[Benchmark] Enable MM Embedding benchmarks (#26310)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/contributing/benchmarks.md

@@ -67,13 +67,13 @@ Legend:
 <details class="admonition abstract" markdown="1">
 <summary>Show more</summary>
 
-First start serving your model
+First start serving your model:
 
 ```bash
 vllm serve NousResearch/Hermes-3-Llama-3.1-8B
 ```
 
-Then run the benchmarking script
+Then run the benchmarking script:
 
 ```bash
 # download dataset
@@ -87,7 +87,7 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-If successful, you will see the following output
+If successful, you will see the following output:
 
 ```text
 ============ Serving Benchmark Result ============
@@ -125,7 +125,7 @@ If the dataset you want to benchmark is not supported yet in vLLM, even then you
 
 ```bash
 # start server
-VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct
+vllm serve meta-llama/Llama-3.1-8B-Instruct
 ```
 
 ```bash
@@ -167,7 +167,7 @@ vllm bench serve \
 ##### InstructCoder Benchmark with Speculative Decoding
 
 ``` bash
-VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
+vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
     --speculative-config $'{"method": "ngram",
     "num_speculative_tokens": 5, "prompt_lookup_max": 5,
     "prompt_lookup_min": 2}'
@@ -184,7 +184,7 @@ vllm bench serve \
 ##### Spec Bench Benchmark with Speculative Decoding
 
 ``` bash
-VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
+vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
     --speculative-config $'{"method": "ngram",
     "num_speculative_tokens": 5, "prompt_lookup_max": 5,
     "prompt_lookup_min": 2}'
@@ -366,7 +366,6 @@ Total num output tokens:  1280
 
 ``` bash
 VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_USE_V1=1 \
 vllm bench throughput \
     --dataset-name=hf \
     --dataset-path=likaixin/InstructCoder \
@@ -781,6 +780,104 @@ This should be seen as an edge case, and if this behavior can be avoided by sett
 
 </details>
 
+#### Embedding Benchmark
+
+Benchmark the performance of embedding requests in vLLM.
+
+<details class="admonition abstract" markdown="1">
+<summary>Show more</summary>
+
+##### Text Embeddings
+
+Unlike generative models which use Completions API or Chat Completions API,
+you should set `--backend openai-embeddings` and `--endpoint /v1/embeddings` to use the Embeddings API.
+
+You can use any text dataset to benchmark the model, such as ShareGPT.
+
+Start the server:
+
+```bash
+vllm serve jinaai/jina-embeddings-v3 --trust-remote-code
+```
+
+Run the benchmark:
+
+```bash
+# download dataset
+# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+vllm bench serve \
+  --model jinaai/jina-embeddings-v3 \
+  --backend openai-embeddings \
+  --endpoint /v1/embeddings \
+  --dataset-name sharegpt \
+  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json
+```
+
+##### Multi-modal Embeddings
+
+Unlike generative models which use Completions API or Chat Completions API,
+you should set `--endpoint /v1/embeddings` to use the Embeddings API. The backend to use depends on the model:
+
+- CLIP: `--backend openai-embeddings-clip`
+- VLM2Vec: `--backend openai-embeddings-vlm2vec`
+
+For other models, please add your own implementation inside <gh-file:vllm/benchmarks/lib/endpoint_request_func.py> to match the expected instruction format.
+
+You can use any text or multi-modal dataset to benchmark the model, as long as the model supports it.
+For example, you can use ShareGPT and VisionArena to benchmark vision-language embeddings.
+
+Serve and benchmark CLIP:
+
+```bash
+# Run this in another process
+vllm serve openai/clip-vit-base-patch32
+
+# Run these one by one after the server is up
+# download dataset
+# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+vllm bench serve \
+  --model openai/clip-vit-base-patch32 \
+  --backend openai-embeddings-clip \
+  --endpoint /v1/embeddings \
+  --dataset-name sharegpt \
+  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json
+
+vllm bench serve \
+  --model openai/clip-vit-base-patch32 \
+  --backend openai-embeddings-clip \
+  --endpoint /v1/embeddings \
+  --dataset-name hf \
+  --dataset-path lmarena-ai/VisionArena-Chat
+```
+
+Serve and benchmark VLM2Vec:
+
+```bash
+# Run this in another process
+vllm serve TIGER-Lab/VLM2Vec-Full --runner pooling \
+  --trust-remote-code \
+  --chat-template examples/template_vlm2vec_phi3v.jinja
+
+# Run these one by one after the server is up
+# download dataset
+# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+vllm bench serve \
+  --model TIGER-Lab/VLM2Vec-Full \
+  --backend openai-embeddings-vlm2vec \
+  --endpoint /v1/embeddings \
+  --dataset-name sharegpt \
+  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json
+
+vllm bench serve \
+  --model TIGER-Lab/VLM2Vec-Full \
+  --backend openai-embeddings-vlm2vec \
+  --endpoint /v1/embeddings \
+  --dataset-name hf \
+  --dataset-path lmarena-ai/VisionArena-Chat
+```
+
+</details>
+
 [](){ #performance-benchmarks }
 
 ## Performance Benchmarks

vllm/benchmarks/datasets.py

@@ -1582,10 +1582,10 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 "like to add support for additional dataset formats."
             )
 
-        if dataset_class.IS_MULTIMODAL and args.backend not in [
-            "openai-chat",
-            "openai-audio",
-        ]:
+        if dataset_class.IS_MULTIMODAL and not (
+            args.backend in ("openai-chat", "openai-audio")
+            or "openai-embeddings-" in args.backend
+        ):
             # multi-modal benchmark is only available on OpenAI Chat
             # endpoint-type.
             raise ValueError(

vllm/benchmarks/lib/endpoint_request_func.py

@@ -10,9 +10,10 @@ import time
 import traceback
 from collections.abc import Awaitable
 from dataclasses import dataclass, field
-from typing import Optional, Protocol, Union
+from typing import Any, Literal, Optional, Protocol, Union
 
 import aiohttp
+import regex as re
 from tqdm.asyncio import tqdm
 
 AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
@@ -103,6 +104,40 @@ class RequestFunc(Protocol):
     ) -> Awaitable[RequestFuncOutput]: ...
 
 
+def _validate_api_url(
+    api_url: str,
+    api_name: str,
+    expected_suffixes: Union[str, set[str]],
+) -> None:
+    if isinstance(expected_suffixes, str):
+        expected_suffixes = {expected_suffixes}
+
+    expected_suffixes = {*expected_suffixes, "profile"}
+
+    if not api_url.endswith(tuple(expected_suffixes)):
+        raise ValueError(f"{api_name} URL must end with one of: {expected_suffixes}.")
+
+
+def _update_payload_common(
+    payload: dict[str, Any],
+    request_func_input: RequestFuncInput,
+) -> None:
+    if request_func_input.ignore_eos:
+        payload["ignore_eos"] = request_func_input.ignore_eos
+    if request_func_input.extra_body:
+        payload.update(request_func_input.extra_body)
+
+
+def _update_headers_common(
+    headers: dict[str, Any],
+    request_func_input: RequestFuncInput,
+) -> None:
+    if request_func_input.extra_headers:
+        headers |= request_func_input.extra_headers
+    if request_func_input.request_id:
+        headers["x-request-id"] = request_func_input.request_id
+
+
 async def async_request_openai_completions(
     request_func_input: RequestFuncInput,
     session: aiohttp.ClientSession,
@@ -118,9 +153,7 @@ async def async_request_openai_completions(
         The output of the request function.
     """
     api_url = request_func_input.api_url
-    assert api_url.endswith(("completions", "profile")), (
-        "OpenAI Completions API URL must end with 'completions' or 'profile'."
-    )
+    _validate_api_url(api_url, "OpenAI Completions API", "completions")
 
     payload = {
         "model": request_func_input.model_name
@@ -136,15 +169,12 @@ async def async_request_openai_completions(
             "include_usage": True,
         },
     }
-    if request_func_input.ignore_eos:
-        payload["ignore_eos"] = request_func_input.ignore_eos
-    if request_func_input.extra_body:
-        payload.update(request_func_input.extra_body)
-    headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
-    if request_func_input.extra_headers:
-        headers |= request_func_input.extra_headers
-    if request_func_input.request_id:
-        headers["x-request-id"] = request_func_input.request_id
+    _update_payload_common(payload, request_func_input)
+
+    headers = {
+        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+    }
+    _update_headers_common(headers, request_func_input)
 
     output = RequestFuncOutput()
     output.prompt_len = request_func_input.prompt_len
@@ -222,27 +252,41 @@ async def async_request_openai_completions(
     return output
 
 
-async def async_request_openai_chat_completions(
+def _get_chat_content(
     request_func_input: RequestFuncInput,
-    session: aiohttp.ClientSession,
-    pbar: Optional[tqdm] = None,
-) -> RequestFuncOutput:
-    api_url = request_func_input.api_url
-    assert api_url.endswith(("chat/completions", "profile")), (
-        "OpenAI Chat Completions API URL must end with 'chat/completions'."
-    )
+    mm_position: Literal["first", "last"] = "last",
+) -> list[dict[str, Any]]:
+    text_contents = [{"type": "text", "text": request_func_input.prompt}]
 
-    content = [{"type": "text", "text": request_func_input.prompt}]
+    mm_contents = []
     if request_func_input.multi_modal_content:
         mm_content = request_func_input.multi_modal_content
         if isinstance(mm_content, list):
-            content.extend(mm_content)
+            mm_contents.extend(request_func_input.multi_modal_content)
         elif isinstance(mm_content, dict):
-            content.append(mm_content)
+            mm_contents.append(request_func_input.multi_modal_content)
         else:
             raise TypeError(
                 "multi_modal_content must be a dict or list[dict] for openai-chat"
             )
+
+    if mm_position == "first":
+        return mm_contents + text_contents
+
+    return text_contents + mm_contents
+
+
+async def async_request_openai_chat_completions(
+    request_func_input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: Optional[tqdm] = None,
+    mm_position: Literal["first", "last"] = "last",
+) -> RequestFuncOutput:
+    api_url = request_func_input.api_url
+    _validate_api_url(api_url, "OpenAI Chat Completions API", "chat/completions")
+
+    content = _get_chat_content(request_func_input, mm_position=mm_position)
+
     payload = {
         "model": request_func_input.model_name
         if request_func_input.model_name
@@ -257,18 +301,13 @@ async def async_request_openai_chat_completions(
             "include_usage": True,
         },
     }
-    if request_func_input.ignore_eos:
-        payload["ignore_eos"] = request_func_input.ignore_eos
-    if request_func_input.extra_body:
-        payload.update(request_func_input.extra_body)
+    _update_payload_common(payload, request_func_input)
+
     headers = {
         "Content-Type": "application/json",
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
     }
-    if request_func_input.extra_headers:
-        headers |= request_func_input.extra_headers
-    if request_func_input.request_id:
-        headers["x-request-id"] = request_func_input.request_id
+    _update_headers_common(headers, request_func_input)
 
     output = RequestFuncOutput()
     output.prompt_len = request_func_input.prompt_len
@@ -343,10 +382,7 @@ async def async_request_openai_audio(
     import soundfile
 
     api_url = request_func_input.api_url
-    assert api_url.endswith(("transcriptions", "translations")), (
-        "OpenAI Chat Completions API URL must end with 'transcriptions' "
-    )
-    "or `translations`."
+    _validate_api_url(api_url, "OpenAI Audio API", {"transcriptions", "translations"})
 
     content = [{"type": "text", "text": request_func_input.prompt}]
     payload = {
@@ -361,15 +397,12 @@ async def async_request_openai_audio(
         "stream_include_usage": True,
         "stream_continuous_usage_stats": True,
     }
-    if request_func_input.extra_body:
-        payload.update(request_func_input.extra_body)
+    _update_payload_common(payload, request_func_input)
+
     headers = {
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
     }
-    if request_func_input.extra_headers:
-        headers |= request_func_input.extra_headers
-    if request_func_input.request_id:
-        headers["x-request-id"] = request_func_input.request_id
+    _update_headers_common(headers, request_func_input)
 
     # Send audio file
     def to_bytes(y, sr):
@@ -451,26 +484,13 @@ async def async_request_openai_audio(
     return output
 
 
-async def async_request_openai_embeddings(
-    request_func_input: RequestFuncInput,
+async def _run_openai_embeddings(
     session: aiohttp.ClientSession,
+    api_url: str,
+    payload: dict[str, Any],
+    headers: dict[str, Any],
     pbar: Optional[tqdm] = None,
-):
-    api_url = request_func_input.api_url
-    assert api_url.endswith("embeddings"), (
-        "OpenAI Embeddings API URL must end with 'embeddings'."
-    )
-
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
-    }
-
-    payload = {
-        "model": request_func_input.model,
-        "input": request_func_input.prompt,
-    }
-
+) -> RequestFuncOutput:
     output = RequestFuncOutput()
     st = time.perf_counter()
     output.start_time = st
@@ -494,6 +514,137 @@ async def async_request_openai_embeddings(
     return output
 
 
+async def async_request_openai_embeddings(
+    request_func_input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: Optional[tqdm] = None,
+) -> RequestFuncOutput:
+    api_url = request_func_input.api_url
+    _validate_api_url(api_url, "OpenAI Embeddings API", "embeddings")
+
+    payload = {
+        "model": request_func_input.model_name
+        if request_func_input.model_name
+        else request_func_input.model,
+        "input": request_func_input.prompt,
+    }
+    _update_payload_common(payload, request_func_input)
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+    }
+    _update_headers_common(headers, request_func_input)
+
+    return await _run_openai_embeddings(
+        session,
+        api_url,
+        payload=payload,
+        headers=headers,
+        pbar=pbar,
+    )
+
+
+async def async_request_openai_embeddings_chat(
+    request_func_input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: Optional[tqdm] = None,
+    mm_position: Literal["first", "last"] = "last",
+) -> RequestFuncOutput:
+    api_url = request_func_input.api_url
+    _validate_api_url(api_url, "OpenAI Embeddings API", "embeddings")
+
+    content = _get_chat_content(request_func_input, mm_position=mm_position)
+
+    payload = {
+        "model": request_func_input.model_name
+        if request_func_input.model_name
+        else request_func_input.model,
+        "messages": [
+            {"role": "user", "content": content},
+        ],
+    }
+    _update_payload_common(payload, request_func_input)
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+    }
+    _update_headers_common(headers, request_func_input)
+
+    return await _run_openai_embeddings(
+        session,
+        api_url,
+        payload=payload,
+        headers=headers,
+        pbar=pbar,
+    )
+
+
+async def async_request_openai_embeddings_clip(
+    request_func_input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: Optional[tqdm] = None,
+) -> RequestFuncOutput:
+    if request_func_input.multi_modal_content:
+        # Image input
+        request_func_input.prompt = ""
+
+    # max_model_len=77 is too short for most datasets,
+    # so by default we truncate the prompt to max_model_len
+    if request_func_input.extra_body is None:
+        request_func_input.extra_body = {}
+    if "truncate_prompt_tokens" not in request_func_input.extra_body:
+        request_func_input.extra_body["truncate_prompt_tokens"] = -1
+
+    return await async_request_openai_embeddings_chat(
+        request_func_input,
+        session,
+        pbar=pbar,
+    )
+
+
+def _try_extract_request_idx(request_func_input: RequestFuncInput):
+    if request_func_input.request_id:
+        match = re.search(r"(\d+)$", request_func_input.request_id)
+        if match:
+            try:
+                return int(match.group(1))
+            except ValueError:
+                pass
+
+    return None
+
+
+async def async_request_openai_embeddings_vlm2vec(
+    request_func_input: RequestFuncInput,
+    session: aiohttp.ClientSession,
+    pbar: Optional[tqdm] = None,
+) -> RequestFuncOutput:
+    if request_func_input.multi_modal_content:
+        request_idx = _try_extract_request_idx(request_func_input)
+
+        # Adjust the ratio manually if needed.
+        use_image_only_prompt = request_idx is None or request_idx % 2 == 0
+
+        if use_image_only_prompt:
+            # Image input
+            request_func_input.prompt = "Represent the given image."
+        else:
+            # Text+Image input
+            request_func_input.prompt = (
+                f"Represent the given image with the following question: "
+                f"{request_func_input.prompt}"
+            )
+
+    return await async_request_openai_embeddings_chat(
+        request_func_input,
+        session,
+        pbar=pbar,
+        mm_position="first",
+    )
+
+
 # TODO: Add more request functions for different API protocols.
 ASYNC_REQUEST_FUNCS: dict[str, RequestFunc] = {
     "vllm": async_request_openai_completions,
@@ -501,6 +652,9 @@ ASYNC_REQUEST_FUNCS: dict[str, RequestFunc] = {
     "openai-chat": async_request_openai_chat_completions,
     "openai-audio": async_request_openai_audio,
     "openai-embeddings": async_request_openai_embeddings,
+    "openai-embeddings-chat": async_request_openai_embeddings_chat,
+    "openai-embeddings-clip": async_request_openai_embeddings_clip,
+    "openai-embeddings-vlm2vec": async_request_openai_embeddings_vlm2vec,
 }
 
 OPENAI_COMPATIBLE_BACKENDS = [

vllm/benchmarks/serve.py

@@ -465,6 +465,7 @@ def calculate_metrics(
 
 
 async def benchmark(
+    task_type: TaskType,
     endpoint_type: str,
     api_url: str,
     base_url: str,
@@ -490,18 +491,10 @@ async def benchmark(
     ramp_up_end_rps: Optional[int] = None,
     ready_check_timeout_sec: int = 600,
 ):
-    task_type = (
-        TaskType.EMBEDDING
-        if api_url.endswith("/v1/embeddings")
-        else TaskType.GENERATION
-    )
-    if endpoint_type in ASYNC_REQUEST_FUNCS:
-        if task_type == TaskType.EMBEDDING:
-            request_func = ASYNC_REQUEST_FUNCS["openai-embeddings"]
-        else:
-            request_func = ASYNC_REQUEST_FUNCS[endpoint_type]
-    else:
-        raise ValueError(f"Unknown backend: {endpoint_type}")
+    try:
+        request_func = ASYNC_REQUEST_FUNCS[endpoint_type]
+    except KeyError:
+        raise ValueError(f"Unknown backend: {endpoint_type}") from None
 
     # Reuses connections across requests to reduce TLS handshake overhead.
     connector = aiohttp.TCPConnector(
@@ -1310,36 +1303,43 @@ async def main_async(args: argparse.Namespace) -> dict[str, Any]:
     input_requests = get_samples(args, tokenizer)
     goodput_config_dict = check_goodput_args(args)
 
+    backend = args.backend
+    task_type = TaskType.EMBEDDING if "embeddings" in backend else TaskType.GENERATION
+
     # Collect the sampling parameters.
-    sampling_params = {
-        k: v
-        for k, v in {
-            "top_p": args.top_p,
-            "top_k": args.top_k,
-            "min_p": args.min_p,
-            "temperature": args.temperature,
-            "frequency_penalty": args.frequency_penalty,
-            "presence_penalty": args.presence_penalty,
-            "repetition_penalty": args.repetition_penalty,
-        }.items()
-        if v is not None
-    }
-
-    # Sampling parameters are only supported by openai-compatible backend.
-    if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
-        raise ValueError(
-            "Sampling parameters are only supported by openai-compatible backends."
-        )
+    if task_type == TaskType.GENERATION:
+        sampling_params = {
+            k: v
+            for k, v in {
+                "top_p": args.top_p,
+                "top_k": args.top_k,
+                "min_p": args.min_p,
+                "temperature": args.temperature,
+                "frequency_penalty": args.frequency_penalty,
+                "presence_penalty": args.presence_penalty,
+                "repetition_penalty": args.repetition_penalty,
+            }.items()
+            if v is not None
+        }
+
+        # Sampling parameters are only supported by openai-compatible backend.
+        if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
+            raise ValueError(
+                "Sampling parameters are only supported by openai-compatible backends."
+            )
 
-    if "temperature" not in sampling_params:
-        sampling_params["temperature"] = 0.0  # Default to greedy decoding.
+        if "temperature" not in sampling_params:
+            sampling_params["temperature"] = 0.0  # Default to greedy decoding.
+    else:
+        sampling_params = {}
 
     # Avoid GC processing "static" data - reduce pause times.
     gc.collect()
     gc.freeze()
 
     benchmark_result = await benchmark(
-        endpoint_type=args.backend,
+        task_type=task_type,
+        endpoint_type=backend,
         api_url=api_url,
         base_url=base_url,
         model_id=model_id,

vllm/entrypoints/chat_utils.py

@@ -498,14 +498,14 @@ def resolve_hf_chat_template(
         tokenizer_name_or_path=model_config.tokenizer,
     )
     if path is not None:
-        logger.info(
+        logger.info_once(
             "Loading chat template fallback for %s as there isn't one "
             "defined on HF Hub.",
             tokenizer.name_or_path,
         )
         chat_template = load_chat_template(path)
     else:
-        logger.debug(
+        logger.debug_once(
             "There is no chat template fallback for %s", tokenizer.name_or_path
         )