: 0.9
-    enforce_eager: false
-    tensor_parallel_size: 1
-    max_num_batched_tokens: 16384
-    devices: "0"
-    limit_mm_per_prompt:
-      video: 1
-    default_sampling_params:
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
-      max_tokens: 2048
-      seed: 42
-      detokenize: true
-      repetition_penalty: 1.1
-
-  - stage_id: 1
-    max_num_seqs: 4
-    gpu_memory_utilization: 0.55
-    enforce_eager: false
-    max_num_batched_tokens: 8192
-    devices: "1"
-    output_connectors:
-      to_stage_2: connector_of_shared_memory
-    # Codec sampling is model-owned by the checkpoint's tts_config (defaults:
-    # T=0.8, top-k=25, top-p=0.85, repetition penalty=1.05, seed=42).
-    # These parameters only govern vLLM's binary continue/stop token.
-    default_sampling_params:
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
-      min_tokens: 50
-      max_tokens: 2048
-      stop_token_ids: [1]
-      seed: 42
-      detokenize: false
-
-  - stage_id: 2
-    max_num_seqs: 4
-    gpu_memory_utilization: 0.35
-    enforce_eager: true
-    enable_chunked_prefill: false
-    max_num_batched_tokens: 65536
-    max_model_len: 65536
-    devices: "1"
-    input_connectors:
-      from_stage_1: connector_of_shared_memory
-    default_sampling_params:
-      temperature: 0.0
-      top_p: 1.0
-      top_k: -1
-      max_tokens: 65536
-      detokenize: true
-
-platforms:
-  npu:
-    stages:
-      - stage_id: 0
-        max_num_batched_tokens: 8192
-        compilation_config:
-          cudagraph_mode: PIECEWISE
-      - stage_id: 1
-        max_num_batched_tokens: 8192
-        gpu_memory_utilization: 0.4
-        compilation_config:
-          cudagraph_mode: PIECEWISE