Unify the model type checking (#1905)

python/sglang/srt/models/llama.py

@@ -409,11 +409,13 @@ class LlamaForCausalLM(nn.Module):
         if (
             hasattr(self.config, "tie_word_embeddings")
             and self.config.tie_word_embeddings
+            and "lm_head.weight" in params_dict
         ):
             # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
             param = self.lm_head.weight
             weight_loader = getattr(param, "weight_loader", default_weight_loader)
             weight_loader(param, self.model.embed_tokens.weight)
+
         apply_torchao_config_(self, params_dict, set(["proj.weight"]))
 
 

python/sglang/srt/models/qwen2_vl.py

@@ -23,7 +23,7 @@
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
 from functools import lru_cache, partial
-from typing import Iterable, List, Mapping, Optional, Tuple, Type, TypedDict, Union
+from typing import Iterable, List, Optional, Tuple, Type, TypedDict
 
 import numpy as np
 import torch
@@ -36,7 +36,6 @@ from vllm.distributed import utils as dist_utils
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import QuickGELU
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.model_executor.models.interfaces import SupportsMultiModal
 
 from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig
 from sglang.srt.hf_transformers_utils import get_processor
@@ -486,7 +485,7 @@ class Qwen2VisionTransformer(nn.Module):
 cached_get_processor = lru_cache(get_processor)
 
 
-class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
+class Qwen2VLForConditionalGeneration(nn.Module):
     def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
         processor = cached_get_processor(self.config._name_or_path)
         grid_t, grid_h, grid_w = image_grid_thw
@@ -536,15 +535,12 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal):
     def __init__(
         self,
         config: Qwen2VLConfig,
-        multimodal_config: MultiModalConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
 
         self.config = config
-        self.multimodal_config = multimodal_config
-
         self.visual = Qwen2VisionTransformer(
             config.vision_config,
             norm_eps=getattr(config, "rms_norm_eps", 1e-6),