diff --git a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
index 817ef0bd6442..44b484b28949 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
@@ -22,7 +22,6 @@
 import torch
 from omegaconf.dictconfig import DictConfig
 from pytorch_lightning.accelerators import CPUAccelerator
-from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
 from pytorch_lightning.trainer.trainer import Trainer
 
 from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import (
@@ -53,7 +52,6 @@
     SamplingParam,
     TextGeneration,
 )
-from nemo.collections.nlp.parts.nlp_overrides import GradScaler
 from nemo.collections.nlp.parts.utils_funcs import get_last_rank
 from nemo.core.classes import Exportable
 from nemo.core.classes.common import PretrainedModelInfo
@@ -512,37 +510,38 @@ def fwd_bwd_step(self, dataloader_iter, batch_idx, forward_only):
 
         return loss_mean
 
+    def initialize_ub_func(self):
+        input_shape = [
+            self.cfg.get('encoder_seq_length') * self.cfg.get('micro_batch_size'),
+            self.cfg.get('hidden_size'),
+        ]
+        ub_cfg_file_name = self.cfg.get('ub_tp_comm_overlap_cfg', None)
+        ub_cfgs = None
+        if ub_cfg_file_name is not None:
+            try:
+                import yaml
+
+                with open(ub_cfg_file_name, 'r') as ub_cfg_file:
+                    ub_cfgs = yaml.safe_load(ub_cfg_file)
+            except (ImportError, TypeError):
+                logging.error(f"Fail to read ub_tp_comm_overlap config file: {ub_cfg_file_name}.")
+        te_module.initialize_ub(
+            shape=input_shape,
+            tp_size=self.cfg.get('tensor_model_parallel_size'),
+            use_fp8=self.cfg.get('fp8'),
+            ub_cfgs=ub_cfgs,
+        )
+        self.initialize_ub = False
+
     def training_step(self, dataloader_iter, batch_idx):
         """
             We pass the dataloader iterator function to the micro-batch scheduler.
             The input batch to each micro-batch is fetched using the dataloader function
             in the micro-batch fwd function.
         """
-        # Initialize userbuffer communicators. Initialization is done only once at the
-        # beginning of the first training step.
+        # Initialize userbuffer communicators.
         if self.initialize_ub:
-            input_shape = [
-                self.cfg.get('encoder_seq_length') * self.cfg.get('micro_batch_size'),
-                self.cfg.get('hidden_size'),
-            ]
-            ub_cfg_file_name = self.cfg.get('ub_tp_comm_overlap_cfg', None)
-            ub_cfgs = None
-            if ub_cfg_file_name is not None:
-                try:
-                    import yaml
-
-                    with open(ub_cfg_file_name, 'r') as ub_cfg_file:
-                        ub_cfgs = yaml.safe_load(ub_cfg_file)
-                except (ImportError, TypeError):
-                    print("Fail to read ub_tp_comm_overlap config file.")
-
-            te_module.initialize_ub(
-                shape=input_shape,
-                tp_size=self.cfg.get('tensor_model_parallel_size'),
-                use_fp8=self.cfg.get('fp8'),
-                ub_cfgs=ub_cfgs,
-            )
-            self.initialize_ub = False
+            self.initialize_ub_func()
 
         if self.rampup_batch_size:
             num_microbatch_calculator = apex.transformer.pipeline_parallel.utils._GLOBAL_NUM_MICROBATCHES_CALCULATOR
@@ -873,6 +872,10 @@ def validation_step(self, dataloader_iter, batch_idx):
             from the dataloader to produce a list of microbatches.
             The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions.
         """
+        # Initialize userbuffer communicators.
+        if self.initialize_ub:
+            self.initialize_ub_func()
+
         if isinstance(self.model, list):
             for model_module in self.model:
                 model_module.eval()