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[Bugfix][Model] Support variable-dimensional M-RoPE - #55436
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WalkthroughThe change derives M-RoPE dimensions from model configuration, propagates them through GPU runtime state and buffers, and filters unsupported ChangesM-RoPE dimension support
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This change enables model-defined M-RoPE position dimensions and avoids unsupported multimodal RoPE arguments, with regression coverage for the affected configuration paths and four-dimensional runtime behavior. No concrete current-head merge-blocking risk remains. Sequence Diagram(s)sequenceDiagram
participant ModelConfig
participant GPUModelRunner
participant RopeState
participant MultimodalAdapter
participant get_rope_index
ModelConfig->>GPUModelRunner: provide mrope_num_dims
ModelConfig->>RopeState: provide mrope_num_dims
GPUModelRunner->>GPUModelRunner: allocate mrope_num_dims position buffer
MultimodalAdapter->>get_rope_index: inspect accepted keyword names
MultimodalAdapter->>get_rope_index: pass supported grid and token-type arguments
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Inline comments:
In `@vllm/config/model.py`:
- Around line 1854-1855: Update the mrope_num_dims logic near the hf_config
traversal to inspect the same thinker_config.text_config configuration used by
uses_mrope, including its four-entry mrope_section, so RopeState receives the
correct dimensions instead of defaulting to 3; add a regression test covering
this configuration and expected shape.
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tests/models/transformers/test_multimodal_mrope.pytests/transformers_utils/test_config.pytests/v1/worker/test_rope_state.pyvllm/config/model.pyvllm/model_executor/models/transformers/multimodal.pyvllm/v1/worker/gpu/mm/rope.pyvllm/v1/worker/gpu_model_runner.py
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Derive the number of M-RoPE position channels from the configured sections, including thinker text configs, instead of assuming three dimensions. Also filter optional grid arguments against each Transformers model's get_rope_index signature. Assisted-by: OpenAI GPT-5 Signed-off-by: Charles xu <charlesxu_mi@163.com>
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Thank you for the PR, I am going to supersede this with #56078 which unifies then the XD-RoPE path on the vLLM side as a way to achieve the same goal |
XD-RoPE was added for the native HunYuan-VL implementation, which has since moved to the Transformers modeling backend. Nothing implements `SupportsXDRoPE` any more, so `uses_xdrope_dim` can only return 0 and every XD-RoPE branch is unreachable; a config that did trip the detection would fail the `supports_xdrope` assertion rather than run. Transformers has collapsed the distinction upstream too: it renames `xdrope_section` to `mrope_section` and validates position ids against `len(mrope_section)`. The two vLLM paths only ever differed in whether decode adds a position delta, and M-RoPE with a model-supplied delta subsumes XD-RoPE, whose delta is structurally zero. Fold XD-RoPE into the M-RoPE path, keep `xdrope_section` as a legacy alias so no config loses support, and size the position buffers from the model's section count instead of a hardcoded 3. That count fixes HunyuanOCR (vllm-project#55140), whose four M-RoPE sections crashed `profile_run` against the 3-channel buffer. Also pass `get_rope_index` only the grid arguments its signature accepts: `HunYuanVLModel` takes no `video_grid_thw` and has no `**kwargs`. Supersedes vllm-project#55436. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01V52AokcYhV51yU6QTD62i2 Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Purpose
Fixes #55140.
HunyuanOCR exposes four M-RoPE sections, while the V1 runner allocated three position channels unconditionally. This change derives the number of M-RoPE dimensions from the model configuration, including nested thinker text configs, uses it for runner and
RopeStateallocations, and filters optionalget_rope_indexkeyword arguments against the Transformers model signature.The explicitly three-dimensional
fused_qk_rmsnorm_rope_gatekernel remains unchanged: it is a Qwen3Next-specific T/H/W optimization, while HunyuanOCR is registered through the Transformers multimodal backend and does not call that kernel.A duplicate search immediately before submission found no open or closed PR referencing #55140 or implementing this fix.
Implementation was assisted by OpenAI GPT-5. The author is responsible for reviewing and validating the change before it is marked ready for merge.
Test Plan
thinker_config.text_config.RopeStatedimensions.get_rope_indexaccepts image but not video grid arguments.The exact HunyuanOCR checkpoint was not available on the target host, and outbound Hugging Face downloads were unavailable, so an end-to-end model evaluation was not run. The regression tests exercise the reported four-channel configuration and strict method signature.
Test Result
5 passed, 16 warnings in 1.74sruff check: all checks passedruff format --check: 7 files already formattedEssential Elements of an Effective PR Description Checklist
BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing