SGLang EAGLE Decoding Bug: Stalls Structured Outputs
Introduction
Hey guys, ever run into a tricky bug that just makes you scratch your head? That's exactly what's happening with SGLang's EAGLE speculative decoding when it comes to structured outputs. We're seeing some weird behavior where the system gets stuck, and I wanted to break it down for you all. This article dives deep into a peculiar bug encountered while using SGLang with EAGLE speculative decoding and structured outputs. This issue causes the system to stall, especially when dealing with formats like JSON or constraints like regex. We'll explore the bug's symptoms, the environment it occurs in, and potential causes. Let's explore the ins and outs of this issue, focusing on how it manifests, the environment it lives in, and what might be the underlying cause.
The Bug: A Structured Output Standstill
So, the main issue is that when you've got EAGLE speculative decoding fired up with a model like GLM-4.5, things go sideways when you ask for structured outputs. Imagine you're trying to get a JSON object back. The system might start okay, spitting out {", but then it hits a snag. If the model throws a curveball and guesses a wrong field name, the whole process grinds to a halt. No more output, just a blank stare. The same thing happens with regex constraints, and while I haven't tested it with EBNF, I suspect it's a similar story. This issue manifests when using EAGLE speculative decoding with models like GLM-4.5, particularly when requesting structured outputs such as JSON objects or when using constraints like regex. The process often begins correctly, but stalls when the model's prediction deviates from the defined structure. For example, when requesting a JSON object, the output might start with {", but if the model predicts an incorrect field name, the generation process stops abruptly.
It feels like the speculative decoding, which tries to predict multiple tokens at once, is clashing with the structure enforcement. The system seems to assume it only needs to roll back the last token if something goes wrong. But with speculative decoding, multiple tokens might be the culprits, and the invalid token just hangs around, messing everything up. The system keeps trying, churning out more tokens until it hits the max_tokens limit, but the structure constraint? It's toast. The core problem appears to stem from a conflict between speculative decoding and structure enforcement. Speculative decoding predicts multiple tokens simultaneously, while structure enforcement is designed to roll back only the last token. When an invalid token is predicted within a sequence, the enforcement mechanism fails to correct it, leading to a standstill. Despite this, the system continues to generate tokens until the max_tokens limit is reached, highlighting the disconnect between the generation process and the structural constraints.
Reproduction Steps
To reproduce this, you'll need SGLang built from source (I was on commit 3b3b3baf9f08a8f2e6180c9f9146b6137ad8032c). Here's the command I used to launch the server:
python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-FP8 --tp-size "8" --ep-size "8" --tool-call-parser glm45 --reasoning-parser glm45 --speculative-algorithm EAGLE --speculative-num-steps "3" --speculative-eagle-topk "1" --speculative-num-draft-tokens "4" --disable-shared-experts-fusion --host "0.0.0.0" --port "30000" --mem-fraction-static "0.9" --enable-metrics
I even tried adding --grammar-backend llguidance, but it didn't change anything.
Then, I sent this request:
curl -X POST http://127.0.0.1:30000/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "glm-4.5", "messages": [{"role": "user", "content": "Specify a random city."}], "max_tokens": 50, "chat_template_kwargs": {"enable_thinking": false}, "regex": "(Paris|Amsterdam}"}'
And the output? Something like this:
{"id":"6064f12fbbd149e1a4016c462672b58c","object":"chat.completion","created":1755171347,"model":"glm-4.5","choices":[{"index":0,"message":"role":"assistant","content":"A","reasoning_content":null,"tool_calls":null},"logprobs":null,"finish_reason":"length","matched_stop":null}],"usage":{"prompt_tokens":14,"total_tokens":64,"completion_tokens":50,"prompt_tokens_details":null}}
Notice how it just… stops? The city never gets specified, and the output is cut short. These steps provide a clear pathway to reproduce the bug. First, the SGLang server is launched with specific configurations, including the use of the GLM-4.5-FP8 model, EAGLE speculative decoding, and various performance-related settings. The inclusion of --grammar-backend llguidance is noted, but it does not resolve the issue. Subsequently, a curl request is sent to the server, prompting it to specify a random city while enforcing a regex constraint that only allows "Paris" or "Amsterdam" as the response. The resulting output demonstrates the bug, where the generation process halts prematurely, failing to produce a complete or valid response. The output typically shows a partial structure, indicating that the model began generating the response but was unable to complete it due to the structural constraints and speculative decoding conflict.
Environment Details
For those who like to get into the nitty-gritty, here's the environment I was working in:
Python: 3.10.12 (main, Feb 4 2025, 14:57:36) [GCC 11.4.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: NVIDIA H100 80GB HBM3
GPU 0,1,2,3,4,5,6,7 Compute Capability: 9.0
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.8, V12.8.93
CUDA Driver Version: 535.230.02
PyTorch: 2.7.0
sglang: 0.4.10.post2
sgl_kernel: 0.3.2
flashinfer_python: 0.2.10
triton: 3.3.0+git96316ce5
transformers: 4.55.0
torchao: 0.9.0
numpy: 2.2.5
aiohttp: 3.11.18
fastapi: 0.116.1
hf_transfer: 0.1.9
huggingface_hub: 0.34.3
interegular: 0.3.3
modelscope: 1.28.2
orjson: 3.11.1
outlines: 0.1.11
packaging: 25.0
psutil: 7.0.0
pydantic: 2.11.3
python-multipart: 0.0.20
pyzmq: 27.0.1
uvicorn: 0.35.0
uvloop: 0.21.0
vllm: Module Not Found
xgrammar: 0.1.22
openai: 1.78.0
tiktoken: 0.10.0
anthropic: 0.61.0
litellm: 1.75.0
decord: 0.6.0
NVIDIA Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11 NIC12 NIC13 NIC14 NIC15 NIC16 CPU Affinity NUMA AffinityGPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE PIX SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS 0,2,4,6,8,10 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX PIX PIX PIX PIX PIX PIX 1,3,5,7,9,11 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE 1,3,5,7,9,11 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE 1,3,5,7,9,11 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS NODE NODE NODE PIX NODE NODE NODE NODE NODE NODE NODE NODE NODE 1,3,5,7,9,11 1 N/A
NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS
NIC1 NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS
NIC2 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS
NIC3 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE PIX PIX PIX PIX PIX PIX PIX PIX PIX
NIC5 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE
NIC6 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE NODE NODE NODE NODE NODE NODE NODE NODE NODE
NIC7 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X NODE NODE NODE NODE NODE NODE NODE NODE NODE
NIC8 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE X PIX PIX PIX PIX PIX PIX PIX PIX
NIC9 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX X PIX PIX PIX PIX PIX PIX PIX
NIC10 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX X PIX PIX PIX PIX PIX PIX
NIC11 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX X PIX PIX PIX PIX PIX
NIC12 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX X PIX PIX PIX PIX
NIC13 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX PIX X PIX PIX PIX
NIC14 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX PIX PIX X PIX PIX
NIC15 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX PIX PIX PIX X PIX
NIC16 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX NODE NODE NODE PIX PIX PIX PIX PIX PIX PIX PIX X
ulimit soft: 1048576
This setup includes a beefy machine with NVIDIA H100 GPUs, CUDA 12.8, PyTorch 2.7.0, and a specific commit of SGLang. It's worth noting that I'm running a fairly recent stack, so this isn't some ancient, dusty corner of the software world. This detailed environment setup is crucial for replicating the bug and understanding its context. The system is equipped with eight NVIDIA H100 80GB HBM3 GPUs, indicating a high-performance computing environment. The CUDA version is 12.8, with driver version 535.230.02, and PyTorch is at version 2.7.0. The SGLang version is 0.4.10.post2, with its kernel component at 0.3.2. Other relevant libraries include flashinfer_python, triton, transformers, and torchao. The NVIDIA Topology section provides insights into the interconnectivity between GPUs and network interface cards (NICs), which can be important for understanding performance bottlenecks. The ulimit soft: 1048576 indicates the maximum number of open file descriptors, which is set to a high value, likely to accommodate the needs of large-scale model serving.
Potential Culprit: Speculative Decoding and Structure Enforcement Clash
My hunch is that the root cause lies in the interplay between speculative decoding and structure enforcement. Speculative decoding, by its nature, tries to predict multiple tokens ahead of time. This is great for speed, but it seems to create a headache when you're trying to enforce a specific structure. If the model speculates down a wrong path, the structure enforcement mechanism might not be equipped to handle the multi-token rollback that's needed. This hypothesis points to the core of the problem: the interaction between speculative decoding and structure enforcement. Speculative decoding aims to accelerate the generation process by predicting multiple tokens in advance. However, this approach can lead to deviations from the desired output structure. The current structure enforcement mechanism appears to be designed for single-token rollbacks, which is insufficient when speculative decoding introduces multiple incorrect tokens. This mismatch results in the system's inability to correct the output and adhere to the specified constraints, ultimately causing the generation process to stall. The challenge lies in developing a more robust structure enforcement mechanism that can effectively handle the multi-token corrections required by speculative decoding.
Next Steps and Potential Solutions
So, what's next? Well, the first step is to confirm this hypothesis with some more testing and debugging. It would be great to see if others are running into the same issue and if there are any workarounds. The ultimate solution probably involves tweaking the structure enforcement logic to be more aware of speculative decoding's multi-token nature. This might involve a more sophisticated rollback mechanism or a way to guide the speculative decoding process to stay within the structural boundaries. To further investigate this issue, several steps can be taken. First, validating the hypothesis through additional testing and debugging is crucial. This involves creating more test cases with varying structural constraints and analyzing the system's behavior. Gathering feedback from other users to identify similar experiences and potential workarounds is also essential. The long-term solution likely involves refining the structure enforcement logic to better accommodate the multi-token predictions of speculative decoding. This could entail developing a more advanced rollback mechanism capable of handling multiple tokens or devising a method to steer speculative decoding toward structurally valid outputs. Collaboration within the SGLang community will be key to finding a comprehensive solution.
Conclusion
This bug highlights the complexities that can arise when you combine different optimization techniques. Speculative decoding is a powerful tool, but it needs to play nicely with other constraints, like structured outputs. Hopefully, by shining a light on this issue, we can get closer to a fix and make SGLang even more robust. So, in a nutshell, we've uncovered a tricky situation where EAGLE speculative decoding and structured outputs in SGLang aren't exactly best buddies right now. The core issue seems to be that the speculative nature of the decoding clashes with the rigid requirements of structure enforcement, leading to stalls and incomplete outputs. While this bug presents a challenge, it also underscores the importance of continuous testing and refinement in complex software systems. By addressing these issues head-on, we can make SGLang an even more powerful and reliable tool for everyone. By identifying this bug and understanding its potential causes, we can work towards a solution that enhances the overall performance and reliability of SGLang. The ongoing efforts to improve SGLang's capabilities demonstrate the commitment to providing a robust and efficient platform for language model serving.
Keywords
EAGLE speculative decoding, SGLang, structured outputs, GLM-4.5, regex constraints, bug, debugging, structure enforcement, multi-token rollback, speculative decoding.
