[Lint] Refactor logging to remove f-string usage for compliance

Signed-off-by: Ahmed Mansy <[email protected]>

vllm/worker/worker.py

@@ -253,31 +253,31 @@ def determine_num_available_blocks(self) -> Tuple[int, int]:
         return num_gpu_blocks, num_cpu_blocks
 
     def _assert_memory_footprint_increased_during_profiling(self):
-    # NOTE(woosuk): Here we assume that the other processes using the same
-    # GPU did not change their memory usage during the profiling.
+        # NOTE(woosuk): Here we assume that the other processes using the same
+        # GPU did not change their memory usage during the profiling.
         free_gpu_memory, total_memory = torch.cuda.mem_get_info()
         memory_diff = self.init_gpu_memory - free_gpu_memory
-        
+
         # If we've loaded model weights but memory shows no change,
         # we're likely in a restricted environment
         model_loaded = hasattr(self.model_runner, 'model')
         memory_is_static = memory_diff == 0
-        
+
         is_restricted_env = model_loaded and memory_is_static
-        
+
         if is_restricted_env:
-            logger.info(
-                "Detected restricted GPU environment. "
-                "Model is loaded but memory reports static usage. "
-                f"Free memory: {free_gpu_memory / (1024**3):.2f}GB, "
-                f"Total memory: {total_memory / (1024**3):.2f}GB"
-            )
-
+            logger.info("Detected restricted GPU environment. "
+                        "Model is loaded but memory reports static usage. "
+                        "Free memory: {:.2f}GB, Total memory: {:.2f}GB".format(
+                            free_gpu_memory / (1024**3),
+                            total_memory / (1024**3)))
+
         assert memory_diff > 0 or is_restricted_env, (
-            "Error in memory profiling. "
+            "Error in memory profiling."
             f"Initial free memory {self.init_gpu_memory}, current free memory"
             f" {free_gpu_memory}. This happens when the GPU memory was "
             "not properly cleaned up before initializing the vLLM instance.")
+
     def initialize_cache(self, num_gpu_blocks: int,
                          num_cpu_blocks: int) -> None:
         """Allocate GPU and CPU KV cache with the specified number of blocks.