diff --git a/tests/worker/test_worker.py b/tests/worker/test_model_runner.py
similarity index 68%
rename from tests/worker/test_worker.py
rename to tests/worker/test_model_runner.py
index b2c61e24efdd..949a7e2292a4 100644
--- a/tests/worker/test_worker.py
+++ b/tests/worker/test_model_runner.py
@@ -2,18 +2,19 @@
 import torch
 
 from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
-from vllm.worker.worker import Worker
+from vllm.worker.model_runner import ModelRunner
 
 
-def test_worker_prepare_inputs_for_prompt():
-    worker = Worker(None, None, None)
-    worker.block_size = 16
+def test_prepare_prompt():
+    model_runner = ModelRunner(None, None, None)
+    model_runner.set_block_size(16)
+
     batch_size = random.randint(1, 256)
     prompt_lens = []
     seq_group_metadata_list = []
     for i in range(batch_size):
         # make sure all tokens fit into one block
-        prompt_len = i % (worker.block_size - 1) + 1
+        prompt_len = i % (model_runner.block_size - 1) + 1
         prompt_lens.append(prompt_len)
         seq_data = list(range(prompt_len))
         seq_group_metadata_list.append(
@@ -24,6 +25,7 @@ def test_worker_prepare_inputs_for_prompt():
                 sampling_params=SamplingParams(temperature=0),
                 block_tables={0: [1]},
             ))
+
     expected_selected_token_indices = []
     selected_token_start_idx = 0
     max_seq_len = max(prompt_lens)
@@ -31,12 +33,15 @@ def test_worker_prepare_inputs_for_prompt():
         expected_selected_token_indices.append(selected_token_start_idx +
                                                prompt_len - 1)
         selected_token_start_idx += max_seq_len
-    input_tokens, input_positions, input_metadata = worker._prepare_inputs(
+    input_tokens, input_positions, _ = model_runner._prepare_prompt(
         seq_group_metadata_list)
-    assert input_tokens.shape == input_positions.shape == (batch_size,
-                                                           max_seq_len)
+    sampling_metadata = model_runner._prepare_sample(seq_group_metadata_list,
+                                                     prompt_lens)
+    assert input_tokens.shape == (batch_size, max_seq_len)
+    assert input_positions.shape == (batch_size, max_seq_len)
     torch.testing.assert_close(input_tokens, input_positions)
-    actual = input_metadata.selected_token_indices
+
+    actual = sampling_metadata.selected_token_indices
     expected = torch.tensor(expected_selected_token_indices,
                             device=actual.device,
                             dtype=actual.dtype)