Measure model memory usage (#3120)

vllm/utils.py

@@ -3,6 +3,7 @@ import os
 import socket
 import subprocess
 import uuid
+import gc
 from platform import uname
 from typing import List, Tuple, Union
 from packaging.version import parse, Version
@@ -309,3 +310,27 @@ def create_kv_caches_with_random(
                 f"Does not support value cache of type {cache_dtype}")
         value_caches.append(value_cache)
     return key_caches, value_caches
+
+
+class measure_cuda_memory:
+
+    def __init__(self, device=None):
+        self.device = device
+
+    def current_memory_usage(self) -> float:
+        # Return the memory usage in bytes.
+        torch.cuda.reset_peak_memory_stats(self.device)
+        mem = torch.cuda.max_memory_allocated(self.device)
+        return mem
+
+    def __enter__(self):
+        self.initial_memory = self.current_memory_usage()
+        # This allows us to call methods of the context manager if needed
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.final_memory = self.current_memory_usage()
+        self.consumed_memory = self.final_memory - self.initial_memory
+
+        # Force garbage collection
+        gc.collect()

vllm/worker/model_runner.py

@@ -21,7 +21,7 @@ from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
 from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
 from vllm.lora.layers import LoRAMapping
 from vllm.lora.request import LoRARequest
-from vllm.utils import in_wsl
+from vllm.utils import in_wsl, measure_cuda_memory
 
 logger = init_logger(__name__)
 
@@ -85,12 +85,18 @@ class ModelRunner:
             self.model_config.enforce_eager = True
 
     def load_model(self) -> None:
+        with measure_cuda_memory() as m:
             self.model = get_model(self.model_config,
                                    self.device_config,
                                    lora_config=self.lora_config,
                                    parallel_config=self.parallel_config,
                                    scheduler_config=self.scheduler_config)
 
+        self.model_memory_usage = m.consumed_memory
+        logger.info(
+            f"Loading model weights took {self.model_memory_usage / float(2**30):.4f} GB"
+        )
+
         vocab_size = self.model.config.vocab_size
 
         if self.lora_config: