update logs

lightx2v/utils/profiler.py

@@ -20,36 +20,22 @@ class _ProfilingContext:
 
     def __enter__(self):
         torch.cuda.synchronize()
-        if torch.cuda.is_available():
-            torch.cuda.reset_peak_memory_stats()
         self.start_time = time.perf_counter()
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         torch.cuda.synchronize()
-        if torch.cuda.is_available():
-            peak_memory = torch.cuda.max_memory_allocated() / (1024**3)  # 转换为GB
-            logger.info(f"[Profile] {self.rank_info} - {self.name} Peak Memory: {peak_memory:.2f} GB")
-        else:
-            logger.info(f"[Profile] {self.rank_info} - {self.name} executed without GPU.")
         elapsed = time.perf_counter() - self.start_time
         logger.info(f"[Profile] {self.rank_info} - {self.name} cost {elapsed:.6f} seconds")
         return False
 
     async def __aenter__(self):
         torch.cuda.synchronize()
-        if torch.cuda.is_available():
-            torch.cuda.reset_peak_memory_stats()
         self.start_time = time.perf_counter()
         return self
 
     async def __aexit__(self, exc_type, exc_val, exc_tb):
         torch.cuda.synchronize()
-        if torch.cuda.is_available():
-            peak_memory = torch.cuda.max_memory_allocated() / (1024**3)  # 转换为GB
-            logger.info(f"[Profile] {self.rank_info} - {self.name} Peak Memory: {peak_memory:.2f} GB")
-        else:
-            logger.info(f"[Profile] {self.rank_info} - {self.name} executed without GPU.")
         elapsed = time.perf_counter() - self.start_time
         logger.info(f"[Profile] {self.rank_info} - {self.name} cost {elapsed:.6f} seconds")
         return False