[BugFix] Propagate 'trust_remote_code' setting in internvl and minicpmv (vllm-project#8250)

Signed-off-by: Amit Garg <mitgarg17495@gmail.com>

Author: zifeitong

vllm/model_executor/models/internvl.py

@@ -230,8 +230,9 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
     else:
         raise TypeError(f"Invalid image type: {type(image_data)}")
 
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
 
     prompt = llm_inputs.get("prompt")
     prompt_token_ids = llm_inputs["prompt_token_ids"]
@@ -278,8 +279,9 @@ def input_mapper_for_internvl(ctx: InputContext, data: object):
                                   use_thumbnail=use_thumbnail) for img in data
         ]
     model_config = ctx.model_config
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
     image_token_id = tokenizer.encode(IMG_CONTEXT,
                                       add_special_tokens=False,
                                       return_tensors="pt")[0]
@@ -298,8 +300,9 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int,
     model_config = ctx.model_config
     hf_config = ctx.get_hf_config()
     vision_config = hf_config.vision_config
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
 
     seq_data = dummy_seq_data_for_clip(
         vision_config,

vllm/model_executor/models/minicpmv.py

@@ -33,6 +33,7 @@
 from torch import nn
 from torch.nn.init import trunc_normal_
 from transformers import PretrainedConfig
+from typing_extensions import NotRequired
 
 from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, MultiModalConfig
@@ -52,6 +53,7 @@
 from vllm.model_executor.models.qwen2 import Qwen2Model
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
@@ -64,6 +66,17 @@
 }
 
 
+class MiniCPMVImageInput(TypedDict):
+    """Input mapper input with auxiliary data for computing image bounds."""
+    image: Image.Image
+
+    # Image bounds token ids in 0-dim scaler tensor.
+    im_start_id: torch.Tensor
+    im_end_id: torch.Tensor
+    slice_start_id: NotRequired[torch.Tensor]
+    slice_end_id: NotRequired[torch.Tensor]
+
+
 class MiniCPMVImagePixelInputs(TypedDict):
     pixel_values: List[torch.Tensor]
     """
@@ -88,8 +101,6 @@ class MiniCPMVImagePixelInputs(TypedDict):
     """
 
 
-MiniCPMVImageInputs = MiniCPMVImagePixelInputs
-
 DEFAULT_LN = partial(nn.LayerNorm, eps=1e-6)
 
 
@@ -234,6 +245,25 @@ def forward(self, x: torch.Tensor,
         return x
 
 
+def _build_image_input(ctx: InputContext,
+                       image: Image.Image) -> MiniCPMVImageInput:
+    tokenizer = cached_get_tokenizer(
+        ctx.model_config.tokenizer,
+        trust_remote_code=ctx.model_config.trust_remote_code)
+    if hasattr(tokenizer, "slice_start_id"):
+        return MiniCPMVImageInput(
+            image=image,
+            im_start_id=torch.tensor(tokenizer.im_start_id),
+            im_end_id=torch.tensor(tokenizer.im_end_id),
+            slice_start_id=torch.tensor(tokenizer.slice_start_id),
+            slice_end_id=torch.tensor(tokenizer.slice_end_id))
+    else:
+        return MiniCPMVImageInput(image=image,
+                                  im_start_id=torch.tensor(
+                                      tokenizer.im_start_id),
+                                  im_end_id=torch.tensor(tokenizer.im_end_id))
+
+
 def get_version_by_config(config: PretrainedConfig) -> Tuple[int, ...]:
     version_float = getattr(config, "version", None)
 
@@ -257,10 +287,13 @@ def dummy_seq_data_for_minicpmv(seq_len: int, num_images: int):
     return SequenceData.from_token_counts((0, seq_len))
 
 
-def dummy_image_for_minicpmv(hf_config: PretrainedConfig, num_images: int):
+def dummy_image_for_minicpmv(ctx: InputContext, hf_config: PretrainedConfig,
+                             num_images: int):
     width = height = hf_config.image_size
-    image = Image.new("RGB", (width, height), color=0)
-    return {"image": image if num_images == 1 else [image] * num_images}
+    image = _build_image_input(ctx,
+                               image=Image.new("RGB", (width, height),
+                                               color=0))
+    return {"image": [image] if num_images == 1 else [image] * num_images}
 
 
 def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int,
@@ -269,7 +302,7 @@ def dummy_data_for_minicpmv(ctx: InputContext, seq_len: int,
     num_images = mm_counts["image"]
 
     seq_data = dummy_seq_data_for_minicpmv(seq_len, num_images)
-    mm_data = dummy_image_for_minicpmv(hf_config, num_images)
+    mm_data = dummy_image_for_minicpmv(ctx, hf_config, num_images)
 
     return seq_data, mm_data
 
@@ -280,8 +313,9 @@ def input_processor_for_minicpmv(ctx: InputContext, llm_inputs: LLMInputs):
         return llm_inputs
     model_config = ctx.model_config
     version = get_version_by_config(model_config.hf_config)
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
     image_processor = cached_get_image_processor(model_config.tokenizer)
 
     def get_placeholder(image_size: Tuple[int, int], num_image: int):
@@ -317,6 +351,10 @@ def get_placeholder(image_size: Tuple[int, int], num_image: int):
         new_prompt = "".join(new_prompt_chunks)
         new_token_ids = tokenizer.encode(new_prompt)
 
+    multi_modal_data["image"] = [
+        _build_image_input(ctx, image) for image in images
+    ]
+
     llm_inputs = LLMInputs(
         prompt_token_ids=new_token_ids,
         prompt=new_prompt,
@@ -325,6 +363,32 @@ def get_placeholder(image_size: Tuple[int, int], num_image: int):
     return llm_inputs
 
 
+def input_mapper_for_minicpmv(ctx: InputContext, data: object):
+    model_config = ctx.model_config
+
+    image_processor = cached_get_image_processor(
+        model_config.model, trust_remote_code=model_config.trust_remote_code)
+    if image_processor is None:
+        raise RuntimeError("No HuggingFace processor is available "
+                           "to process the image object")
+
+    if not isinstance(data, list):
+        raise ValueError(
+            "Image input must be list of MiniCPMVImageInput, got (%s)", data)
+    batch_data = image_processor \
+        .preprocess([img["image"] for img in data], return_tensors="pt") \
+        .data
+
+    if len(data) > 0:
+        batch_data["im_start_id"] = data[0]["im_start_id"]
+        batch_data["im_end_id"] = data[0]["im_end_id"]
+        if "slice_start_id" in data[0]:
+            batch_data["slice_start_id"] = data[0]["slice_start_id"]
+            batch_data["slice_end_id"] = data[0]["slice_end_id"]
+
+    return MultiModalInputs(batch_data)
+
+
 class MiniCPMVBaseModel(nn.Module, SupportsMultiModal):
     """
     The abstract class of MiniCPMV can only be inherited, but cannot be
@@ -365,7 +429,7 @@ def __init__(
     def get_embedding(
         self,
         input_ids: torch.Tensor,
-        image_inputs: Optional[MiniCPMVImageInputs],
+        image_inputs: Optional[MiniCPMVImagePixelInputs],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         vlm_embedding: torch.Tensor = self.llm.embed_tokens(input_ids)
         if hasattr(self.config, "scale_emb"):
@@ -393,14 +457,20 @@ def get_embedding(
 
         return vlm_embedding, vision_hidden_states
 
-    def _get_image_bounds(self, input_ids: torch.Tensor) -> torch.Tensor:
-        tokenizer = cached_get_tokenizer(self.config._name_or_path,
-                                         trust_remote_code=True)
-        start_cond = input_ids == tokenizer.im_start_id
-        end_cond = input_ids == tokenizer.im_end_id
-        if hasattr(tokenizer, "slice_start_id"):
-            start_cond |= (input_ids == tokenizer.slice_start_id)
-            end_cond |= (input_ids == tokenizer.slice_end_id)
+    def _get_image_bounds(
+            self,
+            input_ids: torch.Tensor,
+            im_start_id: torch.Tensor,
+            im_end_id: torch.Tensor,
+            slice_start_id: Optional[torch.Tensor] = None,
+            slice_end_id: Optional[torch.Tensor] = None) -> torch.Tensor:
+        # All the images in the batch should share the same special image
+        # bound token ids.
+        start_cond = input_ids == im_start_id[0]
+        end_cond = input_ids == im_end_id[0]
+        if slice_start_id is not None:
+            start_cond |= (input_ids == slice_start_id[0])
+            end_cond |= (input_ids == slice_end_id[0])
 
         image_start_tokens, = torch.where(start_cond)
         image_start_tokens += 1
@@ -419,7 +489,7 @@ def _parse_and_validate_inputs(
         self,
         input_ids: torch.Tensor,
         **kwargs: object,
-    ) -> Optional[MiniCPMVImageInputs]:
+    ) -> Optional[MiniCPMVImagePixelInputs]:
         pixel_values = kwargs.pop("pixel_values", [])
         tgt_sizes = kwargs.pop("tgt_sizes", [])
 
@@ -456,8 +526,17 @@ def _parse_and_validate_inputs(
         if len(pixel_values_flat) == 0:
             return None
 
-        return MiniCPMVImageInputs(
-            image_bounds=self._get_image_bounds(input_ids),
+        im_start_id = kwargs.pop("im_start_id", None)
+        im_end_id = kwargs.pop("im_end_id", None)
+        slice_start_id = kwargs.pop("slice_start_id", None)
+        slice_end_id = kwargs.pop("slice_end_id", None)
+        if im_start_id is None:
+            return None
+
+        return MiniCPMVImagePixelInputs(
+            image_bounds=self._get_image_bounds(input_ids, im_start_id,
+                                                im_end_id, slice_start_id,
+                                                slice_end_id),
             pixel_values=pixel_values_flat,
             tgt_sizes=torch.stack(tgt_sizes_flat),
         )
@@ -564,8 +643,8 @@ def get_vision_embedding(
     ) -> torch.Tensor:
         raise NotImplementedError
 
-    def get_vision_hidden_states(self,
-                                 data: MiniCPMVImageInputs) -> torch.Tensor:
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         raise NotImplementedError
 
     def is_default_weight_loading(self, name: str) -> bool:
@@ -654,8 +733,8 @@ def get_vision_embedding(
             res.append(self.resampler(vision_embedding, tgt_size))
         return torch.vstack(res)
 
-    def get_vision_hidden_states(self,
-                                 data: MiniCPMVImageInputs) -> torch.Tensor:
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
 
         return self.get_vision_embedding(pixel_values)
@@ -713,8 +792,8 @@ def get_vision_embedding(
         vision_embedding = self.resampler(vision_embedding, tgt_sizes)
         return vision_embedding
 
-    def get_vision_hidden_states(self,
-                                 data: MiniCPMVImageInputs) -> torch.Tensor:
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
         tgt_sizes = data["tgt_sizes"]
 
@@ -807,8 +886,8 @@ def get_vision_embedding(
         ).last_hidden_state
         return vision_embedding
 
-    def get_vision_hidden_states(self,
-                                 data: MiniCPMVImageInputs) -> torch.Tensor:
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
         pixel_values = data["pixel_values"]
         tgt_sizes = data["tgt_sizes"]
 
@@ -851,7 +930,7 @@ def is_default_weight_loading(self, name: str) -> bool:
 }
 
 
-@MULTIMODAL_REGISTRY.register_image_input_mapper()
+@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_minicpmv)
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_minicpmv_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_minicpmv)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_minicpmv)

vllm/model_executor/models/qwen.py

@@ -674,8 +674,9 @@ def input_processor_for_qwen(ctx: InputContext,
     prompt = llm_inputs.get("prompt")
     prompt_token_ids = llm_inputs["prompt_token_ids"]
     model_config = ctx.model_config
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
     image_data = multi_modal_data["image"]
     if isinstance(image_data, torch.Tensor):
         num_dims = len(image_data.shape)
@@ -735,8 +736,9 @@ def input_mapper_for_qwen(ctx: InputContext, data: object) -> MultiModalInputs:
         return MultiModalInputs()
 
     model_config = ctx.model_config
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
 
     image_pair_tok = tokenizer.encode(IMG_START + IMG_END,
                                       add_special_tokens=False,
@@ -824,8 +826,9 @@ def dummy_data_for_qwen(
     # We have a visual component - use images to warm up
     num_images = mm_counts["image"]
     model_config = ctx.model_config
-    tokenizer = cached_get_tokenizer(model_config.tokenizer,
-                                     trust_remote_code=True)
+    tokenizer = cached_get_tokenizer(
+        model_config.tokenizer,
+        trust_remote_code=model_config.trust_remote_code)
 
     # Build the image prompts with no imgpads; the tokenizer will add img pads
     image_prompt = ''.join(