[Core] Revert previous implementation and update worker to check for …

…GPU blocks override

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

vllm/worker/worker.py

@@ -265,29 +265,12 @@ def determine_num_available_blocks(self) -> Tuple[int, int]:
     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.
-        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. "
-            "Free memory: %.2fGB, Total memory: %.2fGB",
-            free_gpu_memory / (1024**3),
-            total_memory / (1024**3))
-
-        assert memory_diff > 0 or is_restricted_env, (
-            "Error in memory profiling."
+        free_gpu_memory, _ = torch.cuda.mem_get_info()
+        assert self.init_gpu_memory - free_gpu_memory > 0, (
+            "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.