[Bugfix] Clean up and fix multi-modal processors (#13012)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/features/compatibility_matrix.md

@@ -297,7 +297,7 @@ Check the '✗' with links to see tracking issue for unsupported feature/hardwar
   * ✅
   * ✅
   * ?
-  * [✗](gh-issue:7968>)
+  * [✗](gh-issue:7968)
   * ?
   * ✅
   *

tests/models/decoder_only/language/test_models.py

@@ -26,6 +26,9 @@ from ...utils import check_logprobs_close
             "google/gemma-1.1-2b-it",  # gemma
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
+        pytest.param(
+            "THUDM/chatglm3-6b",  # ChatGLM (text-only)
+        ),
         pytest.param(
             "meta-llama/Llama-3.2-1B-Instruct",  # llama
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
@@ -43,6 +46,9 @@ from ...utils import check_logprobs_close
             "microsoft/phi-2",  # phi
             marks=[pytest.mark.core_model],
         ),
+        pytest.param(
+            "Qwen/Qwen-7B",  # qwen (text-only)
+        ),
         pytest.param(
             "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
             marks=[pytest.mark.core_model],
@@ -68,6 +74,10 @@ def test_models(
 ) -> None:
 
     with hf_runner(model, dtype=dtype) as hf_model:
+        if model.startswith("THUDM/chatglm3"):
+            hf_model.model.get_output_embeddings = lambda: \
+                hf_model.model.transformer.output_layer
+
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
 

tests/models/multimodal/processing/test_common.py

@@ -89,7 +89,7 @@ def _test_processing_correctness(
         mm_data = {
             k:
             [(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
-             for _ in range(rng.randint(limit))]
+             for _ in range(rng.randint(limit + 1))]
             for k, limit in limit_mm_per_prompt.items()
         }
 

tests/multimodal/utils.py

@@ -17,10 +17,7 @@ def random_video(
     min_wh: int,
     max_wh: int,
 ):
-    # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
     num_frames = rng.randint(min_frames, max_frames)
-    num_frames = (num_frames // 2) * 2
-
     w, h = rng.randint(min_wh, max_wh, size=(2, ))
     return rng.randint(0, 255, size=(num_frames, w, h, 3), dtype=np.uint8)
 

vllm/model_executor/models/chatglm.py

@@ -4,8 +4,8 @@
 # https://github.com/THUDM/CogAgent
 """Inference-only CogAgent model compatible with THUDM weights."""
 from argparse import Namespace
-from typing import (Iterable, List, Mapping, Optional, Sequence, Set, Tuple,
-                    TypedDict, Union)
+from typing import (Iterable, List, Mapping, Optional, Set, Tuple, TypedDict,
+                    Union)
 
 import torch
 from torch import nn
@@ -19,7 +19,6 @@ from transformers.tokenization_utils_base import TextInput
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
-from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -37,12 +36,10 @@ from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors
-from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.parse import MultiModalDataItems
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, BatchFeature,
-                                        BoundPromptReplacement,
                                         MultiModalFieldConfig,
-                                        PlaceholderFeaturesInfo,
                                         PromptReplacement)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
@@ -53,39 +50,6 @@ from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix, merge_multimodal_embeddings)
 
-logger = init_logger(__name__)
-
-IMAGE_TOKEN_ID = 151329
-
-
-def build_normalization_transform(image_size: int) -> transforms.Compose:
-    """
-    Build a normalization transform which can be applied to one or
-    more input images from which we want to extract visual features.
-
-    Args:
-        image_size: size of the image to be processed for visual embeddings.
-    
-    Returns:
-        Callable transform for normalizing and resizing one RGB image.
-    """
-
-    return transforms.Compose([
-        transforms.Resize(
-            (image_size, image_size),
-            interpolation=InterpolationMode.BICUBIC,
-        ),
-        transforms.ToTensor(),
-        transforms.Normalize(
-            (0.48145466, 0.4578275, 0.40821073),
-            (0.26862954, 0.26130258, 0.27577711),
-        ),
-    ])
-
-
-def calculate_image_placeholder(vision_config):
-    return (vision_config["image_size"] // vision_config["patch_size"] // 2)**2
-
 
 class GLMImagePixelInputs(TypedDict):
     pixel_values: torch.Tensor
@@ -109,9 +73,20 @@ class GLM4VProcessor:
         self.config = config
         self.tokenizer = tokenizer
 
-        if hasattr(self.config, "vision_config"):
-            self.image_transform = build_normalization_transform(
-                config.vision_config["image_size"])
+        if vision_config := getattr(config, "vision_config", None):
+            image_size = vision_config["image_size"]
+
+            self.image_transform = transforms.Compose([
+                transforms.Resize(
+                    (image_size, image_size),
+                    interpolation=InterpolationMode.BICUBIC,
+                ),
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=(0.48145466, 0.4578275, 0.40821073),
+                    std=(0.26862954, 0.26130258, 0.27577711),
+                ),
+            ])
         else:
             self.image_transform = None
 
@@ -150,9 +125,19 @@ class GLM4VProcessor:
 
 class GLM4VProcessingInfo(BaseProcessingInfo):
 
-    def __init__(self, ctx):
-        super().__init__(ctx)
-        self._pre_calculate()
+    def get_tokenizer(self):
+        tokenizer = self.ctx.tokenizer
+        assert isinstance(tokenizer, PreTrainedTokenizer)
+        return tokenizer
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(ChatGLMConfig)
+
+    def get_hf_processor(self) -> GLM4VProcessor:
+        return GLM4VProcessor(
+            self.get_hf_config(),
+            self.get_tokenizer(),
+        )
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": 1}
@@ -162,27 +147,21 @@ class GLM4VProcessingInfo(BaseProcessingInfo):
         seq_len: int,
         mm_counts: Mapping[str, int],
     ) -> Mapping[str, int]:
-
-        return {"image": self.image_token_num + 2}
-
-    def _pre_calculate(self):
-        hf_config = self.get_hf_config()
-        vision_config = hf_config.vision_config
-        self.image_token_num = calculate_image_placeholder(vision_config)
-        self.image_size = vision_config["image_size"]
+        return {"image": self.get_num_image_feature_tokens()}
 
     def get_num_image_tokens(self) -> int:
-        return self.image_token_num + 2
-
-    def get_image_size(self) -> ImageSize:
+        hf_config = self.get_hf_config()
+        if not (vision_config := getattr(hf_config, "vision_config", None)):
+            return 0
 
-        return ImageSize(height=self.image_size, width=self.image_size)
+        image_size = vision_config["image_size"]
+        patch_size = vision_config["patch_size"]
+        grid_length = image_size // patch_size // 2
+        return grid_length * grid_length
 
-    def get_hf_processor(self) -> GLM4VProcessor:
-        return GLM4VProcessor(
-            self.get_hf_config(),
-            self.get_tokenizer(),
-        )
+    def get_num_image_feature_tokens(self) -> int:
+        # EVA2CLIPModel has embeddings for boi and eoi tokens as well
+        return self.get_num_image_tokens() + 2
 
 
 class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
@@ -192,8 +171,12 @@ class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
         seq_len: int,
         mm_counts: Mapping[str, int],
     ) -> ProcessorInputs:
+        hf_config = self.info.get_hf_config()
+        if not (vision_config := getattr(hf_config, "vision_config", None)):
+            return ProcessorInputs(prompt_text="", mm_data={})
+
+        target_width = target_height = vision_config["image_size"]
         num_images = mm_counts.get("image", 0)
-        target_width, target_height = self.info.get_image_size()
 
         mm_data = {
             "image":
@@ -201,9 +184,11 @@ class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
                                    height=target_height,
                                    num_images=num_images)
         }
-        text = "<|begin_of_image|><|endoftext|><|end_of_image|>"
+
+        base_text = "<|begin_of_image|><|endoftext|><|end_of_image|>"
+
         return ProcessorInputs(
-            prompt_text=text,
+            prompt_text=base_text * num_images,
             mm_data=mm_data,
         )
 
@@ -223,47 +208,28 @@ class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):
         hf_processor_mm_kwargs: Mapping[str, object],
         out_mm_kwargs: MultiModalKwargs,
     ) -> list[PromptReplacement]:
+        hf_config = self.info.get_hf_config()
+        if not hasattr(hf_config, "vision_config"):
+            return []
+
+        boi_token_id = hf_config.boi_token_id
+        image_token_id = hf_config.pad_token_id
+        eoi_token_id = hf_config.eoi_token_id
 
         def get_replacement(item_idx: int):
-            image_tokens = self.info.image_token_num
-            return [IMAGE_TOKEN_ID] * image_tokens
+            num_image_tokens = self.info.get_num_image_tokens()
+            image_tokens = [image_token_id] * num_image_tokens
+
+            return [boi_token_id] + image_tokens + [eoi_token_id]
 
         return [
             PromptReplacement(
                 modality="image",
-                target=[IMAGE_TOKEN_ID],
+                target=[boi_token_id, image_token_id, eoi_token_id],
                 replacement=get_replacement,
             ),
         ]
 
-    def _apply_prompt_replacements(
-        self,
-        token_ids: list[int],
-        mm_prompt_repls: Mapping[str, Sequence[BoundPromptReplacement]],
-        mm_item_counts: Mapping[str, int],
-    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
-        token_ids, text, placeholders = super()._apply_prompt_replacements(
-            token_ids=token_ids,
-            mm_prompt_repls=mm_prompt_repls,
-            mm_item_counts=mm_item_counts,
-        )
-        hf_config = self.info.get_hf_config()
-        boi_token_id = hf_config.boi_token_id
-        eoi_token_id = hf_config.eoi_token_id
-        placeholders = {
-            modality: [
-                PlaceholderFeaturesInfo(
-                    modality=p.modality,
-                    item_idx=p.item_idx,
-                    start_idx=p.start_idx - 1,
-                    tokens=[boi_token_id] + p.tokens + [eoi_token_id],
-                ) for p in ps
-            ]
-            for modality, ps in placeholders.items()
-        }
-
-        return token_ids, text, placeholders
-
 
 class GLMAttention(nn.Module):
 
@@ -618,7 +584,7 @@ class ChatGLMModel(nn.Module):
                 multimodal_embeddings=multimodal_embeddings,
                 placeholder_token_id=[
                     self.config.boi_token_id,
-                    IMAGE_TOKEN_ID,
+                    self.config.pad_token_id,
                     self.config.eoi_token_id,
                 ],
             )

vllm/model_executor/models/qwen.py

@@ -63,18 +63,6 @@ from .utils import (flatten_bn, is_pp_missing_parameter,
 
 logger = init_logger(__name__)
 
-# NOTE: Qwen models have a few other special tags, e.g., ref, bbox, quad;
-# for the time being, these tags are not considered as special at encoding
-# time. This may change as VLLMs multimodal API changes in the future.
-IMG_START = "<img>"
-IMG_END = "</img>"
-IMG_PAD = "<imgpad>"
-# Image context is fixed at 256 for all images
-MAX_QWEN_IMG_TOKENS = 256
-# Image normalization params
-CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
-CLIP_STD = (0.26862954, 0.26130258, 0.27577711)
-
 
 class QwenImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -622,25 +610,6 @@ class QWenModel(nn.Module):
         return hidden_states
 
 
-def build_normalization_transform(image_size: int) -> transforms.Compose:
-    """
-    Build a normalization transform which can be applied to one or
-    more input images from which we want to extract visual features.
-
-    Args:
-        image_size: size of the image to be processed for visual embeddings.
-    
-    Returns:
-        Callable transform for normalizing and resizing one RGB image.
-    """
-    return transforms.Compose([
-        transforms.Resize((image_size, image_size),
-                          interpolation=InterpolationMode.BICUBIC),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=CLIP_MEAN, std=CLIP_STD),
-    ])
-
-
 @lru_cache(maxsize=1)
 def _get_tokenizer_without_image_pad(
         tokenizer: PreTrainedTokenizer) -> PreTrainedTokenizer:
@@ -716,16 +685,34 @@ class QWenVLProcessor:
         self.config = config
         self.tokenizer = tokenizer
 
-        if hasattr(self.config, "visual"):
-            self.image_transform = build_normalization_transform(
-                config.visual["image_size"])
+        if vision_config := getattr(self.config, "visual", None):
+            image_size = vision_config["image_size"]
+
+            self.image_transform = transforms.Compose([
+                transforms.Resize(
+                    (image_size, image_size),
+                    interpolation=InterpolationMode.BICUBIC,
+                ),
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=(0.48145466, 0.4578275, 0.40821073),
+                    std=(0.26862954, 0.26130258, 0.27577711),
+                ),
+            ])
         else:
             self.image_transform = None
 
-        special_tokens: dict[str,
-                             int] = tokenizer.special_tokens  # type: ignore
-        self.img_start_id = special_tokens[IMG_START]
-        self.img_end_id = special_tokens[IMG_END]
+    @property
+    def image_start_tag(self) -> str:
+        return self.tokenizer.image_start_tag  # type: ignore
+
+    @property
+    def image_end_tag(self) -> str:
+        return self.tokenizer.image_end_tag  # type: ignore
+
+    @property
+    def image_pad_tag(self) -> str:
+        return self.tokenizer.image_pad_tag  # type: ignore
 
     def __call__(
         self,
@@ -787,7 +774,14 @@ class QWenVLProcessingInfo(BaseProcessingInfo):
         return {"image": self.get_num_image_tokens()}
 
     def get_num_image_tokens(self) -> int:
-        return MAX_QWEN_IMG_TOKENS
+        hf_config = self.get_hf_config()
+        if not (vision_config := getattr(hf_config, "visual", None)):
+            return 0
+
+        image_size = vision_config["image_size"]
+        patch_size = vision_config["patch_size"]
+        grid_length = image_size // patch_size // 2
+        return grid_length * grid_length
 
 
 class QWenVLDummyInputsBuilder(BaseDummyInputsBuilder[QWenVLProcessingInfo]):
@@ -798,10 +792,12 @@ class QWenVLDummyInputsBuilder(BaseDummyInputsBuilder[QWenVLProcessingInfo]):
         mm_counts: Mapping[str, int],
     ) -> ProcessorInputs:
         hf_config = self.info.get_hf_config()
-        if not hasattr(hf_config, "visual"):
+        if not (vision_config := getattr(hf_config, "visual", None)):
             return ProcessorInputs(prompt_text="", mm_data={})
 
-        vision_config = hf_config.visual
+        processor = self.info.get_hf_processor()
+        img_start = processor.image_start_tag
+        img_end = processor.image_end_tag
 
         target_width = target_height = vision_config["image_size"]
         num_images = mm_counts.get("image", 0)
@@ -814,7 +810,7 @@ class QWenVLDummyInputsBuilder(BaseDummyInputsBuilder[QWenVLProcessingInfo]):
         }
 
         return ProcessorInputs(
-            prompt_text="".join(f"Picture {i}: {IMG_START}{IMG_END}\n"
+            prompt_text="".join(f"Picture {i}: {img_start}{img_end}\n"
                                 for i in range(1, num_images + 1)),
             mm_data=mm_data,
         )
@@ -869,13 +865,18 @@ class QWenVLMultiModalProcessor(BaseMultiModalProcessor[QWenVLProcessingInfo]):
         hf_processor_mm_kwargs: Mapping[str, object],
         out_mm_kwargs: MultiModalKwargs,
     ) -> list[PromptReplacement]:
+        hf_config = self.info.get_hf_config()
+        if not hasattr(hf_config, "visual"):
+            return []
+
         tokenizer = self.info.get_tokenizer()
         special_tokens: dict[str,
                              int] = tokenizer.special_tokens  # type: ignore
 
-        img_start_id = special_tokens[IMG_START]
-        img_end_id = special_tokens[IMG_END]
-        img_pad_id = special_tokens[IMG_PAD]
+        processor = self.info.get_hf_processor()
+        img_start_id = special_tokens[processor.image_start_tag]
+        img_end_id = special_tokens[processor.image_end_tag]
+        img_pad_id = special_tokens[processor.image_pad_tag]
 
         num_image_tokens = self.info.get_num_image_tokens()
         image_tokens = [img_pad_id] * num_image_tokens

vllm/model_executor/models/qwen2_vl.py

@@ -885,14 +885,10 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self._get_max_video_frames(seq_len -
                                                       max_image_tokens)
-        num_frames = min(max(max_total_frames // max(max_videos, 1), 1),
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
                                    _MAX_FRAMES_PER_VIDEO)
 
-        # Temporary workaround for https://github.com/huggingface/transformers/issues/35412
-        if num_frames > 1 and num_frames % 2 == 1:
-            num_frames += 1
-
-        return num_frames
+        return max(max_frames_per_video, 1)
 
     def get_max_video_tokens(self, seq_len: int) -> int:
         target_width, target_height = self.get_image_size_with_most_features()