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[https://nvbugs/5547434][fix] Fix Qwen2.5-VL device_path error by yechank-nvidia · Pull Request #8057 · NVIDIA/TensorRT-LLM · GitHub
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@yechank-nvidia yechank-nvidia commented Sep 29, 2025

Summary by CodeRabbit

  • Performance Improvements
    • Optimized loading for Qwen2-VL and Qwen2.5-VL models by reducing device-bound multimodal weight data to only essential components. This can lower GPU memory usage and modestly speed up initialization.
    • No changes to public APIs or model behavior; models load and run as before with improved efficiency.

Signed-off-by: yechank <161688079+yechank-nvidia@users.noreply.github.com>
@yechank-nvidia yechank-nvidia requested a review from a team as a code owner September 29, 2025 07:54
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PR_Github #20241 [ run ] triggered by Bot

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coderabbitai bot commented Sep 29, 2025

📝 Walkthrough

Walkthrough

Removed mrope-related entries from multimodal_data_device_paths in Qwen2VL and Qwen2_5_VL model implementations, returning only "multimodal_embedding" for device-bound multimodal data. No changes to method signatures, load_weights, or other control flow.

Changes

Cohort / File(s) Summary of Edits
Multimodal device path pruning
tensorrt_llm/_torch/models/modeling_qwen2vl.py
Updated multimodal_data_device_paths for Qwen2VLModel and Qwen2_5_VLModel to exclude mrope_config.mrope_position_ids, retaining only "multimodal_embedding". No other logic or API changes.

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❌ Failed checks (2 warnings)
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Description Check ⚠️ Warning The pull request has no description and does not follow the repository’s PR template, leaving out the summary, detailed issue and solution explanation, test coverage details, and confirmation of checklist items. Please add a description that follows the template, including a brief summary of the change and rationale, details on tests covering the new behavior, and confirmation of checklist items such as coding guidelines adherence and documentation updates.
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✅ Passed checks (1 passed)
Check name Status Explanation
Title Check ✅ Passed The pull request title correctly uses the NVBugs format and descriptive fix type, clearly indicating that it addresses a device_path error in the Qwen2.5-VL model; however it omits the corresponding change to the Qwen2-VL variant, so it only partially captures the full scope of the updates.
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Actionable comments posted: 0

🧹 Nitpick comments (2)
tensorrt_llm/_torch/models/modeling_qwen2vl.py (2)

861-877: Make host→device move explicit before assignment.

Today you rely on implicit cross-device copy when assigning CPU mrope_position_ids into a CUDA buffer. Make it explicit to avoid surprises and enable non_blocking copies.

Apply this minimal change:

@@
-                        mrope_position_ids = multimodal_param.multimodal_data[
-                            'mrope_config']['mrope_position_ids']
+                        mrope_position_ids = multimodal_param.multimodal_data[
+                            'mrope_config']['mrope_position_ids']
+                        # Explicit H2D to match buffer device and allow async copy
+                        mrope_position_ids = mrope_position_ids.to('cuda', non_blocking=True)
@@
-                        self.mrope_position_ids_padding_cuda[:, :, :
-                                                             mrope_position_ids.
-                                                             shape[
-                                                                 -1]] = mrope_position_ids
+                        self.mrope_position_ids_padding_cuda[:, :, : mrope_position_ids.shape[-1]] = mrope_position_ids

280-282: Python 3.8+ typing compatibility.

Use typing.Dict/Any (or add from future import annotations) and avoid lowercase any.

As per coding guidelines

-    def _preprocess(self, text: dict[str, any], mm_data: dict[str, any],
+    def _preprocess(self, text: Dict[str, Any], mm_data: Dict[str, Any],
                     mm_processor_kwargs: Dict[str, Any]):
@@
-            second_per_grid_ts: torch.Tensor = None) -> dict[str, torch.Tensor]:
+            second_per_grid_ts: torch.Tensor = None) -> Dict[str, torch.Tensor]:

Also applies to: 306-313

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  • tensorrt_llm/_torch/models/modeling_qwen2vl.py (2 hunks)
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🔇 Additional comments (3)
tensorrt_llm/_torch/models/modeling_qwen2vl.py (3)

1031-1037: SM≥100: device paths look right.

Keeping pixel values and grid_thw on device and dropping mrope paths is consistent with HF vision path. No issues spotted.

Please sanity-check image-only, video-only, and mixed prompts on SM 100+ to ensure there are no KeyErrors from the device mover.


1038-1041: SM<100: device paths simplified; OK.

For the TRT-LLM vision path, moving only pixel values (plus embeddings) and excluding mrope is appropriate.

Validate text-only prompts still work (mrope stays CPU, embeddings absent) and that video/image flows don’t regress.


977-983: Approve removal of mrope entries from multimodal_data_device_paths
mrope tensors are transferred via dedicated logic (e.g., in model_engine and functional layers), so they no longer need to be included in the generic device paths.

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PR_Github #20241 [ run ] completed with state SUCCESS
/LLM/release-1.1/L0_MergeRequest_PR pipeline #14 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.

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@yechank-nvidia yechank-nvidia self-assigned this Oct 10, 2025
@kaiyux kaiyux merged commit 3d3d494 into NVIDIA:release/1.1 Oct 13, 2025
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