[Bugfix] Fix multi-modal processors for transformers 4.48

vllm/model_executor/models/llava.py

@@ -5,9 +5,11 @@
 
 import torch
 import torch.nn as nn
+from packaging.version import Version
 from transformers import (BatchFeature, CLIPVisionConfig, LlavaConfig,
                           PixtralVisionConfig, PretrainedConfig,
                           SiglipVisionConfig)
+from transformers import __version__ as TRANSFORMERS_VERSION
 from transformers.models.llava import LlavaProcessor
 from transformers.models.pixtral import PixtralProcessor
 
@@ -716,6 +718,27 @@ def load_weights(self, weights: Iterable[Tuple[str,
         return loader.load_weights(weights)
 
 
+class MantisProcessingInfo(LlavaProcessingInfo):
+
+    def get_hf_processor(self):
+        hf_config = self.get_hf_config()
+        vision_info = self.get_vision_encoder_info()
+
+        if Version(TRANSFORMERS_VERSION) < Version("4.48"):
+            # BUG: num_additional_image_tokens = 0 but treated as 1,
+            # so we set vision_feature_select_strategy to None to offset this
+            vision_feature_select_strategy = None
+        else:
+            # FIXED: https://github.com/huggingface/transformers/pull/33424/files#diff-6a37acc21efcadaae622b079b2712a131131448ff64262bd219aa346aeec38faL150
+            vision_feature_select_strategy = hf_config.vision_feature_select_strategy  # noqa: E501
+
+        return self.ctx.get_hf_processor(
+            LlavaProcessor,
+            patch_size=vision_info.get_patch_size(),
+            vision_feature_select_strategy=vision_feature_select_strategy,
+        )
+
+
 class MantisMultiModalProcessor(LlavaMultiModalProcessor):
 
     def apply(
@@ -794,7 +817,7 @@ def get_replacement_mantis(item_idx: int):
 # To use this model, please use
 # `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'`
 @MULTIMODAL_REGISTRY.register_processor(MantisMultiModalProcessor,
-                                        info=LlavaProcessingInfo,
+                                        info=MantisProcessingInfo,
                                         dummy_inputs=LlavaDummyInputsBuilder)
 class MantisForConditionalGeneration(LlavaForConditionalGeneration):
     pass

vllm/model_executor/models/qwen2_audio.py

@@ -36,8 +36,9 @@
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
-                                    NestedTensors)
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalInputsV2, MultiModalKwargs,
+                                    NestedTensors, PlaceholderRange)
 from vllm.multimodal.parse import (AudioProcessorItems, MultiModalDataItems,
                                    MultiModalDataParser)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
@@ -153,29 +154,24 @@ def _call_hf_processor(
         mm_data: Mapping[str, object],
         mm_kwargs: Mapping[str, Any],
     ) -> BatchFeature:
-        mm_data = dict(mm_data)
-        audios = mm_data.pop("audios", [])
-
-        if audios:
-            mm_data["audios"] = audios
-
-            feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
-            mm_kwargs = dict(
-                **mm_kwargs,
-                sampling_rate=feature_extractor.sampling_rate,
-            )
-        else:
-            # NOTE: WhisperFeatureExtractor cannot handle empty list of audios
-            pass
+        # Text-only input not supported in composite processor
+        if not mm_data or not mm_data.get("audios", []):
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        feature_extractor = self.info.get_feature_extractor(**mm_kwargs)
+        mm_kwargs = dict(
+            **mm_kwargs,
+            sampling_rate=feature_extractor.sampling_rate,
+        )
 
-        processed_outputs = super()._call_hf_processor(
+        return super()._call_hf_processor(
             prompt=prompt,
             mm_data=mm_data,
             mm_kwargs=mm_kwargs,
         )
 
-        return processed_outputs
-
     def _get_mm_fields_config(
         self,
         hf_inputs: BatchFeature,
@@ -192,8 +188,14 @@ def _get_prompt_replacements(
         hf_processor_mm_kwargs: Mapping[str, object],
         out_mm_kwargs: MultiModalKwargs,
     ) -> list[PromptReplacement]:
-        hf_config = self.info.get_hf_config()
-        placeholder = hf_config.audio_token_index
+        processor = self.info.get_hf_processor()
+
+        # Use getattr with default to be compatible with transformers<4.48
+        audio_token = getattr(processor, "audio_token", "<|AUDIO|>")
+        audio_bos_token = getattr(processor, "audio_bos_token",
+                                  "<|audio_bos|>")
+        audio_eos_token = getattr(processor, "audio_eos_token",
+                                  "<|audio_eos|>")
 
         feature_attention_mask = out_mm_kwargs.get("feature_attention_mask")
         if feature_attention_mask is None:
@@ -214,12 +216,16 @@ def get_replacement_qwen2_audio(item_idx: int):
                     f"The audio {audio} (len={len(audio)}) is too short "
                     "to be represented inside the model")
 
-            return [placeholder] * num_placeholders
+            return "".join([
+                audio_bos_token,
+                audio_token * num_placeholders,
+                audio_eos_token,
+            ])
 
         return [
             PromptReplacement(
                 modality="audio",
-                target=[placeholder],
+                target=audio_token,
                 replacement=get_replacement_qwen2_audio,
             )
         ]
@@ -234,6 +240,26 @@ def _always_apply_prompt_replacements(self) -> bool:
         # tokens than the number of audio items)
         return not hasattr(self.info.get_hf_processor(), "audio_token")
 
+    def apply(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> MultiModalInputsV2:
+        result = super().apply(prompt, mm_data, hf_processor_mm_kwargs)
+
+        # Only <|AUDIO|> tokens should be considered as placeholders,
+        # so we ignore the audio_bos_token and audio_eos_token
+        result["mm_placeholders"] = {
+            modality: [
+                PlaceholderRange(offset=p["offset"] + 1,
+                                 length=p["length"] - 2) for p in ps
+            ]
+            for modality, ps in result["mm_placeholders"].items()
+        }
+
+        return result
+
 
 @MULTIMODAL_REGISTRY.register_processor(
     Qwen2AudioMultiModalProcessor,

vllm/model_executor/models/ultravox.py

@@ -137,7 +137,7 @@ def _call_hf_processor(
         mm_kwargs: Mapping[str, object],
     ) -> BatchFeature:
         # Text-only input not supported in composite processor
-        if not mm_data:
+        if not mm_data or not mm_data.get("audios", []):
             prompt_ids = self.info.get_tokenizer().encode(prompt)
             prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids)
             return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
@@ -146,13 +146,6 @@ def _call_hf_processor(
         audios = mm_data.pop("audios", [])
         assert isinstance(audios, list)
 
-        if not audios:
-            return super()._call_hf_processor(
-                prompt=prompt,
-                mm_data=mm_data,
-                mm_kwargs=mm_kwargs,
-            )
-
         feature_extractor = self.info.get_feature_extractor()
         mm_kwargs = dict(
             **mm_kwargs,

vllm/transformers_utils/config.py

@@ -22,10 +22,10 @@
 from vllm.logger import init_logger
 # yapf conflicts with isort for this block
 # yapf: disable
-from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
-                                             DbrxConfig, DeepseekVLV2Config,
-                                             EAGLEConfig, ExaoneConfig,
-                                             H2OVLChatConfig,
+from vllm.transformers_utils.configs import (AriaConfig, ChatGLMConfig,
+                                             Cohere2Config, DbrxConfig,
+                                             DeepseekVLV2Config, EAGLEConfig,
+                                             ExaoneConfig, H2OVLChatConfig,
                                              InternVLChatConfig, JAISConfig,
                                              MedusaConfig, MllamaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
@@ -52,6 +52,7 @@
 }
 
 _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
+    "aria": AriaConfig,
     "chatglm": ChatGLMConfig,
     "cohere2": Cohere2Config,
     "dbrx": DbrxConfig,

vllm/transformers_utils/configs/__init__.py

@@ -1,3 +1,4 @@
+from vllm.transformers_utils.configs.aria import AriaConfig
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.cohere2 import Cohere2Config
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
@@ -23,6 +24,7 @@
 from vllm.transformers_utils.configs.ultravox import UltravoxConfig
 
 __all__ = [
+    "AriaConfig",
     "ChatGLMConfig",
     "Cohere2Config",
     "DbrxConfig",

vllm/transformers_utils/configs/aria.py

@@ -1,7 +1,32 @@
+# Copyright 2024 Rhymes AI. All rights reserved.
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from typing import Mapping
+
+from transformers import PretrainedConfig
 from transformers.models.idefics2.configuration_idefics2 import (
     Idefics2VisionConfig)
 from transformers.models.llama.configuration_llama import LlamaConfig
 
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
 
 class AriaVisionConfig(Idefics2VisionConfig):
     model_type = "aria_vision_model"
@@ -45,3 +70,96 @@ def __init__(
         self.moe_num_experts = moe_num_experts
         self.moe_topk = moe_topk
         self.moe_num_shared_experts = moe_num_shared_experts
+
+
+class AriaConfig(PretrainedConfig):
+    """
+    Configuration class for Aria model.
+    This class handles the configuration for both vision and text components of
+    the Aria model,
+    as well as additional parameters for image token handling and projector
+    mapping.
+
+    Args:
+        vision_config (AriaVisionConfig or dict): Configuration for the vision
+            component.
+        text_config (AriaMoELMConfig or dict): Configuration for the text
+            component.
+        projector_patch_to_query_dict (dict): Mapping of patch sizes to query
+            dimensions.
+        ignore_index (int): Index to ignore in loss calculation.
+        image_token_index (int): Index used to represent image tokens.
+        **kwargs: Additional keyword arguments passed to the parent class.
+    Attributes:
+        model_type (str): Type of the model, set to "aria".
+        is_composition (bool): Whether the model is a composition of multiple
+            components.
+        ignore_index (int): Index to ignore in loss calculation.
+        image_token_index (int): Index used to represent image tokens.
+        projector_patch_to_query_dict (dict): Mapping of patch sizes to query
+            dimensions.
+        vision_config (AriaVisionConfig): Configuration for the vision
+            component.
+        text_config (AriaMoELMConfig): Configuration for the text component.
+    """
+
+    model_type = "aria"
+    is_composition = False
+
+    def __init__(
+        self,
+        vision_config: AriaVisionConfig = AriaVisionConfig(),  # noqa: B008
+        text_config: AriaMoELMConfig = AriaMoELMConfig(),  # noqa: B008
+        projector_patch_to_query_dict: Mapping[int, int] = {
+            1225: 128,
+            4900: 256,
+        },
+        ignore_index=-100,
+        image_token_index=32000,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.ignore_index = ignore_index
+        self.image_token_index = image_token_index
+        self.tie_word_embeddings = tie_word_embeddings
+        attn_implementation = kwargs.pop("attn_implementation", None)
+
+        # Set the default attention implementation to flash_attention_2 if not
+        # specified
+        self._attn_implementation = ("flash_attention_2"
+                                     if attn_implementation is None else
+                                     attn_implementation)
+
+        # Convert the keys and values of projector_patch_to_query_dict to
+        # integers
+        # This ensures consistency even if they were provided as strings
+        self.projector_patch_to_query_dict = {
+            int(k): int(v)
+            for k, v in projector_patch_to_query_dict.items()
+        }
+
+        if isinstance(vision_config, dict) and "model_type" in vision_config:
+            vision_config = AriaVisionConfig(**vision_config)
+            if attn_implementation is None:
+                vision_attn_implementation = "flash_attention_2"
+            elif attn_implementation == "sdpa":
+                logger.warning("SDPA is not supported for vit, using "
+                               "flash_attention_2 instead")
+                vision_attn_implementation = "flash_attention_2"
+            else:
+                vision_attn_implementation = attn_implementation
+            vision_config._attn_implementation = vision_attn_implementation
+
+        self.vision_config = vision_config
+
+        if isinstance(text_config, dict) and "model_type" in text_config:
+            text_attn_implementation = ("sdpa" if attn_implementation is None
+                                        else attn_implementation)
+            text_config = AriaMoELMConfig(**text_config)
+            text_config._attn_implementation = text_attn_implementation
+
+        self.text_config = text_config
+
+        # This is needed for the static kv cache
+        self.num_hidden_layers = self.text_config.num_hidden_layers