[Model] Enable LoRA support for tower and connector in Cosmos3-Edge

[charitarthchugh]

Purpose

An operator serving nvidia/Cosmos3-Edge can attach a LoRA adapter to the language model, but not
to the vision tower or the connector, so visual feature extraction and its projection into
the language model's space are frozen. Adapting the model to a new visual domain — a different
imaging modality, an unusual camera, a specialised document type — currently means full
fine-tuning. Today the engine refuses to start at all: Cosmos3EdgeForConditionalGeneration does not support LoRA yet.

The machinery for this already exists; each model opts in by declaring SupportsLoRA and
implementing two token-count helpers that convert an LLM sequence token count into the row counts
entering the tower and the connector. Cosmos3-Edge implemented neither, although its
get_mm_mapping and packed_modules_mapping were already correct.

Getting those counts wrong fails silently rather than loudly, which is what shapes the
verification below: the LoRA metadata buffer is not cleared between forward passes, so any row an
undercount fails to cover reads a stale adapter index from the previous pass, and the operator
receives plausible output produced by the wrong adapter. Nothing raises. For that reason the row
counts here are measured against real tensors, not derived on paper.

Part of #31479, claimed there on 2026-08-07. That issue tracks this feature across many models and
stays open until they are all done, so this PR references it without a closing keyword — matching
every merged sibling in the series.

This PR:

• Declares SupportsLoRA on the model class.
• Implements get_num_mm_encoder_tokens and get_num_mm_connector_tokens.
• Fills in the LoRA cell for this model in docs/models/supported_models.md.

Technical Details

The two helpers are chained: get_num_mm_connector_tokens receives the encoder row count
returned by get_num_mm_encoder_tokens, not the LLM sequence token count.

LLM sequence tokens  --x merge²-->  encoder rows  --// merge²-->  connector rows

merge is spatial_merge_size. The connector body is the exact inverse of the encoder body, so
the round trip returns the original count.

The merge factor is read from self.visual.spatial_merge_size, which differs from Qwen2.5-VL
and Qwen3-VL, which read config.vision_config.spatial_merge_size. The reason is specific to this
checkpoint: Cosmos3EdgeConfig.__init__ assigns

self.vision_config.spatial_merge_size = self.projector_config.spatial_merge_size

(vllm/transformers_utils/configs/cosmos3_edge.py:126). Siglip2VisionConfig has no such field, so
the vision config's attribute is a synthesized alias; projector_config is where the checkpoint
stores it and what the projector is built from (cosmos3_edge.py:227). cosmos3_edge.py:597
already computes this same quantity the same way when splitting vision embeddings per item.

Both helpers read the factor once and the connector body is the exact inverse of the encoder body,
so the round trip cancels regardless of the factor's value. Integer division only, since the runtime
mapping multiplies a list by the result. No divisibility assertion: vllm/lora/model_manager.py:250
calls the encoder helper once at engine init with a whole-batch budget that carries no divisibility
guarantee. A non-positive input returns zero — the connector floor-divides, so a negative count
would otherwise yield a silently empty mapping rather than an error.

Duplicate check

Per AGENTS.md, re-run immediately before opening:

gh issue view 31479 --repo vllm-project/vllm --comments
gh pr list --repo vllm-project/vllm --state open --search "31479 in:body"
gh pr list --repo vllm-project/vllm --state all  --search "Cosmos3-Edge LoRA"
gh pr list --repo vllm-project/vllm --state open --search "tower connector lora"
gh pr list --repo vllm-project/vllm --state open --search "Cosmos3EdgeForConditionalGeneration"

No open or merged PR adds tower/connector LoRA to Cosmos3-Edge; the model-name and class-name
searches return nothing. The eleven open PRs in this family target other models. Three neighbours a
reviewer may find, none of which this duplicates:

• [Model] Add EVS video pruning support for Cosmos3-Edge #51221 — adds EVS video pruning to this same model. Different feature, overlapping files; a
  rebase may be needed depending on merge order. See Known limitations.
• [LoRA][Gemma4] Support vision tower LoRA #42662 — migrates the helper interface to get_mm_lora_token_counts. Its default
  implementation delegates to the two methods added here, so this model migrates unchanged.
• [Bugfix] Skip cache-served mm items in tower/connector LoRA mapping #47400 — fixes a different defect in the same mapping builder: cache-served items are
  counted although the encoder forward skips them. Independent of this change.

Test Plan

# CI-visible: mocked models, no weights, no GPU
.venv/bin/python -m pytest tests/v1/worker/test_gpu_model_runner.py -k "cosmos3_edge or lora"

# Processing regression. tests/models/registry.py marks this model
# is_available_online=False, so the file is skipped unless given a local path:
SNAPSHOT=$(.venv/bin/python -c \
  "from huggingface_hub import snapshot_download; print(snapshot_download('nvidia/Cosmos3-Edge'))")
COSMOS3_EDGE_MODEL_PATH="$SNAPSHOT" .venv/bin/python -m pytest \
    tests/models/multimodal/processing/test_cosmos3_edge.py

.venv/bin/python -m pytest tests/models/test_registry.py
pre-commit run --all-files

End-to-end runs: single RTX 3090 (24 GB), CUDA 13.0, at this PR's base commit.

Test Result

Before / after. Starting the engine with --enable-lora --enable-tower-connector-lora:

# before — engine refuses to start
ValueError: Cosmos3EdgeForConditionalGeneration does not support LoRA yet.
$ echo $?
1

# after — engine starts with the feature active
WARNING [model_manager.py:242] LoRA for the tower and connector of multimodal models is
experimental and may contain bugs. Please report any related issues on GitHub if you encounter them.
$ echo $?
0

Helper output vs. real tensor rows, with --enable-tower-connector-lora. Forward hooks on
visual.encoder.encoder.layers.0.self_attn.qkv_proj (tower) and visual.projector.linear_fc1
(connector):

input	LLM tokens	encoder rows observed	helper	connector rows observed	helper
image	1107	4428	4428	1107	1107
video, 4 frames	4180	16720	16720	4180	4180

The same counts hold with an adapter attached — applying LoRA does not perturb them.

Extended on CPU across a range of image sizes and video lengths: encoder rows,
connector rows and the round trip are exact in every case.

Script that produces the table above

import os

os.environ.setdefault("VLLM_ENABLE_V1_MULTIPROCESSING", "0")

from types import SimpleNamespace

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.model_executor.models.cosmos3_edge import (
    Cosmos3EdgeForConditionalGeneration as C3E,
)
from vllm.transformers_utils.config import get_config

MODEL = "nvidia/Cosmos3-Edge"
TOWER = "visual.encoder.encoder.layers.0.self_attn.qkv_proj"
CONNECTOR = "visual.projector.linear_fc1"

seen: dict[str, list[int]] = {"tower": [], "connector": []}


def install_hooks(model):
    def record(key):
        def hook(_module, args):
            seen[key].append(args[0].shape[-2])

        return hook

    for name, module in model.named_modules():
        if name == TOWER:
            module.register_forward_pre_hook(record("tower"))
        elif name == CONNECTOR:
            module.register_forward_pre_hook(record("connector"))


llm = LLM(
    model=MODEL,
    max_model_len=16384,
    limit_mm_per_prompt={"image": 1, "video": 1},
    gpu_memory_utilization=0.85,
    enforce_eager=True,
    allowed_local_media_path="/",
    mm_processor_cache_gb=0,
)
llm.apply_model(install_hooks)

config = get_config(MODEL, trust_remote_code=False)
tokenizer = llm.get_tokenizer()
start = tokenizer.decode([config.vision_start_token_id])
end = tokenizer.decode([config.vision_end_token_id])
image_pad = tokenizer.decode([config.image_token_id])
params = SamplingParams(temperature=0.0, max_tokens=1)


def check(label, output, token_id):
    llm_tokens = output[0].prompt_token_ids.count(token_id)
    encoder_rows, connector_rows = seen["tower"][-1], seen["connector"][-1]
    stub = SimpleNamespace(
        visual=SimpleNamespace(
            spatial_merge_size=config.projector_config.spatial_merge_size
        )
    )
    predicted_encoder = C3E.get_num_mm_encoder_tokens(stub, llm_tokens)
    predicted_connector = C3E.get_num_mm_connector_tokens(stub, predicted_encoder)
    print(
        f"{label:<8} llm_tokens={llm_tokens}  "
        f"encoder observed={encoder_rows} helper={predicted_encoder} "
        f"{'OK' if encoder_rows == predicted_encoder else 'MISMATCH'}  "
        f"connector observed={connector_rows} helper={predicted_connector} "
        f"{'OK' if connector_rows == predicted_connector else 'MISMATCH'}"
    )


check(
    "image",
    llm.generate(
        {
            "prompt": f"<|im_start|>user\n{start}{image_pad}{end}Describe."
            f"<|im_end|>\n<|im_start|>assistant\n",
            "multi_modal_data": {"image": ImageAsset("stop_sign").pil_image},
        },
        params,
    ),
    config.image_token_id,
)

video = VideoAsset(name="baby_reading", num_frames=4)
check(
    "video",
    llm.chat(
        [
            {
                "role": "user",
                "content": [
                    {
                        "type": "video_url",
                        "video_url": {"url": f"file://{video.video_path}"},
                    },
                    {"type": "text", "text": "Describe."},
                ],
            }
        ],
        params,
    ),
    config.video_token_id,
)

The video prompt goes through the chat template. A hand-built video placeholder omits the
per-frame timestamp tokens, which silently misplaces the embeddings and would invalidate the
comparison.

The adapter is applied, not silently skipped. Serving with --enable-tower-connector-lora and
a rank-8 adapter targeting one tower layer and the connector: the engine emits the
tower/connector-LoRA experimental warning at startup, the Punica shrink and expand kernels are
invoked during the adapter request, and the output tensors of the wrapped modules differ from the
no-adapter run for images and for video alike. This shows the adapter path is reached; it is not
a model-quality measurement.

Unit and regression tests — all pass:

tests/v1/worker/test_gpu_model_runner.py -k "cosmos3_edge or lora"
tests/models/multimodal/processing/test_cosmos3_edge.py
tests/models/test_registry.py
pre-commit run --all-files

Known limitations

• Video pruning (EVS) is not addressed. This model does not implement
  SupportsMultiModalPruning at HEAD, so the interaction is unreachable here. On models that do
  support pruning, PlaceholderRange.get_num_embeds() returns a post-prune count while the tower
  has already processed the unpruned set, so these helpers undercount by roughly 1/(1-q), bounded
  by the frame count. That follows from the shared call site at vllm/v1/worker/gpu/mm/lora.py:45
  rather than from any one model, and already applies to Qwen2_5_VLForConditionalGeneration,
  Qwen3VLForConditionalGeneration and Qwen3VLMoeForConditionalGeneration. Images are unaffected.
  This PR neither introduces nor widens it.
• visual.encoder.embeddings.patch_embedding is a plain nn.Linear, so LoRA skips it. It lives
  in lfm2_siglip2.py, shared with two other models. Pre-existing, out of scope, unchanged here.

Acceptance criteria

• New tests added that exercise the changed code path
• Existing test suite passes locally (model-runner, multi-modal processing, model registry)
• Follows the project style guide — pre-commit run --all-files clean, including mypy
• No breaking changes: the change is additive, and no existing behaviour is modified
• Documentation updated (docs/models/supported_models.md)
• Duplicate-work checks re-run immediately before opening, per AGENTS.md
• AI assistance disclosed

AI assistance

AI assistance was used. I reviewed every changed line, and the results above are from real runs on
the stated hardware.

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

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Documentation preview: https://vllm--51949.org.readthedocs.build/en/51949/