[Bugfix] Fix prefix strings for quantized VLMs

vllm/model_executor/model_loader/loader.py

@@ -147,15 +147,20 @@ def _get_model_initialization_kwargs(
     return extra_kwargs
 
 
-def build_model(model_class: Type[nn.Module], hf_config: PretrainedConfig,
+def build_model(model_class: Type[nn.Module],
+                hf_config: PretrainedConfig,
                 cache_config: Optional[CacheConfig],
-                quant_config: Optional[QuantizationConfig], *,
+                quant_config: Optional[QuantizationConfig],
+                *,
                 lora_config: Optional[LoRAConfig],
                 multimodal_config: Optional[MultiModalConfig],
-                scheduler_config: Optional[SchedulerConfig]) -> nn.Module:
+                scheduler_config: Optional[SchedulerConfig],
+                prefix: Optional[str] = None) -> nn.Module:
     extra_kwargs = _get_model_initialization_kwargs(model_class, lora_config,
                                                     multimodal_config,
                                                     scheduler_config)
+    if prefix:
+        extra_kwargs["prefix"] = prefix
 
     return model_class(config=hf_config,
                        cache_config=cache_config,

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/internlm2.py

@@ -30,7 +30,8 @@
 
 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 InternLM2MLP(nn.Module):
@@ -41,16 +42,23 @@ def __init__(
         intermediate_size: int,
         hidden_act: str,
         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,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.w2 = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
             bias=False,
-            quant_config=quant_config)
-        self.w2 = RowParallelLinear(intermediate_size,
-                                    hidden_size,
-                                    bias=False,
-                                    quant_config=quant_config)
+            quant_config=quant_config,
+            prefix=f"{prefix}.w2",
+        )
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -75,6 +83,7 @@ def __init__(
         max_position_embeddings: int = 8192,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -108,12 +117,14 @@ def __init__(
             self.total_num_kv_heads,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.wqkv",
         )
         self.wo = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            prefix=f"{prefix}.wo",
         )
 
         self.rotary_emb = get_rope(
@@ -123,12 +134,15 @@ def __init__(
             base=rope_theta,
             rope_scaling=rope_scaling,
         )
-        self.attn = Attention(self.num_heads,
-                              self.head_dim,
-                              self.scaling,
-                              num_kv_heads=self.num_kv_heads,
-                              cache_config=cache_config,
-                              quant_config=quant_config)
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attn",
+        )
 
     def split_qkv(self, qkv: torch.Tensor):
         seq_len = qkv.shape[0]
@@ -176,6 +190,7 @@ def __init__(
         config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -192,15 +207,18 @@ def __init__(
             max_position_embeddings=max_position_embeddings,
             cache_config=cache_config,
             quant_config=quant_config,
+            prefix=f"{prefix}.attention",
         )
         self.feed_forward = InternLM2MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
             quant_config=quant_config,
+            prefix=f"{prefix}.feed_forward",
         )
         self.attention_norm = RMSNorm(config.hidden_size,
-                                      eps=config.rms_norm_eps)
+                                      eps=config.rms_norm_eps,
+                                      prefix=f"{prefix}.attention_norm")
         self.ffn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def forward(
@@ -251,8 +269,8 @@ def __init__(
         )
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
-            lambda prefix: InternLMDecoderLayer(config, cache_config,
-                                                quant_config),
+            lambda prefix: InternLMDecoderLayer(
+                config, cache_config, quant_config, prefix=prefix),
             prefix=f"{prefix}.layers")
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.make_empty_intermediate_tensors = (
@@ -306,14 +324,19 @@ def __init__(
         config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.config = config
         self.quant_config = quant_config
-        self.model = InternLM2Model(config, cache_config, quant_config)
+        self.model = InternLM2Model(config,
+                                    cache_config,
+                                    quant_config,
+                                    prefix=maybe_prefix(prefix, "model"))
         self.output = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
-                                     quant_config=quant_config)
+                                     quant_config=quant_config,
+                                     prefix=maybe_prefix(prefix, "output"))
         if self.config.tie_word_embeddings:
             self.output.weight = self.model.tok_embeddings.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)

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/llama.py

@@ -55,7 +55,8 @@
 
 from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
-                    make_empty_intermediate_tensors_factory, make_layers)
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class LlamaMLP(nn.Module):
@@ -500,6 +501,7 @@ def __init__(
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         lora_config: Optional[LoRAConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
 
@@ -510,7 +512,7 @@ def __init__(
                                 cache_config,
                                 quant_config,
                                 lora_config=lora_config,
-                                prefix="model")
+                                prefix=maybe_prefix(prefix, "model"))
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
             if lora_config:
@@ -526,6 +528,7 @@ def __init__(
                     if not lora_config else
                     lora_config.lora_vocab_padding_size),
                 quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
             )
             if config.tie_word_embeddings:
                 self.lm_head = self.lm_head.tie_weights(