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[BugFix] Fix 3D rope in transformers backend #35097

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Merged
hmellor merged 9 commits into from
Feb 27, 2026
Merged

[BugFix] Fix 3D rope in transformers backend #35097

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dce95b8
mrope-index
zucchini-nlp Feb 23, 2026
e75a62b
timestampls
zucchini-nlp Feb 23, 2026
3836c06
fix
zucchini-nlp Feb 24, 2026
c852324
bc for v4!
zucchini-nlp Feb 24, 2026
cc0d738
update comment
zucchini-nlp Feb 25, 2026
c6f5eae
Merge branch 'main' into qwen2-vl
hmellor Feb 25, 2026
4d1efbf
revert ig?
zucchini-nlp Feb 27, 2026
e7596be
Merge branch 'main' into qwen2-vl
hmellor Feb 27, 2026
108074e
revert this time
zucchini-nlp Feb 27, 2026
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4 changes: 2 additions & 2 deletions vllm/model_executor/models/glm4_1v.py
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@hmellor hmellor Feb 25, 2026

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cc @Isotr0py for this change

Original file line number Diff line number Diff line change
Expand Up @@ -990,7 +990,7 @@ def _get_video_second_idx_glm4v(
uniq.append(uniq[-1])
frame_indices = uniq

full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
full_second_idxs = [idx / video_fps for idx in frame_indices]

@Isotr0py Isotr0py Feb 25, 2026

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Is this change also necessary for GLM4.1V? I remember GLM4.1V use int for timestamp while GLM4.6V is float with decimal seconds:

<|end_of_image|>0<|end_of_video|>

<|end_of_image|>0.0 seconds<|end_of_video|>

@zucchini-nlp zucchini-nlp Feb 25, 2026

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in transformers we use the same timestamps format for all GLM models, so I am relying on it. Do you want to check-in with GLM authors, I can ask in slack?

@Isotr0py Isotr0py Feb 25, 2026

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cc @zRzRzRzRzRzRzR

timestamps_list = full_second_idxs[::2]
selected_timestamps = []
for idx in range(0, len(timestamps_list)):
Expand Down Expand Up @@ -1067,7 +1067,7 @@ def _get_video_second_idx_glm46v(
uniq.append(uniq[-1])

frame_indices = uniq
full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
full_second_idxs = [idx / video_fps for idx in frame_indices]
timestamps_list = full_second_idxs[::2]
selected_timestamps = []
for idx in range(len(timestamps_list)):
Expand Down
13 changes: 11 additions & 2 deletions vllm/model_executor/models/transformers/multimodal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,7 @@ def apply(
if "mm_token_type_ids" in processed_data
else "token_type_ids"
)
mm_token_type_ids = processed_data.pop(token_type_key)
mm_token_type_ids = processed_data.get(token_type_key)

# We can infer vLLM style placeholder from token type ids, if we split
# it for each input `mm_data`.
Expand Down Expand Up @@ -351,6 +351,7 @@ def embed_multimodal(self, **kwargs):

num_image_patches = kwargs.pop("num_image_patches")
kwargs.pop("token_type_ids", None) # used only in `forward`
kwargs.pop("mm_token_type_ids", None) # used only in `model.get_rope_index`

if pixel_values is not None:
# ROCm: Force math SDP backend for vision encoder to avoid accuracy issues
Expand Down Expand Up @@ -441,6 +442,7 @@ def get_mrope_input_positions(
{
"image_grid_thw",
"video_grid_thw",
"mm_token_type_ids",
"second_per_grid_ts",
"audio_feature_lengths",
"use_audio_in_video",
Expand All @@ -449,14 +451,15 @@ def get_mrope_input_positions(
if any(
v
for k, v in kwargs.items()
if k not in {"image_grid_thw", "video_grid_thw"}
if k not in {"image_grid_thw", "mm_token_type_ids"}
):
raise NotImplementedError(
"Transformers modeling backend only supports images."
)

image_grid_thw = kwargs.get("image_grid_thw", [])
video_grid_thw = kwargs.get("video_grid_thw", [])
mm_token_type_ids = kwargs.get("mm_token_type_ids")

image_grid_thw = (torch.stack if image_grid_thw else torch.tensor)(
image_grid_thw
Expand All @@ -465,10 +468,16 @@ def get_mrope_input_positions(
video_grid_thw
)

# In v4 this utility didn't accept any `kwargs`, thus we filter

@hmellor hmellor Feb 25, 2026

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I don't understand this comment.

Will mm_token_type_ids only exist in v5 and we keep kwargs empty otherwise because get_rope_index would error in v4 if we explicitly passed the None value?

kwargs = {}
if mm_token_type_ids:
kwargs["mm_token_type_ids"] = torch.cat(mm_token_type_ids)

mrope_positions, mrope_position_delta = self.model.get_rope_index(
input_ids=torch.tensor(input_tokens).unsqueeze(0),
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
**kwargs,
)

mrope_positions = mrope_positions[:, 0]
Expand Down