[Model][LoRA]LoRA support added for idefics3 (#10281)

Signed-off-by: B-201 <Joy25810@foxmail.com>

docs/source/models/supported_models.rst

@@ -450,7 +450,7 @@ Text Generation
     - Idefics3
     - T + I
     - :code:`HuggingFaceM4/Idefics3-8B-Llama3` etc.
-    - 
+    - ✅︎
     - 
   * - :code:`InternVLChatModel`
     - InternVL2

vllm/model_executor/models/idefics3.py

@@ -33,6 +33,7 @@ from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.image import cached_get_image_processor
@@ -44,7 +45,7 @@ from vllm.utils import is_list_of
 from .idefics2_vision_model import (
     Idefics2VisionTransformer as Idefics3VisionTransformer)
 # yapf: enable
-from .interfaces import SupportsMultiModal
+from .interfaces import SupportsLoRA, SupportsMultiModal
 from .llama import LlamaModel
 from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
                     merge_multimodal_embeddings)
@@ -58,8 +59,6 @@ class Idefics3ImagePixelInputs(TypedDict):
     """
     Shape: `(batch_size * num_images, num_channels, height, width)`
     """
-    rows: List[int]
-    cols: List[int]
     pixel_attention_mask: Optional[torch.BoolTensor]
 
 
@@ -356,8 +355,15 @@ def dummy_data_for_idefics3(
     image_seq_len = processor.image_seq_len
     max_llm_image_tokens = max_num_image_patches * image_seq_len * num_images
 
+    if seq_len - max_llm_image_tokens < 0:
+        raise RuntimeError(
+            f"Idefics3 cannot process {num_images} images in a prompt, "
+            "please increase max_model_len or reduce image limit by "
+            "--limit-mm-per-prompt.")
+
     seq_data = SequenceData.from_prompt_token_counts(
-        (hf_config.image_token_id, max_llm_image_tokens), (0, seq_len))
+        (hf_config.image_token_id, max_llm_image_tokens),
+        (0, seq_len - max_llm_image_tokens))
 
     width = height = hf_config.vision_config.image_size
     image = Image.new("RGB", (width, height), color=0)
@@ -463,8 +469,6 @@ class Idefics3Model(nn.Module):
             self, **kwargs: object) -> Optional[ImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
         image_embeds = kwargs.pop("image_embeds", None)
-        rows = kwargs.pop("rows", None)
-        cols = kwargs.pop("cols", None)
         pixel_attention_mask = kwargs.pop("pixel_attention_mask", None)
 
         if pixel_values is None and image_embeds is None:
@@ -489,8 +493,6 @@ class Idefics3Model(nn.Module):
                                             data=self._validate_pixel_values(
                                                 flatten_bn(pixel_values,
                                                            concat=True)),
-                                            rows=rows,
-                                            cols=cols,
                                             pixel_attention_mask=flatten_bn(
                                                 pixel_attention_mask,
                                                 concat=True))
@@ -610,7 +612,33 @@ class Idefics3Model(nn.Module):
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_idefics3_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_idefics3)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_idefics3)
-class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal):
+class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                       SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+    # LoRA specific attributes
+    supported_lora_modules = [
+        # vision_model
+        "fc1",
+        "fc2",
+        "out_proj",
+        # text_model
+        "qkv_proj",  # same name with vision encoder
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+    embedding_modules = {}
+    embedding_padding_modules = []
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -672,3 +700,12 @@ class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal):
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         loader = AutoWeightsLoader(self)
         loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="model.text_model",
+            connector="model.connector",
+            tower_model="model.vision_model")