[Feature] Universal speculative decoding for heterogeneous vocabularies (TLI) by wan-danfeng · Pull Request #38174 · vllm-project/vllm

wan-danfeng · 2026-03-26T02:09:50Z

Summary

Implements Token-Level Intersection (TLI) speculative decoding, allowing target and draft models to have different (but overlapping) vocabularies.

Closes #38173

Algorithm

Based on the ICML 2025 oral paper:

Accelerating LLM Inference with Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies
Timor et al., https://arxiv.org/abs/2502.05202

How it works:

At startup, build a normalized token intersection between target and draft vocabularies
Draft model generates tokens constrained to the intersection (logits of non-intersection tokens → -inf)
Intersection tokens are mapped to target token IDs before rejection sampling
Rejection sampling runs unchanged — the algorithm is provably lossless

Changes（Original）

File	Change
`vllm/v1/spec_decode/universal_draft_model.py`	New:`UniversalDraftModelProposer`
`vllm/v1/spec_decode/vocab_mapping.py`	New: `VocabMapping` (intersection + ID mapping)
`vllm/config/speculative.py`	Register `"universal_draft"`, skip same-vocab check
`vllm/v1/worker/gpu_model_runner.py`	Instantiate proposer for`universal_draft` method

Changes（Refactored）

File	Change
`vllm/v1/spec_decode/vocab_mapping.py`	New: `VocabMapping` (intersection + ID mapping)
`vllm/config/speculative.py`	add flag `"use_heterogeneous_vocab"`, skip same-vocab check
`vllm/v1/spec_decode/llm_base_proposer.py`	base process when`use_heterogeneous_vocab` is True
`vllm/v1/spec_decode/draft_model.py`	process heterogeneous-vocab models when `"use_heterogeneous_vocab"`is True
`examples/features/speculative_decoding/spec_decoding_offline.py`	add `"use_heterogeneous_vocab"` examples
`tests/v1/spec_decode/test_vocab_mapping.py`	tests for vocab mapping UNK

Testing

Functional test (Qwen2.5-1.5B + Qwen2.5-0.5B, A800 80GB):

Vocab intersection: 151665 / 151936 = 99.8%
Mean acceptance length: 2.83 / 3
Per-position acceptance rate: 77.5%, 60.6%, 45.1%
Avg draft acceptance rate: 61%

Regression (existing methods unaffected):

✅ No speculative decoding (baseline)
✅ ngram
✅ draft_model

Attribution

This implementation is based on the TLI algorithm by Timor et al. The original authors have a reference implementation in HuggingFace Transformers (PR #35029).

claude

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github-actions · 2026-03-26T02:09:59Z

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Code Review

This pull request introduces a new speculative decoding method called "universal_draft" to support heterogeneous vocabularies between target and draft models. This involves adding a new UniversalDraftModelProposer and a VocabMapping class to handle token ID mapping and constrain logits. The gpu_model_runner.py is updated to integrate this new method. Review feedback indicates a critical issue with the UniversalDraftModelProposer's inheritance and initialization, which needs to be corrected to avoid runtime errors. Additionally, the VocabMapping's default handling of unk_token_id to 0 is problematic and should be revised to prevent incorrect token mapping for models where 0 is a special token.

gemini-code-assist · 2026-03-26T02:14:02Z

+class UniversalDraftModelProposer(SpecDecodeBaseProposer):
+    """Draft model proposer supporting heterogeneous vocabularies via TLI."""
+
+    def __init__(self, vllm_config: VllmConfig, device: torch.device, runner=None):
+        super().__init__(vllm_config=vllm_config, device=device,
+                         pass_hidden_states_to_model=False, runner=runner)


UniversalDraftModelProposer should inherit from EagleProposer to gain access to the propose method, which is called in gpu_model_runner.py. Currently, it inherits from SpecDecodeBaseProposer, which will lead to an AttributeError at runtime.

Additionally, the __init__ method needs to be adjusted. After changing the base class to EagleProposer, the super().__init__ call would invoke EagleProposer.__init__, which incorrectly sets pass_hidden_states_to_model=True. To correctly set it to False, you should directly call SpecDecodeBaseProposer.__init__. You'll also need to update the imports to include EagleProposer and SpecDecodeBaseProposer.

Suggested change

class UniversalDraftModelProposer(SpecDecodeBaseProposer):

"""Draft model proposer supporting heterogeneous vocabularies via TLI."""

def __init__(self, vllm_config: VllmConfig, device: torch.device, runner=None):

super().__init__(vllm_config=vllm_config, device=device,

pass_hidden_states_to_model=False, runner=runner)

class UniversalDraftModelProposer(EagleProposer):

"""Draft model proposer supporting heterogeneous vocabularies via TLI."""

def __init__(self, vllm_config: VllmConfig, device: torch.device, runner=None):

SpecDecodeBaseProposer.__init__(self, vllm_config=vllm_config, device=device,

pass_hidden_states_to_model=False, runner=runner)

SpecDecodeBaseProposer already contains the propose method (line 388 of eagle.py). EagleProposer does not override it. The current inheritance is correct and has been verified working with 61% draft acceptance rate in testing.

gemini-code-assist · 2026-03-26T02:14:02Z

+        self.target_unk_token_id = getattr(target_tokenizer, "unk_token_id", None) or 0
+        self.draft_unk_token_id = getattr(draft_tokenizer, "unk_token_id", None) or 0


Defaulting to 0 for unk_token_id when it's not present in the tokenizer can be problematic. For many models like Llama, token 0 is a special token (e.g., <s>) and not an "unknown" token. Mapping out-of-vocabulary tokens to a BOS token is incorrect and can degrade the performance of speculative decoding. It would be safer to raise an error if a tokenizer without an unk_token is used with this feature.

Suggested change

self.target_unk_token_id = getattr(target_tokenizer, "unk_token_id", None) or 0

self.draft_unk_token_id = getattr(draft_tokenizer, "unk_token_id", None) or 0

self.target_unk_token_id = target_tokenizer.unk_token_id

if self.target_unk_token_id is None:

raise ValueError("Target tokenizer must have an `unk_token_id`.")

self.draft_unk_token_id = draft_tokenizer.unk_token_id

if self.draft_unk_token_id is None:

raise ValueError("Draft tokenizer must have an `unk_token_id`.")

Fixed in the latest commit. Raising a ValueError is intentional — silently mapping to an arbitrary fallback token would corrupt the draft KV cache without any visible error to the user.

…g (TLI) Add AscendUniversalDraftProposer that extends AscendDraftModelProposer with heterogeneous vocabulary support via Token-Level Intersection (TLI). This allows the draft and target models to use different tokenizers (e.g., Llama-8B target + Qwen-0.5B draft). Key changes: - New universal_draft_proposer.py with AscendUniversalDraftProposer that skips the vocab-size-mismatch check, initialises VocabMapping after model loading, and wraps _propose to translate token IDs between draft and target vocabularies. - Register "universal_draft" method in get_spec_decode_method(). Depends on upstream vllm PR: vllm-project/vllm#38174 Signed-off-by: wan-danfeng <[email protected]>

jmamou · 2026-03-26T15:40:06Z

Great work, @wan-danfeng

It would be awesome to see some additional experiments with different levels of overlap between the draft and target vocabularies.

wan-danfeng · 2026-03-27T02:18:19Z

Great work, @wan-danfeng

It would be awesome to see some additional experiments with different levels of overlap between the draft and target vocabularies.

Thanks for the suggestion! We actually have results across different vocabulary overlap ratios:

Draft → Target	Vocab Overlap	AcceptanceRate
Llama-3.2-1B → Llama-3.2-8B (homogeneous)	100%	~80%
Qwen2.5-0.5B → Qwen2.5-7B (TLI)	99.8%	~61%
SmolLM2-1.7B → Qwen2.5-7B (TLI)	79.8%	~25%
TinyLlama-1.1B → Qwen2.5-7B (TLI)	69.6%	~15%

Acceptance rate scales with vocabulary overlap — TLI is most effective for high-overlap pairs (≥90%).

▎Note: The SmolLM2 and TinyLlama results are from a pre-fix version — those tokenizers lack unk_token_id, which the current code now explicitly rejects. The algorithm is theoretically valid for any overlap ratio; The current implementation requires both tokenizers to have unk_token_id. Models without it (e.g., Llama 3, SmolLM2) cannot currently be used as draft models for universal_draft. Models that do have unk_token_id and are compatible include Qwen (all generations), Llama 1/2, Mistral, and Gemma.

To clarify the pre-fix version behavior: in that version, tokenizers without unk_token_id would silently fall back to token 0, corrupting the draft KV cache for out-of-intersection prompt tokens and slightly deflating the reported acceptance rates. So the SmolLM2 and TinyLlama numbers above are likely conservative.

jmamou · 2026-03-29T11:55:52Z

Great work, @wan-danfeng
It would be awesome to see some additional experiments with different levels of overlap between the draft and target vocabularies.

Thanks for the suggestion! We actually have results across different vocabulary overlap ratios:

Draft → Target Vocab Overlap AcceptanceRate
Llama-3.2-1B → Llama-3.2-8B (homogeneous) 100% ~80%
Qwen2.5-0.5B → Qwen2.5-7B (TLI) 99.8% ~61%
SmolLM2-1.7B → Qwen2.5-7B (TLI) 79.8% ~25%
TinyLlama-1.1B → Qwen2.5-7B (TLI) 69.6% ~15%
Acceptance rate scales with vocabulary overlap — TLI is most effective for high-overlap pairs (≥90%).

▎Note: The SmolLM2 and TinyLlama results are from a pre-fix version — those tokenizers lack unk_token_id, which the current code now explicitly rejects. The algorithm is theoretically valid for any overlap ratio; The current implementation requires both tokenizers to have unk_token_id. Models without it (e.g., Llama 3, SmolLM2) cannot currently be used as draft models for universal_draft. Models that do have unk_token_id and are compatible include Qwen (all generations), Llama 1/2, Mistral, and Gemma.

To clarify the pre-fix version behavior: in that version, tokenizers without unk_token_id would silently fall back to token 0, corrupting the draft KV cache for out-of-intersection prompt tokens and slightly deflating the reported acceptance rates. So the SmolLM2 and TinyLlama numbers above are likely conservative.

@wan-danfeng
Do you have also speedup (comparing to vanilla - target only)?

benchislett

A large amount of complexity in this PR comes from implementing this as a new kind of proposer. As I see it, this could be implemented in a much simpler way by reusing / extending the draft_model.py class and enabling the feature either using a field on SpeculativeConfig, or just turning it on automatically when there is a vocab mismatch.

Please attempt some cleanup along these lines.

Overall, this is a nice PR and a good feature to have, thanks for the contribution!

mergify · 2026-03-30T19:15:03Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @WanDanfeng0802.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

jmamou · 2026-06-09T13:33:52Z

Thanks @jkallini! Good point about SLEM.

That said, I think the practical impact of UNK-mapping in TLI is fairly contained: it only hits the bonus token, and for high-overlap pairs (e.g. Qwen2.5 family, ~99.8% intersection) it almost never triggers. For lower-overlap pairs (e.g. SmolLM2 -> Qwen2.5, ~80%) it matters more, but those pairs already have low acceptance rates for other reasons.

As a follow-up PR, we can contribute the decode+retokenize path to vLLM. The cleanest approach might be to handle it as a special case just for the bonus token (which is always exactly 1 target token -> N draft tokens), sidestepping the batch-shape complexity for the speculative proposals themselves.

@jkallini
FYI I have opened a PR with SLEM implementation #45018

Co-authored-by: Benjamin Chislett <[email protected]> Signed-off-by: Wonderful <[email protected]>

…documentation for visibility Signed-off-by: wan-danfeng <[email protected]>

mergify · 2026-06-10T02:48:02Z

Hi @WanDanfeng0802, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Signed-off-by: wan-danfeng <[email protected]>

wan-danfeng · 2026-06-10T03:28:33Z

Looks much better. Added a few nitpicks. Please also add a brief paragraph to the vllm documentation for visibility.

Otherwise LGTM

@benchislett
Thanks for the feedback! I've added a dedicated section in speculative decoding README.md and draft_model.md about use_heterogeneous_vocab.
For the probabilistic draft sampling issue, I've added a validator that raises an error upfront when use_heterogeneous_vocab=True and draft_sample_method != "greedy", rather than silently dropping draft_probs at runtime. The runtime path now has an assert as a safety fallback in case the config validation is bypassed.

wan-danfeng · 2026-06-15T10:10:33Z

Hi @benchislett — thanks for the review! The latest CI run has two failing jobs, and both are unrelated to this PR. Quick breakdown:

1. test_spec_decode_logprobs[ngram-raw_logprobs] / [ngram-processed_logprobs] — AMD mi325 (ROCm) step

These exercise NgramProposer, which does not inherit SpecDecodeBaseProposer. Every change in this PR is gated behind use_heterogeneous_vocab , so the ngram code path is unchanged.
This test also has a documented history of ROCm flakiness (e.g. [ROCm][CI] Fix spec decode logprobs flakiness and parametrize tree attention backends #34599).

2. Kernels FusedMoE Layer Test (2 B200s) — MoE kernels, unrelated to speculative decoding.

Both of these same failures also show up on other unrelated PRs, for example #45466. An unrelated kernel PR reproducing both of these confirms they're environmental/flaky rather than introduced here.

Details

Could you re-run these two jobs — or, if you agree they're unrelated, proceed to merge? Happy to rebase onto latest main if that would help. Thanks again!

benchislett · 2026-06-16T01:23:08Z

Seems fine. I set both failing tests to retry and will ask for a force-merge if they do not pass

wan-danfeng · 2026-06-22T01:07:56Z

Seems fine. I set both failing tests to retry and will ask for a force-merge if they do not pass
@benchislett hi, the retries both failed again with the same failures. Whenever you get a chance, could you go ahead and request the force-merge? Appreciate the help!

TLI (Token-Level Intersection) will be contributed separately via PR vllm-project#38174. This PR now implements only SLEM (String-Level Exact Match) speculative decoding for cross-vocabulary draft/target pairs. Removed: - vocab_mapping.py and its tests (TLI-only) - All TLI proposer paths (logit constraining, ID mapping in sampling loop) Signed-off-by: jmamou <[email protected]>

Signed-off-by: Wonderful <[email protected]>

Sync 2026-07-03: picks up Model Runner V2 improvements (motivated by V2 viability reopening via PR#26). Notable: MRV2 scheduler req-slot bugfix (vllm-project#46974), MRV2 spec-decode int32 block-verify overflow fix (vllm-project#47383), MRV2 DSA-indexer prefill warmup for GLM-5.2/DSA (vllm-project#47285), V2-default for dense models (vllm-project#44443 — DSv4 is MoE so stays V1), DeepGEMM tag -> nv-dev a6b593d2 for SM120 (vllm-project#47304), TLI heterogeneous-vocab spec decode (vllm-project#38174), Delete PagedAttention (vllm-project#47361 — no impact, our sparse-MLA runner is a flashinfer symbol), Xqa decode kernels (vllm-project#43232). Conflict: vllm/v1/spec_decode/llm_base_proposer.py (our DSv4 MTP spec_step_idx routing vs upstream TLI) — resolved keeping BOTH: TLI's heterogeneous-vocab branch routed through our _compute_logits(spec_step_idx). Our SM12x stack preserved (persistent_topk, topk.cu, PR#26 padded-Q kernel, dspark, attention, R1 hardening all UNCHANGED); config/vllm.py DSv4->V1 default + DSpark env-control intact.

…es (TLI) (vllm-project#38174) Signed-off-by: wan-danfeng <[email protected]> Signed-off-by: Wonderful <[email protected]> Co-authored-by: Wan_DF <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]>

…es (TLI) (vllm-project#38174) Signed-off-by: wan-danfeng <[email protected]> Signed-off-by: Wonderful <[email protected]> Co-authored-by: Wan_DF <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]> Signed-off-by: mayuyuace <[email protected]>

…es (TLI) (vllm-project#38174) Signed-off-by: wan-danfeng <[email protected]> Signed-off-by: Wonderful <[email protected]> Co-authored-by: Wan_DF <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]> Co-authored-by: Benjamin Chislett <[email protected]>

wan-danfeng requested review from MatthewBonanni, ProExpertProg, WoosukKwon, benchislett, hmellor, houseroad, luccafong, mgoin, njhill, robertgshaw2-redhat, tlrmchlsmth, yewentao256 and youkaichao as code owners March 26, 2026 02:09

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wan-danfeng force-pushed the feat/universal-draft-tli branch from 64b60f4 to 7498075 Compare March 26, 2026 02:33

This was referenced Mar 26, 2026

[Feature] Support universal speculative decoding for heterogeneous vocabularies (TLI) vllm-project/vllm-ascend#7663

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[Feature] Heterogeneous-vocab (TLI) support for draft_model spec decode vllm-project/vllm-ascend#7664

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wan-danfeng force-pushed the feat/universal-draft-tli branch from 7498075 to 2a6ab57 Compare April 8, 2026 10:47

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wan-danfeng and others added 2 commits June 10, 2026 08:10

Update vllm/v1/worker/gpu_model_runner.py

03fa9d8

Co-authored-by: Benjamin Chislett <[email protected]> Signed-off-by: Wonderful <[email protected]>

fix: validate greedy draft sampling only when TLI is enabled and add …

4b82b77

…documentation for visibility Signed-off-by: wan-danfeng <[email protected]>

doc: pre-commit check

6b568aa

Signed-off-by: wan-danfeng <[email protected]>

Merge branch 'main' into feat/universal-draft-tli

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benchislett and others added 2 commits June 14, 2026 20:37

Merge branch 'main' into feat/universal-draft-tli

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Merge branch 'main' into feat/universal-draft-tli

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Merge branch 'main' into feat/universal-draft-tli

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Signed-off-by: Wonderful <[email protected]>

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Merge branch 'main' into feat/universal-draft-tli

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ywang96 merged commit 3af8789 into vllm-project:main Jul 2, 2026
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alityb mentioned this pull request Jul 2, 2026

[Bug]: SpeculativeConfig silently overrides the user's explicit speculative method (model_type and path-substring auto-detection) #47486

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1 task

goldsbd mentioned this pull request Jul 18, 2026

[Bug]: v0.25.x breaks Gemma4 MTP spec decode — drafter projection fed concat(target_hidden, drafter_hidden) [3840] instead of concat(target_hidden, target_embedding) [5632] #49008

Closed

1 task

		self.target_unk_token_id = getattr(target_tokenizer, "unk_token_id", None) or 0
		self.draft_unk_token_id = getattr(draft_tokenizer, "unk_token_id", None) or 0

-        self.target_unk_token_id = getattr(target_tokenizer, "unk_token_id", None) or 0
-        self.draft_unk_token_id = getattr(draft_tokenizer, "unk_token_id", None) or 0
+        self.target_unk_token_id = target_tokenizer.unk_token_id
+        if self.target_unk_token_id is None:
+            raise ValueError("Target tokenizer must have an `unk_token_id`.")
+        self.draft_unk_token_id = draft_tokenizer.unk_token_id
+        if self.draft_unk_token_id is None:
+            raise ValueError("Draft tokenizer must have an `unk_token_id`.")

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