Fix input_metadata.selected_token_indices in worker prepare_inputs (#1546)

tests/worker/test_worker.py

@@ -0,0 +1,44 @@
+# pylint: disable=protected-access
+import random
+import torch
+
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
+from vllm.worker.worker import Worker
+
+
+def test_worker_prepare_inputs_for_prompt():
+    worker = Worker(None, None, None)
+    worker.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_lens.append(prompt_len)
+        seq_data = list(range(prompt_len))
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData(seq_data)},
+                sampling_params=SamplingParams(temperature=0),
+                block_tables={0: [1]},
+            ))
+    expected_selected_token_indices = []
+    selected_token_start_idx = 0
+    max_seq_len = max(prompt_lens)
+    for prompt_len in prompt_lens:
+        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(
+        seq_group_metadata_list)
+    assert input_tokens.shape == input_positions.shape == (batch_size,
+                                                           max_seq_len)
+    torch.testing.assert_close(input_tokens, input_positions)
+    actual = input_metadata.selected_token_indices
+    expected = torch.tensor(expected_selected_token_indices,
+                            device=actual.device,
+                            dtype=actual.dtype)
+    torch.testing.assert_close(actual, expected)

vllm/worker/worker.py

@@ -211,12 +211,14 @@ class Worker:
         context_lens: List[int] = []
         generation_block_tables: List[List[int]] = []
         max_seq_len = max(prompt_lens) if prompt_lens else 1
-        for seq_group_metadata in seq_group_metadata_list:
+        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
             if seq_group_metadata.is_prompt:
                 # We need to do this in this loop as we need to know max_seq_len
                 assert len(
                     seq_ids) == 1, "Prompt input should have only one seq."
                 sampling_params = seq_group_metadata.sampling_params
+                assert len(prompt_lens) == len(seq_group_metadata_list)
+                prompt_len = prompt_lens[i]
                 if sampling_params.prompt_logprobs is not None:
                     selected_token_indices.extend(
                         range(selected_token_start_idx,