adding deferred init as Ke advised

examples/llama/2d_llama.py

@@ -27,7 +27,8 @@ def modify_view(
 
 # Grab the model
 llama = AutoModelForCausalLM.from_pretrained(
-    "meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True
+    "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")
 
@@ -46,8 +47,8 @@ def modify_view(
 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.to(device).eval()
-inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(device)
+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
@@ -90,7 +91,7 @@ def modify_view(
 parallelize_module(
     stage.submod, tp_mesh, {**attn_plan, **mlp_plan}
 )
-
+inputs = inputs.to(device)
 # Run
 if stage_idx == 0:
     args = inputs["input_ids"]