diff --git a/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py b/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
index 02858b119bfa..6cce2b42be9c 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
@@ -149,7 +149,7 @@ def __init__(
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
+        attention_mask: torch.Tensor = None,
         encoder_output: Optional[torch.Tensor] = None,
         enc_dec_attn_mask: Optional[torch.Tensor] = None,
         inference_params: Optional[Any] = None,
@@ -169,7 +169,7 @@ def forward(
         with torch.autocast(device_type="cuda", dtype=self.dtype):
             return super().forward(
                 hidden_states,
-                attention_mask,
+                attention_mask=attention_mask,
                 encoder_output=encoder_output,
                 enc_dec_attn_mask=enc_dec_attn_mask,
                 inference_params=inference_params,
@@ -242,25 +242,30 @@ def __init__(self, config, layer_number=1, hidden_dropout=None):
     def forward(
         self,
         hidden_states,
-        attention_mask,
+        is_first_microbatch=None,
+        attention_mask=None,
         context=None,
         context_mask=None,
         rotary_pos_emb=None,
         inference_params=None,
         packed_seq_params=None,  # TODO: handle this
     ):
+        # Use is_first_microbatch argument during CUDA graph capture. Use self.is_first_microbatch otherwise.
         hidden_states = super().forward(
             hidden_states,
             attention_mask=attention_mask,
             encoder_output=context,
             enc_dec_attn_mask=context_mask,
             inference_params=inference_params,
-            is_first_microbatch=self.is_first_microbatch,
+            is_first_microbatch=is_first_microbatch if is_first_microbatch is not None else self.is_first_microbatch,
             # checkpoint_core_attention,
         )
         self.is_first_microbatch = False
         context = None
 
+        # CUDA graph requires returned values to be Tensors
+        if self.config.enable_cuda_graph and self.training:
+            return hidden_states
         return hidden_states, context
 
     def _get_layer_offset(self):
diff --git a/nemo/collections/nlp/models/language_modeling/megatron_base_model.py b/nemo/collections/nlp/models/language_modeling/megatron_base_model.py
index f431d43716b9..31b2809476be 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron_base_model.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron_base_model.py
@@ -204,6 +204,7 @@ def __init__(self, cfg: DictConfig, trainer: Trainer, no_lm_init=True):
             init_mpi_proc_group=cfg.get('ub_tp_comm_overlap', False),
             seed=self.cfg.get('seed', 1234),
             apex_transformer_log_level=self.cfg.get('apex_transformer_log_level', 30),
+            use_te_rng_tracker=self.cfg.get('use_te_rng_tracker', False),
         )
 
         # This must be called after initialize model parallel since it needs to know the data parallel size
diff --git a/nemo/collections/nlp/modules/common/megatron/megatron_init.py b/nemo/collections/nlp/modules/common/megatron/megatron_init.py
index 5d5b65b360ee..496229f40da3 100644
--- a/nemo/collections/nlp/modules/common/megatron/megatron_init.py
+++ b/nemo/collections/nlp/modules/common/megatron/megatron_init.py
@@ -76,6 +76,7 @@ def initialize_model_parallel_for_nemo(
     seed=1234,
     apex_transformer_log_level=30,
     use_tp_pp_dp_mapping=False,
+    use_te_rng_tracker=False,
 ):
 
     if virtual_pipeline_model_parallel_size is not None and not HAVE_INTERLEAVED:
@@ -128,6 +129,7 @@ def initialize_model_parallel_for_nemo(
     set_pipeline_model_parallel_world_size(app_state.pipeline_model_parallel_size)
     set_pipeline_model_parallel_split_rank(app_state.pipeline_model_parallel_split_rank)
 
+    tensor_parallel.random.initialize_rng_tracker(use_te_rng_tracker=use_te_rng_tracker)
     if seed is not None:
         # @chcui not setting seed is for model conversion. always set seed for training/inference.
         _set_random_seed(seed)
diff --git a/nemo/core/optim/distributed_adam.py b/nemo/core/optim/distributed_adam.py
index 32bd7e6c1154..750fd0fcd93d 100644
--- a/nemo/core/optim/distributed_adam.py
+++ b/nemo/core/optim/distributed_adam.py
@@ -487,8 +487,6 @@ def _param_copy_fragments(self, fragments: Iterable[DistributedFusedAdam.Paramet
         for param in params:
             if is_float8tensor(param):
                 param._reset_caches()
-                param.transpose(update_cache=True)
-                param._lazy_transpose_cache = True
 
     @torch.no_grad()
     def _check_params_shard_dtypes(self, params_buckets: Dict[int, DistributedFusedAdam.ParameterBucket]) -> None: