Init

Signed-off-by: Jee Jee Li <[email protected]>

vllm/model_executor/models/qwen.py

@@ -20,7 +20,7 @@
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig, MultiModalConfig
+from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
                          token_inputs)
@@ -39,14 +39,15 @@
 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.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer
 from vllm.sequence import IntermediateTensors, SequenceData
 from vllm.utils import is_list_of
 
-from .interfaces import SupportsMultiModal, SupportsPP
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
 from .utils import (flatten_bn, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers)
 
@@ -860,18 +861,15 @@ def dummy_data_for_qwen(
     return seq_data, mm_data
 
 
-@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_qwen)
-@MULTIMODAL_REGISTRY.register_max_image_tokens(MAX_QWEN_IMG_TOKENS)
-@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen)
-@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen)
-class QWenLMHeadModel(nn.Module, SupportsMultiModal, SupportsPP):
+class QWenBaseModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
 
     def __init__(
         self,
         config: PretrainedConfig,
         multimodal_config: MultiModalConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config = config
@@ -990,3 +988,106 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)
                 weight_loader(param, loaded_weight)
+
+
+class QWenLLM(QWenBaseModel):
+    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 encoder
+        "fc1",
+        "fc2",
+        "out_proj",
+        # language model
+        "qkv_proj",  # same name with vision encoder
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        # resampler
+        "kv_proj",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+
+class QWenVL(QWenBaseModel):
+    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 encoder
+        "fc1",
+        "fc2",
+        "out_proj",
+        # language model
+        "qkv_proj",  # same name with vision encoder
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+        # resampler
+        "kv_proj",
+    ]
+
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(language_model="llm",
+                                                connector="resampler",
+                                                tower_model="vpm")
+
+
+@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_qwen)
+@MULTIMODAL_REGISTRY.register_max_image_tokens(MAX_QWEN_IMG_TOKENS)
+@INPUT_REGISTRY.register_dummy_data(dummy_data_for_qwen)
+@INPUT_REGISTRY.register_input_processor(input_processor_for_qwen)
+class QWenLMHeadModel(QWenBaseModel):
+    """
+    QWenLMHeadModel is not only applicable to LLM  but also to VL, which is not 
+    conducive to the current integration logic of LoRA in vLLM. Therefore, it 
+    is necessary to separate them.
+    """
+    # Ensure that the LoRA support check passes when the class is not
+    # initialized, but set all these attributes to empty.
+    packed_modules_mapping = {}
+    supported_lora_modules = []
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def __new__(
+        cls,
+        config: PretrainedConfig,
+        multimodal_config: MultiModalConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ):
+        if multimodal_config is None:
+            return QWenLLM(config, multimodal_config, cache_config,
+                           quant_config)
+        else:
+            return QWenVL(config, multimodal_config, cache_config,
+                          quant_config)