Merge tag 'v0.14.0rc0' into v0.14.0rc0-ori

tests/engine/test_arg_utils.py

@@ -9,7 +9,7 @@ from typing import Annotated, Literal
 
 import pytest
 
-from vllm.config import CompilationConfig, config
+from vllm.config import AttentionConfig, CompilationConfig, config
 from vllm.engine.arg_utils import (
     EngineArgs,
     contains_type,
@@ -298,6 +298,139 @@ def test_compilation_config():
     )
 
 
+def test_attention_config():
+    from vllm.attention.backends.registry import AttentionBackendEnum
+
+    parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
+
+    # default value
+    args = parser.parse_args([])
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    assert engine_args.attention_config == AttentionConfig()
+
+    # set backend via dot notation
+    args = parser.parse_args(["--attention-config.backend", "FLASH_ATTN"])
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    assert engine_args.attention_config.backend is not None
+    assert engine_args.attention_config.backend.name == "FLASH_ATTN"
+
+    # set backend via --attention-backend shorthand
+    args = parser.parse_args(["--attention-backend", "FLASHINFER"])
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    assert engine_args.attention_backend is not None
+    assert engine_args.attention_backend == "FLASHINFER"
+
+    # set all fields via dot notation
+    args = parser.parse_args(
+        [
+            "--attention-config.backend",
+            "FLASH_ATTN",
+            "--attention-config.flash_attn_version",
+            "3",
+            "--attention-config.use_prefill_decode_attention",
+            "true",
+            "--attention-config.flash_attn_max_num_splits_for_cuda_graph",
+            "16",
+            "--attention-config.use_cudnn_prefill",
+            "true",
+            "--attention-config.use_trtllm_ragged_deepseek_prefill",
+            "true",
+            "--attention-config.use_trtllm_attention",
+            "true",
+            "--attention-config.disable_flashinfer_prefill",
+            "true",
+            "--attention-config.disable_flashinfer_q_quantization",
+            "true",
+        ]
+    )
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    assert engine_args.attention_config.backend is not None
+    assert engine_args.attention_config.backend.name == "FLASH_ATTN"
+    assert engine_args.attention_config.flash_attn_version == 3
+    assert engine_args.attention_config.use_prefill_decode_attention is True
+    assert engine_args.attention_config.flash_attn_max_num_splits_for_cuda_graph == 16
+    assert engine_args.attention_config.use_cudnn_prefill is True
+    assert engine_args.attention_config.use_trtllm_ragged_deepseek_prefill is True
+    assert engine_args.attention_config.use_trtllm_attention is True
+    assert engine_args.attention_config.disable_flashinfer_prefill is True
+    assert engine_args.attention_config.disable_flashinfer_q_quantization is True
+
+    # set to string form of a dict with all fields
+    args = parser.parse_args(
+        [
+            "--attention-config="
+            '{"backend": "FLASHINFER", "flash_attn_version": 2, '
+            '"use_prefill_decode_attention": false, '
+            '"flash_attn_max_num_splits_for_cuda_graph": 8, '
+            '"use_cudnn_prefill": false, '
+            '"use_trtllm_ragged_deepseek_prefill": false, '
+            '"use_trtllm_attention": false, '
+            '"disable_flashinfer_prefill": false, '
+            '"disable_flashinfer_q_quantization": false}',
+        ]
+    )
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    assert engine_args.attention_config.backend is not None
+    assert engine_args.attention_config.backend.name == "FLASHINFER"
+    assert engine_args.attention_config.flash_attn_version == 2
+    assert engine_args.attention_config.use_prefill_decode_attention is False
+    assert engine_args.attention_config.flash_attn_max_num_splits_for_cuda_graph == 8
+    assert engine_args.attention_config.use_cudnn_prefill is False
+    assert engine_args.attention_config.use_trtllm_ragged_deepseek_prefill is False
+    assert engine_args.attention_config.use_trtllm_attention is False
+    assert engine_args.attention_config.disable_flashinfer_prefill is False
+    assert engine_args.attention_config.disable_flashinfer_q_quantization is False
+
+    # test --attention-backend flows into VllmConfig.attention_config
+    args = parser.parse_args(
+        [
+            "--model",
+            "facebook/opt-125m",
+            "--attention-backend",
+            "FLASH_ATTN",
+        ]
+    )
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    vllm_config = engine_args.create_engine_config()
+    assert vllm_config.attention_config.backend == AttentionBackendEnum.FLASH_ATTN
+
+    # test --attention-config.backend flows into VllmConfig.attention_config
+    args = parser.parse_args(
+        [
+            "--model",
+            "facebook/opt-125m",
+            "--attention-config.backend",
+            "FLASHINFER",
+        ]
+    )
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    vllm_config = engine_args.create_engine_config()
+    assert vllm_config.attention_config.backend == AttentionBackendEnum.FLASHINFER
+
+    # test --attention-backend and --attention-config.backend are mutually exclusive
+    args = parser.parse_args(
+        [
+            "--model",
+            "facebook/opt-125m",
+            "--attention-backend",
+            "FLASH_ATTN",
+            "--attention-config.backend",
+            "FLASHINFER",
+        ]
+    )
+    assert args is not None
+    engine_args = EngineArgs.from_cli_args(args)
+    with pytest.raises(ValueError, match="mutually exclusive"):
+        engine_args.create_engine_config()
+
+
 def test_prefix_cache_default():
     parser = EngineArgs.add_cli_args(FlexibleArgumentParser())
     args = parser.parse_args([])

tests/entrypoints/openai/test_metrics.py → tests/entrypoints/instrumentator/test_metrics.py

@@ -14,11 +14,10 @@ import requests
 from prometheus_client.parser import text_string_to_metric_families
 from transformers import AutoTokenizer
 
+from tests.conftest import LocalAssetServer
+from tests.utils import RemoteOpenAIServer
 from vllm import version
 
-from ...conftest import LocalAssetServer
-from ...utils import RemoteOpenAIServer
-
 MODELS = {
     "text": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
     "multimodal": "HuggingFaceTB/SmolVLM-256M-Instruct",

tests/entrypoints/openai/test_chat_error.py

@@ -76,6 +76,7 @@ def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
         lora_request,
         trace_headers,
         priority,
+        data_parallel_rank,
     ):
         return dict(engine_prompt), {}
 

tests/entrypoints/openai/test_completion_error.py

@@ -73,6 +73,7 @@ def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
         lora_request,
         trace_headers,
         priority,
+        data_parallel_rank,
     ):
         return dict(engine_prompt), {}
 

tests/entrypoints/openai/test_response_api_parsable_context.py

@@ -165,6 +165,7 @@ async def test_mcp_tool_call(client: OpenAI, model_name: str):
         model=model_name,
         input="What is 13 * 24? Use python to calculate the result.",
         tools=[{"type": "code_interpreter", "container": {"type": "auto"}}],
+        extra_body={"enable_response_messages": True},
         temperature=0.0,
     )
 
@@ -178,3 +179,8 @@ async def test_mcp_tool_call(client: OpenAI, model_name: str):
     # make sure the correct math is in the final output
     assert response.output[3].type == "message"
     assert "312" in response.output[3].content[0].text
+
+    # test raw input_messages / output_messages
+    assert len(response.input_messages) == 1
+    assert len(response.output_messages) == 3
+    assert "312" in response.output_messages[2]["message"]

tests/entrypoints/openai/test_response_api_simple.py

@@ -87,3 +87,48 @@ async def test_reasoning_item(client: OpenAI, model_name: str):
     assert response.output[0].type == "reasoning"
     assert response.output[1].type == "message"
     assert type(response.output[1].content[0].text) is str
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_streaming_output_consistency(client: OpenAI, model_name: str):
+    """Test that streaming delta text matches the final response output_text.
+
+    This test verifies that when using streaming mode:
+    1. The concatenated text from all 'response.output_text.delta' events
+    2. Matches the 'output_text' in the final 'response.completed' event
+    """
+    response = await client.responses.create(
+        model=model_name,
+        input="Say hello in one sentence.",
+        stream=True,
+    )
+
+    events = []
+    async for event in response:
+        events.append(event)
+
+    assert len(events) > 0
+
+    # Concatenate all delta text from streaming events
+    streaming_text = "".join(
+        event.delta for event in events if event.type == "response.output_text.delta"
+    )
+
+    # Get the final response from the last event
+    response_completed_event = events[-1]
+    assert response_completed_event.type == "response.completed"
+    assert response_completed_event.response.status == "completed"
+
+    # Get output_text from the final response
+    final_output_text = response_completed_event.response.output_text
+
+    # Verify final response has output
+    assert len(response_completed_event.response.output) > 0
+
+    # Verify streaming text matches final output_text
+    assert streaming_text == final_output_text, (
+        f"Streaming text does not match final output_text.\n"
+        f"Streaming: {streaming_text!r}\n"
+        f"Final: {final_output_text!r}"
+    )

tests/entrypoints/openai/test_serving_chat.py

@@ -52,8 +52,19 @@ def with_tool_parser(request) -> bool:
     return request.param
 
 
+@pytest.fixture(
+    scope="module",
+    params=[True],
+    ids=["exclude_tools_when_tool_choice_none"],
+)
+def exclude_tools_when_tool_choice_none(request) -> bool:
+    return request.param
+
+
 @pytest.fixture(scope="module")
-def default_server_args(with_tool_parser: bool):
+def default_server_args(
+    with_tool_parser: bool, exclude_tools_when_tool_choice_none: bool
+):
     args = [
         # use half precision for speed and memory savings in CI environment
         "--enforce-eager",
@@ -72,19 +83,16 @@ def default_server_args(with_tool_parser: bool):
                 "--enable-auto-tool-choice",
             ]
         )
+    if exclude_tools_when_tool_choice_none:
+        args.append("--exclude-tools-when-tool-choice-none")
     return args
 
 
 @pytest.fixture(scope="module")
-def gptoss_server(
-    monkeypatch_module: pytest.MonkeyPatch, default_server_args: list[str]
-):
-    with monkeypatch_module.context() as m:
-        m.setenv("VLLM_ATTENTION_BACKEND", "TRITON_ATTN")
-        with RemoteOpenAIServer(
-            GPT_OSS_MODEL_NAME, default_server_args
-        ) as remote_server:
-            yield remote_server
+def gptoss_server(default_server_args: list[str]):
+    server_args = default_server_args + ["--attention-backend=TRITON_ATTN"]
+    with RemoteOpenAIServer(GPT_OSS_MODEL_NAME, server_args) as remote_server:
+        yield remote_server
 
 
 @pytest_asyncio.fixture
@@ -340,6 +348,69 @@ async def test_gpt_oss_tool_message_array_content(
     assert response_multi_array.choices[0].message is not None
 
 
+@pytest.mark.asyncio
+async def test_gpt_oss_tool_choice_none(
+    gptoss_client: OpenAI,
+    with_tool_parser: bool,
+    exclude_tools_when_tool_choice_none: bool,
+):
+    if not (with_tool_parser and exclude_tools_when_tool_choice_none):
+        pytest.skip(
+            "skip tool_choice tests when non-tool or "
+            "--exclude-tools-when-tool-choice-none not set"
+        )
+
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "city": {"type": "string"},
+                        "state": {"type": "string"},
+                        "unit": {
+                            "type": "string",
+                            "enum": ["celsius", "fahrenheit"],
+                        },
+                    },
+                    "required": ["city", "state", "unit"],
+                },
+            },
+        }
+    ]
+
+    messages = [
+        {
+            "role": "user",
+            "content": "What's the temperature(in degrees Celsius) in Dallas?",
+        },
+    ]
+
+    tool_choice_auto = await gptoss_client.chat.completions.create(
+        model=GPT_OSS_MODEL_NAME,
+        messages=messages,
+        tools=tools,
+        tool_choice="auto",
+        temperature=0.0,
+    )
+    msg = tool_choice_auto.choices[0].message
+    assert len(msg.tool_calls) == 1
+
+    tool_choice_none = await gptoss_client.chat.completions.create(
+        model=GPT_OSS_MODEL_NAME,
+        messages=messages,
+        tools=tools,
+        tool_choice="none",
+        temperature=0.0,
+    )
+
+    msg = tool_choice_none.choices[0].message
+    assert len(msg.tool_calls) == 0
+
+
 MODEL_NAME = "openai-community/gpt2"
 MODEL_NAME_SHORT = "gpt2"
 CHAT_TEMPLATE = "Dummy chat template for testing {}"
@@ -401,6 +472,7 @@ def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
         lora_request,
         trace_headers,
         priority,
+        data_parallel_rank,
     ):
         return dict(engine_prompt), {}
 

tests/entrypoints/openai/test_transcription_validation_whisper.py

@@ -244,3 +244,35 @@ async def test_audio_with_timestamp(mary_had_lamb, whisper_client):
     )
     assert transcription.segments is not None
     assert len(transcription.segments) > 0
+
+
+@pytest.mark.asyncio
+async def test_audio_with_max_tokens(whisper_client, mary_had_lamb):
+    transcription = await whisper_client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        response_format="text",
+        temperature=0.0,
+        extra_body={"max_completion_tokens": 1},
+    )
+    out = json.loads(transcription)
+    out_text = out["text"]
+    from transformers import AutoTokenizer
+
+    tok = AutoTokenizer.from_pretrained(MODEL_NAME)
+    out_tokens = tok(out_text, add_special_tokens=False)["input_ids"]
+    assert len(out_tokens) == 1
+    # max_completion_tokens > max_model_len
+    transcription = await whisper_client.audio.transcriptions.create(
+        model=MODEL_NAME,
+        file=mary_had_lamb,
+        language="en",
+        response_format="text",
+        temperature=0.0,
+        extra_body={"max_completion_tokens": int(1e6)},
+    )
+    out = json.loads(transcription)
+    out_text = out["text"]
+    out_tokens = tok(out_text, add_special_tokens=False)["input_ids"]
+    assert len(out_tokens) < 450  # ~Whisper max output len

tests/entrypoints/openai/test_translation_validation.py

@@ -227,3 +227,36 @@ async def test_long_audio_request(foscolo, client_and_model):
     )
     out = json.loads(translation)["text"].strip().lower()
     assert out.count("greek sea") == 2
+
+
+@pytest.mark.asyncio
+async def test_audio_with_max_tokens(mary_had_lamb, client_and_model):
+    client, model_name = client_and_model
+    transcription = await client.audio.translations.create(
+        model=model_name,
+        file=mary_had_lamb,
+        response_format="text",
+        temperature=0.0,
+        extra_body={"max_completion_tokens": 1},
+    )
+    out = json.loads(transcription)
+    out_text = out["text"]
+    print(out_text)
+    from transformers import AutoTokenizer
+
+    tok = AutoTokenizer.from_pretrained(model_name)
+    out_tokens = tok(out_text, add_special_tokens=False)["input_ids"]
+    assert len(out_tokens) == 1
+    # max_completion_tokens > max_model_len
+    transcription = await client.audio.transcriptions.create(
+        model=model_name,
+        file=mary_had_lamb,
+        response_format="text",
+        temperature=0.0,
+        extra_body={"max_completion_tokens": int(1e6)},
+    )
+    out = json.loads(transcription)
+    out_text = out["text"]
+    print(out_text)
+    out_tokens = tok(out_text, add_special_tokens=False)["input_ids"]
+    assert len(out_tokens) < 450  # ~Whisper max output len

tests/entrypoints/openai/test_collective_rpc.py → tests/entrypoints/rpc/test_collective_rpc.py

@@ -37,7 +37,7 @@ def server():
         "--max-num-seqs",
         "128",
         "--worker-extension-cls",
-        "tests.entrypoints.openai.test_collective_rpc.TestWorkerExtension",
+        "tests.entrypoints.rpc.test_collective_rpc.TestWorkerExtension",
     ]
     with RemoteOpenAIServer(
         MODEL_NAME,

tests/entrypoints/openai/test_sleep.py → tests/entrypoints/sleep/test_sleep.py

@@ -4,7 +4,7 @@
 import requests
 from prometheus_client.parser import text_string_to_metric_families
 
-from ...utils import RemoteOpenAIServer
+from tests.utils import RemoteOpenAIServer
 
 MODEL_NAME = "meta-llama/Llama-3.2-1B"
 

tests/evals/gsm8k/README.md

@@ -7,9 +7,8 @@ This directory contains a replacement for the lm-eval-harness GSM8K evaluation,
 ### Run tests with pytest (like buildkite)
 
 ```bash
-pytest -s -v tests/gsm8k/test_gsm8k_correctness.py \
-    --config-list-file=configs/models-small.txt \
-    --tp-size=1
+pytest -s -v tests/evals/gsm8k/test_gsm8k_correctness.py \
+    --config-list-file=configs/models-small.txt
 ```
 
 ### Run standalone evaluation script
@@ -31,5 +30,11 @@ model_name: "Qwen/Qwen2.5-1.5B-Instruct"
 accuracy_threshold: 0.54  # Minimum expected accuracy
 num_questions: 1319       # Number of questions (default: full test set)
 num_fewshot: 5            # Few-shot examples from train set
-max_model_len: 4096       # Model context length
+server_args: "--max-model-len 4096 --tensor-parallel-size 2"  # Server arguments
+env:                      # Environment variables (optional)
+  VLLM_USE_FLASHINFER_MOE_FP4: "1"
 ```
+
+The `server_args` field accepts any arguments that can be passed to `vllm serve`.
+
+The `env` field accepts a dictionary of environment variables to set for the server process.

tests/evals/gsm8k/configs/DeepSeek-V2-Lite-Instruct-FP8.yaml

@@ -2,5 +2,4 @@ model_name: "RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8"
 accuracy_threshold: 0.72
 num_questions: 1319
 num_fewshot: 5
-max_model_len: 4096
-
+server_args: "--enforce-eager --max-model-len 4096"

tests/evals/gsm8k/configs/Llama-3-8B-Instruct-nonuniform-CT.yaml

@@ -2,4 +2,4 @@ model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
 accuracy_threshold: 0.74
 num_questions: 1319
 num_fewshot: 5
-max_model_len: 4096
+server_args: "--enforce-eager --max-model-len 4096"
\ No newline at end of file

tests/evals/gsm8k/configs/Llama-3.2-1B-Instruct-INT8-CT.yaml

@@ -2,4 +2,4 @@ model_name: "RedHatAI/Llama-3.2-1B-Instruct-quantized.w8a8"
 accuracy_threshold: 0.31
 num_questions: 1319
 num_fewshot: 5
-max_model_len: 4096
+server_args: "--enforce-eager --max-model-len 4096"
\ No newline at end of file

tests/evals/gsm8k/configs/Qwen1.5-MoE-W4A16-CT.yaml

@@ -2,4 +2,4 @@ model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
 accuracy_threshold: 0.45
 num_questions: 1319
 num_fewshot: 5
-max_model_len: 4096
+server_args: "--enforce-eager --max-model-len 4096"

tests/evals/gsm8k/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -2,4 +2,4 @@ model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
 accuracy_threshold: 0.60
 num_questions: 1319
 num_fewshot: 5
-max_model_len: 4096
+server_args: "--enforce-eager --max-model-len 4096"
\ No newline at end of file