[Spec Decode][Benchmark] Add Spec Bench Dataset for benchmarking

vllm/benchmarks/datasets.py

@@ -1020,7 +1020,7 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         default="random",
         choices=[
             "sharegpt", "burstgpt", "sonnet", "random", "random-mm", "hf", 
-            "custom", "prefix_repetition"
+            "custom", "prefix_repetition", "spec_bench"
         ],
         help="Name of the dataset to benchmark on.",
     )
@@ -1053,6 +1053,22 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         "Skip applying chat template to prompt, used only for custom dataset.",
     )
 
+    spec_bench_group = parser.add_argument_group("spec bench dataset options")
+    spec_bench_group.add_argument(
+        "--spec-bench-output-len",
+        type=int,
+        default=256,
+        help=
+        "Num of output tokens per request, used only for spec bench dataset.",
+    )
+    spec_bench_group.add_argument(
+        "--spec-bench-category",
+        type=str,
+        default=None,
+        help=
+        "Category for spec bench dataset. If None, use all categories.",
+    )
+
     sonnet_group = parser.add_argument_group("sonnet dataset options")
     sonnet_group.add_argument(
         "--sonnet-input-len",
@@ -1404,6 +1420,14 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
     else:
         # For datasets that follow a similar structure, use a mapping.
         dataset_mapping = {
+            "spec_bench":
+            lambda: SpecBench(dataset_path=args.dataset_path, 
+                              category=args.spec_bench_category).sample(
+                num_requests=args.num_prompts,
+                tokenizer=tokenizer,
+                output_len=args.spec_bench_output_len,
+                request_id_prefix=args.request_id_prefix,
+            ),
             "sharegpt": lambda: ShareGPTDataset(
                 random_seed=args.seed, dataset_path=args.dataset_path
             ).sample(
@@ -1541,6 +1565,14 @@ def sample(
         request_id_prefix: str = "",
         **kwargs,
     ) -> list:
+        # load all data if needed
+        self.num_available_samples = len(self.data)
+        if num_requests <= 0:
+            num_requests = self.num_available_samples
+            logger.info("num_requests is set to 0 or negative, "
+                        "so using all available samples: %d",
+                        num_requests)
+
         sampled_requests = []
         for i, item in enumerate(self.data):
             if len(sampled_requests) >= num_requests:
@@ -1572,6 +1604,52 @@ def sample(
         return sampled_requests
 
 
+# -----------------------------------------------------------------------------
+# Spec Bench Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class SpecBench(CustomDataset):
+    """
+    Implements the SpecBench dataset: https://github.com/hemingkx/Spec-Bench
+    Download the dataset using: 
+    wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl
+    """ # noqa: E501
+
+    def __init__(self, **kwargs) -> None:
+        self.category = kwargs.pop("category", 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 = []
+
+        # Load the JSONL file
+        jsonl_data = pd.read_json(path_or_buf=self.dataset_path,
+                                    lines=True)
+
+        # check if the JSONL file has a 'turns' column
+        if "turns" not in jsonl_data.columns:
+            raise ValueError("JSONL file must contain a 'turns' column.")
+
+        for _, row in jsonl_data.iterrows():
+            # sample only from a specific category if specified
+            if (not self.category) or (self.category == row['category']):
+                prompt = row["turns"][0]
+                self.data.append({"prompt": prompt})
+
+        random.seed(self.random_seed)
+        random.shuffle(self.data)
+
+    def sample(self, **kwargs) -> list:
+        # leverage CustomDataset sample
+        kwargs["skip_chat_template"] = False
+        return super().sample(**kwargs)
+
+
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------