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

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

vllm/model_executor/models/gemma2.py

@@ -43,7 +43,8 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 logger = init_logger(__name__)
 
@@ -243,11 +244,7 @@ class Gemma2DecoderLayer(nn.Module):
 @support_torch_compile
 class Gemma2Model(nn.Module):
 
-    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
@@ -399,13 +396,8 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "up_proj": ("gate_up_proj", 1),
     }
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ) -> None:
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         config = vllm_config.model_config.hf_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
         del lora_config  # Unused.
@@ -414,7 +406,8 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         # currently all existing Gemma models have `tie_word_embeddings` enabled
         assert config.tie_word_embeddings
         self.quant_config = quant_config
-        self.model = Gemma2Model(config, cache_config, quant_config)
+        self.model = Gemma2Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
         self.logits_processor = LogitsProcessor(
             config.vocab_size, soft_cap=config.final_logit_softcapping)
         self.sampler = get_sampler()
@@ -471,14 +464,11 @@ class Gemma2EmbeddingModel(nn.Module, SupportsPP):
         _pooler: An instance of Pooler used for pooling operations.
     """
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ) -> None:
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
 
-        self.model = Gemma2Model(vllm_config, prefix)
+        self.model = Gemma2Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
         self._pooler = Pooler.from_config_with_defaults(
             vllm_config.model_config.pooler_config,
             pooling_type=PoolingType.LAST,