sglang/docs/backend/speculative_decoding.ipynb

Speculative Decoding

SGLang now provides an EAGLE-based speculative decoding option. The implementation aims to maximize speed and efficiency and is considered to be among the fastest in open-source LLM engines.

Note: Currently, Speculative Decoding in SGLang does not support radix cache.

Performance Highlights

• Official EAGLE code (SafeAILab/EAGLE): ~200 tokens/s
• Standard SGLang Decoding: ~156 tokens/s
• EAGLE Decoding in SGLang: ~297 tokens/s
• EAGLE Decoding in SGLang (w/ torch.compile): ~316 tokens/s

All benchmarks below were run on a single H100.

EAGLE Decoding

To enable EAGLE-based speculative decoding, specify the draft model (--speculative-draft-model-path) and the relevant EAGLE parameters:

In [ ]:

from sglang.test.test_utils import is_in_ci

if is_in_ci():
    from patch import launch_server_cmd
else:
    from sglang.utils import launch_server_cmd

from sglang.utils import wait_for_server, print_highlight, terminate_process

server_process, port = launch_server_cmd(
    """
python3 -m sglang.launch_server --model meta-llama/Llama-2-7b-chat-hf  --speculative-algorithm EAGLE \
    --speculative-draft-model-path lmzheng/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 5 \
    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64
"""
)

wait_for_server(f"http://localhost:{port}")

In [ ]:

import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print_highlight(f"Response: {response}")

In [ ]:

terminate_process(server_process)

EAGLE Decoding with torch.compile

You can also enable torch.compile for further optimizations and optionally set --cuda-graph-max-bs:

In [ ]:

server_process, port = launch_server_cmd(
    """
python3 -m sglang.launch_server --model meta-llama/Llama-2-7b-chat-hf  --speculative-algorithm EAGLE \
    --speculative-draft-model-path lmzheng/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 5 \
        --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --mem-fraction 0.6 \
            --enable-torch-compile --cuda-graph-max-bs 2
"""
)

wait_for_server(f"http://localhost:{port}")

In [ ]:

import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print_highlight(f"Response: {response}")

In [ ]:

terminate_process(server_process)

EAGLE Decoding via Frequency-Ranked Speculative Sampling

By employing a truncated high-frequency token vocabulary in the draft model, Eagle speculative decoding reduces lm_head computational overhead while accelerating the pipeline without quality degradation. For more details, checkout the paper.

In our implementation, set --speculative-token-map to enable the optimization. You can get the high-frequency token in FR-Spec from this model. Or you can obtain high-frequency token by directly downloading these token from this repo.

Thanks for the contribution from Weilin Zhao and Zhousx.

In [ ]:

from sglang.test.test_utils import is_in_ci

if is_in_ci():
    from patch import launch_server_cmd
else:
    from sglang.utils import launch_server_cmd

from sglang.utils import wait_for_server, print_highlight, terminate_process

server_process, port = launch_server_cmd(
    """
python3 -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --speculative-algorithm EAGLE \
    --speculative-draft-model-path lmzheng/sglang-EAGLE-LLaMA3-Instruct-8B --speculative-num-steps 5 \
    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --speculative-token-map thunlp/LLaMA3-Instruct-8B-FR-Spec/freq_32768.pt \
    --mem-fraction 0.7 --cuda-graph-max-bs 2 --dtype float16 
"""
)

wait_for_server(f"http://localhost:{port}")

In [ ]:

import openai

client = openai.Client(base_url=f"http://127.0.0.1:{port}/v1", api_key="None")

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    messages=[
        {"role": "user", "content": "List 3 countries and their capitals."},
    ],
    temperature=0,
    max_tokens=64,
)

print_highlight(f"Response: {response}")

In [ ]:

terminate_process(server_process)