[BugFix] Fix 3D rope in transformers backend (#35097)
Signed-off-by: raushan <raushan@huggingface.co> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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@@ -218,7 +218,7 @@ class MultiModalProcessor(BaseMultiModalProcessor[MultiModalProcessingInfo]):
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if "mm_token_type_ids" in processed_data
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else "token_type_ids"
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)
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mm_token_type_ids = processed_data.pop(token_type_key)
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mm_token_type_ids = processed_data.get(token_type_key)
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# We can infer vLLM style placeholder from token type ids, if we split
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# it for each input `mm_data`.
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@@ -353,6 +353,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
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num_image_patches = kwargs.pop("num_image_patches")
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kwargs.pop("token_type_ids", None) # used only in `forward`
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kwargs.pop("mm_token_type_ids", None) # used only in `model.get_rope_index`
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if pixel_values is not None:
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# ROCm: Force math SDP backend for vision encoder to avoid accuracy issues
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@@ -443,6 +444,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
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{
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"image_grid_thw",
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"video_grid_thw",
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"mm_token_type_ids",
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"second_per_grid_ts",
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"audio_feature_lengths",
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"use_audio_in_video",
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@@ -451,7 +453,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
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if any(
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v
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for k, v in kwargs.items()
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if k not in {"image_grid_thw", "video_grid_thw"}
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if k not in {"image_grid_thw", "mm_token_type_ids"}
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):
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raise NotImplementedError(
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"Transformers modeling backend only supports images."
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@@ -459,6 +461,7 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
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image_grid_thw = kwargs.get("image_grid_thw", [])
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video_grid_thw = kwargs.get("video_grid_thw", [])
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mm_token_type_ids = kwargs.get("mm_token_type_ids")
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image_grid_thw = (torch.stack if image_grid_thw else torch.tensor)(
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image_grid_thw
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@@ -467,10 +470,17 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
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video_grid_thw
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)
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# In v4 `get_rope_index` doesn't have wildcard `kwargs`, and
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# can't accept arbitrary args, even if its value is `None`
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kwargs = {}
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if mm_token_type_ids:
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kwargs["mm_token_type_ids"] = torch.cat(mm_token_type_ids)
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mrope_positions, mrope_position_delta = self.model.get_rope_index(
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input_ids=torch.tensor(input_tokens).unsqueeze(0),
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image_grid_thw=image_grid_thw,
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video_grid_thw=video_grid_thw,
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**kwargs,
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)
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mrope_positions = mrope_positions[:, 0]
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