[Model] Rename MiniCPMVQwen2 to MiniCPMV2.6

vllm/model_executor/models/minicpmv.py

@@ -216,16 +216,7 @@ def __init__(
 
         self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
         trunc_normal_(self.query, std=0.02)
-
-        if kv_dim is not None and kv_dim != embed_dim:
-            self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
-        else:
-            # Maintain the same return value with ReplicatedLinear.forward
-            self.kv_proj = lambda *args, **kwargs: (
-                nn.Identity()(*args, **kwargs),
-                None,
-            )
-
+        self.kv_proj = ReplicatedLinear(kv_dim, embed_dim, bias=False)
         self.attn = nn.MultiheadAttention(embed_dim, num_heads)
         self.ln_q = norm_layer(embed_dim)
         self.ln_kv = norm_layer(embed_dim)
@@ -261,7 +252,6 @@ def __init__(
                          norm_layer)
 
         self.adaptive = adaptive
-
         pos_embed_arr = get_2d_sincos_pos_embed(embed_dim,
                                                 grid_size,
                                                 version=(2, 0))
@@ -717,7 +707,7 @@ def is_default_weight_loading(self, name: str) -> bool:
         raise NotImplementedError
 
 
-class MiniCPMV2(MiniCPMVBaseModel):
+class MiniCPMV2_0(MiniCPMVBaseModel):
 
     def __init__(
         self,
@@ -890,10 +880,7 @@ def is_default_weight_loading(self, name: str) -> bool:
         return "resampler" in name
 
 
-# NOTE: Currently, information about this model is unavailable. We are
-# temporarily using `MiniCPMVQwen2` as it's name. The name may need
-# to be modified in the future.
-class MiniCPMVQwen2(MiniCPMVBaseModel):
+class MiniCPMV2_6(MiniCPMVBaseModel):
 
     def __init__(
         self,
@@ -903,6 +890,7 @@ def __init__(
         quant_config: Optional[QuantizationConfig] = None,
     ):
         super().__init__(config, multimodal_config, cache_config, quant_config)
+        assert self.version == (2, 6)
 
     def init_llm(
         self,
@@ -930,6 +918,7 @@ def init_vision_module(self) -> nn.Module:
 
     def init_resampler(self, embed_dim: int, vision_dim: int) -> nn.Module:
         with set_default_torch_dtype(torch.float16):
+            # The resampler in 2.6 remains consistent with the one in 2.5.
             resampler = Resampler2_5(
                 num_queries=self.config.query_num,
                 embed_dim=embed_dim,
@@ -989,6 +978,13 @@ def is_default_weight_loading(self, name: str) -> bool:
         return "resampler" in name or "vpm" in name
 
 
+_SUPPORT_VERSION = {
+    (2, 0): MiniCPMV2_0,
+    (2, 5): MiniCPMV2_5,
+    (2, 6): MiniCPMV2_6
+}
+
+
 @MULTIMODAL_REGISTRY.register_image_input_mapper()
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_minicpmv_image_tokens)
 @INPUT_REGISTRY.register_dummy_data(dummy_data_for_minicpmv)
@@ -1007,20 +1003,11 @@ def __new__(
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
     ):
-        if not hasattr(config, "version"):
-            if config.hidden_size == 2304 and config.query_num == 64:
-                version = (2, 0)
-            else:
-                version = (2, 5)
-        else:
-            version = str(config.version).split(".")
-            version = tuple([int(x) for x in version])
+        version = str(config.version).split(".")
+        version = tuple([int(x) for x in version])
         # Dispatch class based on version
-        if version == (2, 0):
-            instance_class = MiniCPMV2
-        elif version == (2, 5):
-            instance_class = MiniCPMV2_5
-        else:
-            instance_class = MiniCPMVQwen2
+        instance_class = _SUPPORT_VERSION.get(version, None)
+        if instance_class is None:
+            raise NotImplementedError
         return instance_class(config, multimodal_config, cache_config,
                               quant_config)