[Model][LoRA]LoRA support added for MolmoForCausalLM (vllm-project#11439

)

Signed-off-by: Matthias Vogler <[email protected]>
Signed-off-by: Jee Jee Li <[email protected]>
Co-authored-by: Matthias Vogler <[email protected]>
Co-authored-by: Jee Jee Li <[email protected]>

docs/source/models/supported_models.md

@@ -666,7 +666,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - Molmo
   - T + I
   - `allenai/Molmo-7B-D-0924`, `allenai/Molmo-72B-0924`, etc.
-  -
+  - ✅︎
   - ✅︎
   - ✅︎
 * - `NVLM_D_Model`

vllm/model_executor/models/molmo.py

@@ -36,14 +36,15 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
 from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
                            SequenceData)
 from vllm.transformers_utils.processor import get_processor
 
-from .interfaces import SupportsMultiModal, SupportsPP
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
 from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix, merge_multimodal_embeddings)
@@ -1161,8 +1162,8 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_molmo_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_molmo)
 @INPUT_REGISTRY.register_input_processor(input_processor_for_molmo)
-class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
-
+class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
+                       SupportsLoRA):
     hf_to_vllm_mapper = WeightsMapper(
         orig_to_new_substr={
             # vision backbone mapping
@@ -1191,6 +1192,32 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         },
     )
 
+    packed_modules_mapping = {
+        "qkv_proj": ["qkv_proj"],
+        "gate_up_proj": ["gate_up_proj"],  # language model
+        "merged_linear": ["gate_proj", "up_proj"]  # image_projector
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        # language model
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",  # same name with image_projector
+        # vision tower
+        "wq",
+        "wk",
+        "wv",
+        "wo",
+        "w1",
+        "w2",
+        # image_projector
+        "merged_linear",
+    ]
+    embedding_modules = {}
+    embedding_padding_modules = []
+
     # BitandBytes specific attributes
     bitsandbytes_stacked_params_mapping = {
         "gate_proj": ("merged_linear", 0),
@@ -1202,8 +1229,10 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
+        lora_config = vllm_config.lora_config
         self.config = config
         self.multimodal_config = multimodal_config
+        self.lora_config = lora_config
 
         vision_config = VisionBackboneConfig()
         self.vision_backbone = MolmoVisionBackbone(config, vision_config,
@@ -1377,6 +1406,16 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         weights = _get_weights_with_merged_embedding(weights)
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="model",
+            connector="vision_backbone.image_projector",
+            tower_model="vision_backbone",
+        )
+
 
 def _get_weights_with_merged_embedding(
     weights: Iterable[Tuple[str, torch.Tensor]]