diff --git a/examples/llama/2d_llama.py b/examples/llama/2d_llama.py
index bc7e380ff..f967e6d2b 100644
--- a/examples/llama/2d_llama.py
+++ b/examples/llama/2d_llama.py
@@ -3,8 +3,8 @@
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from pippy import Pipe, PipeSplitWrapper, annotate_split_points, PipelineStage
-from torch.distributed._tensor import init_device_mesh
-from torch.distributed._tensor import DTensor
+from torch.distributed._tensor import init_device_mesh, DTensor
+from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel, RowwiseParallel
 
 
 # We set this flag to true to allow operations on a mix of tensor and dtensor
@@ -15,28 +15,27 @@
 # Grab the model
 llama = AutoModelForCausalLM.from_pretrained(
     "meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True,
-    torch_dtype=torch.float16
 )
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+llama.eval()
 
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
 prompts = (
     "How do you", "I like to", "Can I help", "You need to",
     "The weather is", "I found a", "What is your", "You are so",
 )  # bs = 8
 tokenizer.pad_token = tokenizer.eos_token
+inputs = tokenizer(prompts, return_tensors="pt", padding=True)
 
 rank = int(os.environ["RANK"])
 world_size = int(os.environ["WORLD_SIZE"])
 device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
 
+# Initialize 2D device mesh
 pp_group_size = 2
 tp_group_size = 4
 mesh_2d = init_device_mesh("cuda", (pp_group_size, tp_group_size), mesh_dim_names=("pp", "tp"))
 pp_group = mesh_2d["pp"].get_group()
 
-llama.eval()
-inputs = tokenizer(prompts, return_tensors="pt", padding=True)
-
 # Cut model by equal number of layers per rank
 layers_per_stage = llama.config.num_hidden_layers // pp_group_size
 for i in range(1, pp_group_size):
@@ -51,7 +50,6 @@
 stage = PipelineStage(llama_pipe, stage_idx, device=device, group=pp_group)
 
 # Tensor parallel
-from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel, RowwiseParallel
 starting_layer = stage_idx * layers_per_stage
 attn_plan = {}
 mlp_plan = {}
@@ -77,8 +75,9 @@
 parallelize_module(
     stage.submod, tp_mesh, {**attn_plan, **mlp_plan}
 )
-inputs = inputs.to(device)
+
 # Run
+inputs = inputs.to(device)
 if stage_idx == 0:
     args = inputs["input_ids"]
 else:
diff --git a/pippy/PipelineStage.py b/pippy/PipelineStage.py
index 767939113..3efca131d 100644
--- a/pippy/PipelineStage.py
+++ b/pippy/PipelineStage.py
@@ -478,7 +478,9 @@ def _send_activations(
                 work = dist.isend(
                     # HACK: we convert DTensor to regular tensor here for it to
                     # work with send ops. DTensor may show up in PP + TP cases.
-                    out.to_local() if isinstance(out, torch.distributed._tensor.DTensor) else out,
+                    out.to_local()
+                    if isinstance(out, torch.distributed._tensor.DTensor)
+                    else out,
                     peer_rank
                     if self.group is None
                     else dist.get_global_rank(self.group, peer_rank),  # TODO