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

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

vllm/model_executor/models/llama.py

@@ -28,7 +28,7 @@ from transformers import LlamaConfig
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, LoRAConfig, VllmConfig
+from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import SiluAndMul
@@ -271,15 +271,14 @@ class LlamaDecoderLayer(nn.Module):
 @support_torch_compile
 class LlamaModel(nn.Module):
 
-    def __init__(
-        self,
-        config: LlamaConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        lora_config: Optional[LoRAConfig] = None,
-        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
+        lora_config = vllm_config.lora_config
+
         self.config = config
         self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
@@ -492,24 +491,16 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "norm": "model.norm"
     }
 
-    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
         lora_config = vllm_config.lora_config
         pooler_config = vllm_config.model_config.pooler_config
         self.config = config
         self.lora_config = lora_config
 
-        self.model = LlamaModel(config,
-                                cache_config,
-                                quant_config,
-                                lora_config=lora_config,
+        self.model = LlamaModel(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
@@ -652,23 +643,12 @@ class LlamaEmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
     }
     embedding_padding_modules = []
 
-    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
-        lora_config = vllm_config.lora_config
         pooler_config = vllm_config.model_config.pooler_config
 
-        self.model = LlamaModel(config,
-                                cache_config,
-                                quant_config,
-                                lora_config,
+        self.model = LlamaModel(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
         self._pooler = Pooler.from_config_with_defaults(
             pooler_config,