fix: texttransformer

server/clip_server/model/model.py

@@ -82,8 +82,15 @@ def __init__(
 
 
 class TextTransformer(_TextTransformer):
-    def __init__(self, dtype: torch.dtype = torch.float32, **kwargs):
-        super().__init__(**kwargs)
+    def __init__(
+        self,
+        context_length: int,
+        vocab_size: int,
+        output_dim: int,
+        dtype: torch.dtype = torch.float32,
+        **kwargs,
+    ):
+        super().__init__(context_length, vocab_size, output_dim=output_dim, **kwargs)
         self.transformer = Transformer(dtype=dtype, **kwargs)
         self.init_parameters()
 
@@ -249,22 +256,7 @@ def __init__(
         quick_gelu: bool = False,
         dtype: Optional[torch.dtype] = torch.float32,
     ):
-        self.visual = _build_vision_tower(
-            embed_dim=embed_dim,
-            vision_cfg=vision_cfg,
-            quick_gelu=quick_gelu,
-            dtype=dtype,
-        )
-        text = _build_text_tower(
-            embed_dim=embed_dim, text_cfg=text_cfg, quick_gelu=quick_gelu, dtype=dtype
-        )
-        self.transformer = text.transformer
-        self.vocab_size = text.vocab_size
-        self.token_embedding = text.token_embedding
-        self.positional_embedding = text.positional_embedding
-        self.ln_final = text.ln_final
-        self.text_projection = text.text_projection
-        self.register_buffer('attn_mask', text.attn_mask, persistent=False)
+        super().__init__(embed_dim, vision_cfg, text_cfg, quick_gelu, dtype)
 
 
 def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):