[Bug] Mismatch between `get_multimodal_embedding` output and `PlaceholderRange`

[DarkLight1337]

In V1, we expect the output of get_multimodal_embedding to correspond to the PlaceholderRange, which is in turn constructed based on PromptUpdateDetails.features. However, the current V1 code doesn't validate this, causing the model to crash during inference when under high load (e.g. #14897, #14963).

From a quick look at the code, these models output embedding sizes which are inconsistent with the placeholder range:

• Fuyu (fixed by [Bugfix] Added embed_is_patch mask for fuyu model #15731)
• Gemma3 (fixed by [Bugfix] Re-enable Gemma3 for V1 #14980)
• Idefics3 (fixed by [Bugfix] embed_is_patch for Idefics3 #15696)
• InternVL-based models (fixed by [Bugfix] Fix embedding assignment for InternVL-based models #15086)
• MiniCPM-V (fixed by [Model] MiniCPM-V/O supports V1 #15487)

(Basically, any model that has image newline/column tokens after applying HF processor needs a mask to map image patch features to image embeddings, as described below.)

To fix this, we can follow these steps:

1. Update the multi-modal processor to output a mask to indicate which positions in the PlaceholderRange-aligned embeddings should the patch features (outputted by vision encoder) be assigned to. This mask can be called embed_is_patch.
2. Use scatter_patch_features to scatter the patch features into the image embedding tensor.
3. When merging multimodal embeddings, use select_patch_features to recover the patch features from the image embeddings. The number of patch features should correspond to the number of image tokens (which is a subset of the feature tokens in PromptUpdateDetails).

Follow-up work:

• [V1] Scatter and gather placeholders in the model runner #15712 (assigned to @DarkLight1337)
• Directly use individual token IDs instead of range of IDs (assigned to @ywang96 )