from typing import List, Optional, Union

from tqdm import tqdm
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast

from vllm.engine.arg_utils import EngineArgs
from vllm.engine.llm_engine import LLMEngine
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
from vllm.utils import Counter


class LLM:
    """An LLM for generating texts from given prompts and sampling parameters.

    This class includes a tokenizer, a language model (possibly distributed
    across multiple GPUs), and GPU memory space allocated for intermediate
    states (aka KV cache). Given a batch of prompts and sampling parameters,
    this class generates texts from the model, using an intelligent batching
    mechanism and efficient memory management.

    NOTE: This class is intended to be used for offline inference. For online
    serving, use the `AsyncLLMEngine` class instead.
    NOTE: For the comprehensive list of arguments, see `EngineArgs`.

    Args:
        model: The name or path of a HuggingFace Transformers model.
        tokenizer: The name or path of a HuggingFace Transformers tokenizer.
        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
            if available, and "slow" will always use the slow tokenizer.
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
        tensor_parallel_size: The number of GPUs to use for distributed
            execution with tensor parallelism.
        dtype: The data type for the model weights and activations. Currently,
            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
            the `torch_dtype` attribute specified in the model config file.
            However, if the `torch_dtype` in the config is `float32`, we will
            use `float16` instead.
        seed: The seed to initialize the random number generator for sampling.
    """

    def __init__(
        self,
        model: str,
        tokenizer: Optional[str] = None,
        tokenizer_mode: str = "auto",
        trust_remote_code: bool = False,
        tensor_parallel_size: int = 1,
        dtype: str = "auto",
        seed: int = 0,
        **kwargs,
    ) -> None:
        if "disable_log_stats" not in kwargs:
            kwargs["disable_log_stats"] = True
        engine_args = EngineArgs(
            model=model,
            tokenizer=tokenizer,
            tokenizer_mode=tokenizer_mode,
            trust_remote_code=trust_remote_code,
            tensor_parallel_size=tensor_parallel_size,
            dtype=dtype,
            seed=seed,
            **kwargs,
        )
        self.llm_engine = LLMEngine.from_engine_args(engine_args)
        self.request_counter = Counter()

    def get_tokenizer(
            self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
        return self.llm_engine.tokenizer

    def set_tokenizer(
        self,
        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
    ) -> None:
        self.llm_engine.tokenizer = tokenizer

    def generate(
        self,
        prompts: Optional[Union[str, List[str]]] = None,
        sampling_params: Optional[SamplingParams] = None,
        prompt_token_ids: Optional[List[List[int]]] = None,
        use_tqdm: bool = True,
    ) -> List[RequestOutput]:
        """Generates the completions for the input prompts.

        NOTE: This class automatically batches the given prompts, considering
        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
            prompts: A list of prompts to generate completions for.
            sampling_params: The sampling parameters for text generation. If
                None, we use the default sampling parameters.
            prompt_token_ids: A list of token IDs for the prompts. If None, we
                use the tokenizer to convert the prompts to token IDs.
            use_tqdm: Whether to use tqdm to display the progress bar.

        Returns:
            A list of `RequestOutput` objects containing the generated
            completions in the same order as the input prompts.
        """
        if prompts is None and prompt_token_ids is None:
            raise ValueError("Either prompts or prompt_token_ids must be "
                             "provided.")
        if isinstance(prompts, str):
            # Convert a single prompt to a list.
            prompts = [prompts]
        if prompts is not None and prompt_token_ids is not None:
            if len(prompts) != len(prompt_token_ids):
                raise ValueError("The lengths of prompts and prompt_token_ids "
                                 "must be the same.")
        if sampling_params is None:
            # Use default sampling params.
            sampling_params = SamplingParams()

        # Add requests to the engine.
        if prompts is not None:
            num_requests = len(prompts)
        else:
            num_requests = len(prompt_token_ids)
        for i in range(num_requests):
            prompt = prompts[i] if prompts is not None else None
            if prompt_token_ids is None:
                token_ids = None
            else:
                token_ids = prompt_token_ids[i]
            self._add_request(prompt, sampling_params, token_ids)
        return self._run_engine(use_tqdm)

    def _add_request(
        self,
        prompt: Optional[str],
        sampling_params: SamplingParams,
        prompt_token_ids: Optional[List[int]],
    ) -> None:
        request_id = str(next(self.request_counter))
        self.llm_engine.add_request(request_id, prompt, sampling_params,
                                    prompt_token_ids)

    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
        # Initialize tqdm.
        if use_tqdm:
            num_requests = self.llm_engine.get_num_unfinished_requests()
            pbar = tqdm(total=num_requests, desc="Processed prompts")
        # Run the engine.
        outputs: List[RequestOutput] = []
        while self.llm_engine.has_unfinished_requests():
            step_outputs = self.llm_engine.step()
            for output in step_outputs:
                if output.finished:
                    outputs.append(output)
                    if use_tqdm:
                        pbar.update(1)
        if use_tqdm:
            pbar.close()
        return outputs

vllm/entrypoints/llm.py
from typing import List, Optional, Union

from tqdm import tqdm
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast

from vllm.engine.arg_utils import EngineArgs
from vllm.engine.llm_engine import LLMEngine
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
from vllm.utils import Counter


class LLM:
    """An LLM for generating texts from given prompts and sampling parameters.

    This class includes a tokenizer, a language model (possibly distributed
    across multiple GPUs), and GPU memory space allocated for intermediate
    states (aka KV cache). Given a batch of prompts and sampling parameters,
    this class generates texts from the model, using an intelligent batching
    mechanism and efficient memory management.

    NOTE: This class is intended to be used for offline inference. For online
    serving, use the `AsyncLLMEngine` class instead.
    NOTE: For the comprehensive list of arguments, see `EngineArgs`.

    Args:
        model: The name or path of a HuggingFace Transformers model.
        tokenizer: The name or path of a HuggingFace Transformers tokenizer.
        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
            if available, and "slow" will always use the slow tokenizer.
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
        tensor_parallel_size: The number of GPUs to use for distributed
            execution with tensor parallelism.
        dtype: The data type for the model weights and activations. Currently,
            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
            the `torch_dtype` attribute specified in the model config file.
            However, if the `torch_dtype` in the config is `float32`, we will
            use `float16` instead.
        seed: The seed to initialize the random number generator for sampling.
    """

    def __init__(
        self,
        model: str,
        tokenizer: Optional[str] = None,
        tokenizer_mode: str = "auto",
        trust_remote_code: bool = False,
        tensor_parallel_size: int = 1,
        dtype: str = "auto",
        seed: int = 0,
        **kwargs,
    ) -> None:
        if "disable_log_stats" not in kwargs:
            kwargs["disable_log_stats"] = True
        engine_args = EngineArgs(
            model=model,
            tokenizer=tokenizer,
            tokenizer_mode=tokenizer_mode,
            trust_remote_code=trust_remote_code,
            tensor_parallel_size=tensor_parallel_size,
            dtype=dtype,
            seed=seed,
            **kwargs,
        )
        self.llm_engine = LLMEngine.from_engine_args(engine_args)
        self.request_counter = Counter()

    def get_tokenizer(
            self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
        return self.llm_engine.tokenizer

    def set_tokenizer(
        self,
        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
    ) -> None:
        self.llm_engine.tokenizer = tokenizer

    def generate(
        self,
        prompts: Optional[Union[str, List[str]]] = None,
        sampling_params: Optional[SamplingParams] = None,
        prompt_token_ids: Optional[List[List[int]]] = None,
        use_tqdm: bool = True,
    ) -> List[RequestOutput]:
        """Generates the completions for the input prompts.

        NOTE: This class automatically batches the given prompts, considering
        the memory constraint. For the best performance, put all of your prompts
        into a single list and pass it to this method.

        Args:
            prompts: A list of prompts to generate completions for.
            sampling_params: The sampling parameters for text generation. If
                None, we use the default sampling parameters.
            prompt_token_ids: A list of token IDs for the prompts. If None, we
                use the tokenizer to convert the prompts to token IDs.
            use_tqdm: Whether to use tqdm to display the progress bar.

        Returns:
            A list of `RequestOutput` objects containing the generated
            completions in the same order as the input prompts.
        """
        if prompts is None and prompt_token_ids is None:
            raise ValueError("Either prompts or prompt_token_ids must be "
                             "provided.")
        if isinstance(prompts, str):
            # Convert a single prompt to a list.
            prompts = [prompts]
        if prompts is not None and prompt_token_ids is not None:
            if len(prompts) != len(prompt_token_ids):
                raise ValueError("The lengths of prompts and prompt_token_ids "
                                 "must be the same.")
        if sampling_params is None:
            # Use default sampling params.
            sampling_params = SamplingParams()

        # Add requests to the engine.
        if prompts is not None:
            num_requests = len(prompts)
        else:
            num_requests = len(prompt_token_ids)
        for i in range(num_requests):
            prompt = prompts[i] if prompts is not None else None
            if prompt_token_ids is None:
                token_ids = None
            else:
                token_ids = prompt_token_ids[i]
            self._add_request(prompt, sampling_params, token_ids)
        return self._run_engine(use_tqdm)

    def _add_request(
        self,
        prompt: Optional[str],
        sampling_params: SamplingParams,
        prompt_token_ids: Optional[List[int]],
    ) -> None:
        request_id = str(next(self.request_counter))
        self.llm_engine.add_request(request_id, prompt, sampling_params,
                                    prompt_token_ids)

    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
        # Initialize tqdm.
        if use_tqdm:
            num_requests = self.llm_engine.get_num_unfinished_requests()
            pbar = tqdm(total=num_requests, desc="Processed prompts")
        # Run the engine.
        outputs: List[RequestOutput] = []
        while self.llm_engine.has_unfinished_requests():
            step_outputs = self.llm_engine.step()
            for output in step_outputs:
                if output.finished:
                    outputs.append(output)
                    if use_tqdm:
                        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

vllm/entrypoints/llm.py
diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py
index 9c3d3bb454c..c9ab6852550 100644
--- a/vllm/entrypoints/llm.py
+++ b/vllm/entrypoints/llm.py
@@ -155,4 +155,8 @@ def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
                         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

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!
