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

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

vllm/model_executor/models/gpt_bigcode.py

@@ -25,7 +25,7 @@ from transformers import GPTBigCodeConfig
 
 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_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -189,15 +189,14 @@ class GPTBigCodeBlock(nn.Module):
 @support_torch_compile
 class GPTBigCodeModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        lora_config: Optional[LoRAConfig] = 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
+        lora_config = vllm_config.lora_config
+
         self.config = config
         assert not config.add_cross_attention
 
@@ -265,7 +264,6 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     ):
         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
 
@@ -273,8 +271,8 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.lora_config = lora_config
 
         self.quant_config = quant_config
-        self.transformer = GPTBigCodeModel(config, cache_config, quant_config,
-                                           lora_config)
+        self.transformer = GPTBigCodeModel(vllm_config=vllm_config,
+                                           prefix=prefix)
         if self.config.tie_word_embeddings:
             self.lm_head = self.transformer.wte
         else:

vllm/model_executor/models/gpt_j.py

@@ -42,7 +42,8 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
 from .utils import (is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class GPTJAttention(nn.Module):
@@ -177,14 +178,13 @@ class GPTJBlock(nn.Module):
 @support_torch_compile
 class GPTJModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTJConfig,
-        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.embed_dim = config.n_embd
         self.wte = VocabParallelEmbedding(
@@ -236,12 +236,13 @@ class GPTJForCausalLM(nn.Module, SupportsPP):
     ):
         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.quant_config = quant_config
         assert not config.tie_word_embeddings
-        self.transformer = GPTJModel(config, cache_config, quant_config)
+        self.transformer = GPTJModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
         self.lm_head = ParallelLMHead(
             config.vocab_size,
             config.n_embd,

vllm/model_executor/models/gpt_neox.py

@@ -41,7 +41,8 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
 from .utils import (is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class GPTNeoXAttention(nn.Module):
@@ -189,14 +190,13 @@ class GPTNeoXLayer(nn.Module):
 @support_torch_compile
 class GPTNeoXModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        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.embed_in = VocabParallelEmbedding(
@@ -249,11 +249,11 @@ class GPTNeoXForCausalLM(nn.Module, SupportsPP):
     ):
         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.quant_config = quant_config
-        self.gpt_neox = GPTNeoXModel(config, cache_config, quant_config)
+        self.gpt_neox = GPTNeoXModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "gpt_neox"))
         self.embed_out = ParallelLMHead(
             config.vocab_size,
             config.hidden_size,

vllm/model_executor/models/granite.py

@@ -28,7 +28,7 @@ from transformers import GraniteConfig
 
 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
@@ -52,7 +52,8 @@ from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
-from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+from .utils import (PPMissingLayer, is_pp_missing_parameter, make_layers,
+                    maybe_prefix)
 
 
 class GraniteMLP(nn.Module):
@@ -257,15 +258,14 @@ class GraniteDecoderLayer(nn.Module):
 @support_torch_compile
 class GraniteModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GraniteConfig,
-        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 *
@@ -370,25 +370,17 @@ class GraniteForCausalLM(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 = ""):
         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.lora_config = lora_config
 
-        self.model = GraniteModel(config,
-                                  cache_config,
-                                  quant_config,
-                                  lora_config=lora_config,
-                                  prefix="model")
+        self.model = GraniteModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
             if lora_config:

vllm/model_executor/models/granitemoe.py

@@ -28,7 +28,7 @@ from transformers.models.granitemoe import GraniteMoeConfig
 
 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_world_size
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -47,7 +47,7 @@ from vllm.sequence import IntermediateTensors
 
 from . import mixtral
 from .interfaces import SupportsLoRA, SupportsPP
-from .utils import make_layers
+from .utils import make_layers, maybe_prefix
 
 
 class GraniteMoeMoE(nn.Module):
@@ -247,15 +247,14 @@ class GraniteMoeDecoderLayer(nn.Module):
 @support_torch_compile
 class GraniteMoeModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GraniteMoeConfig,
-        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.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
@@ -333,25 +332,17 @@ class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     }
     embedding_padding_modules = ["lm_head"]
 
-    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
 
         self.config = config
         self.lora_config = lora_config
 
-        self.model = GraniteMoeModel(config,
-                                     cache_config,
-                                     quant_config,
-                                     lora_config=lora_config,
-                                     prefix="model")
+        self.model = GraniteMoeModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "model"))
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size

vllm/model_executor/models/idefics3.py

@@ -22,17 +22,15 @@ import torch.utils.checkpoint
 from PIL import Image
 from torch import nn
 # Temporary solution for transformers below 4.46.0.
-from transformers import PretrainedConfig as Idefics3Config
 from transformers import ProcessorMixin as Idefics3ImageProcessor
 
 from vllm.attention import AttentionMetadata
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import VllmConfig
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext, token_inputs)
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
-from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -48,7 +46,8 @@ from .idefics2_vision_model import (
 # yapf: enable
 from .interfaces import SupportsMultiModal
 from .llama import LlamaModel
-from .utils import AutoWeightsLoader, flatten_bn, merge_multimodal_embeddings
+from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
+                    merge_multimodal_embeddings)
 
 logger = init_logger(__name__)
 
@@ -417,13 +416,13 @@ class Idefics3Connector(nn.Module):
 
 class Idefics3Model(nn.Module):
 
-    def __init__(
-        self,
-        config: Idefics3Config,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = 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
+
         self.config = config
         self.padding_idx = self.config.text_config.pad_token_id
         self.vocab_size = self.config.text_config.vocab_size
@@ -613,22 +612,18 @@ class Idefics3Model(nn.Module):
 @INPUT_REGISTRY.register_input_processor(input_processor_for_idefics3)
 class Idefics3ForConditionalGeneration(nn.Module, SupportsMultiModal):
 
-    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.model = Idefics3Model(config, cache_config, quant_config)
+        self.model = Idefics3Model(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))
         self.image_token_id = self.config.image_token_id
 
         self.lm_head = ParallelLMHead(

vllm/model_executor/models/internlm2.py

@@ -250,14 +250,13 @@ class InternLMDecoderLayer(nn.Module):
 @support_torch_compile
 class InternLM2Model(nn.Module):
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = 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
+
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
@@ -317,20 +316,13 @@ class InternLM2Model(nn.Module):
 
 class InternLM2ForCausalLM(nn.Module, SupportsPP):
 
-    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
         self.config = config
         self.quant_config = quant_config
-        self.model = InternLM2Model(config,
-                                    cache_config,
-                                    quant_config,
+        self.model = InternLM2Model(vllm_config=vllm_config,
                                     prefix=maybe_prefix(prefix, "model"))
         self.output = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,

vllm/model_executor/models/internlm2_ve.py

@@ -104,14 +104,13 @@ class InternLM2VEDecoderLayer(nn.Module):
 
 class InternLM2VEModel(InternLM2Model):
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__(config, cache_config, quant_config)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
             lambda prefix: InternLM2VEDecoderLayer(
@@ -159,12 +158,8 @@ class InternLM2VEModel(InternLM2Model):
 
 class InternLM2VEForCausalLM(InternLM2ForCausalLM):
 
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        prefix: str = "",
-    ) -> None:
-        super().__init__(vllm_config, prefix=prefix)
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
 
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config

vllm/model_executor/models/internvl.py

@@ -35,7 +35,7 @@ from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,
                    get_clip_num_patches)
 from .interfaces import SupportsMultiModal, SupportsPP
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+                    maybe_prefix, merge_multimodal_embeddings)
 
 IMG_START = '<img>'
 IMG_END = '</img>'
@@ -435,13 +435,13 @@ class InternVLChatModel(nn.Module, SupportsMultiModal, SupportsPP):
             config,
             quant_config=quant_config,
             is_mono=self.is_mono,
-            prefix="vision_model",
+            prefix=maybe_prefix(prefix, "vision_model"),
         )
 
         self.language_model = init_vllm_registered_model(
             config.text_config,
             vllm_config=vllm_config,
-            prefix="language_model")
+            prefix=maybe_prefix(prefix, "language_model"))
 
         self.mlp1 = self._init_mlp1(config)
 

vllm/model_executor/models/jais.py

@@ -44,7 +44,8 @@ from vllm.transformers_utils.configs import JAISConfig
 
 from .interfaces import SupportsPP
 from .utils import (is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class SwiGLUActivation(nn.Module):
@@ -215,14 +216,13 @@ class JAISBlock(nn.Module):
 @support_torch_compile
 class JAISModel(nn.Module):
 
-    def __init__(
-        self,
-        config: JAISConfig,
-        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
         assert not config.add_cross_attention
         assert not config.scale_attn_by_inverse_layer_idx
@@ -293,11 +293,12 @@ class JAISLMHeadModel(nn.Module, SupportsPP):
     ):
         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.quant_config = quant_config
-        self.transformer = JAISModel(config, cache_config, quant_config)
+        self.transformer = JAISModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(
+                                         prefix, "transformer"))
         if self.config.tie_word_embeddings:
             self.lm_head = self.transformer.wte
         else:

vllm/model_executor/models/jamba.py

@@ -7,7 +7,7 @@ from transformers import JambaConfig
 
 from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.attention.layer import Attention
-from vllm.config import CacheConfig, LoRAConfig, VllmConfig
+from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -29,6 +29,7 @@ from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
                                       _get_graph_batch_size)
 
 from .interfaces import HasInnerState, SupportsLoRA
+from .utils import maybe_prefix
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -258,14 +259,14 @@ ALL_DECODER_LAYER_TYPES = {
 
 class JambaModel(nn.Module):
 
-    def __init__(
-        self,
-        config: JambaConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
-        lora_config: Optional[LoRAConfig] = None,
-    ) -> 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 *
@@ -348,14 +349,9 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
     }
     embedding_padding_modules = ["lm_head"]
 
-    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
         scheduler_config = vllm_config.scheduler_config
         assert not cache_config.enable_prefix_caching, \
@@ -364,10 +360,8 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
         super().__init__()
         self.config = config
         self.scheduler_config = scheduler_config
-        self.model = JambaModel(config,
-                                cache_config=cache_config,
-                                quant_config=quant_config,
-                                lora_config=lora_config)
+        self.model = JambaModel(vllm_config=vllm_config,
+                                prefix=maybe_prefix(prefix, "model"))
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size

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,

vllm/model_executor/models/llava.py

@@ -32,7 +32,7 @@ from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
                      input_processor_for_siglip)
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+                    maybe_prefix, merge_multimodal_embeddings)
 
 
 class LlavaImagePixelInputs(TypedDict):
@@ -282,7 +282,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             config,
             quant_config,
             require_post_norm=False,
-            prefix="vision_tower")
+            prefix=maybe_prefix(prefix, "vision_tower"))
         self.multi_modal_projector = LlavaMultiModalProjector(
             vision_hidden_size=config.vision_config.hidden_size,
             text_hidden_size=config.text_config.hidden_size,
@@ -291,7 +291,7 @@ class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
         self.language_model = init_vllm_registered_model(
             config.text_config,
             vllm_config=vllm_config,
-            prefix="language_model")
+            prefix=maybe_prefix(prefix, "language_model"))
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_next.py

@@ -31,7 +31,7 @@ from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_siglip_image_feature_size,
                      get_siglip_patch_grid_length, input_processor_for_siglip)
 from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn,
-                    init_vllm_registered_model)
+                    init_vllm_registered_model, maybe_prefix)
 
 
 class LlavaNextImagePixelInputs(TypedDict):
@@ -296,7 +296,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
             config,
             quant_config,
             require_post_norm=False,
-            prefix="vision_tower")
+            prefix=maybe_prefix(prefix, "vision_tower"))
         self.image_newline = nn.Parameter(
             torch.empty(config.text_config.hidden_size))
         self.multi_modal_projector = LlavaMultiModalProjector(
@@ -307,7 +307,7 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.language_model = init_vllm_registered_model(
             config.text_config,
             vllm_config=vllm_config,
-            prefix="language_model")
+            prefix=maybe_prefix(prefix, "language_model"))
 
         # The same model class supports both language generation and embedding
         # because the architecture name is the same

vllm/model_executor/models/llava_next_video.py

@@ -29,7 +29,7 @@ from .llava import init_vision_tower_for_llava
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip)
 from .utils import (AutoWeightsLoader, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+                    maybe_prefix, merge_multimodal_embeddings)
 
 # For profile run
 _MAX_FRAMES_PER_VIDEO = 32
@@ -267,7 +267,7 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
             config,
             quant_config,
             require_post_norm=False,
-            prefix="vision_tower")
+            prefix=maybe_prefix(prefix, "vision_tower"))
         self.vision_resampler = LlavaNextVideoPooler(config)
         self.multi_modal_projector = LlavaNextMultiModalProjector(
             vision_hidden_size=config.vision_config.hidden_size,
@@ -276,7 +276,7 @@ class LlavaNextVideoForConditionalGeneration(nn.Module, SupportsMultiModal,
         self.language_model = init_vllm_registered_model(
             config.text_config,
             vllm_config=vllm_config,
-            prefix="language_model")
+            prefix=maybe_prefix(prefix, "language_model"))
 
         self.make_empty_intermediate_tensors = (
             self.language_model.model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_onevision.py

@@ -35,7 +35,7 @@ from .siglip import (SiglipVisionModel, dummy_seq_data_for_siglip,
                      dummy_video_for_siglip, get_siglip_image_feature_size,
                      get_siglip_patch_grid_length, input_processor_for_siglip)
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+                    maybe_prefix, merge_multimodal_embeddings)
 
 # Result in the max possible feature size (2x2 grid of 336x336px tiles)
 MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
@@ -418,12 +418,12 @@ class LlavaOnevisionForConditionalGeneration(nn.Module, SupportsMultiModal,
             config,
             quant_config,
             require_post_norm=False,
-            prefix="vision_tower")
+            prefix=maybe_prefix(prefix, "vision_tower"))
         self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
         self.language_model = init_vllm_registered_model(
             config.text_config,
             vllm_config=vllm_config,
-            prefix="language_model")
+            prefix=maybe_prefix(prefix, "language_model"))
         self.image_newline = nn.Parameter(
             torch.empty(config.text_config.hidden_size))
 

vllm/model_executor/models/mamba.py

@@ -6,7 +6,7 @@ from torch import nn
 from transformers import MambaConfig
 
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.config import CacheConfig, LoRAConfig, VllmConfig
+from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -26,6 +26,8 @@ from vllm.sequence import IntermediateTensors
 from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
                                       _get_graph_batch_size)
 
+from .utils import maybe_prefix
+
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 
@@ -73,14 +75,14 @@ class MambaDecoderLayer(nn.Module):
 
 class MambaModel(nn.Module):
 
-    def __init__(
-        self,
-        config: MambaConfig,
-        quant_config: Optional[QuantizationConfig] = None,
-        cache_config: Optional[CacheConfig] = None,
-        lora_config: Optional[LoRAConfig] = None,
-    ) -> 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 *
@@ -130,14 +132,9 @@ class MambaModel(nn.Module):
 
 class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
 
-    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
         scheduler_config = vllm_config.scheduler_config
         assert not cache_config.enable_prefix_caching, \
@@ -146,10 +143,8 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
         super().__init__()
         self.config = config
         self.scheduler_config = scheduler_config
-        self.backbone = MambaModel(config,
-                                   cache_config=cache_config,
-                                   quant_config=quant_config,
-                                   lora_config=lora_config)
+        self.backbone = MambaModel(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "backbone"))
         self.unpadded_vocab_size = config.vocab_size
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size

vllm/model_executor/models/minicpm.py

@@ -29,7 +29,7 @@ from transformers import PretrainedConfig
 
 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,
                               tensor_model_parallel_all_reduce)
@@ -53,7 +53,8 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class MiniCPMMoE(nn.Module):
@@ -351,15 +352,14 @@ class MiniCPMDecoderLayer(nn.Module):
 @support_torch_compile
 class MiniCPMModel(nn.Module):
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        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.cache_config = cache_config
         self.quant_config = quant_config
@@ -461,24 +461,22 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     }
     embedding_padding_modules = ["lm_head"]
 
-    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
 
+        self.prefix = prefix
+        self.vllm_config = vllm_config
         self.config = config
         self.lora_config = lora_config
         self.cache_config = cache_config
         self.quant_config = quant_config
 
         self.num_experts = getattr(self.config, "num_experts", 0)
-        self._init_model()
+        self._init_model(vllm_config=vllm_config, prefix=prefix)
         unpadded_vocab_size = config.vocab_size
         if lora_config:
             unpadded_vocab_size += lora_config.lora_extra_vocab_size
@@ -502,11 +500,9 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
-    def _init_model(self):
-        self.model = MiniCPMModel(config=self.config,
-                                  cache_config=self.cache_config,
-                                  quant_config=self.quant_config,
-                                  lora_config=self.lora_config)
+    def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        self.model = MiniCPMModel(vllm_config=vllm_config,
+                                  prefix=maybe_prefix(prefix, "model"))
 
     def forward(
         self,

vllm/model_executor/models/minicpm3.py

@@ -28,7 +28,7 @@ from torch import nn
 from transformers import PretrainedConfig
 
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig
+from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -40,7 +40,7 @@ from vllm.model_executor.models.minicpm import (MiniCPMDecoderLayer,
                                                 MiniCPMForCausalLM,
                                                 MiniCPMModel)
 
-from .utils import make_layers
+from .utils import make_layers, maybe_prefix
 
 
 class MiniCPM3Attention(nn.Module):
@@ -238,8 +238,6 @@ class MiniCPM3ForCausalLM(MiniCPMForCausalLM):
     # `embedding_modules` and `embedding_padding_modules`
     # are inherited from MiniCPMForCausalLM
 
-    def _init_model(self):
-        self.model = MiniCPM3Model(config=self.config,
-                                   cache_config=self.cache_config,
-                                   quant_config=self.quant_config,
-                                   lora_config=self.lora_config)
+    def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        self.model = MiniCPM3Model(vllm_config=vllm_config,
+                                   prefix=maybe_prefix(prefix, "model"))

vllm/model_executor/models/minicpmv.py

@@ -34,7 +34,7 @@ from transformers import PretrainedConfig
 from typing_extensions import NotRequired
 
 from vllm.attention import AttentionMetadata
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import VllmConfig
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext, token_inputs)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -59,7 +59,7 @@ from vllm.sequence import IntermediateTensors, SequenceData
 
 from .idefics2_vision_model import Idefics2VisionTransformer
 from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
-from .utils import is_pp_missing_parameter
+from .utils import is_pp_missing_parameter, maybe_prefix
 
 _KEYS_TO_MODIFY_MAPPING = {
     "llm.lm_head": "lm_head",
@@ -390,7 +390,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
     ):
         config = vllm_config.model_config.hf_config
         multimodal_config = vllm_config.model_config.multimodal_config
-        cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         super().__init__()
         # All MiniCPM-V models disable `tie_word_embeddings` but
@@ -401,11 +400,11 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         self.multimodal_config = multimodal_config
 
         self.version = get_version_by_config(self.config)
-        self.llm = self.init_llm(config,
-                                 cache_config,
-                                 quant_config,
-                                 prefix="llm")
-        self.vpm = self.init_vision_module(config, quant_config, prefix="vpm")
+        self.llm = self.init_llm(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "llm"))
+        self.vpm = self.init_vision_module(config,
+                                           quant_config,
+                                           prefix=maybe_prefix(prefix, "vpm"))
         param_dtype = torch.get_default_dtype()
         self.vpm.to(dtype=param_dtype)
         self.vision_dim = (self.vpm.embed_dim if self.version == (2, 0) else
@@ -414,13 +413,15 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         self.resampler = self.init_resampler(self.embed_dim,
                                              self.vision_dim,
                                              quant_config=quant_config,
-                                             prefix="resampler")
+                                             prefix=maybe_prefix(
+                                                 prefix, "resampler"))
         self.resampler.to(device="cuda", dtype=param_dtype)
         # TODO: why is there _KEYS_TO_MODIFY_MAPPING? lm_head should be in llm
         self.lm_head = ParallelLMHead(config.vocab_size,
                                       config.hidden_size,
                                       quant_config=quant_config,
-                                      prefix="llm.lm_head")
+                                      prefix=maybe_prefix(
+                                          prefix, "llm.lm_head"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = get_sampler()
 
@@ -661,9 +662,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
 
     def init_llm(
         self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        vllm_config: VllmConfig,
         prefix: str = "",
     ) -> nn.Module:
         raise NotImplementedError
@@ -711,16 +710,10 @@ class MiniCPMV2_0(MiniCPMVBaseModel):
 
     def init_llm(
         self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        vllm_config: VllmConfig,
         prefix: str = "",
     ) -> nn.Module:
-
-        return LLMWrapper(MiniCPMModel(config,
-                                       cache_config=cache_config,
-                                       quant_config=quant_config,
-                                       prefix=prefix),
+        return LLMWrapper(MiniCPMModel(vllm_config=vllm_config, prefix=prefix),
                           name="model")
 
     def init_vision_module(
@@ -875,15 +868,10 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
 
     def init_llm(
         self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        vllm_config: VllmConfig,
         prefix: str = "",
     ) -> nn.Module:
-        return LLMWrapper(LlamaModel(config,
-                                     cache_config=cache_config,
-                                     quant_config=quant_config,
-                                     prefix=prefix),
+        return LLMWrapper(LlamaModel(vllm_config=vllm_config, prefix=prefix),
                           name="model")
 
     def init_vision_module(
@@ -1022,16 +1010,10 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
 
     def init_llm(
         self,
-        config: PretrainedConfig,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
+        vllm_config: VllmConfig,
         prefix: str = "",
     ) -> nn.Module:
-
-        return LLMWrapper(Qwen2Model(config,
-                                     cache_config=cache_config,
-                                     quant_config=quant_config,
-                                     prefix=prefix),
+        return LLMWrapper(Qwen2Model(vllm_config=vllm_config, prefix=prefix),
                           name="model")
 
     def init_vision_module(
@@ -1151,4 +1133,4 @@ class MiniCPMV(MiniCPMVBaseModel, SupportsLoRA):
         if instance_class is None:
             raise ValueError(
                 "Currently, MiniCPMV only supports versions 2.0, 2.5, and 2.6")
-        return instance_class(vllm_config, prefix=prefix)
+        return instance_class(vllm_config=vllm_config, prefix=prefix)