[Model] Add NemotronLabsDiffusion support with tri-mode inference by sajadn · Pull Request #58387 · vllm-project/vllm

sajadn · 2026-09-23T15:33:59Z

Overview

Add native Nemotron Labs Diffusion 3B/8B text-model support: masked block diffusion and ordinary autoregressive inference. This builds on #48279 and is the foundation for #60287 and #60288; tracking issue #60283 covers the stack.

Claims

Load the Ministral backbone and untied diffusion head using checkpoint dimensions and RoPE settings.
Use causal prompt prefill/block commit and bidirectional masked denoising, with literal per-request temperatures.
Support ordinary AR through hf_overrides={"ar_mode": true}.
Defer linear speculation as requested in review. There are no changes to gpu/model_runner.py or the model-state interface in this version.
Use the Transformers v5 PreTrainedConfig name required by current main.

Validation

Rebased onto upstream main 1a3a7a8f01a8c1a3184151b8185072efda11daab.

CPU-only tests of the complete stacked branch: 93 passed, 9 skipped. This runs the sampling-parameter suite and the Nemotron model unit/configuration suite with CUDA disabled. The nine skips are CUDA/checkpoint-gated cases, not GPU validation.

The applicable full pre-commit suite passed, including type checking, test tethering and forbidden-import checks. GPU-serving and model-quality evaluations have not been rerun after this rebase. CUDA/checkpoint-gated tests must not be counted as passing GPU validation.

Historical public 3B results, before this rebase:

Mode	Correct / total	GSM8K accuracy
Confidence-based diffusion	1,155 / 1,319	87.57%
Ordinary AR	1,182 / 1,319	89.61%

Diffusion was evaluated at 83594fb280; AR at 0df15ea7e5. Configuration: official 3B checkpoint, BF16, H100, TP=1, Triton attention, eager backbone, temperature 0, concurrency 8, context 1,024 and maximum 750 generated tokens. Diffusion used block size 32 and confidence threshold 0.9. These are historical scores, not measurements of the new head.

Reproduction commands:

.venv/bin/python -m pytest --noconftest tests/test_sampling_params.py \
  tests/models/language/generation/test_nemotron_dllm.py -q

NEMOTRON_DLM_MODEL_PATH=/path/to/Nemotron-Labs-Diffusion-3B \
VLLM_USE_V2_MODEL_RUNNER=1 \
  .venv/bin/python -m pytest --confcutdir=tests/models/language/generation \
  tests/models/language/generation/test_nemotron_dllm.py -v

.venv/bin/pre-commit run --from-ref origin/main --to-ref HEAD

Details

The previous implementation, including linear speculation, is preserved at deferred/nemotron-linear-spec-20261009. A separate follow-up can restore its speculative sampler, configuration, prefill-logit hook and tests without holding up masked decisions.

The diffusion warmup guard remains because masked diffusion does not support structured-output grammar masks. Vision inputs are outside this change. Mixed-batch logprob bookkeeping and unsupported pipeline-parallel configurations are addressed by #60287, not claimed fixed in this foundation. The original fork's 3B diffusion result of 1,161/1,319 versus this implementation's historical 1,155/1,319 remains unexplained. No new speedup, long-context, quantization or multi-GPU accuracy claim is made.

AI assistance: OpenAI Codex assisted with the scope reduction, rebase, checks and review update. The submitting human must review the revised diff and confirm relevant validation before merge.

Pull Request Checklist

I used vLLM's /pr-checklist skill.
AI assistance was used during the creation of this PR.
Design Fit: Model-specific behavior is isolated; linear speculation is deferred.
Testing and Validation: Fresh CUDA serving and model evaluation remain outstanding; historical results and current checks are distinguished above.
Code Quality and Style: Applicable checks pass; model documentation matches the reduced scope.
Pull Request Contents: Scope, evidence, dependencies and limitations are stated.

mergify · 2026-09-23T15:34:36Z

Documentation preview: https://vllm--58387.org.readthedocs.build/en/58387/

github-actions · 2026-09-23T15:38:04Z

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mergify · 2026-09-23T15:40:23Z

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

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

Merge Linnan Wang's original Nemotron model commit from vllm-project#48279: 792ab07 linnanwang@792ab07 Preserve that exact commit, its authorship, and its sign-off as an ancestor. The original model port is already incorporated into the upstream adaptation; resolve the old port to the validated tree at a31615f. The resulting tree is byte-for-byte identical. This consolidates the follow-up AR, per-request temperature, linear-speculation, test, and documentation work from PR vllm-project#58387 into vllm-project#48279 without rewriting either branch's existing history. Official 3B GSM8K results: diffusion 1155/1319 (87.57%), AR 1182/1319 (89.61%), linear speculation 1179/1319 (89.39%). Pre-commit passes on the complete feature diff; model tests/evaluations are unchanged by this merge. Not ready to merge: batch-dependent diffusion logprobs and unsupported pipeline-parallel diffusion/linear execution remain unresolved review findings. The previously reported 3B diffusion accuracy difference also remains open. Assisted-by: OpenAI Codex Signed-off-by: Sajad Norouzi <snorouzi@nvidia.com>

mergify · 2026-09-23T15:56:00Z

⚠️ The sha of the head commit of this PR conflicts with #48279. Mergify cannot evaluate rules on this PR. Once #48279 is merged or closed, Mergify will resume processing this PR. ⚠️

mergify · 2026-09-23T16:26:03Z

Documentation preview: https://vllm--58387.org.readthedocs.build/en/58387/

mergify · 2026-09-23T16:26:03Z

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

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

claude

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

sajadn · 2026-09-23T18:46:35Z

@benchislett @LucasWilkinson can you please review this PR?

pst2154 · 2026-10-06T16:18:25Z

Codex assisting @pst2154 here. We are building directly on this full-model implementation instead of opening the separate scoring adapter described earlier.

The two follow-ups are now upstream and ready for review, both targeting main:

[Fix][Stacked] Preserve Nemotron diffusion scores and async sampling #60287 fixes diffusion logprob isolation, asynchronous sampler bookkeeping and unsupported configuration handling. It depends on this model PR.
[Feat][Stacked] Add Nemotron seeded decision reads to structured gateway #60288 depends on this PR and [Fix][Stacked] Preserve Nemotron diffusion scores and async sampling #60287, and adds seeded, single-forward Nemotron decision reads through the existing structured gateway for Choice, Noul and Score. It does not duplicate the native /v1/systemone endpoint work in [Feat] Structured decisions endpoint (/v1/systemone) #59299 or add ordering heuristics.

Because the dependencies are unmerged, each follow-up links its isolated change delta for review. Rebase and rerun affected validation as dependencies land; do not merge the follow-ups ahead of them. The earlier fork-targeted PRs were closed as superseded, with branches and commits retained.

Tracking all three upstream PRs: #60283.

Affected paths were validated on H100, A100 and B200; reproduction commands, results and limitations are in the PR descriptions. No new model-quality or end-to-end speedup claim is made. The original full-model implementation remains in this PR.

LucasWilkinson · 2026-10-06T21:07:47Z

            total_num_draft_tokens = int(num_draft_tokens_per_req.sum())
            total_num_logits = num_reqs * num_bonus_tokens + total_num_draft_tokens
            num_logits = num_draft_tokens_per_req + num_bonus_tokens
+            if self.model_state.needs_prefill_logits:


this is only needed for linear_spec mode right? in the interest of getting decision reads (#60283) in faster could we maybe just talk about linear_spec in a follow up? then we dont need to the model runner

Codex here, assisting @pst2154. Addressed: linear speculation is deferred from this PR. The speculative sampler, algorithm configuration, prefill-logit hook and associated tests are removed, so gpu/model_runner.py and the model-state interface are unchanged relative to main. Masked diffusion and ordinary AR remain supported. The diffusion warmup guard remains because it also applies to masked diffusion. The previous complete implementation is preserved at sajadn/vllm:deferred/nemotron-linear-spec-20261009 for a later follow-up.

This PR and #60287/#60288 are rebased onto current main. CPU checks of the complete stack: 93 passed, 9 CUDA/checkpoint-gated skips. Full local pre-commit and OpenAPI validation passed. Fresh CUDA serving/quality reruns remain outstanding; historical GPU results are not claimed as validation of this rebase.

mergify · 2026-10-09T10:10:53Z

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

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

Rebase the 3B/8B masked-diffusion and AR foundation onto upstream main. Defer the linear-speculation sampler and prefill-logit model-runner changes as requested by LucasWilkinson. Preserve the mixed-causality attention and diffusion warmup behavior. Based on the implementation by Sajad Norouzi <snorouzi@nvidia.com> in upstream PR vllm-project#58387. AI assistance: OpenAI Codex. Co-authored-by: Sajad Norouzi <snorouzi@nvidia.com> Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Alex Steiner <asteiner@nvidia.com>

Current upstream main rejects legacy Transformers imports. Use PreTrainedConfig in the local checkpoint config and model annotations without changing inference behavior. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Alex Steiner <asteiner@nvidia.com> Signed-off-by: "Alex Steiner" <asteiner@nvidia.com>

pst2154 · 2026-10-09T17:32:55Z

Codex update, assisting @pst2154: the branch now contains masked diffusion and ordinary AR support, with linear speculation deferred as requested in review. The old implementation is retained on https://github.com/sajadn/vllm/tree/deferred/nemotron-linear-spec-20261009. The model-runner and model-state-interface diffs are gone; the diffusion warmup guard remains. #60287 and #60288 have been restacked on this version.

Current CPU/unit validation of the full stack: 93 passed, 9 CUDA/checkpoint-gated skips. The full local pre-commit suite and OpenAPI validation passed. Fresh CUDA serving and quality validation remain outstanding; existing GPU scores are historical.

LucasWilkinson

A few more (AI assisted) review comments

LucasWilkinson · 2026-10-13T03:11:24Z

        grammar_output = None
-        if model_runner.is_last_pp_rank:
+        if model_runner.is_last_pp_rank and not model_runner.model_config.is_diffusion:
+            # Diffusion samplers do not support structured outputs.


Can we drop this? Diffusion prefills have no logit rows, so the bitmask is already skipped in sample(). Warmup works for me with this reverted.

LucasWilkinson · 2026-10-13T03:11:24Z

-        # supported. Penalties are ignored by the sampler with a warning.
+        # Nemotron supports ordinary per-request temperatures; other
+        # diffusion models retain their engine-level schedule.
+        per_request_temperature = (


Can we avoid checking the model name here? e.g. a flag on the model's hf config.

LucasWilkinson · 2026-10-13T03:11:24Z

    """Maximum number of denoising iterations per canvas block.
    If not set, read from the model's generation_config.json."""
+
+    temperature: float | None = Field(default=None, ge=0)


Can these move under a nested, model-specific model_options? DiffusionConfig would keep only the shared options, and each model validates its own section. Suggestion: sajadn#2

LucasWilkinson · 2026-10-13T03:11:24Z

    @cached_property
    def is_diffusion(self) -> bool:
        """Detect discrete diffusion (dLLM) models from HF config."""
+        # Nemotron can serve the same checkpoint through the ordinary AR path.


Can this move into NemotronLabsDiffusionConfig (e.g. canvas_length = None in AR mode) so is_diffusion stays generic?

LucasWilkinson · 2026-10-13T03:11:25Z

        vllm_config=SimpleNamespace(
-            scheduler_config=SimpleNamespace(async_scheduling=async_scheduling)
+            scheduler_config=SimpleNamespace(async_scheduling=async_scheduling),
+            diffusion_config=diffusion_config,


Still needed? _validate_params reads self.diffusion_config.

LucasWilkinson · 2026-10-13T03:11:25Z

+MAX_MODEL_LEN = 2048
+
+
+def _weights_available() -> bool:


MODEL_PATH defaults to a hub ID, so this is always False and the GPU tests always skip.

mergify Bot added documentation Improvements or additions to documentation new-model Requests to new models mrv2 Model Runner V2 specific labels Sep 23, 2026

mergify Bot added the needs-rebase label Sep 23, 2026

sajadn closed this Sep 23, 2026

sajadn reopened this Sep 23, 2026

sajadn force-pushed the nemotron-labs-diffusion-upstream branch from 7f6fa8a to 7d5ad9e Compare September 23, 2026 16:25

sajadn marked this pull request as ready for review September 23, 2026 17:19

sajadn requested review from AndreasKaratzas, DarkLight1337, NickLucche, ProExpertProg, WoosukKwon, houseroad, mgoin, njhill, robertgshaw2-redhat, tlrmchlsmth, tomeras91, yewentao256, youkaichao and ywang96 as code owners September 23, 2026 17:19

claude Bot reviewed Sep 23, 2026

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sajadn force-pushed the nemotron-labs-diffusion-upstream branch from 7d5ad9e to 943c956 Compare September 23, 2026 17:48

sajadn changed the title ~~[Model] Add Nemotron diffusion, AR, and greedy linear speculation~~ [Model] Add NemotronLabsDiffusion diffusion, AR, and greedy linear speculation Sep 23, 2026

sajadn changed the title ~~[Model] Add NemotronLabsDiffusion diffusion, AR, and greedy linear speculation~~ [Model] Add NemotronLabsDiffusion support for diffusion, AR, and greedy linear speculation inference Sep 23, 2026

mergify Bot removed the needs-rebase label Sep 23, 2026

sajadn changed the title ~~[Model] Add NemotronLabsDiffusion support for diffusion, AR, and greedy linear speculation inference~~ [Model] Add NemotronLabsDiffusion support with tri-mode inference Sep 23, 2026

sajadn mentioned this pull request Sep 24, 2026

feat(research): add Block JustGRPO and Fast Block JustGRPO NVIDIA-NeMo/RL#4258

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LucasWilkinson reviewed Oct 6, 2026

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mergify Bot added the needs-rebase label Oct 9, 2026

pst2154 and others added 2 commits October 9, 2026 10:24

pst2154 force-pushed the nemotron-labs-diffusion-upstream branch from 943c956 to 7dca058 Compare October 9, 2026 17:31

mergify Bot removed the needs-rebase label Oct 9, 2026

LucasWilkinson mentioned this pull request Oct 13, 2026

[Config] Nest model-specific diffusion options under DiffusionConfig.model_options sajadn/vllm#2

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LucasWilkinson reviewed Oct 13, 2026

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