Revert "[FEAT] Support GGUF format" (#2285)

python/sglang/srt/models/qwen2.py

@@ -230,7 +230,6 @@ class Qwen2Model(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
-            quant_config=quant_config,
         )
         self.layers = make_layers(
             config.num_hidden_layers,
@@ -277,12 +276,7 @@ class Qwen2ForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = Qwen2Model(config, quant_config=quant_config)
-        if config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
-        else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size, config.hidden_size, quant_config=quant_config
-            )
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
@@ -298,7 +292,7 @@ class Qwen2ForCausalLM(nn.Module):
         hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
         if not get_embedding:
             return self.logits_processor(
-                input_ids, hidden_states, self.lm_head, forward_batch
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
             )
         else:
             return self.pooler(hidden_states, forward_batch)
@@ -312,7 +306,6 @@ class Qwen2ForCausalLM(nn.Module):
             ("gate_up_proj", "gate_proj", 0),
             ("gate_up_proj", "up_proj", 1),
         ]
-
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name or "projector" in name:
@@ -342,6 +335,11 @@ class Qwen2ForCausalLM(nn.Module):
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader", default_weight_loader)
                 weight_loader(param, loaded_weight)
+                if (
+                    self.config.tie_word_embeddings
+                    and name == "model.embed_tokens.weight"
+                ):
+                    weight_loader(params_dict["lm_head.weight"], loaded_weight)
 
 
 EntryClass = Qwen2ForCausalLM