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[SpecDecode][Benchmark] Add SPEED-bench support to benchmarking CLI (#36029)

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Signed-off-by: talora <talora@nvidia.com>
Co-authored-by: Benjamin Chislett <bchislett@nvidia.com>
This commit is contained in:
Talor Abramovich
2026-04-15 12:00:07 -04:00
committed by GitHub
co-authored by Benjamin Chislett
parent 3beb57a238
commit 3cc328a4be
2 changed files with 152 additions and 0 deletions
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docs/benchmarking/cli.md
+64
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@@ -37,6 +37,7 @@ th {
| HuggingFace-Blazedit | ✅ | ✅ | `vdaita/edit_5k_char`, `vdaita/edit_10k_char` |
| HuggingFace-ASR | ✅ | ✅ | `openslr/librispeech_asr`, `facebook/voxpopuli`, `LIUM/tedlium`, `edinburghcstr/ami`, `speechcolab/gigaspeech`, `kensho/spgispeech` |
| Spec Bench | ✅ | ✅ | `wget https://raw.githubusercontent.com/hemingkx/Spec-Bench/refs/heads/main/data/spec_bench/question.jsonl` |
| SPEED-Bench | ✅ | ✅ | `curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -` |
| Custom | ✅ | ✅ | Local file: `data.jsonl` |
| Custom MM | ✅ | ✅ | Local file: `mm_data.jsonl` |
@@ -239,6 +240,69 @@ vllm bench serve \
--spec-bench-category "summarization"
```
#### SPEED-Bench Benchmark with Speculative Decoding
[SPEED-Bench](https://huggingface.co/datasets/nvidia/SPEED-Bench) is a unified and diverse dataset for speculative decoding, supporting acceptance rate and length measurements using the Qualitative split and throughput measurements using the Throughput splits in 5 configuration of input sequence length (1k, 2k, 8k, 16k, 32k).
!!! note
This dataset is governed by the [NVIDIA Evaluation Dataset License Agreement](https://huggingface.co/datasets/nvidia/SPEED-Bench/blob/main/License.pdf). For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose. The `prepare.py` script automatically fetches data from all the source datasets.
First, download the dataset to a folder, using this one liner:
```bash
curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py \| python3 -
```
The command supports also the following arguments:
- `--config`: download only a subset of the dataset: `qualitative`, `throughput_1k`, `throughput_2k`, `throughput_8k`, `throughput_16k` and `throughput_32k`. By default, it will download all subsets.
- `--output_dir`: download to a specified folder. By default, it will download to the current directory.
Start a server with speculative decoding:
```bash
vllm serve meta-llama/Llama-3.3-70B-Instruct \
--speculative-config $'{"method": "eagle3",
"num_speculative_tokens": 3,
"model": "nvidia/Llama-3.3-70B-Instruct-Eagle3"}'
```
Run all categories in the Qualitative split:
```bash
vllm bench serve \
--model meta-llama/Llama-3.3-70B-Instruct \
--dataset-name speed_bench \
--dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
--num-prompts -1
```
Available categories include `[writing, roleplay, reasoning, math, coding, stem, humanities, multilingual, summarization, qa, rag]`.
Run only a specific category like "multilingual":
```bash
vllm bench serve \
--model meta-llama/Llama-3.3-70B-Instruct \
--dataset-name speed_bench \
--dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench" \
--num-prompts -1
--speed-bench-category "multilingual"
```
Run all categories in the Throughput split (2k ISL):
```bash
vllm bench serve \
--model meta-llama/Llama-3.3-70B-Instruct \
--dataset-name speed_bench \
--speed-bench-dataset-subset throughput_2k
--dataset-path "<YOUR_DOWNLOADED_PATH>/data/speed_bench/" \
--num-prompts -1
```
Available categories include `[high_entropy, mixed, low_entropy]`, where high entropy data contains unstructued data such as creative writing while low entropy data contains more structured data such as coding, more details are in the dataset card.
#### Other HuggingFaceDataset Examples
```bash
vllm/benchmarks/datasets/datasets.py
+88
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@@ -25,6 +25,7 @@ from contextlib import suppress
from dataclasses import dataclass, replace
from functools import cache
from io import BytesIO
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Any, cast
@@ -1422,6 +1423,7 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
"custom_mm",
"prefix_repetition",
"spec_bench",
"speed_bench",
],
help="Name of the dataset to benchmark on.",
)
@@ -1606,6 +1608,34 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
"repetition dataset.",
)
speed_bench_group = parser.add_argument_group("speed bench dataset options")
speed_bench_group.add_argument(
"--speed-bench-dataset-subset",
type=str,
default="qualitative",
choices={
"qualitative",
"throughput_1k",
"throughput_2k",
"throughput_8k",
"throughput_16k",
"throughput_32k",
},
help="Subset of the SPEED-Bench dataset.",
)
speed_bench_group.add_argument(
"--speed-bench-output-len",
type=int,
default=4096,
help="Num of output tokens per request, used only for speed bench dataset.",
)
speed_bench_group.add_argument(
"--speed-bench-category",
type=str,
default=None,
help="Category for speed bench dataset. If None, use all categories.",
)
def add_random_dataset_base_args(
parser_or_group: FlexibleArgumentParser | argparse._ArgumentGroup,
@@ -2074,6 +2104,19 @@ def get_samples(args, tokenizer: TokenizerLike) -> list[SampleRequest]:
request_id_prefix=args.request_id_prefix,
no_oversample=args.no_oversample,
),
"speed_bench": lambda: SpeedBench(
dataset_path=args.dataset_path,
dataset_subset=args.speed_bench_dataset_subset,
category=args.speed_bench_category,
disable_shuffle=args.disable_shuffle,
).sample(
num_requests=args.num_prompts,
tokenizer=tokenizer,
output_len=args.speed_bench_output_len,
enable_multimodal_chat=args.enable_multimodal_chat,
request_id_prefix=args.request_id_prefix,
no_oversample=args.no_oversample,
),
}
try:
@@ -3551,3 +3594,48 @@ class MMStarDataset(HuggingFaceDataset):
sampled_requests, num_requests, request_id_prefix, no_oversample
)
return sampled_requests
# -----------------------------------------------------------------------------
# Speed Bench Dataset Implementation
# -----------------------------------------------------------------------------
class SpeedBench(CustomDataset):
"""
Implements the SPEED-Bench dataset: https://huggingface.co/datasets/nvidia/SPEED-Bench
Download the dataset using:
curl -LsSf https://raw.githubusercontent.com/NVIDIA-NeMo/Skills/refs/heads/main/nemo_skills/dataset/speed-bench/prepare.py | python3 -
""" # noqa: E501
def __init__(self, **kwargs) -> None:
self.dataset_subset = kwargs.pop("dataset_subset", "qualitative")
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=Path(self.dataset_path) / f"{self.dataset_subset}.jsonl",
lines=True,
)
# check if the JSONL file has a 'turns' column
if "messages" not in jsonl_data.columns:
raise ValueError("JSONL file must contain a 'messages' 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["messages"][0]["content"]
self.data.append({"prompt": prompt})
random.seed(self.random_seed)
if not getattr(self, "disable_shuffle", False):
random.shuffle(self.data)