[6/N] pass whole config to inner model (#10205)

Signed-off-by: youkaichao <youkaichao@gmail.com>

vllm/model_executor/models/qwen.py

@@ -50,7 +50,8 @@ from vllm.utils import is_list_of
 
 from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
 from .utils import (flatten_bn, is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 logger = init_logger(__name__)
 
@@ -552,14 +553,13 @@ class QWenBlock(nn.Module):
 @support_torch_compile
 class QWenModel(nn.Module):
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
         self.config = config
         self.vocab_size = config.vocab_size
 
@@ -865,20 +865,17 @@ def dummy_data_for_qwen(
 
 class QWenBaseModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ) -> None:
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         multimodal_config = vllm_config.model_config.multimodal_config
         self.config = config
         self.multimodal_config = multimodal_config
         self.quant_config = quant_config
-        self.transformer = QWenModel(config, cache_config, quant_config)
+        self.transformer = QWenModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
                                       quant_config=quant_config)