added support for tied weights in qwen pipeline parallelism (#6546)

python/sglang/srt/models/qwen2.py

@@ -386,15 +386,36 @@ class Qwen2ForCausalLM(nn.Module):
         self.model = Qwen2Model(
             config, quant_config=quant_config, prefix=add_prefix("model", prefix)
         )
-        if config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
+
+        # handle the lm head on different pp ranks
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and 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,
+                    prefix=add_prefix("lm_head", prefix),
+                )
         else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=add_prefix("lm_head", prefix),
-            )
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
+        # perform weight tying for PP
+        if self.pp_group.world_size > 1 and config.tie_word_embeddings:
+            if self.pp_group.is_first_rank:
+                self.pp_group.send(
+                    self.model.embed_tokens.weight, dst=self.pp_group.last_rank
+                )
+            else:
+                emb_token_weight = self.pp_group.recv(
+                    size=(config.vocab_size, config.hidden_size),
+                    dtype=next(self.model.parameters()).dtype,
+                    src=self.pp_group.first_rank,
+                )
+                self.lm_head.weight.copy_(emb_token_weight)
+
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
@@ -470,7 +491,15 @@ class Qwen2ForCausalLM(nn.Module):
                 # the checkpoint. Skip them.
                 continue
             if self.config.tie_word_embeddings and "lm_head.weight" in name:
-                continue
+                if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
+                    # Handle pp weight tying here
+                    # find the embed_tokens.weight in the weights
+                    embed_token_weights = next(
+                        filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
+                    )[1]
+                    loaded_weight = embed_token_weights
+                else:
+                    continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue
 

python/sglang/srt/models/qwen3.py

@@ -21,7 +21,7 @@ from sglang.srt.layers.pooler import Pooler, PoolingType
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.rotary_embedding import get_rope
-from sglang.srt.layers.utils import get_layer_id
+from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_loader.weight_utils import default_weight_loader
@@ -249,15 +249,36 @@ class Qwen3ForCausalLM(nn.Module):
         self.model = Qwen3Model(
             config, quant_config=quant_config, prefix=add_prefix("model", prefix)
         )
-        if config.tie_word_embeddings:
-            self.lm_head = self.model.embed_tokens
+
+        # handle the lm head on different pp ranks
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and 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,
+                    prefix=add_prefix("lm_head", prefix),
+                )
         else:
-            self.lm_head = ParallelLMHead(
-                config.vocab_size,
-                config.hidden_size,
-                quant_config=quant_config,
-                prefix=add_prefix("lm_head", prefix),
-            )
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
+        # perform weight tying for PP
+        if self.pp_group.world_size > 1 and config.tie_word_embeddings:
+            if self.pp_group.is_first_rank:
+                self.pp_group.send(
+                    self.model.embed_tokens.weight, dst=self.pp_group.last_rank
+                )
+            else:
+                emb_token_weight = self.pp_group.recv(
+                    size=(config.vocab_size, config.hidden_size),
+                    dtype=next(self.model.parameters()).dtype,
+                    src=self.pp_group.first_rank,
+                )
+                self.lm_head.weight.copy_(emb_token_weight)
+
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
@@ -330,7 +351,15 @@ class Qwen3ForCausalLM(nn.Module):
                 # the checkpoint. Skip them.
                 continue
             if self.config.tie_word_embeddings and "lm_head.weight" in name:
-                continue
+                if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
+                    # Handle pp weight tying here
+                    # find the embed_tokens.weight in the weights
+                    embed_token_weights = next(
+                        filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
+                    )[1]
+                    loaded_weight = embed_token_weights
+                else:
+                    continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue