Add support for tie_word_embeddings when loading weights + support fo…

…r SmolLM (#1508)

README.md

@@ -263,6 +263,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 - BaiChuan2
 - MiniCPM / MiniCPM 3
 - XVERSE / XVERSE MoE
+- SmolLM
 
 
 **Embedding Models**

python/sglang/srt/models/llama.py

@@ -403,6 +403,14 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
 
+        if (
+            hasattr(self.config, "tie_word_embeddings")
+            and self.config.tie_word_embeddings
+        ):
+            # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
+            param = self.lm_head.weight
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, self.model.embed_tokens.weight)
         apply_torchao_config_(self, params_dict, set(["proj.weight"]))
 
 

test/srt/models/test_generation_models.py

@@ -51,6 +51,7 @@ class ModelCase:
 # All other models
 ALL_OTHER_MODELS = [
     ModelCase("Qwen/Qwen2-1.5B"),
+    ModelCase("HuggingFaceTB/SmolLM-135M-Instruct"),
 ]
 
 TORCH_DTYPES = [torch.float16]