[LoRA] Much faster startup when LoRA is enabled (#23777)

Signed-off-by: Andy Lo <[email protected]>
Co-authored-by: Jee Jee Li <[email protected]>

Author: andylolu2

vllm/v1/worker/gpu_model_runner.py

@@ -2213,6 +2213,7 @@ def _dummy_run(
         uniform_decode: bool = False,
         skip_eplb: bool = False,
         is_profile: bool = False,
+        remove_lora: bool = True,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Run a dummy forward pass to warm up/profile run or capture the
@@ -2230,6 +2231,7 @@ def _dummy_run(
             uniform_decode: If True, the batch is a uniform decode batch.
             skip_eplb: If True, skip EPLB state update.
             is_profile: If True, this is a profile run.
+            remove_lora: If False, dummy LoRAs are not destroyed after the run
         """
         assert cudagraph_runtime_mode in {
             CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL
@@ -2317,7 +2319,7 @@ def _dummy_run(
                         attn_metadata[layer_name] = attn_metadata_i
 
         with self.maybe_dummy_run_with_lora(self.lora_config,
-                                            num_scheduled_tokens):
+                                            num_scheduled_tokens, remove_lora):
             if self.supports_mm_inputs:
                 input_ids = None
                 inputs_embeds = self.inputs_embeds[:num_tokens]
@@ -2708,11 +2710,14 @@ def _capture_cudagraphs(self, compilation_cases: list[int],
                                 cudagraph_runtime_mode=CUDAGraphMode.NONE,
                                 force_attention=force_attention,
                                 uniform_decode=uniform_decode,
-                                skip_eplb=True)
+                                skip_eplb=True,
+                                remove_lora=False)
             self._dummy_run(num_tokens,
                             cudagraph_runtime_mode=cudagraph_runtime_mode,
                             uniform_decode=uniform_decode,
-                            skip_eplb=True)
+                            skip_eplb=True,
+                            remove_lora=False)
+        self.maybe_remove_all_loras(self.lora_config)
 
     def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
         """

vllm/v1/worker/gpu_worker.py

@@ -308,7 +308,10 @@ def compile_or_warm_up_model(self) -> None:
         # We skip EPLB here since we don't want to record dummy metrics
         for size in sorted(warmup_sizes, reverse=True):
             logger.info("Compile and warming up model for size %d", size)
-            self.model_runner._dummy_run(size, skip_eplb=True)
+            self.model_runner._dummy_run(size,
+                                         skip_eplb=True,
+                                         remove_lora=False)
+        self.model_runner.maybe_remove_all_loras(self.model_runner.lora_config)
 
         # Warmup and tune the kernels used during model execution before
         # cuda graph capture.

vllm/v1/worker/lora_model_runner_mixin.py

@@ -5,7 +5,7 @@
 """
 
 from contextlib import contextmanager
-from typing import Union
+from typing import Optional, Union
 
 import numpy as np
 import torch
@@ -87,7 +87,9 @@ def set_active_loras(self, input_batch: InputBatch,
                                       lora_requests)
 
     @contextmanager
-    def maybe_setup_dummy_loras(self, lora_config):
+    def maybe_setup_dummy_loras(self,
+                                lora_config: Optional[LoRAConfig],
+                                remove_lora: bool = True):
         if lora_config is None:
             yield
         else:
@@ -114,10 +116,11 @@ def maybe_setup_dummy_loras(self, lora_config):
                 yield
 
             # __exit__ code
-            self.lora_manager.remove_all_adapters()
+            if remove_lora:
+                self.lora_manager.remove_all_adapters()
 
     @contextmanager
-    def maybe_select_dummy_loras(self, lora_config: LoRAConfig,
+    def maybe_select_dummy_loras(self, lora_config: Optional[LoRAConfig],
                                  num_scheduled_tokens: np.ndarray):
         if lora_config is None:
             yield
@@ -151,13 +154,22 @@ def maybe_select_dummy_loras(self, lora_config: LoRAConfig,
             yield
 
     @contextmanager
-    def maybe_dummy_run_with_lora(self, lora_config: LoRAConfig,
-                                  num_scheduled_tokens: np.ndarray):
-        with self.maybe_setup_dummy_loras(
-                lora_config), self.maybe_select_dummy_loras(
-                    lora_config, num_scheduled_tokens):
+    def maybe_dummy_run_with_lora(self,
+                                  lora_config: Optional[LoRAConfig],
+                                  num_scheduled_tokens: np.ndarray,
+                                  remove_lora: bool = True):
+        with (
+                self.maybe_setup_dummy_loras(lora_config, remove_lora),
+                self.maybe_select_dummy_loras(lora_config,
+                                              num_scheduled_tokens),
+        ):
             yield
 
+    def maybe_remove_all_loras(self, lora_config: Optional[LoRAConfig]):
+        if lora_config is None:
+            return
+        self.lora_manager.remove_all_adapters()
+
     def add_lora(self, lora_request: LoRARequest) -> bool:
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")