[TRTLLM-6825][fix] Update lora for phi4-mm

tensorrt_llm/_torch/models/modeling_phi4mm.py

@@ -243,23 +243,21 @@ def forward(
     @staticmethod
     def lora_config(model_dir: str):
         _lora_config = LoraConfig(
-            lora_dir=[
-                f"{model_dir}/vision-lora",
-                f"{model_dir}/speech-lora",
-            ],
             lora_target_modules=[
                 "attn_qkv",
                 "attn_dense",
-                "mlp_h_to_4h",
+                "mlp_gate_up",
                 "mlp_4h_to_h",
             ],
             trtllm_modules_to_hf_modules={
                 "attn_qkv": "qkv_proj",
                 "attn_dense": "o_proj",
-                "mlp_h_to_4h": "gate_up_proj",
+                "mlp_gate_up": "gate_up_proj",
                 "mlp_4h_to_h": "down_proj",
             },
             max_lora_rank=320,  # Max rank for Phi4MM.
+            swap_gate_up_proj_lora_b_weight=
+            False,  # Disable swap gate_up_proj.lora_B.weight for Phi4MM.
         )
         return _lora_config
 

tensorrt_llm/_torch/pyexecutor/_util.py

@@ -509,7 +509,8 @@ def create_py_executor_instance(
         resources[ResourceManagerType.PEFT_CACHE_MANAGER] = peft_cache_manager
         model_engine.set_lora_model_config(
             lora_config.lora_target_modules,
-            lora_config.trtllm_modules_to_hf_modules)
+            lora_config.trtllm_modules_to_hf_modules,
+            lora_config.swap_gate_up_proj_lora_b_weight)
 
     max_num_sequences = executor_config.max_batch_size * mapping.pp_size
 

tensorrt_llm/_torch/pyexecutor/model_engine.py

@@ -438,13 +438,16 @@ def __init__(
         else:
             self.cache_indirection_attention = None
 
-    def set_lora_model_config(self, lora_target_modules: list[str],
-                              trtllm_modules_to_hf_modules: dict[str, str]):
+    def set_lora_model_config(self,
+                              lora_target_modules: list[str],
+                              trtllm_modules_to_hf_modules: dict[str, str],
+                              swap_gate_up_proj_lora_b_weight: bool = True):
         self.lora_model_config = LoraModelConfig(
             lora_target_modules=lora_target_modules,
             trtllm_modules_to_hf_modules=trtllm_modules_to_hf_modules,
             hidden_size=self.model.config.hidden_size,
-            dtype=torch_dtype_to_str(self.model.config.torch_dtype))
+            dtype=torch_dtype_to_str(self.model.config.torch_dtype),
+            swap_gate_up_proj_lora_b_weight=swap_gate_up_proj_lora_b_weight)
 
     @property
     def use_mrope(self):

tensorrt_llm/_torch/pyexecutor/resource_manager.py

@@ -1206,7 +1206,8 @@ def __init__(self,
         self._lora_model_config = LoraModelConfig(
             lora_config.lora_target_modules,
             lora_config.trtllm_modules_to_hf_modules, model_config.hidden_size,
-            binding_to_str_dtype(model_config.data_type))
+            binding_to_str_dtype(model_config.data_type),
+            lora_config.swap_gate_up_proj_lora_b_weight)
         self._lora_manager = LoraManager()
 
     def add_request_peft(self, request: LlmRequest):

tensorrt_llm/lora_manager.py

@@ -241,6 +241,7 @@ class LoraConfig(DictConversion):
     trtllm_modules_to_hf_modules: Dict[str, str] = field(default_factory=dict)
     max_loras: int | None = None
     max_cpu_loras: int | None = None
+    swap_gate_up_proj_lora_b_weight: bool = True
 
     def __post_init__(self):
         assert self.lora_ckpt_source in ["hf", "nemo"], (
@@ -258,6 +259,7 @@ class LoraModelConfig:
     trtllm_modules_to_hf_modules: dict[str, str]
     hidden_size: int
     dtype: str
+    swap_gate_up_proj_lora_b_weight: bool = True
 
 
 class HfLoraLoader:
@@ -1026,16 +1028,17 @@ def load_from_hf(
         )
         hf_modules = set(hf_modules_to_trtllm_modules.keys())
 
-        def preprocess_lora_weights(lora_model):
+        def preprocess_lora_weights(lora_model, model_config):
             # Swap weights of gate_up_proj
-            for key, value in lora_model.items():
-                if "gate_up_proj.lora_B.weight" in key:
-                    original_weights = value.contiguous().clone()
-                    half_split = original_weights.shape[0] // 2
-                    first_half = original_weights[:half_split, :]
-                    second_half = original_weights[half_split:, :]
-                    value = torch.cat((second_half, first_half), dim=0)
-                    lora_model[key] = value
+            if getattr(model_config, "swap_gate_up_proj_lora_b_weight", True):
+                for key, value in lora_model.items():
+                    if "gate_up_proj.lora_B.weight" in key:
+                        original_weights = value.contiguous().clone()
+                        half_split = original_weights.shape[0] // 2
+                        first_half = original_weights[:half_split, :]
+                        second_half = original_weights[half_split:, :]
+                        value = torch.cat((second_half, first_half), dim=0)
+                        lora_model[key] = value
             return lora_model
 
         def load_from_model_dir(uid, model_dir, hf_config):
@@ -1047,7 +1050,7 @@ def load_from_model_dir(uid, model_dir, hf_config):
             lora_model = load_state_dict(get_model_path(model_dir, "adapter_model"))
             if lora_model is None:
                 raise ValueError(f"Failed to load adapter_model from {model_dir}")
-            lora_model = preprocess_lora_weights(lora_model)
+            lora_model = preprocess_lora_weights(lora_model, model_config)
             all_weights = get_all_hf_lora_weights(lora_model, hf_modules, component)
             rank = int(hf_config["r"])
             rs_lora = bool(hf_config.get("use_rslora", False))

tests/integration/defs/perf/pytorch_model_config.py

@@ -198,15 +198,17 @@ def get_model_yaml_config(model_label: str,
         }
         if 'phi_4_multimodal_instruct' in model_label:
             lora_config['lora_config']['lora_target_modules'] = [
-                "attn_qkv", "attn_dense", "mlp_h_to_4h", "mlp_4h_to_h"
+                "attn_qkv", "attn_dense", "mlp_gate_up", "mlp_4h_to_h"
             ]
             lora_config['lora_config']['trtllm_modules_to_hf_modules'] = {
                 "attn_qkv": "qkv_proj",
                 "attn_dense": "o_proj",
-                "mlp_h_to_4h": "gate_up_proj",
+                "mlp_gate_up": "gate_up_proj",
                 "mlp_4h_to_h": "down_proj"
             }
             lora_config['lora_config']['max_lora_rank'] = 320
+            lora_config['lora_config'][
+                'swap_gate_up_proj_lora_b_weight'] = False
         base_config.update(lora_config)
 
     kv_cache_config = base_config.get('kv_cache_config', {})

tests/integration/defs/test_e2e.py

@@ -2486,15 +2486,15 @@ def test_ptp_quickstart_multimodal_phi4mm(llm_root, llm_venv, modality):
     }
     expected_keywords = {
         "image": [
-            ["image", "depicts", "mountain", "half", "rock"],
-            ["road", "car", "lane", "traffic", "bus"],
+            ["object", "mountain", "weather", "clear", "clouds"],
+            ["traffic", "road", "vehicles", "cars", "bus"],
         ],
         "audio": [
             ["what", "is", "the", "traffic", "sign", "in", "image"],
             ["what", "is", "shown", "in", "this", "image"],
         ],
         "image_audio": [
-            ["image", "depicts", "Grand", "rock", "scene"],
+            ["image", "depicts", "scenic", "famous", "landmark"],
         ],
     }