[Misc][Benchmark] Add support for CustomDataset (#18511)

benchmarks/README.md

@@ -64,6 +64,12 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
     </tr>
+    <tr>
+      <td><strong>Custom</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>Local file: <code>data.jsonl</code></td>
+    </tr>
   </tbody>
 </table>
 
@@ -124,6 +130,38 @@ P99 ITL (ms):                            8.39
 ==================================================
 ```
 
+### Custom Dataset
+If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
+
+```
+{"prompt": "What is the capital of India?"}
+{"prompt": "What is the capital of Iran?"}
+{"prompt": "What is the capital of China?"}
+``` 
+
+```bash
+# start server
+VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
+```
+
+```bash
+# run benchmarking script
+python3 benchmarks/benchmark_serving.py --port 9001 --save-result --save-detailed \
+  --backend vllm \
+  --model meta-llama/Llama-3.1-8B-Instruct \
+  --endpoint /v1/completions \
+  --dataset-name custom \
+  --dataset-path <path-to-your-data-jsonl> \
+  --custom-skip-chat-template \
+  --num-prompts 80 \
+  --max-concurrency 1 \
+  --temperature=0.3 \
+  --top-p=0.75 \
+  --result-dir "./log/"
+```
+
+You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`.
+
 ### VisionArena Benchmark for Vision Language Models
 
 ```bash
@@ -203,6 +241,16 @@ python3 vllm/benchmarks/benchmark_serving.py \
     --seed 42
 ```
 
+**`philschmid/mt-bench`**
+
+``` bash
+python3 vllm/benchmarks/benchmark_serving.py \
+    --model Qwen/QwQ-32B \
+    --dataset-name hf \
+    --dataset-path philschmid/mt-bench \
+    --num-prompts 80
+```
+
 ### Running With Sampling Parameters
 
 When using OpenAI-compatible backends such as `vllm`, optional sampling

benchmarks/benchmark_dataset.py

@@ -9,9 +9,6 @@ generation. Supported dataset types include:
   - BurstGPT
   - HuggingFace
   - VisionArena
-
-TODO: Implement CustomDataset to parse a JSON file and convert its contents into
-SampleRequest instances, similar to the approach used in ShareGPT.
 """
 
 import base64
@@ -442,6 +439,97 @@ class ShareGPTDataset(BenchmarkDataset):
         return samples
 
 
+# -----------------------------------------------------------------------------
+# Custom Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class CustomDataset(BenchmarkDataset):
+    """
+    Implements the Custom dataset.  Loads data from a JSONL file and generates
+    sample requests based on conversation turns. E.g.,
+    ```
+    {"prompt": "What is the capital of India?"}
+    {"prompt": "What is the capital of Iran?"}
+    {"prompt": "What is the capital of China?"}
+    ```
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        # self.data will be a list of dictionaries
+        # e.g., [{"prompt": "What is the capital of India?"}, ...]
+        # This will be the standardized format which load_data()
+        # has to convert into depending on the filetype of dataset_path.
+        # sample() will assume this standardized format of self.data
+        self.data = []
+
+        # Load the JSONL file
+        if self.dataset_path.endswith(".jsonl"):
+            jsonl_data = pd.read_json(path_or_buf=self.dataset_path, lines=True)
+
+            # check if the JSONL file has a 'prompt' column
+            if "prompt" not in jsonl_data.columns:
+                raise ValueError("JSONL file must contain a 'prompt' column.")
+
+            # Convert each row to a dictionary and append to self.data
+            # This will convert the DataFrame to a list of dictionaries
+            # where each dictionary corresponds to a row in the DataFrame.
+            # This is the standardized format we want for self.data
+            for _, row in jsonl_data.iterrows():
+                self.data.append(row.to_dict())
+        else:
+            raise NotImplementedError(
+                "Only JSONL format is supported for CustomDataset."
+            )
+
+        random.seed(self.random_seed)
+        random.shuffle(self.data)
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        lora_path: Optional[str] = None,
+        max_loras: Optional[int] = None,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        skip_chat_template: bool = False,
+        **kwargs,
+    ) -> list:
+        sampled_requests = []
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item["prompt"]
+
+            # apply template
+            if not skip_chat_template:
+                prompt = tokenizer.apply_chat_template(
+                    [{"role": "user", "content": prompt}],
+                    add_generation_prompt=True,
+                    tokenize=False,
+                )
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                )
+            )
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+
+        return sampled_requests
+
+
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------

benchmarks/benchmark_serving.py

@@ -60,6 +60,7 @@ from benchmark_dataset import (
     ASRDataset,
     BurstGPTDataset,
     ConversationDataset,
+    CustomDataset,
     HuggingFaceDataset,
     InstructCoderDataset,
     MTBenchDataset,
@@ -627,7 +628,16 @@ def main(args: argparse.Namespace):
             "'--dataset-path' if required."
         )
 
-    if args.dataset_name == "sonnet":
+    if args.dataset_name == "custom":
+        dataset = CustomDataset(dataset_path=args.dataset_path)
+        input_requests = dataset.sample(
+            num_requests=args.num_prompts,
+            tokenizer=tokenizer,
+            output_len=args.custom_output_len,
+            skip_chat_template=args.custom_skip_chat_template,
+        )
+
+    elif args.dataset_name == "sonnet":
         dataset = SonnetDataset(dataset_path=args.dataset_path)
         # For the "sonnet" dataset, formatting depends on the backend.
         if args.backend == "openai-chat":
@@ -838,6 +848,8 @@ def main(args: argparse.Namespace):
             ]:
                 if field in result_json:
                     del result_json[field]
+                if field in benchmark_result:
+                    del benchmark_result[field]
 
         # Save to file
         base_model_id = model_id.split("/")[-1]
@@ -850,6 +862,7 @@ def main(args: argparse.Namespace):
         if args.result_filename:
             file_name = args.result_filename
         if args.result_dir:
+            os.makedirs(args.result_dir, exist_ok=True)
             file_name = os.path.join(args.result_dir, file_name)
         with open(
             file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
@@ -890,7 +903,7 @@ if __name__ == "__main__":
         "--dataset-name",
         type=str,
         default="sharegpt",
-        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
+        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf", "custom"],
         help="Name of the dataset to benchmark on.",
     )
     parser.add_argument(
@@ -1060,6 +1073,19 @@ if __name__ == "__main__":
     )
 
     # group for dataset specific arguments
+    custom_group = parser.add_argument_group("custom dataset options")
+    custom_group.add_argument(
+        "--custom-output-len",
+        type=int,
+        default=256,
+        help="Number of output tokens per request, used only for custom dataset.",
+    )
+    custom_group.add_argument(
+        "--custom-skip-chat-template",
+        action="store_true",
+        help="Skip applying chat template to prompt, used only for custom dataset.",
+    )
+
     sonnet_group = parser.add_argument_group("sonnet dataset options")
     sonnet_group.add_argument(
         "--sonnet-input-len",

vllm/benchmarks/datasets.py

@@ -9,9 +9,6 @@ generation. Supported dataset types include:
   - BurstGPT
   - HuggingFace
   - VisionArena
-
-TODO: Implement CustomDataset to parse a JSON file and convert its contents into
-SampleRequest instances, similar to the approach used in ShareGPT.
 """
 import base64
 import io
@@ -26,6 +23,7 @@ from io import BytesIO
 from typing import Any, Callable, Optional, Union
 
 import numpy as np
+import pandas as pd
 from PIL import Image
 from transformers import PreTrainedTokenizerBase
 
@@ -443,6 +441,99 @@ class ShareGPTDataset(BenchmarkDataset):
         return samples
 
 
+# -----------------------------------------------------------------------------
+# Custom Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class CustomDataset(BenchmarkDataset):
+    """
+    Implements the Custom dataset.  Loads data from a JSONL file and generates
+    sample requests based on conversation turns. E.g.,
+    ```
+    {"prompt": "What is the capital of India?"}
+    {"prompt": "What is the capital of Iran?"}
+    {"prompt": "What is the capital of China?"}
+    ```
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        # self.data will be a list of dictionaries
+        # e.g., [{"prompt": "What is the capital of India?"}, ...]
+        # This will be the standardized format which load_data()
+        # has to convert into depending on the filetype of dataset_path.
+        # sample() will assume this standardized format of self.data
+        self.data = []
+
+        # Load the JSONL file
+        if self.dataset_path.endswith(".jsonl"):
+            jsonl_data = pd.read_json(path_or_buf=self.dataset_path,
+                                      lines=True)
+
+            # check if the JSONL file has a 'prompt' column
+            if "prompt" not in jsonl_data.columns:
+                raise ValueError("JSONL file must contain a 'prompt' column.")
+
+            # Convert each row to a dictionary and append to self.data
+            # This will convert the DataFrame to a list of dictionaries
+            # where each dictionary corresponds to a row in the DataFrame.
+            # This is the standardized format we want for self.data
+            for _, row in jsonl_data.iterrows():
+                self.data.append(row.to_dict())
+        else:
+            raise NotImplementedError(
+                "Only JSONL format is supported for CustomDataset.")
+
+        random.seed(self.random_seed)
+        random.shuffle(self.data)
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        lora_path: Optional[str] = None,
+        max_loras: Optional[int] = None,
+        output_len: Optional[int] = None,
+        enable_multimodal_chat: bool = False,
+        skip_chat_template: bool = False,
+        **kwargs,
+    ) -> list:
+        sampled_requests = []
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item["prompt"]
+
+            # apply template
+            if not skip_chat_template:
+                prompt = tokenizer.apply_chat_template(
+                    [{
+                        "role": "user",
+                        "content": prompt
+                    }],
+                    add_generation_prompt=True,
+                    tokenize=False,
+                )
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+
+        return sampled_requests
+
+
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------

vllm/benchmarks/serve.py

@@ -1110,6 +1110,8 @@ def main(args: argparse.Namespace):
             ]:
                 if field in result_json:
                     del result_json[field]
+                if field in benchmark_result:
+                    del benchmark_result[field]
 
         # Save to file
         base_model_id = model_id.split("/")[-1]
@@ -1120,6 +1122,7 @@ def main(args: argparse.Namespace):
         if args.result_filename:
             file_name = args.result_filename
         if args.result_dir:
+            os.makedirs(args.result_dir, exist_ok=True)
             file_name = os.path.join(args.result_dir, file_name)
         with open(file_name,
                   mode="a+" if args.append_result else "w",