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[V0 Deprecation] Remove unused context_len and seq_len from M-RoPE (#28395)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2025-11-11 00:30:06 +08:00 committed by GitHub
parent f080a83511
commit d0e186c16f
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GPG Key ID: B5690EEEBB952194
13 changed files with 2 additions and 39 deletions
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3
vllm/model_executor/models/ernie45_vl.py
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@ -1435,8 +1435,6 @@ class Ernie4_5_VLMoeForConditionalGeneration(
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
context_len: int = 0,
seq_len: int | None = None,
second_per_grid_ts: list[float] | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
@ -1569,7 +1567,6 @@ class Ernie4_5_VLMoeForConditionalGeneration(
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1))
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
llm_positions = llm_positions[:, context_len:seq_len]
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/glm4_1v.py
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@ -1622,8 +1622,6 @@ class Glm4vForConditionalGeneration(
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1754,7 +1752,6 @@ class Glm4vForConditionalGeneration(
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1))
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
llm_positions = llm_positions[:, context_len:seq_len]
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/glm4v.py
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@ -625,8 +625,6 @@ class GLM4VForCausalLM(
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
context_len: int = 0,
seq_len: int | None = None,
second_per_grid_ts: list[float] | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
@ -758,7 +756,6 @@ class GLM4VForCausalLM(
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1))
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
llm_positions = llm_positions[:, context_len:seq_len]
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
return llm_positions, mrope_position_delta

4
vllm/model_executor/models/interfaces.py
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@ -995,8 +995,6 @@ class SupportsMRoPE(Protocol):
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1012,8 +1010,6 @@ class SupportsMRoPE(Protocol):
image_grid_thw: Image grid dimensions (t, h, w)
video_grid_thw: Video grid dimensions (t, h, w)
second_per_grid_ts: Seconds per grid timestep for videos
context_len: Context length
seq_len: Sequence length
audio_feature_lengths: Audio feature lengths for multimodal models
use_audio_in_video: Whether to use audio in video for interleaving

3
vllm/model_executor/models/keye.py
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@ -1630,8 +1630,6 @@ class KeyeForConditionalGeneration(
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
context_len: int = 0,
seq_len: int | None = None,
second_per_grid_ts: list[float] | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
@ -1759,6 +1757,5 @@ class KeyeForConditionalGeneration(
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/keye_vl1_5.py
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@ -600,8 +600,6 @@ class KeyeVL1_5ForConditionalGeneration(
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
context_len: int = 0,
seq_len: int | None = None,
second_per_grid_ts: list[float] | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
@ -729,6 +727,5 @@ class KeyeVL1_5ForConditionalGeneration(
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/paddleocr_vl.py
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@ -1179,8 +1179,6 @@ class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, Support
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
second_per_grid_ts: list[float],
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1293,7 +1291,6 @@ class PaddleOCRVLForConditionalGeneration(nn.Module, SupportsMultiModal, Support
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/qwen2_5_omni_thinker.py
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@ -927,8 +927,6 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1125,7 +1123,6 @@ class Qwen2_5OmniThinkerForConditionalGeneration(
mrope_position_delta = (
torch.cat(llm_pos_ids_list, dim=1).max() + 1 - len(src_item)
)
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/qwen2_5_vl.py
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@ -1118,8 +1118,6 @@ class Qwen2_5_VLForConditionalGeneration(
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
second_per_grid_ts: list[float],
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1232,7 +1230,6 @@ class Qwen2_5_VLForConditionalGeneration(
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

3
vllm/model_executor/models/qwen2_vl.py
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@ -1240,8 +1240,6 @@ class Qwen2VLForConditionalGeneration(
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -1360,7 +1358,6 @@ class Qwen2VLForConditionalGeneration(
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta

2
vllm/model_executor/models/qwen3_omni_moe_thinker.py
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@ -1417,8 +1417,6 @@ class Qwen3OmniMoeThinkerForConditionalGeneration(
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:

4
vllm/model_executor/models/qwen3_vl.py
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@ -1419,8 +1419,6 @@ class Qwen3VLForConditionalGeneration(
hf_config: PretrainedConfig,
image_grid_thw: list[list[int]] | torch.Tensor,
video_grid_thw: list[list[int]] | torch.Tensor,
context_len: int = 0,
seq_len: int | None = None,
second_per_grid_ts: list[float] | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
@ -1519,7 +1517,7 @@ class Qwen3VLForConditionalGeneration(
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
llm_positions = llm_positions[:, context_len:seq_len]
return llm_positions, mrope_position_delta
def get_language_model(self) -> torch.nn.Module:

4
vllm/model_executor/models/transformers/multimodal.py
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@ -371,8 +371,6 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
image_grid_thw: list[list[int]] | torch.Tensor | None,
video_grid_thw: list[list[int]] | torch.Tensor | None,
second_per_grid_ts: list[float] | None = None,
context_len: int = 0,
seq_len: int | None = None,
audio_feature_lengths: torch.Tensor | None = None,
use_audio_in_video: bool = False,
) -> tuple[torch.Tensor, int]:
@ -390,7 +388,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
video_grid_thw=video_grid_thw,
)
mrope_positions = mrope_positions[:, 0, context_len:seq_len]
mrope_positions = mrope_positions[:, 0]
mrope_position_delta = mrope_position_delta[0].item()
return mrope_positions, mrope_position_delta