[feat]: Create interface for model-specific M-RoPE (vllm-project#24194)

Signed-off-by: AzizCode92 <azizbenothman76@gmail.com>
Signed-off-by: Aziz <azizbenothman76@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

Author: 3 people

vllm/model_executor/models/__init__.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal,
-                         SupportsPP, SupportsTranscription, SupportsV0Only,
-                         has_inner_state, supports_lora, supports_multimodal,
-                         supports_pp, supports_transcription, supports_v0_only)
+from .interfaces import (HasInnerState, SupportsLoRA, SupportsMRoPE,
+                         SupportsMultiModal, SupportsPP, SupportsTranscription,
+                         SupportsV0Only, has_inner_state, supports_lora,
+                         supports_mrope, supports_multimodal, supports_pp,
+                         supports_transcription, supports_v0_only)
 from .interfaces_base import (VllmModelForPooling, VllmModelForTextGeneration,
                               is_pooling_model, is_text_generation_model)
 from .registry import ModelRegistry
@@ -21,6 +22,8 @@
     "supports_lora",
     "SupportsMultiModal",
     "supports_multimodal",
+    "SupportsMRoPE",
+    "supports_mrope",
     "SupportsPP",
     "supports_pp",
     "SupportsTranscription",

vllm/model_executor/models/interfaces.py

@@ -8,6 +8,7 @@
 import numpy as np
 import torch
 from torch import Tensor
+from transformers import PretrainedConfig
 from transformers.models.whisper.tokenization_whisper import LANGUAGES
 from typing_extensions import Self, TypeIs
 
@@ -852,3 +853,70 @@ def supports_eagle3(
     model: Union[type[object], object],
 ) -> Union[TypeIs[type[SupportsEagle3]], TypeIs[SupportsEagle3]]:
     return isinstance(model, SupportsEagle3)
+
+
+@runtime_checkable
+class SupportsMRoPE(Protocol):
+    """The interface required for all models that support M-RoPE."""
+
+    supports_mrope: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports M-RoPE.
+    
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """
+        Get M-RoPE input positions and delta value for this specific model.
+        
+        This method should be implemented by each model that supports M-RoPE
+        to provide model-specific logic for computing input positions.
+        
+        Args:
+            input_tokens: List of input token IDs
+            hf_config: HuggingFace model configuration
+            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
+            
+        Returns:
+            Tuple of (llm_positions, mrope_position_delta)
+            - llm_positions: Tensor of shape [3, num_tokens]
+                with T/H/W positions
+            - mrope_position_delta: Delta for position calculations
+        """
+        ...
+
+
+@overload
+def supports_mrope(model: type[object]) -> TypeIs[type[SupportsMRoPE]]:
+    ...
+
+
+@overload
+def supports_mrope(model: object) -> TypeIs[SupportsMRoPE]:
+    ...
+
+
+def supports_mrope(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsMRoPE]], TypeIs[SupportsMRoPE]]:
+    return isinstance(model, SupportsMRoPE)

vllm/model_executor/models/qwen2_vl.py

@@ -32,7 +32,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange, repeat
-from transformers import AutoConfig, BatchFeature
+from transformers import AutoConfig, BatchFeature, PretrainedConfig
 from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
                                           Qwen2VLProcessor)
 from transformers.models.qwen2_vl.configuration_qwen2_vl import (
@@ -73,7 +73,7 @@
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE,
                          SupportsMultiModal, SupportsPP)
 from .utils import (AutoWeightsLoader, WeightsMapper,
                     init_vllm_registered_model, maybe_prefix,
@@ -1096,7 +1096,7 @@ def _get_mm_fields_config(
                                         info=Qwen2VLProcessingInfo,
                                         dummy_inputs=Qwen2VLDummyInputsBuilder)
 class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                      SupportsLoRA, SupportsPP):
+                                      SupportsLoRA, SupportsPP, SupportsMRoPE):
 
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
@@ -1109,6 +1109,118 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             "model.": "language_model.model.",
         })
 
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """Get M-RoPE input positions for Qwen2-VL model."""
+        if image_grid_thw is None:
+            image_grid_thw = []
+        if video_grid_thw is None:
+            video_grid_thw = []
+        if second_per_grid_ts is None:
+            second_per_grid_ts = []
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(hf_config.vision_config,
+                                    "tokens_per_second", 1.0)
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            video_second_per_grid_t = 0.0
+            if remain_images > 0:
+                try:
+                    ed_image = input_tokens.index(image_token_id, st)
+                except ValueError:
+                    ed_image = len(input_tokens) + 1
+            else:
+                ed_image = len(input_tokens) + 1
+            if remain_videos > 0:
+                try:
+                    ed_video = input_tokens.index(video_token_id, st)
+                except ValueError:
+                    ed_video = len(input_tokens) + 1
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_second_per_grid_t = 1.0
+                if second_per_grid_ts:
+                    video_second_per_grid_t = second_per_grid_ts[video_index]
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
+                -1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
+                       tokens_per_second).long().flatten()
+
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        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
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):

vllm/v1/worker/gpu_model_runner.py

@@ -42,6 +42,7 @@
 from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
 from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
                                                    supports_eagle3,
+                                                   supports_mrope,
                                                    supports_transcription)
 from vllm.model_executor.models.interfaces_base import (
     VllmModelForPooling, is_pooling_model, is_text_generation_model)
@@ -730,16 +731,28 @@ def _init_mrope_positions(self, req_state: CachedRequestState):
             if mm_input.get("use_audio_in_video") is True:
                 use_audio_in_video = True
 
-        req_state.mrope_positions, req_state.mrope_position_delta = \
-            MRotaryEmbedding.get_input_positions_tensor(
-                req_state.prompt_token_ids,
-                hf_config=self.model_config.hf_config,
-                image_grid_thw=image_grid_thw,
-                video_grid_thw=video_grid_thw,
-                second_per_grid_ts=second_per_grid_ts,
-                audio_feature_lengths=audio_feature_lengths,
-                use_audio_in_video=use_audio_in_video,
-            )
+        if supports_mrope(self.model):
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                self.model.get_mrope_input_positions(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
+        else:
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                MRotaryEmbedding.get_input_positions_tensor(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
 
     def _extract_mm_kwargs(
         self,

vllm/worker/model_runner.py

@@ -41,7 +41,8 @@
                                                 get_sampler)
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
-from vllm.model_executor.models import supports_lora, supports_multimodal
+from vllm.model_executor.models import (supports_lora, supports_mrope,
+                                        supports_multimodal)
 from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs, MultiModalPlaceholderMap,
@@ -670,18 +671,33 @@ def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup,
                     inter_data.seq_ids[seq_idx]]
                 token_ids = seq_data.get_token_ids()
 
-                mrope_input_positions, mrope_position_delta = \
-                    MRotaryEmbedding.get_input_positions(
-                        token_ids,
-                        hf_config=hf_config,
-                        image_grid_thw=image_grid_thw,
-                        video_grid_thw=video_grid_thw,
-                        second_per_grid_ts=second_per_grid_ts,
-                        context_len=inter_data.context_lens[seq_idx],
-                        seq_len=inter_data.seq_lens[seq_idx],
-                        audio_feature_lengths=audio_feature_lengths,
-                        use_audio_in_video=use_audio_in_video,
-                    )
+                if supports_mrope(self.runner.model):
+                    mrope_input_positions, mrope_position_delta = \
+                        self.runner.model.get_mrope_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
+                    mrope_input_positions = mrope_input_positions.tolist()
+                else:
+                    mrope_input_positions, mrope_position_delta = \
+                        MRotaryEmbedding.get_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
 
                 seq_data.mrope_position_delta = mrope_position_delta
                 inter_data.mrope_input_positions[