[Bugfix] Make the deviceprofiler include LoRA memory. (#14469)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

vllm/worker/model_runner.py

@@ -1111,41 +1111,41 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
         with DeviceMemoryProfiler(self.device) as m:
             time_before_load = time.perf_counter()
             self.model = get_model(vllm_config=self.vllm_config)
+            if self.lora_config:
+                assert supports_lora(
+                    self.model
+                ), f"{self.model.__class__.__name__} does not support LoRA yet."
+
+                if supports_multimodal(self.model):
+                    logger.warning(
+                        "Regarding multimodal models, vLLM currently "
+                        "only supports adding LoRA to language model.")
+                # It's necessary to distinguish between the
+                # max_position_embeddings of VLMs and LLMs.
+                if hasattr(self.model.config, "max_position_embeddings"):
+                    max_pos_embeddings = (
+                        self.model.config.max_position_embeddings)
+                else:
+                    max_pos_embeddings = (
+                        self.model.config.text_config.max_position_embeddings)
+
+                self.lora_manager = LRUCacheWorkerLoRAManager(
+                    self.scheduler_config.max_num_seqs,
+                    self.scheduler_config.max_num_batched_tokens,
+                    self.vocab_size,
+                    self.lora_config,
+                    self.device,
+                    self.model.embedding_modules,
+                    self.model.embedding_padding_modules,
+                    max_position_embeddings=max_pos_embeddings,
+                )
+                self.model = self.lora_manager.create_lora_manager(self.model)
             time_after_load = time.perf_counter()
 
         self.model_memory_usage = m.consumed_memory
         logger.info("Model loading took %.4f GB and %.6f seconds",
                     self.model_memory_usage / float(2**30),
                     time_after_load - time_before_load)
-
-        if self.lora_config:
-            assert supports_lora(
-                self.model
-            ), f"{self.model.__class__.__name__} does not support LoRA yet."
-
-            if supports_multimodal(self.model):
-                logger.warning("Regarding multimodal models, vLLM currently "
-                               "only supports adding LoRA to language model.")
-            # It's necessary to distinguish between the max_position_embeddings
-            # of VLMs and LLMs.
-            if hasattr(self.model.config, "max_position_embeddings"):
-                max_pos_embeddings = self.model.config.max_position_embeddings
-            else:
-                max_pos_embeddings = (
-                    self.model.config.text_config.max_position_embeddings)
-
-            self.lora_manager = LRUCacheWorkerLoRAManager(
-                self.scheduler_config.max_num_seqs,
-                self.scheduler_config.max_num_batched_tokens,
-                self.vocab_size,
-                self.lora_config,
-                self.device,
-                self.model.embedding_modules,
-                self.model.embedding_padding_modules,
-                max_position_embeddings=max_pos_embeddings,
-            )
-            self.model = self.lora_manager.create_lora_manager(self.model)
-
         if self.prompt_adapter_config:
             self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager(
                 self.scheduler_config.max_num_seqs,