Refactor LoRA handling to support adapter tensors in fused format (#6585)

python/sglang/srt/models/phi4mm.py

@@ -17,6 +17,7 @@
 
 import logging
 import math
+import re
 from collections.abc import Iterable
 from typing import List, Optional, Tuple
 
@@ -392,6 +393,10 @@ class Phi4MMForCausalLM(nn.Module):
         "gate_up_proj": ["gate_proj", "up_proj"],
     }
 
+    lora_pattern = re.compile(
+        r"^language_model\.model\.layers\.(\d+)\.(?:self_attn|mlp)\.(?:qkv_proj|o_proj|down_proj|gate_up_proj)"
+    )
+
     def __init__(
         self,
         config: PretrainedConfig,
@@ -446,6 +451,9 @@ 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]:
+        return self.lora_pattern.match(module_name)
+
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)