We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Input and output orders are different
I was trying to do batch inference by feeding a list of texts into `llm.generate()`. But I found that the outputs have different orders from examples in the input list. For example, the result for the first example in the input list may be the second in the output list. Anything suggestions that I may have done something wrong? Thanks!

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `vllm/entrypoints/llm.py`
Content:
```
1 from typing import List, Optional, Union
2 
3 from tqdm import tqdm
4 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
5 
6 from vllm.engine.arg_utils import EngineArgs
7 from vllm.engine.llm_engine import LLMEngine
8 from vllm.outputs import RequestOutput
9 from vllm.sampling_params import SamplingParams
10 from vllm.utils import Counter
11 
12 
13 class LLM:
14     """An LLM for generating texts from given prompts and sampling parameters.
15 
16     This class includes a tokenizer, a language model (possibly distributed
17     across multiple GPUs), and GPU memory space allocated for intermediate
18     states (aka KV cache). Given a batch of prompts and sampling parameters,
19     this class generates texts from the model, using an intelligent batching
20     mechanism and efficient memory management.
21 
22     NOTE: This class is intended to be used for offline inference. For online
23     serving, use the `AsyncLLMEngine` class instead.
24     NOTE: For the comprehensive list of arguments, see `EngineArgs`.
25 
26     Args:
27         model: The name or path of a HuggingFace Transformers model.
28         tokenizer: The name or path of a HuggingFace Transformers tokenizer.
29         tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
30             if available, and "slow" will always use the slow tokenizer.
31         trust_remote_code: Trust remote code (e.g., from HuggingFace) when
32             downloading the model and tokenizer.
33         tensor_parallel_size: The number of GPUs to use for distributed
34             execution with tensor parallelism.
35         dtype: The data type for the model weights and activations. Currently,
36             we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
37             the `torch_dtype` attribute specified in the model config file.
38             However, if the `torch_dtype` in the config is `float32`, we will
39             use `float16` instead.
40         seed: The seed to initialize the random number generator for sampling.
41     """
42 
43     def __init__(
44         self,
45         model: str,
46         tokenizer: Optional[str] = None,
47         tokenizer_mode: str = "auto",
48         trust_remote_code: bool = False,
49         tensor_parallel_size: int = 1,
50         dtype: str = "auto",
51         seed: int = 0,
52         **kwargs,
53     ) -> None:
54         if "disable_log_stats" not in kwargs:
55             kwargs["disable_log_stats"] = True
56         engine_args = EngineArgs(
57             model=model,
58             tokenizer=tokenizer,
59             tokenizer_mode=tokenizer_mode,
60             trust_remote_code=trust_remote_code,
61             tensor_parallel_size=tensor_parallel_size,
62             dtype=dtype,
63             seed=seed,
64             **kwargs,
65         )
66         self.llm_engine = LLMEngine.from_engine_args(engine_args)
67         self.request_counter = Counter()
68 
69     def get_tokenizer(
70             self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
71         return self.llm_engine.tokenizer
72 
73     def set_tokenizer(
74         self,
75         tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
76     ) -> None:
77         self.llm_engine.tokenizer = tokenizer
78 
79     def generate(
80         self,
81         prompts: Optional[Union[str, List[str]]] = None,
82         sampling_params: Optional[SamplingParams] = None,
83         prompt_token_ids: Optional[List[List[int]]] = None,
84         use_tqdm: bool = True,
85     ) -> List[RequestOutput]:
86         """Generates the completions for the input prompts.
87 
88         NOTE: This class automatically batches the given prompts, considering
89         the memory constraint. For the best performance, put all of your prompts
90         into a single list and pass it to this method.
91 
92         Args:
93             prompts: A list of prompts to generate completions for.
94             sampling_params: The sampling parameters for text generation. If
95                 None, we use the default sampling parameters.
96             prompt_token_ids: A list of token IDs for the prompts. If None, we
97                 use the tokenizer to convert the prompts to token IDs.
98             use_tqdm: Whether to use tqdm to display the progress bar.
99 
100         Returns:
101             A list of `RequestOutput` objects containing the generated
102             completions in the same order as the input prompts.
103         """
104         if prompts is None and prompt_token_ids is None:
105             raise ValueError("Either prompts or prompt_token_ids must be "
106                              "provided.")
107         if isinstance(prompts, str):
108             # Convert a single prompt to a list.
109             prompts = [prompts]
110         if prompts is not None and prompt_token_ids is not None:
111             if len(prompts) != len(prompt_token_ids):
112                 raise ValueError("The lengths of prompts and prompt_token_ids "
113                                  "must be the same.")
114         if sampling_params is None:
115             # Use default sampling params.
116             sampling_params = SamplingParams()
117 
118         # Add requests to the engine.
119         if prompts is not None:
120             num_requests = len(prompts)
121         else:
122             num_requests = len(prompt_token_ids)
123         for i in range(num_requests):
124             prompt = prompts[i] if prompts is not None else None
125             if prompt_token_ids is None:
126                 token_ids = None
127             else:
128                 token_ids = prompt_token_ids[i]
129             self._add_request(prompt, sampling_params, token_ids)
130         return self._run_engine(use_tqdm)
131 
132     def _add_request(
133         self,
134         prompt: Optional[str],
135         sampling_params: SamplingParams,
136         prompt_token_ids: Optional[List[int]],
137     ) -> None:
138         request_id = str(next(self.request_counter))
139         self.llm_engine.add_request(request_id, prompt, sampling_params,
140                                     prompt_token_ids)
141 
142     def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
143         # Initialize tqdm.
144         if use_tqdm:
145             num_requests = self.llm_engine.get_num_unfinished_requests()
146             pbar = tqdm(total=num_requests, desc="Processed prompts")
147         # Run the engine.
148         outputs: List[RequestOutput] = []
149         while self.llm_engine.has_unfinished_requests():
150             step_outputs = self.llm_engine.step()
151             for output in step_outputs:
152                 if output.finished:
153                     outputs.append(output)
154                     if use_tqdm:
155                         pbar.update(1)
156         if use_tqdm:
157             pbar.close()
158         return outputs
159
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py
--- a/vllm/entrypoints/llm.py
+++ b/vllm/entrypoints/llm.py
@@ -155,4 +155,8 @@
                         pbar.update(1)
         if use_tqdm:
             pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        outputs = sorted(outputs, key=lambda x: int(x.request_id))
         return outputs

{"golden_diff": "diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py\n--- a/vllm/entrypoints/llm.py\n+++ b/vllm/entrypoints/llm.py\n@@ -155,4 +155,8 @@\n                         pbar.update(1)\n         if use_tqdm:\n             pbar.close()\n+        # Sort the outputs by request ID.\n+        # This is necessary because some requests may be finished earlier than\n+        # its previous requests.\n+        outputs = sorted(outputs, key=lambda x: int(x.request_id))\n         return outputs\n", "issue": "Input and output orders are different\nI was trying to do batch inference by feeding a list of texts into `llm.generate()`. But I found that the outputs have different orders from examples in the input list. For example, the result for the first example in the input list may be the second in the output list. Anything suggestions that I may have done something wrong? Thanks!\n", "before_files": [{"content": "from typing import List, Optional, Union\n\nfrom tqdm import tqdm\nfrom transformers import PreTrainedTokenizer, PreTrainedTokenizerFast\n\nfrom vllm.engine.arg_utils import EngineArgs\nfrom vllm.engine.llm_engine import LLMEngine\nfrom vllm.outputs import RequestOutput\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.utils import Counter\n\n\nclass LLM:\n    \"\"\"An LLM for generating texts from given prompts and sampling parameters.\n\n    This class includes a tokenizer, a language model (possibly distributed\n    across multiple GPUs), and GPU memory space allocated for intermediate\n    states (aka KV cache). Given a batch of prompts and sampling parameters,\n    this class generates texts from the model, using an intelligent batching\n    mechanism and efficient memory management.\n\n    NOTE: This class is intended to be used for offline inference. For online\n    serving, use the `AsyncLLMEngine` class instead.\n    NOTE: For the comprehensive list of arguments, see `EngineArgs`.\n\n    Args:\n        model: The name or path of a HuggingFace Transformers model.\n        tokenizer: The name or path of a HuggingFace Transformers tokenizer.\n        tokenizer_mode: The tokenizer mode. \"auto\" will use the fast tokenizer\n            if available, and \"slow\" will always use the slow tokenizer.\n        trust_remote_code: Trust remote code (e.g., from HuggingFace) when\n            downloading the model and tokenizer.\n        tensor_parallel_size: The number of GPUs to use for distributed\n            execution with tensor parallelism.\n        dtype: The data type for the model weights and activations. Currently,\n            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use\n            the `torch_dtype` attribute specified in the model config file.\n            However, if the `torch_dtype` in the config is `float32`, we will\n            use `float16` instead.\n        seed: The seed to initialize the random number generator for sampling.\n    \"\"\"\n\n    def __init__(\n        self,\n        model: str,\n        tokenizer: Optional[str] = None,\n        tokenizer_mode: str = \"auto\",\n        trust_remote_code: bool = False,\n        tensor_parallel_size: int = 1,\n        dtype: str = \"auto\",\n        seed: int = 0,\n        **kwargs,\n    ) -> None:\n        if \"disable_log_stats\" not in kwargs:\n            kwargs[\"disable_log_stats\"] = True\n        engine_args = EngineArgs(\n            model=model,\n            tokenizer=tokenizer,\n            tokenizer_mode=tokenizer_mode,\n            trust_remote_code=trust_remote_code,\n            tensor_parallel_size=tensor_parallel_size,\n            dtype=dtype,\n            seed=seed,\n            **kwargs,\n        )\n        self.llm_engine = LLMEngine.from_engine_args(engine_args)\n        self.request_counter = Counter()\n\n    def get_tokenizer(\n            self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:\n        return self.llm_engine.tokenizer\n\n    def set_tokenizer(\n        self,\n        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],\n    ) -> None:\n        self.llm_engine.tokenizer = tokenizer\n\n    def generate(\n        self,\n        prompts: Optional[Union[str, List[str]]] = None,\n        sampling_params: Optional[SamplingParams] = None,\n        prompt_token_ids: Optional[List[List[int]]] = None,\n        use_tqdm: bool = True,\n    ) -> List[RequestOutput]:\n        \"\"\"Generates the completions for the input prompts.\n\n        NOTE: This class automatically batches the given prompts, considering\n        the memory constraint. For the best performance, put all of your prompts\n        into a single list and pass it to this method.\n\n        Args:\n            prompts: A list of prompts to generate completions for.\n            sampling_params: The sampling parameters for text generation. If\n                None, we use the default sampling parameters.\n            prompt_token_ids: A list of token IDs for the prompts. If None, we\n                use the tokenizer to convert the prompts to token IDs.\n            use_tqdm: Whether to use tqdm to display the progress bar.\n\n        Returns:\n            A list of `RequestOutput` objects containing the generated\n            completions in the same order as the input prompts.\n        \"\"\"\n        if prompts is None and prompt_token_ids is None:\n            raise ValueError(\"Either prompts or prompt_token_ids must be \"\n                             \"provided.\")\n        if isinstance(prompts, str):\n            # Convert a single prompt to a list.\n            prompts = [prompts]\n        if prompts is not None and prompt_token_ids is not None:\n            if len(prompts) != len(prompt_token_ids):\n                raise ValueError(\"The lengths of prompts and prompt_token_ids \"\n                                 \"must be the same.\")\n        if sampling_params is None:\n            # Use default sampling params.\n            sampling_params = SamplingParams()\n\n        # Add requests to the engine.\n        if prompts is not None:\n            num_requests = len(prompts)\n        else:\n            num_requests = len(prompt_token_ids)\n        for i in range(num_requests):\n            prompt = prompts[i] if prompts is not None else None\n            if prompt_token_ids is None:\n                token_ids = None\n            else:\n                token_ids = prompt_token_ids[i]\n            self._add_request(prompt, sampling_params, token_ids)\n        return self._run_engine(use_tqdm)\n\n    def _add_request(\n        self,\n        prompt: Optional[str],\n        sampling_params: SamplingParams,\n        prompt_token_ids: Optional[List[int]],\n    ) -> None:\n        request_id = str(next(self.request_counter))\n        self.llm_engine.add_request(request_id, prompt, sampling_params,\n                                    prompt_token_ids)\n\n    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:\n        # Initialize tqdm.\n        if use_tqdm:\n            num_requests = self.llm_engine.get_num_unfinished_requests()\n            pbar = tqdm(total=num_requests, desc=\"Processed prompts\")\n        # Run the engine.\n        outputs: List[RequestOutput] = []\n        while self.llm_engine.has_unfinished_requests():\n            step_outputs = self.llm_engine.step()\n            for output in step_outputs:\n                if output.finished:\n                    outputs.append(output)\n                    if use_tqdm:\n                        pbar.update(1)\n        if use_tqdm:\n            pbar.close()\n        return outputs\n", "path": "vllm/entrypoints/llm.py"}], "after_files": [{"content": "from typing import List, Optional, Union\n\nfrom tqdm import tqdm\nfrom transformers import PreTrainedTokenizer, PreTrainedTokenizerFast\n\nfrom vllm.engine.arg_utils import EngineArgs\nfrom vllm.engine.llm_engine import LLMEngine\nfrom vllm.outputs import RequestOutput\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.utils import Counter\n\n\nclass LLM:\n    \"\"\"An LLM for generating texts from given prompts and sampling parameters.\n\n    This class includes a tokenizer, a language model (possibly distributed\n    across multiple GPUs), and GPU memory space allocated for intermediate\n    states (aka KV cache). Given a batch of prompts and sampling parameters,\n    this class generates texts from the model, using an intelligent batching\n    mechanism and efficient memory management.\n\n    NOTE: This class is intended to be used for offline inference. For online\n    serving, use the `AsyncLLMEngine` class instead.\n    NOTE: For the comprehensive list of arguments, see `EngineArgs`.\n\n    Args:\n        model: The name or path of a HuggingFace Transformers model.\n        tokenizer: The name or path of a HuggingFace Transformers tokenizer.\n        tokenizer_mode: The tokenizer mode. \"auto\" will use the fast tokenizer\n            if available, and \"slow\" will always use the slow tokenizer.\n        trust_remote_code: Trust remote code (e.g., from HuggingFace) when\n            downloading the model and tokenizer.\n        tensor_parallel_size: The number of GPUs to use for distributed\n            execution with tensor parallelism.\n        dtype: The data type for the model weights and activations. Currently,\n            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use\n            the `torch_dtype` attribute specified in the model config file.\n            However, if the `torch_dtype` in the config is `float32`, we will\n            use `float16` instead.\n        seed: The seed to initialize the random number generator for sampling.\n    \"\"\"\n\n    def __init__(\n        self,\n        model: str,\n        tokenizer: Optional[str] = None,\n        tokenizer_mode: str = \"auto\",\n        trust_remote_code: bool = False,\n        tensor_parallel_size: int = 1,\n        dtype: str = \"auto\",\n        seed: int = 0,\n        **kwargs,\n    ) -> None:\n        if \"disable_log_stats\" not in kwargs:\n            kwargs[\"disable_log_stats\"] = True\n        engine_args = EngineArgs(\n            model=model,\n            tokenizer=tokenizer,\n            tokenizer_mode=tokenizer_mode,\n            trust_remote_code=trust_remote_code,\n            tensor_parallel_size=tensor_parallel_size,\n            dtype=dtype,\n            seed=seed,\n            **kwargs,\n        )\n        self.llm_engine = LLMEngine.from_engine_args(engine_args)\n        self.request_counter = Counter()\n\n    def get_tokenizer(\n            self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:\n        return self.llm_engine.tokenizer\n\n    def set_tokenizer(\n        self,\n        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],\n    ) -> None:\n        self.llm_engine.tokenizer = tokenizer\n\n    def generate(\n        self,\n        prompts: Optional[Union[str, List[str]]] = None,\n        sampling_params: Optional[SamplingParams] = None,\n        prompt_token_ids: Optional[List[List[int]]] = None,\n        use_tqdm: bool = True,\n    ) -> List[RequestOutput]:\n        \"\"\"Generates the completions for the input prompts.\n\n        NOTE: This class automatically batches the given prompts, considering\n        the memory constraint. For the best performance, put all of your prompts\n        into a single list and pass it to this method.\n\n        Args:\n            prompts: A list of prompts to generate completions for.\n            sampling_params: The sampling parameters for text generation. If\n                None, we use the default sampling parameters.\n            prompt_token_ids: A list of token IDs for the prompts. If None, we\n                use the tokenizer to convert the prompts to token IDs.\n            use_tqdm: Whether to use tqdm to display the progress bar.\n\n        Returns:\n            A list of `RequestOutput` objects containing the generated\n            completions in the same order as the input prompts.\n        \"\"\"\n        if prompts is None and prompt_token_ids is None:\n            raise ValueError(\"Either prompts or prompt_token_ids must be \"\n                             \"provided.\")\n        if isinstance(prompts, str):\n            # Convert a single prompt to a list.\n            prompts = [prompts]\n        if prompts is not None and prompt_token_ids is not None:\n            if len(prompts) != len(prompt_token_ids):\n                raise ValueError(\"The lengths of prompts and prompt_token_ids \"\n                                 \"must be the same.\")\n        if sampling_params is None:\n            # Use default sampling params.\n            sampling_params = SamplingParams()\n\n        # Add requests to the engine.\n        if prompts is not None:\n            num_requests = len(prompts)\n        else:\n            num_requests = len(prompt_token_ids)\n        for i in range(num_requests):\n            prompt = prompts[i] if prompts is not None else None\n            if prompt_token_ids is None:\n                token_ids = None\n            else:\n                token_ids = prompt_token_ids[i]\n            self._add_request(prompt, sampling_params, token_ids)\n        return self._run_engine(use_tqdm)\n\n    def _add_request(\n        self,\n        prompt: Optional[str],\n        sampling_params: SamplingParams,\n        prompt_token_ids: Optional[List[int]],\n    ) -> None:\n        request_id = str(next(self.request_counter))\n        self.llm_engine.add_request(request_id, prompt, sampling_params,\n                                    prompt_token_ids)\n\n    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:\n        # Initialize tqdm.\n        if use_tqdm:\n            num_requests = self.llm_engine.get_num_unfinished_requests()\n            pbar = tqdm(total=num_requests, desc=\"Processed prompts\")\n        # Run the engine.\n        outputs: List[RequestOutput] = []\n        while self.llm_engine.has_unfinished_requests():\n            step_outputs = self.llm_engine.step()\n            for output in step_outputs:\n                if output.finished:\n                    outputs.append(output)\n                    if use_tqdm:\n                        pbar.update(1)\n        if use_tqdm:\n            pbar.close()\n        # Sort the outputs by request ID.\n        # This is necessary because some requests may be finished earlier than\n        # its previous requests.\n        outputs = sorted(outputs, key=lambda x: int(x.request_id))\n        return outputs\n", "path": "vllm/entrypoints/llm.py"}]}