[Bugfix] Fix prefix strings for quantized VLMs (#9772)

vllm/model_executor/models/gemma.py

@@ -43,7 +43,8 @@ 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)
 
 logger = init_logger(__name__)
 
@@ -83,16 +84,23 @@ class GemmaMLP(nn.Module):
         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 @@ class GemmaMLP(nn.Module):
 
 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 @@ class GemmaAttention(nn.Module):
             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 @@ class GemmaDecoderLayer(nn.Module):
         config: GemmaConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -198,6 +212,7 @@ class GemmaDecoderLayer(nn.Module):
             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,
@@ -205,6 +220,7 @@ class GemmaDecoderLayer(nn.Module):
             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 @@ class GemmaModel(nn.Module):
         )
         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 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -375,7 +392,10 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         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 = (