Fix incorrect LoRA weight loading for fused gate_up_proj (#6734)

python/sglang/srt/conversation.py

@@ -680,8 +680,8 @@ register_conv_template(
 register_conv_template(
     Conversation(
         name="phi-4-mm",
-        system_message="You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
+        system_message="",
-        system_template="<|system|>{system_message}<|end|>",
+        system_template="{system_message}",
         roles=("<|user|>", "<|assistant|>"),
         sep_style=SeparatorStyle.NO_COLON_SINGLE,
         sep="<|end|>",

python/sglang/srt/lora/lora.py

@@ -209,4 +209,12 @@ class LoRAAdapter(nn.Module):
                 gate_up_name = weight_name
                 if "lora_A" in weight_name:
                     weights[gate_up_name] = weights[gate_up_name].repeat(2, 1)
-                # else: "lora_B" is already stacked, no operations is needed.
+                else:
+                    output_dim = weights[gate_up_name].shape[0] // 2
+                    weights[gate_up_name] = torch.stack(
+                        [
+                            weights[gate_up_name][:output_dim, :],
+                            weights[gate_up_name][output_dim:, :],
+                        ],
+                        dim=0,
+                    )

python/sglang/srt/models/idefics2.py

@@ -296,23 +296,30 @@ class Idefics2VisionTransformer(nn.Module):
     def compute_cu_seqlens(
         self,
         tgt_sizes: Optional[torch.Tensor] = None,
-        atch_attention_mask: Optional[torch.BoolTensor] = None,
+        input_embeds: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         # shape: (batch_size,)
         if tgt_sizes is not None:
-            patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+            seqlen = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+        elif input_embeds is not None:
+            seqlen = torch.full(
+                size=(input_embeds.shape[0],),
+                fill_value=input_embeds.shape[1],
+                dtype=torch.int32,
+                device=input_embeds.device,
+            )
         else:
-            patch_len = atch_attention_mask[:, :, 0].sum(dim=1) * atch_attention_mask[
+            raise ValueError(
-                :, 0, :
+                "Either `tgt_sizes` or `input_embeds` must be provided to compute cu_seqlens."
-            ].sum(dim=1)
+            )
 
         cu_seqlens = torch.cat(
             [
-                torch.tensor([0], device=patch_len.device, dtype=torch.int32),
+                torch.tensor([0], device=seqlen.device, dtype=torch.int32),
-                torch.cumsum(patch_len, dim=0, dtype=torch.int32),
+                torch.cumsum(seqlen, dim=0, dtype=torch.int32),
             ],
             dim=0,
-        ).to(patch_len.device)
+        ).to(seqlen.device)
         return cu_seqlens
 
     def forward(
@@ -326,7 +333,7 @@ class Idefics2VisionTransformer(nn.Module):
             patch_attention_mask=patch_attention_mask,
             tgt_sizes=tgt_sizes,
         )
-        cu_seqlens = self.compute_cu_seqlens(tgt_sizes, patch_attention_mask)
+        cu_seqlens = self.compute_cu_seqlens(tgt_sizes, hidden_states)
         encoder_outputs = self.encoder(
             hidden_states,
             cu_seqlens=cu_seqlens,

python/sglang/srt/models/phi4mm.py

@@ -451,8 +451,8 @@ class Phi4MMForCausalLM(nn.Module):
         pattern = MultiModalityDataPaddingPatternMultimodalTokens([im_token_id])
         return pattern.pad_input_tokens(input_ids, mm_inputs)
 
-    def should_apply_lora(self, module_name: str) -> Optional[str]:
+    def should_apply_lora(self, module_name: str) -> bool:
-        return self.lora_pattern.match(module_name)
+        return bool(self.lora_pattern.match(module_name))
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [