[Misc] Add request_id into benchmark_serve.py (#23065)

Signed-off-by: yangxia <yangxiast@gmail.com>

benchmarks/backend_request_func.py

@@ -34,6 +34,7 @@ class RequestFuncInput:
     multi_modal_content: Optional[dict | list[dict]] = None
     ignore_eos: bool = False
     language: Optional[str] = None
+    request_id: Optional[str] = None
 
 
 @dataclass
@@ -71,6 +72,9 @@ async def async_request_tgi(
             "inputs": request_func_input.prompt,
             "parameters": params,
         }
+        headers = None
+        if request_func_input.request_id:
+            headers = {"x-request-id": request_func_input.request_id}
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
         if request_func_input.ignore_eos:
@@ -82,7 +86,9 @@ async def async_request_tgi(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(url=api_url, json=payload) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                 if response.status == 200:
                     async for chunk_bytes in response.content:
                         chunk_bytes = chunk_bytes.strip()
@@ -145,6 +151,9 @@ async def async_request_trt_llm(
         }
         if request_func_input.ignore_eos:
             payload["min_length"] = request_func_input.output_len
+        headers = None
+        if request_func_input.request_id:
+            headers = {"x-request-id": request_func_input.request_id}
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
@@ -152,7 +161,9 @@ async def async_request_trt_llm(
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
-            async with session.post(url=api_url, json=payload) as response:
+            async with session.post(
+                url=api_url, json=payload, headers=headers
+            ) as response:
                 if response.status == 200:
                     async for chunk_bytes in response.content:
                         chunk_bytes = chunk_bytes.strip()
@@ -211,6 +222,8 @@ async def async_request_deepspeed_mii(
             "top_p": 1.0,
         }
         headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"}
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id
 
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
@@ -283,6 +296,8 @@ async def async_request_openai_completions(
         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.request_id:
+            headers["x-request-id"] = request_func_input.request_id
 
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
@@ -395,6 +410,8 @@ async def async_request_openai_chat_completions(
             "Content-Type": "application/json",
             "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
         }
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id
 
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
@@ -491,6 +508,8 @@ async def async_request_openai_audio(
         headers = {
             "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
         }
+        if request_func_input.request_id:
+            headers["x-request-id"] = request_func_input.request_id
 
         # Send audio file
         def to_bytes(y, sr):

benchmarks/benchmark_dataset.py

@@ -19,6 +19,7 @@ import logging
 import random
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
+from copy import deepcopy
 from dataclasses import dataclass
 from functools import cache
 from io import BytesIO
@@ -54,6 +55,7 @@ class SampleRequest:
     expected_output_len: int
     multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
     lora_request: Optional[LoRARequest] = None
+    request_id: Optional[str] = None
 
 
 # -----------------------------------------------------------------------------
@@ -155,7 +157,10 @@ class BenchmarkDataset(ABC):
 
     @abstractmethod
     def sample(
-        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        request_id_prefix: str = "",
     ) -> list[SampleRequest]:
         """
         Abstract method to generate sample requests from the dataset.
@@ -167,6 +172,7 @@ class BenchmarkDataset(ABC):
             tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
              for processing the dataset's text.
             num_requests (int): The number of sample requests to generate.
+            request_id_prefix (str) The prefix of request_id.
 
         Returns:
             list[SampleRequest]: A list of sample requests generated from the
@@ -175,7 +181,10 @@ class BenchmarkDataset(ABC):
         raise NotImplementedError("sample must be implemented in subclasses.")
 
     def maybe_oversample_requests(
-        self, requests: list[SampleRequest], num_requests: int
+        self,
+        requests: list[SampleRequest],
+        num_requests: int,
+        request_id_prefix: str = "",
     ) -> None:
         """
         Oversamples the list of requests if its size is less than the desired
@@ -183,11 +192,18 @@ class BenchmarkDataset(ABC):
 
         Args:
             requests (List[SampleRequest]): The current list of sampled
-            requests.  num_requests (int): The target number of requests.
+            requests.
+            num_requests (int): The target number of requests.
+            request_id_prefix (str) The prefix of the request ids.
         """
         if len(requests) < num_requests:
             random.seed(self.random_seed)
-            additional = random.choices(requests, k=num_requests - len(requests))
+            additional = deepcopy(
+                random.choices(requests, k=num_requests - len(requests))
+            )
+            for i in range(len(additional)):
+                req = additional[i]
+                req.request_id = request_id_prefix + str(len(requests) + i)
             requests.extend(additional)
             logger.info("Oversampled requests to reach %d total samples.", num_requests)
 
@@ -303,6 +319,7 @@ class RandomDataset(BenchmarkDataset):
         range_ratio: float = DEFAULT_RANGE_RATIO,
         input_len: int = DEFAULT_INPUT_LEN,
         output_len: int = DEFAULT_OUTPUT_LEN,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         # Enforce range_ratio < 1
@@ -363,8 +380,10 @@ class RandomDataset(BenchmarkDataset):
                     prompt=prompt,
                     prompt_len=total_input_len,
                     expected_output_len=int(output_lens[i]),
+                    request_id=request_id_prefix + str(i),
                 )
             )
+
         return requests
 
 
@@ -406,9 +425,11 @@ class ShareGPTDataset(BenchmarkDataset):
         max_loras: Optional[int] = None,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         samples: list = []
+        ind = 0
         for entry in self.data:
             if len(samples) >= num_requests:
                 break
@@ -444,9 +465,11 @@ class ShareGPTDataset(BenchmarkDataset):
                     expected_output_len=new_output_len,
                     lora_request=lora_request,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 )
             )
-        self.maybe_oversample_requests(samples, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
         return samples
 
 
@@ -512,10 +535,11 @@ class CustomDataset(BenchmarkDataset):
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
         skip_chat_template: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = item["prompt"]
@@ -534,9 +558,12 @@ class CustomDataset(BenchmarkDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
+                )
             )
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
         )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
 
         return sampled_requests
 
@@ -578,6 +605,7 @@ class SonnetDataset(BenchmarkDataset):
         input_len: int = DEFAULT_INPUT_LEN,
         output_len: int = DEFAULT_OUTPUT_LEN,
         return_prompt_formatted: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         # Calculate average token length for a poem line.
@@ -603,6 +631,7 @@ class SonnetDataset(BenchmarkDataset):
         prefix_lines = self.data[:num_prefix_lines]
 
         samples = []
+        ind = 0
         while len(samples) < num_requests:
             extra_lines = random.choices(
                 self.data, k=num_input_lines - num_prefix_lines
@@ -613,14 +642,17 @@ class SonnetDataset(BenchmarkDataset):
                 msg, add_generation_prompt=True, tokenize=False
             )
             prompt_len = len(tokenizer(prompt_formatted).input_ids)
+
             if prompt_len <= input_len:
                 samples.append(
                     SampleRequest(
                         prompt=prompt_formatted if return_prompt_formatted else prompt,
                         prompt_len=prompt_len,
                         expected_output_len=output_len,
+                        request_id=request_id_prefix + str(ind),
                     )
                 )
+                ind += 1
         return samples
 
 
@@ -672,6 +704,7 @@ class BurstGPTDataset(BenchmarkDataset):
         num_requests: int,
         max_loras: Optional[int] = None,
         lora_path: Optional[str] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         samples = []
@@ -693,6 +726,7 @@ class BurstGPTDataset(BenchmarkDataset):
                     prompt_len=input_len,
                     expected_output_len=output_len,
                     lora_request=lora_req,
+                    request_id=request_id_prefix + str(i),
                 )
             )
         return samples
@@ -752,12 +786,14 @@ class ConversationDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         # Filter examples with at least 2 conversations
         filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
         sampled_requests = []
         dynamic_output = output_len is None
+        ind = 0
 
         for item in filtered_data:
             if len(sampled_requests) >= num_requests:
@@ -785,9 +821,13 @@ class ConversationDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 )
             )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
         return sampled_requests
 
 
@@ -814,11 +854,12 @@ class VisionArenaDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
@@ -838,9 +879,12 @@ class VisionArenaDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(i),
+                )
             )
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
         )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
 
@@ -870,11 +914,12 @@ class InstructCoderDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = f"{item['input']}\n\n{item['instruction']} Just output \
@@ -892,9 +937,12 @@ class InstructCoderDataset(HuggingFaceDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
+                )
             )
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
         )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
 
@@ -924,12 +972,13 @@ class MTBenchDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
         sampled_requests = []
 
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = item["turns"][0]
@@ -947,9 +996,12 @@ class MTBenchDataset(HuggingFaceDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                 )
             )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
         return sampled_requests
 
 
@@ -974,10 +1026,12 @@ class AIMODataset(HuggingFaceDataset):
         tokenizer: PreTrainedTokenizerBase,
         num_requests: int,
         output_len: Optional[int] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         sampled_requests = []
         dynamic_output = output_len is None
+        ind = 0
 
         for item in self.data:
             if len(sampled_requests) >= num_requests:
@@ -1000,9 +1054,13 @@ class AIMODataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=None,
+                    request_id=request_id_prefix + str(ind),
+                )
             )
+            ind += 1
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
         )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
 
 
@@ -1072,12 +1130,18 @@ class NextEditPredictionDataset(HuggingFaceDataset):
         "zed-industries/zeta": _format_zeta_prompt,
     }
 
-    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        request_id_prefix: str = "",
+        **kwargs,
+    ):
         formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
         if formatting_prompt_func is None:
             raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
         samples = []
-        for sample in self.data:
+        for i, sample in enumerate(self.data):
             sample = formatting_prompt_func(sample)
             samples.append(
                 SampleRequest(
@@ -1086,11 +1150,12 @@ class NextEditPredictionDataset(HuggingFaceDataset):
                     expected_output_len=len(
                         tokenizer(sample["expected_output"]).input_ids
                     ),
+                    request_id=request_id_prefix + str(i),
                 )
             )
             if len(samples) >= num_requests:
                 break
-        self.maybe_oversample_requests(samples, num_requests)
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
         return samples
 
 
@@ -1139,6 +1204,7 @@ class ASRDataset(HuggingFaceDataset):
         tokenizer: PreTrainedTokenizerBase,
         num_requests: int,
         output_len: Optional[int] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         import librosa
@@ -1148,6 +1214,7 @@ class ASRDataset(HuggingFaceDataset):
         prompt_len = len(tokenizer(prompt).input_ids)
         sampled_requests = []
         skipped = 0
+        ind = 0
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
@@ -1166,8 +1233,10 @@ class ASRDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 )
             )
+            ind += 1
         if skipped:
             logger.warning(
                 "%d samples discarded from dataset due to"
@@ -1175,5 +1244,7 @@ class ASRDataset(HuggingFaceDataset):
                 " what Whisper supports.",
                 skipped,
             )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(
+            sampled_requests, num_requests, request_id_prefix
+        )
         return sampled_requests

benchmarks/benchmark_serving.py

@@ -375,11 +375,12 @@ async def benchmark(
                     rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
                 last_int_rps = current_int_rps
 
-        prompt, prompt_len, output_len, mm_content = (
+        prompt, prompt_len, output_len, mm_content, request_id = (
             request.prompt,
             request.prompt_len,
             request.expected_output_len,
             request.multi_modal_data,
+            request.request_id,
         )
         req_model_id, req_model_name = model_id, model_name
         if lora_modules:
@@ -397,6 +398,7 @@ async def benchmark(
             multi_modal_content=mm_content,
             ignore_eos=ignore_eos,
             extra_body=extra_body,
+            request_id=request_id,
         )
         task = limited_request_func(request_func_input=request_func_input, pbar=pbar)
         tasks.append(asyncio.create_task(task))
@@ -665,6 +667,7 @@ def main(args: argparse.Namespace):
             tokenizer=tokenizer,
             output_len=args.custom_output_len,
             skip_chat_template=args.custom_skip_chat_template,
+            request_id_prefix=args.request_id_prefix,
         )
 
     elif args.dataset_name == "sonnet":
@@ -678,6 +681,7 @@ def main(args: argparse.Namespace):
                 prefix_len=args.sonnet_prefix_len,
                 tokenizer=tokenizer,
                 return_prompt_formatted=False,
+                request_id_prefix=args.request_id_prefix,
             )
         else:
             assert tokenizer.chat_template or tokenizer.default_chat_template, (
@@ -690,6 +694,7 @@ def main(args: argparse.Namespace):
                 prefix_len=args.sonnet_prefix_len,
                 tokenizer=tokenizer,
                 return_prompt_formatted=True,
+                request_id_prefix=args.request_id_prefix,
             )
 
     elif args.dataset_name == "hf":
@@ -751,6 +756,7 @@ def main(args: argparse.Namespace):
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
             output_len=args.hf_output_len,
+            request_id_prefix=args.request_id_prefix,
         )
 
     else:
@@ -762,10 +768,15 @@ def main(args: argparse.Namespace):
                 tokenizer=tokenizer,
                 num_requests=args.num_prompts,
                 output_len=args.sharegpt_output_len,
+                request_id_prefix=args.request_id_prefix,
             ),
             "burstgpt": lambda: BurstGPTDataset(
                 random_seed=args.seed, dataset_path=args.dataset_path
-            ).sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+            ).sample(
+                tokenizer=tokenizer,
+                num_requests=args.num_prompts,
+                request_id_prefix=args.request_id_prefix,
+            ),
             "random": lambda: RandomDataset(dataset_path=args.dataset_path).sample(
                 tokenizer=tokenizer,
                 num_requests=args.num_prompts,
@@ -773,6 +784,7 @@ def main(args: argparse.Namespace):
                 input_len=args.random_input_len,
                 output_len=args.random_output_len,
                 range_ratio=args.random_range_ratio,
+                request_id_prefix=args.request_id_prefix,
             ),
         }
 
@@ -1118,6 +1130,13 @@ def create_argument_parser():
         "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
         "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
     )
+    parser.add_argument(
+        "--request-id-prefix",
+        type=str,
+        required=False,
+        default="benchmark-serving",
+        help="Specify the prefix of request id.",
+    )
 
     # group for dataset specific arguments
     custom_group = parser.add_argument_group("custom dataset options")

vllm/benchmarks/datasets.py

@@ -18,6 +18,7 @@ import logging
 import random
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
+from copy import deepcopy
 from dataclasses import dataclass
 from functools import cache
 from io import BytesIO
@@ -76,6 +77,7 @@ class SampleRequest:
         Union[MultiModalDataDict, dict, list[dict]]
     ] = None
     lora_request: Optional[LoRARequest] = None
+    request_id: Optional[str] = None
 
 
 # -----------------------------------------------------------------------------
@@ -183,7 +185,8 @@ class BenchmarkDataset(ABC):
 
     @abstractmethod
     def sample(self, tokenizer: PreTrainedTokenizerBase,
-               num_requests: int) -> list[SampleRequest]:
+               num_requests: int, 
+               request_id_prefix: str = "") -> list[SampleRequest]:
         """
         Abstract method to generate sample requests from the dataset.
 
@@ -194,6 +197,8 @@ class BenchmarkDataset(ABC):
             tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
                 for processing the dataset's text.
             num_requests (int): The number of sample requests to generate.
+            request_id_prefix (str) The prefix of request_id.
+            
 
         Returns:
             list[SampleRequest]: A list of sample requests generated from the
@@ -201,8 +206,12 @@ class BenchmarkDataset(ABC):
         """
         raise NotImplementedError("sample must be implemented in subclasses.")
 
-    def maybe_oversample_requests(self, requests: list[SampleRequest],
-                                  num_requests: int) -> None:
+    def maybe_oversample_requests(
+        self,
+        requests: list[SampleRequest],
+        num_requests: int,
+        request_id_prefix: str = "",
+    ) -> None:
         """
         Oversamples the list of requests if its size is less than the desired
         number.
@@ -211,11 +220,17 @@ class BenchmarkDataset(ABC):
             requests (List[SampleRequest]): The current list of sampled
                 requests.
             num_requests (int): The target number of requests.
+            request_id_prefix (str) The prefix of the request ids.
+
         """
         if len(requests) < num_requests:
             random.seed(self.random_seed)
-            additional = random.choices(requests,
-                                        k=num_requests - len(requests))
+            additional = deepcopy(
+                random.choices(requests, k=num_requests - len(requests))
+            )
+            for i in range(len(additional)):
+                req = additional[i]
+                req.request_id = request_id_prefix + str(len(requests) + i)
             requests.extend(additional)
             logger.info("Oversampled requests to reach %d total samples.",
                         num_requests)
@@ -334,6 +349,7 @@ class RandomDataset(BenchmarkDataset):
         range_ratio: float = DEFAULT_RANGE_RATIO,
         input_len: int = DEFAULT_INPUT_LEN,
         output_len: int = DEFAULT_OUTPUT_LEN,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         # Enforce range_ratio < 1
@@ -391,6 +407,7 @@ class RandomDataset(BenchmarkDataset):
                     prompt=prompt,
                     prompt_len=total_input_len,
                     expected_output_len=int(output_lens[i]),
+                    request_id=request_id_prefix + str(i),
                 ))
         return requests
 
@@ -432,9 +449,11 @@ class ShareGPTDataset(BenchmarkDataset):
         max_loras: Optional[int] = None,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         samples: list = []
+        ind = 0
         for entry in self.data:
             if len(samples) >= num_requests:
                 break
@@ -470,8 +489,10 @@ class ShareGPTDataset(BenchmarkDataset):
                     expected_output_len=new_output_len,
                     lora_request=lora_request,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 ))
-        self.maybe_oversample_requests(samples, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
         return samples
 
 
@@ -647,6 +668,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
             tokenizer=tokenizer,
             output_len=args.custom_output_len,
             skip_chat_template=args.custom_skip_chat_template,
+            request_id_prefix=args.request_id_prefix,
         )
 
     elif args.dataset_name == "sonnet":
@@ -660,6 +682,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 prefix_len=args.sonnet_prefix_len,
                 tokenizer=tokenizer,
                 return_prompt_formatted=False,
+                request_id_prefix=args.request_id_prefix,
             )
         else:
             assert tokenizer.chat_template or tokenizer.default_chat_template, (
@@ -671,6 +694,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 prefix_len=args.sonnet_prefix_len,
                 tokenizer=tokenizer,
                 return_prompt_formatted=True,
+                request_id_prefix=args.request_id_prefix,
             )
 
     elif args.dataset_name == "hf":
@@ -730,6 +754,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
             output_len=args.hf_output_len,
+            request_id_prefix=args.request_id_prefix,
         )
 
     else:
@@ -741,11 +766,13 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                                         tokenizer=tokenizer,
                                         num_requests=args.num_prompts,
                                         output_len=args.sharegpt_output_len,
+                                        request_id_prefix=args.request_id_prefix,
                                     ),
             "burstgpt":
             lambda: BurstGPTDataset(random_seed=args.seed,
                                     dataset_path=args.dataset_path).
-            sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+            sample(tokenizer=tokenizer, num_requests=args.num_prompts, 
+                   request_id_prefix=args.request_id_prefix,),
             "random":
             lambda: RandomDataset(random_seed=args.seed,
                                   dataset_path=args.dataset_path).sample(
@@ -755,6 +782,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 input_len=args.random_input_len,
                 output_len=args.random_output_len,
                 range_ratio=args.random_range_ratio,
+                request_id_prefix=args.request_id_prefix,
             ),
             "prefix_repetition":
             lambda: PrefixRepetitionRandomDataset(
@@ -766,6 +794,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 suffix_len=args.prefix_repetition_suffix_len,
                 num_prefixes=args.prefix_repetition_num_prefixes,
                 output_len=args.prefix_repetition_output_len,
+                request_id_prefix=args.request_id_prefix,
             ),
         }
 
@@ -839,10 +868,11 @@ class CustomDataset(BenchmarkDataset):
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
         skip_chat_template: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = item["prompt"]
@@ -864,8 +894,10 @@ class CustomDataset(BenchmarkDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
 
         return sampled_requests
 
@@ -909,6 +941,7 @@ class SonnetDataset(BenchmarkDataset):
         input_len: int = DEFAULT_INPUT_LEN,
         output_len: int = DEFAULT_OUTPUT_LEN,
         return_prompt_formatted: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         # Calculate average token length for a poem line.
@@ -934,6 +967,7 @@ class SonnetDataset(BenchmarkDataset):
         prefix_lines = self.data[:num_prefix_lines]
 
         samples = []
+        ind = 0
         while len(samples) < num_requests:
             extra_lines = random.choices(self.data,
                                          k=num_input_lines - num_prefix_lines)
@@ -949,7 +983,9 @@ class SonnetDataset(BenchmarkDataset):
                         if return_prompt_formatted else prompt,
                         prompt_len=prompt_len,
                         expected_output_len=output_len,
+                         request_id=request_id_prefix + str(ind),
                     ))
+                ind += 1
         return samples
 
 
@@ -1000,6 +1036,7 @@ class BurstGPTDataset(BenchmarkDataset):
         num_requests: int,
         max_loras: Optional[int] = None,
         lora_path: Optional[str] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         samples = []
@@ -1020,6 +1057,7 @@ class BurstGPTDataset(BenchmarkDataset):
                     prompt_len=input_len,
                     expected_output_len=output_len,
                     lora_request=lora_req,
+                    request_id=request_id_prefix + str(i),
                 ))
         return samples
 
@@ -1075,11 +1113,13 @@ class ConversationDataset(HuggingFaceDataset):
                num_requests: int,
                output_len: Optional[int] = None,
                enable_multimodal_chat: bool = False,
+               request_id_prefix: str = "",
                **kwargs) -> list:
         # Filter examples with at least 2 conversations
         filtered_data = self.data.filter(
             lambda x: len(x["conversations"]) >= 2)
         sampled_requests = []
+        ind = 0
         dynamic_output = output_len is None
 
         for item in filtered_data:
@@ -1111,8 +1151,11 @@ class ConversationDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1141,12 +1184,13 @@ class VisionArenaDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = (output_len
                       if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
@@ -1168,8 +1212,10 @@ class VisionArenaDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(i),
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1198,11 +1244,12 @@ class InstructCoderDataset(HuggingFaceDataset):
                num_requests: int,
                output_len: Optional[int] = None,
                enable_multimodal_chat: bool = False,
+               request_id_prefix: str = "",
                **kwargs) -> list:
         output_len = (output_len
                       if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = f"{item['input']}\n\n{item['instruction']} Just output \
@@ -1224,8 +1271,10 @@ class InstructCoderDataset(HuggingFaceDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1255,13 +1304,14 @@ class MTBenchDataset(HuggingFaceDataset):
         num_requests: int,
         output_len: Optional[int] = None,
         enable_multimodal_chat: bool = False,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = (output_len
                       if output_len is not None else self.DEFAULT_OUTPUT_LEN)
         sampled_requests = []
 
-        for item in self.data:
+        for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
                 break
             prompt = item["turns"][0]
@@ -1282,8 +1332,10 @@ class MTBenchDataset(HuggingFaceDataset):
                     prompt=prompt,
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
+                    request_id=request_id_prefix + str(i),
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1305,8 +1357,10 @@ class AIMODataset(HuggingFaceDataset):
                tokenizer: PreTrainedTokenizerBase,
                num_requests: int,
                output_len: Optional[int] = None,
+               request_id_prefix: str = "",
                **kwargs) -> list:
         sampled_requests = []
+        ind = 0
         dynamic_output = output_len is None
 
         for item in self.data:
@@ -1331,8 +1385,12 @@ class AIMODataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=None,
+                    request_id=request_id_prefix + str(ind),
+                    
                 ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+            ind += 1
+        self.maybe_oversample_requests(sampled_requests, num_requests,
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1403,13 +1461,14 @@ class NextEditPredictionDataset(HuggingFaceDataset):
     }
 
     def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
+               request_id_prefix: str = "",
                **kwargs):
         formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
             self.dataset_path)
         if formatting_prompt_func is None:
             raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
         samples = []
-        for sample in self.data:
+        for i, sample in enumerate(self.data):
             sample = formatting_prompt_func(sample)
             samples.append(
                 SampleRequest(
@@ -1417,10 +1476,11 @@ class NextEditPredictionDataset(HuggingFaceDataset):
                     prompt_len=len(tokenizer(sample["prompt"]).input_ids),
                     expected_output_len=len(
                         tokenizer(sample["expected_output"]).input_ids),
+                    request_id=request_id_prefix + str(i),
                 ))
             if len(samples) >= num_requests:
                 break
-        self.maybe_oversample_requests(samples, num_requests)
+        self.maybe_oversample_requests(samples, num_requests, request_id_prefix)
         return samples
 
 
@@ -1470,6 +1530,7 @@ class ASRDataset(HuggingFaceDataset):
         tokenizer: PreTrainedTokenizerBase,
         num_requests: int,
         output_len: Optional[int] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list:
         output_len = (output_len
@@ -1477,6 +1538,7 @@ class ASRDataset(HuggingFaceDataset):
         prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
         prompt_len = len(tokenizer(prompt).input_ids)
         sampled_requests = []
+        ind = 0
         skipped = 0
         for item in self.data:
             if len(sampled_requests) >= num_requests:
@@ -1496,7 +1558,9 @@ class ASRDataset(HuggingFaceDataset):
                     prompt_len=prompt_len,
                     expected_output_len=output_len,
                     multi_modal_data=mm_content,
+                    request_id=request_id_prefix + str(ind),
                 ))
+            ind += 1
         if skipped:
             logger.warning(
                 "%d samples discarded from dataset due to"
@@ -1504,7 +1568,8 @@ class ASRDataset(HuggingFaceDataset):
                 " what Whisper supports.",
                 skipped,
             )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1541,11 +1606,13 @@ class MLPerfDataset(HuggingFaceDataset):
         tokenizer: PreTrainedTokenizerBase,
         num_requests: int,
         output_len: Optional[int] = None,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         # Force dynamic output length based on reference completion.
         dynamic_output = output_len is None
         sampled_requests: list[SampleRequest] = []
+        ind = 0
 
         for item in self.data:
             if len(sampled_requests) >= num_requests:
@@ -1580,10 +1647,13 @@ class MLPerfDataset(HuggingFaceDataset):
                     prompt=prompt_formatted,
                     prompt_len=prompt_len,
                     expected_output_len=expected_output_len,
+                    request_id=request_id_prefix + str(ind),
                 )
             )
+            ind += 1
 
-        self.maybe_oversample_requests(sampled_requests, num_requests)
+        self.maybe_oversample_requests(sampled_requests, num_requests, 
+                                       request_id_prefix)
         return sampled_requests
 
 
@@ -1616,6 +1686,7 @@ class PrefixRepetitionRandomDataset(BenchmarkDataset):
         suffix_len: int = DEFAULT_SUFFIX_LEN,
         num_prefixes: int = DEFAULT_NUM_PREFIXES,
         output_len: int = DEFAULT_OUTPUT_LEN,
+        request_id_prefix: str = "",
         **kwargs,
     ) -> list[SampleRequest]:
         vocab_size = tokenizer.vocab_size

vllm/benchmarks/lib/endpoint_request_func.py

@@ -31,6 +31,7 @@ class RequestFuncInput:
     multi_modal_content: Optional[dict | list[dict]] = None
     ignore_eos: bool = False
     language: Optional[str] = None
+    request_id: Optional[str] = None
 
 
 @dataclass
@@ -87,6 +88,8 @@ async def async_request_openai_completions(
     headers = {
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"
     }
+    if request_func_input.request_id:
+        headers["x-request-id"] = request_func_input.request_id
 
     output = RequestFuncOutput()
     output.prompt_len = request_func_input.prompt_len
@@ -210,6 +213,8 @@ async def async_request_openai_chat_completions(
         "Content-Type": "application/json",
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
     }
+    if request_func_input.request_id:
+        headers["x-request-id"] = request_func_input.request_id
 
     output = RequestFuncOutput()
     output.prompt_len = request_func_input.prompt_len
@@ -311,6 +316,8 @@ async def async_request_openai_audio(
     headers = {
         "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
     }
+    if request_func_input.request_id:
+        headers["x-request-id"] = request_func_input.request_id
 
     # Send audio file
     def to_bytes(y, sr):

vllm/benchmarks/serve.py

@@ -478,11 +478,12 @@ async def benchmark(
                         "timestamp": timestamp
                     })
                 last_int_rps = current_int_rps
-        prompt, prompt_len, output_len, mm_content = (
+        prompt, prompt_len, output_len, mm_content, request_id = (
             request.prompt,
             request.prompt_len,
             request.expected_output_len,
             request.multi_modal_data,
+            request.request_id,
         )
         req_model_id, req_model_name = model_id, model_name
         if lora_modules:
@@ -498,7 +499,8 @@ async def benchmark(
                                               logprobs=logprobs,
                                               multi_modal_content=mm_content,
                                               ignore_eos=ignore_eos,
-                                              extra_body=extra_body)
+                                              extra_body=extra_body,
+                                              request_id=request_id,)
         tasks.append(
             asyncio.create_task(
                 limited_request_func(request_func_input=request_func_input,
@@ -865,6 +867,14 @@ def add_cli_args(parser: argparse.ArgumentParser):
         "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
         "and the blog: https://hao-ai-lab.github.io/blogs/distserve",
     )
+    parser.add_argument(
+        "--request-id-prefix",
+        type=str,
+        required=False,
+        default="benchmark-serving",
+        help="Specify the prefix of request id.",
+    )
+
 
     sampling_group = parser.add_argument_group("sampling parameters")
     sampling_group.add_argument(