refactor function to get tensor bytes and add logging

fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_training.py

@@ -1714,10 +1714,16 @@ def _report_duration(
             tbe_id=self.uuid,
         )
 
+    def _get_tensor_memory(self, tensor_name: str) -> int:
+        """Get memory usage of a tensor in bytes."""
+        if not hasattr(self, tensor_name):
+            self.log(f"Tensor '{tensor_name}' not found, using 0 bytes")
+            return 0
+        tensor = getattr(self, tensor_name)
+        return tensor.numel() * tensor.element_size()
+
     @torch.jit.ignore
-    def _report_tbe_mem_usage(
-        self,
-    ) -> None:
+    def _report_tbe_mem_usage(self) -> None:
         if self.stats_reporter is None:
             return
 
@@ -1726,18 +1732,17 @@ def _report_tbe_mem_usage(
             return
 
         total_mem_usage = sum(
-            param.numel() * param.element_size() for param in self.parameters()
-        ) + sum(buffer.numel() * buffer.element_size() for buffer in self.buffers())
+            p.numel() * p.element_size() for p in self.parameters()
+        ) + sum(b.numel() * b.element_size() for b in self.buffers())
+
         if self.use_cpu:
             total_hbm_usage = 0
             total_uvm_usage = total_mem_usage
         else:
-            # hbm usage is total usage minus uvm usage
             total_uvm_usage = sum(
-                getattr(self, tensor_name).numel()
-                * getattr(self, tensor_name).element_size()
-                for tensor_name in self._uvm_tensors_log
-                if hasattr(self, tensor_name)
+                self._get_tensor_memory(name)
+                for name in self._uvm_tensors_log
+                if hasattr(self, name)
             )
             total_hbm_usage = total_mem_usage - total_uvm_usage