[Model] Rename MiniCPMVQwen2 to MiniCPMV2.6 (#7273)

vllm/model_executor/models/minicpmv.py

@@ -216,7 +216,6 @@ class BaseResampler(nn.Module):
 
         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:
@@ -225,7 +224,6 @@ class BaseResampler(nn.Module):
                 nn.Identity()(*args, **kwargs),
                 None,
             )
-
         self.attn = nn.MultiheadAttention(embed_dim, num_heads)
         self.ln_q = norm_layer(embed_dim)
         self.ln_kv = norm_layer(embed_dim)
@@ -261,7 +259,6 @@ class Resampler2(BaseResampler):
                          norm_layer)
 
         self.adaptive = adaptive
-
         pos_embed_arr = get_2d_sincos_pos_embed(embed_dim,
                                                 grid_size,
                                                 version=(2, 0))
@@ -717,7 +714,7 @@ class MiniCPMVBaseModel(nn.Module, SupportsVision):
         raise NotImplementedError
 
 
-class MiniCPMV2(MiniCPMVBaseModel):
+class MiniCPMV2_0(MiniCPMVBaseModel):
 
     def __init__(
         self,
@@ -890,10 +887,7 @@ class MiniCPMV2_5(MiniCPMVBaseModel):
         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 +897,7 @@ class MiniCPMVQwen2(MiniCPMVBaseModel):
         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 +925,7 @@ class MiniCPMVQwen2(MiniCPMVBaseModel):
 
     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 +985,13 @@ class MiniCPMVQwen2(MiniCPMVBaseModel):
         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)
@@ -1016,11 +1019,9 @@ class MiniCPMV(MiniCPMVBaseModel):
             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 ValueError(
+                "Currently, MiniCPMV only supports versions 2.0, 2.5, and 2.6")
         return instance_class(config, multimodal_config, cache_config,
                               quant_config)