#0: work around segfault in main

models/demos/llama3/tests/test_llama_model_prefill.py

@@ -58,7 +58,7 @@ def test_llama_model_inference(mesh_device, seq_len, use_program_cache, reset_se
     # Use instruct weights instead of general weights
     instruct = True
 
-    model_args = TtModelArgs(mesh_device, instruct=instruct)
+    model_args = TtModelArgs(mesh_device, instruct=instruct, max_batch_size=1)
     tokenizer = Tokenizer(model_args.tokenizer_path)
 
     logger.info("Loading weights...")
@@ -84,12 +84,12 @@ def test_llama_model_inference(mesh_device, seq_len, use_program_cache, reset_se
     prompt_file = os.path.join(current_file_dir, "tale-of-two-cities.txt.bz2")
 
     with bz2.open(prompt_file, "rt", encoding="utf-8") as f:
-        prompts = f.read()
+        prompt = f.read()
 
     if instruct:
-        encoded_prompts = [encode_prompt_llama_instruct(tokenizer, prompt) for prompt in prompts]
+        encoded_prompt = encode_prompt_llama_instruct(tokenizer, prompt)[:seq_len]
     else:
-        encoded_prompts = tokenizer.encode(prompts, bos=True, eos=False)[:seq_len]
+        encoded_prompt = tokenizer.encode(prompt, bos=True, eos=False)[:seq_len]
 
     if run_ref_pt:
         reference_model = Transformer(model_args)
@@ -126,9 +126,9 @@ def test_llama_model_inference(mesh_device, seq_len, use_program_cache, reset_se
 
     batch = 1
 
-    # Select the first token from the prompts for initial decoding
-    encoded_prompts_tensor = torch.tensor(encoded_prompts)  # [:,0]
-    pt_decode_input = embd(encoded_prompts_tensor).view(batch, seq_len, -1)
+    # Select the first token from the prompt for initial decoding
+    encoded_prompt_tensor = torch.tensor(encoded_prompt)  # [:,0]
+    pt_decode_input = embd(encoded_prompt_tensor).view(batch, seq_len, -1)
 
     tt_decode_input = pt_decode_input