Fix prefix strings for quantized VLMs

vllm/model_executor/models/blip2.py

@@ -507,7 +507,10 @@ def __init__(self,
         )
 
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            prefix="language_model")
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)

vllm/model_executor/models/gemma.py

@@ -43,7 +43,8 @@
 
 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)
 
 logger = init_logger(__name__)
 
@@ -83,16 +84,23 @@ def __init__(
         hidden_act: Optional[str] = None,
         hidden_activation: Optional[str] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+            hidden_size,
+            [intermediate_size] * 2,
             bias=False,
-            quant_config=quant_config)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           quant_config=quant_config)
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
         self.act_fn = _get_gemma_act_fn(hidden_act, hidden_activation)
 
     def forward(self, x):
@@ -104,15 +112,18 @@ def forward(self, x):
 
 class GemmaAttention(nn.Module):
 
-    def __init__(self,
-                 hidden_size: int,
-                 num_heads: int,
-                 num_kv_heads: int,
-                 head_dim: int,
-                 max_position_embeddings: int = 8192,
-                 rope_theta: float = 10000,
-                 cache_config: Optional[CacheConfig] = None,
-                 quant_config: Optional[QuantizationConfig] = None) -> None:
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        max_position_embeddings: int = 8192,
+        rope_theta: float = 10000,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
         tp_size = get_tensor_model_parallel_world_size()
@@ -142,12 +153,14 @@ def __init__(self,
             self.total_num_kv_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
         )
 
         self.rotary_emb = get_rope(
@@ -186,6 +199,7 @@ def __init__(
         config: GemmaConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -198,13 +212,15 @@ def __init__(
             rope_theta=config.rope_theta,
             cache_config=cache_config,
             quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
         )
         self.mlp = GemmaMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
             hidden_activation=getattr(config, "hidden_activation", None),
             quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
         )
         self.input_layernorm = GemmaRMSNorm(config.hidden_size,
                                             eps=config.rms_norm_eps)
@@ -259,8 +275,8 @@ def __init__(
         )
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
-            lambda prefix: GemmaDecoderLayer(config, cache_config, quant_config
-                                             ),
+            lambda prefix: GemmaDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
             prefix=f"{prefix}.layers")
         self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
@@ -366,6 +382,7 @@ def __init__(
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -375,7 +392,10 @@ def __init__(
         self.lora_config = lora_config
 
         self.quant_config = quant_config
-        self.model = GemmaModel(config, cache_config, quant_config)
+        self.model = GemmaModel(config,
+                                cache_config,
+                                quant_config,
+                                prefix=maybe_prefix(prefix, "model"))
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
         self.make_empty_intermediate_tensors = (

vllm/model_executor/models/internvl.py

@@ -439,7 +439,10 @@ def __init__(self,
         )
 
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            prefix="language_model")
 
         self.mlp1 = self._init_mlp1(config)
 

vllm/model_executor/models/llava.py

@@ -210,6 +210,7 @@ def init_vision_tower_for_llava(
     quant_config: Optional[QuantizationConfig],
     *,
     require_post_norm: Optional[bool] = None,
+    prefix: str = "",
 ):
     vision_config = hf_config.vision_config
 
@@ -224,23 +225,26 @@ def init_vision_tower_for_llava(
     if isinstance(vision_config, CLIPVisionConfig):
         return CLIPVisionModel(
             vision_config,
-            quant_config,
+            quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers,
             require_post_norm=require_post_norm,
+            prefix=prefix,
         )
     elif isinstance(vision_config, SiglipVisionConfig):
         return SiglipVisionModel(
             vision_config,
-            quant_config,
+            quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers,
             require_post_norm=require_post_norm,
+            prefix=prefix,
         )
     elif isinstance(vision_config, PixtralVisionConfig):
         return PixtralHFVisionModel(
             vision_config,
-            quant_config,
+            quant_config=quant_config,
             num_hidden_layers_override=num_hidden_layers,
             require_post_norm=require_post_norm,
+            prefix=prefix,
         )
 
     msg = f"Unsupported vision config: {type(vision_config)}"
@@ -274,14 +278,20 @@ def __init__(self,
 
         # TODO: Optionally initializes this for supporting embeddings.
         self.vision_tower = init_vision_tower_for_llava(
-            config, quant_config, require_post_norm=False)
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix="vision_tower")
         self.multi_modal_projector = LlavaMultiModalProjector(
             vision_hidden_size=config.vision_config.hidden_size,
             text_hidden_size=config.text_config.hidden_size,
             projector_hidden_act=config.projector_hidden_act)
 
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            prefix="language_model")
 
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_next.py

@@ -293,7 +293,10 @@ def __init__(self,
 
         # TODO: Optionally initializes this for supporting embeddings.
         self.vision_tower = init_vision_tower_for_llava(
-            config, quant_config, require_post_norm=False)
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix="vision_tower")
         self.image_newline = nn.Parameter(
             torch.empty(config.text_config.hidden_size))
         self.multi_modal_projector = LlavaMultiModalProjector(
@@ -302,7 +305,10 @@ def __init__(self,
             projector_hidden_act=config.projector_hidden_act)
 
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            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

@@ -257,14 +257,20 @@ def __init__(self,
 
         # Initialize the vision tower only up to the required feature layer
         self.vision_tower = init_vision_tower_for_llava(
-            config, quant_config, require_post_norm=False)
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix="vision_tower")
         self.vision_resampler = LlavaNextVideoPooler(config)
         self.multi_modal_projector = LlavaNextMultiModalProjector(
             vision_hidden_size=config.vision_config.hidden_size,
             text_hidden_size=config.text_config.hidden_size,
             projector_hidden_act=config.projector_hidden_act)
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            prefix="language_model")
 
         self.make_empty_intermediate_tensors = (
             self.language_model.model.make_empty_intermediate_tensors)

vllm/model_executor/models/llava_onevision.py

@@ -401,10 +401,16 @@ def __init__(self,
 
         # Initialize the vision tower only up to the required feature layer
         self.vision_tower = init_vision_tower_for_llava(
-            config, quant_config, require_post_norm=False)
+            config,
+            quant_config,
+            require_post_norm=False,
+            prefix="vision_tower")
         self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
         self.language_model = init_vllm_registered_model(
-            config.text_config, cache_config, quant_config)
+            config.text_config,
+            cache_config,
+            quant_config,
+            prefix="language_model")
         self.image_newline = nn.Parameter(
             torch.empty(config.text_config.hidden_size))