benchmark_dataset.py

# SPDX-License-Identifier: Apache-2.0
"""
This module defines a framework for sampling benchmark requests from various
datasets. Each dataset subclass of BenchmarkDataset must implement sample
generation. Supported dataset types include:
  - ShareGPT
  - Random (synthetic)
  - Sonnet
  - BurstGPT
  - HuggingFace
  - VisionArena
"""

import base64
import io
import json
import logging
import random
from abc import ABC, abstractmethod
from collections.abc import Mapping
from dataclasses import dataclass
from functools import cache
from io import BytesIO
from typing import Any, Callable, Optional, Union

import numpy as np
import pandas as pd
from datasets import load_dataset
from PIL import Image
from transformers import PreTrainedTokenizerBase

from vllm.lora.request import LoRARequest
from vllm.lora.utils import get_adapter_absolute_path
from vllm.multimodal import MultiModalDataDict
from vllm.multimodal.image import convert_image_mode
from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer

logger = logging.getLogger(__name__)

# -----------------------------------------------------------------------------
# Data Classes
# -----------------------------------------------------------------------------


@dataclass
class SampleRequest:
    """
    Represents a single inference request for benchmarking.
    """

    prompt: Union[str, Any]
    prompt_len: int
    expected_output_len: int
    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
    lora_request: Optional[LoRARequest] = None


# -----------------------------------------------------------------------------
# Benchmark Dataset Base Class
# -----------------------------------------------------------------------------


class BenchmarkDataset(ABC):
    DEFAULT_SEED = 0
    IS_MULTIMODAL = False

    def __init__(
        self,
        dataset_path: Optional[str] = None,
        random_seed: int = DEFAULT_SEED,
    ) -> None:
        """
        Initialize the BenchmarkDataset with an optional dataset path and random
        seed.  Args:
            dataset_path (Optional[str]): Path to the dataset. If None, it
            indicates that a default or random dataset might be used.
            random_seed (int): Seed value for reproducible shuffling or
            sampling. Defaults to DEFAULT_SEED.
        """
        self.dataset_path = dataset_path
        # Set the random seed, ensuring that a None value is replaced with the
        # default seed.
        self.random_seed = random_seed if random_seed is not None else self.DEFAULT_SEED
        self.data = None

    def apply_multimodal_chat_transformation(
        self, prompt: str, mm_content: Optional[MultiModalDataDict] = None
    ) -> list[dict]:
        """
        Transform a prompt and optional multimodal content into a chat format.
        This method is used for chat models that expect a specific conversation
        format.
        """
        content = [{"text": prompt, "type": "text"}]
        if mm_content is not None:
            content.append(mm_content)
        return [{"role": "user", "content": content}]

    def load_data(self) -> None:
        """
        Load data from the dataset path into self.data.

        This method must be overridden by subclasses since the method to load
        data will vary depending on the dataset format and source.

        Raises:
            NotImplementedError: If a subclass does not implement this method.
        """
        # TODO (jenniferzhao): add support for downloading data
        raise NotImplementedError("load_data must be implemented in subclasses.")

    def get_random_lora_request(
        self,
        tokenizer: PreTrainedTokenizerBase,
        max_loras: Optional[int] = None,
        lora_path: Optional[str] = None,
    ) -> tuple[Optional[LoRARequest], AnyTokenizer]:
        """
        Optionally select a random LoRA request and return its associated
        tokenizer.

        This method is used when LoRA parameters are provided.  It randomly
        selects a LoRA based on max_loras and retrieves a cached tokenizer for
        that LoRA if available. Otherwise, it returns the base tokenizer.

        Args:
            tokenizer (PreTrainedTokenizerBase): The base tokenizer to use if no
            LoRA is selected.  max_loras (Optional[int]): The maximum number of
            LoRAs available. If None, LoRA is not used.  lora_path
            (Optional[str]): Path to the LoRA parameters on disk. If None, LoRA
            is not used.

        Returns:
            tuple[Optional[LoRARequest], AnyTokenizer]: A tuple where the first
            element is a LoRARequest (or None if not applicable) and the second
            element is the tokenizer associated with the LoRA request (or the
            base tokenizer).
        """
        if max_loras is None or lora_path is None:
            return None, tokenizer

        # Generate a random LoRA ID in the range [1, max_loras].
        lora_id = random.randint(1, max_loras)
        lora_request = LoRARequest(
            lora_name=str(lora_id),
            lora_int_id=lora_id,
            lora_path=lora_path_on_disk(lora_path),
        )
        if lora_id not in lora_tokenizer_cache:
            lora_tokenizer_cache[lora_id] = get_lora_tokenizer(lora_request)
        # Return lora_request and the cached tokenizer if available; otherwise,
        # return the base tokenizer
        return lora_request, lora_tokenizer_cache[lora_id] or tokenizer

    @abstractmethod
    def sample(
        self, tokenizer: PreTrainedTokenizerBase, num_requests: int
    ) -> list[SampleRequest]:
        """
        Abstract method to generate sample requests from the dataset.

        Subclasses must override this method to implement dataset-specific logic
        for generating a list of SampleRequest objects.

        Args:
            tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
             for processing the dataset's text.
            num_requests (int): The number of sample requests to generate.

        Returns:
            list[SampleRequest]: A list of sample requests generated from the
            dataset.
        """
        raise NotImplementedError("sample must be implemented in subclasses.")

    def maybe_oversample_requests(
        self, requests: list[SampleRequest], num_requests: int
    ) -> None:
        """
        Oversamples the list of requests if its size is less than the desired
        number.

        Args:
            requests (List[SampleRequest]): The current list of sampled
            requests.  num_requests (int): The target number of requests.
        """
        if len(requests) < num_requests:
            random.seed(self.random_seed)
            additional = random.choices(requests, k=num_requests - len(requests))
            requests.extend(additional)
            logger.info("Oversampled requests to reach %d total samples.", num_requests)


# -----------------------------------------------------------------------------
# Utility Functions and Global Caches
# -----------------------------------------------------------------------------


def is_valid_sequence(
    prompt_len: int,
    output_len: int,
    min_len: int = 4,
    max_prompt_len: int = 1024,
    max_total_len: int = 2048,
    skip_min_output_len_check: bool = False,
) -> bool:
    """
    Validate a sequence based on prompt and output lengths.

    Default pruning criteria are copied from the original `sample_hf_requests`
    and `sample_sharegpt_requests` functions in benchmark_serving.py, as well as
    from `sample_requests` in benchmark_throughput.py.
    """
    # Check for invalid conditions
    prompt_too_short = prompt_len < min_len
    output_too_short = (not skip_min_output_len_check) and (output_len < min_len)
    prompt_too_long = prompt_len > max_prompt_len
    combined_too_long = (prompt_len + output_len) > max_total_len

    # Return True if none of the invalid conditions are met
    return not (
        prompt_too_short or output_too_short or prompt_too_long or combined_too_long
    )


@cache
def lora_path_on_disk(lora_path: str) -> str:
    return get_adapter_absolute_path(lora_path)


# Global cache for LoRA tokenizers.
lora_tokenizer_cache: dict[int, AnyTokenizer] = {}


def process_image(image: Any) -> Mapping[str, Any]:
    """
    Process a single image input and return a multimedia content dictionary.

    Supports three input types:

    1. Dictionary with raw image bytes: - Expects a dict with a 'bytes' key
       containing raw image data.  - Loads the bytes as a PIL.Image.Image.

    2. PIL.Image.Image input: - Converts the image to RGB.  - Saves the image as
       a JPEG in memory.  - Encodes the JPEG data as a base64 string.  - Returns
       a dictionary with the image as a base64 data URL.

    3. String input: - Treats the string as a URL or local file path.  -
       Prepends "file://" if the string doesn't start with "http://" or
       "file://".  - Returns a dictionary with the image URL.

    Raises:
        ValueError: If the input is not a supported type.
    """
    if isinstance(image, dict) and "bytes" in image:
        image = Image.open(BytesIO(image["bytes"]))
    if isinstance(image, Image.Image):
        image = convert_image_mode(image, "RGB")
        with io.BytesIO() as image_data:
            image.save(image_data, format="JPEG")
            image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
        return {
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
        }

    if isinstance(image, str):
        image_url = (
            image if image.startswith(("http://", "file://")) else f"file://{image}"
        )
        return {"type": "image_url", "image_url": {"url": image_url}}

    raise ValueError(
        f"Invalid image input {image}. Must be a PIL.Image.Image"
        " or str or dictionary with raw image bytes."
    )


# -----------------------------------------------------------------------------
# Random Dataset Implementation (Synthetic Data)
# -----------------------------------------------------------------------------


class RandomDataset(BenchmarkDataset):
    # Default values copied from benchmark_serving.py for the random dataset.
    DEFAULT_PREFIX_LEN = 0
    DEFAULT_RANGE_RATIO = 0.0
    DEFAULT_INPUT_LEN = 1024
    DEFAULT_OUTPUT_LEN = 128

    def __init__(
        self,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        prefix_len: int = DEFAULT_PREFIX_LEN,
        range_ratio: float = DEFAULT_RANGE_RATIO,
        input_len: int = DEFAULT_INPUT_LEN,
        output_len: int = DEFAULT_OUTPUT_LEN,
        **kwargs,
    ) -> list[SampleRequest]:
        # Enforce range_ratio < 1
        assert range_ratio < 1.0, (
            "random_range_ratio must be < 1.0 to ensure a valid sampling range"
        )

        vocab_size = tokenizer.vocab_size
        num_special_tokens = tokenizer.num_special_tokens_to_add()
        real_input_len = input_len - num_special_tokens

        prefix_token_ids = (
            np.random.randint(0, vocab_size, size=prefix_len).tolist()
            if prefix_len > 0
            else []
        )

        # New sampling logic: [X * (1 - b), X * (1 + b)]
        input_low = int(real_input_len * (1 - range_ratio))
        input_high = int(real_input_len * (1 + range_ratio))
        output_low = int(output_len * (1 - range_ratio))
        output_high = int(output_len * (1 + range_ratio))

        # Add logging for debugging
        logger.info("Sampling input_len from [%s, %s]", input_low, input_high)
        logger.info("Sampling output_len from [%s, %s]", output_low, output_high)

        input_lens = np.random.randint(input_low, input_high + 1, size=num_requests)
        output_lens = np.random.randint(output_low, output_high + 1, size=num_requests)
        offsets = np.random.randint(0, vocab_size, size=num_requests)

        requests = []
        for i in range(num_requests):
            inner_seq = (
                (offsets[i] + i + np.arange(input_lens[i])) % vocab_size
            ).tolist()
            token_sequence = prefix_token_ids + inner_seq
            prompt = tokenizer.decode(token_sequence)
            # After decoding the prompt we have to encode and decode it again.
            # This is done because in some cases N consecutive tokens
            # give a string tokenized into != N number of tokens.
            # For example for GPT2Tokenizer:
            # [6880, 6881] -> ['Ġcalls', 'here'] ->
            # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
            # To avoid uncontrolled change of the prompt length,
            # the encoded sequence is truncated before being decode again.
            re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
                : input_lens[i]
            ]
            prompt = tokenizer.decode(re_encoded_sequence)
            total_input_len = prefix_len + int(input_lens[i])
            requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=total_input_len,
                    expected_output_len=int(output_lens[i]),
                )
            )
        return requests


# -----------------------------------------------------------------------------
# ShareGPT Dataset Implementation
# -----------------------------------------------------------------------------


class ShareGPTDataset(BenchmarkDataset):
    """
    Implements the ShareGPT dataset.  Loads data from a JSON file and generates
    sample requests based on conversation turns.
    """

    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        self.load_data()

    def load_data(self) -> None:
        if self.dataset_path is None:
            raise ValueError("dataset_path must be provided for loading data.")

        with open(self.dataset_path, encoding="utf-8") as f:
            self.data = json.load(f)
        # Filter entries with at least two conversation turns.
        self.data = [
            entry
            for entry in self.data
            if "conversations" in entry and len(entry["conversations"]) >= 2
        ]
        random.seed(self.random_seed)
        random.shuffle(self.data)

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        lora_path: Optional[str] = None,
        max_loras: Optional[int] = None,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        samples: list = []
        for entry in self.data:
            if len(samples) >= num_requests:
                break
            prompt, completion = (
                entry["conversations"][0]["value"],
                entry["conversations"][1]["value"],
            )

            lora_request, tokenizer = self.get_random_lora_request(
                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
            )
            prompt_ids = tokenizer(prompt).input_ids
            completion_ids = tokenizer(completion).input_ids
            prompt_len = len(prompt_ids)
            new_output_len = len(completion_ids) if output_len is None else output_len
            if not is_valid_sequence(
                prompt_len,
                new_output_len,
                skip_min_output_len_check=output_len is not None,
            ):
                continue
            if enable_multimodal_chat:
                prompt = self.apply_multimodal_chat_transformation(prompt, None)
            samples.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=new_output_len,
                    lora_request=lora_request,
                )
            )
        self.maybe_oversample_requests(samples, num_requests)
        return samples


# -----------------------------------------------------------------------------
# Custom Dataset Implementation
# -----------------------------------------------------------------------------


class CustomDataset(BenchmarkDataset):
    """
    Implements the Custom dataset.  Loads data from a JSONL file and generates
    sample requests based on conversation turns. E.g.,
    ```
    {"prompt": "What is the capital of India?"}
    {"prompt": "What is the capital of Iran?"}
    {"prompt": "What is the capital of China?"}
    ```
    """

    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        self.load_data()

    def load_data(self) -> None:
        if self.dataset_path is None:
            raise ValueError("dataset_path must be provided for loading data.")

        # self.data will be a list of dictionaries
        # e.g., [{"prompt": "What is the capital of India?"}, ...]
        # This will be the standardized format which load_data()
        # has to convert into depending on the filetype of dataset_path.
        # sample() will assume this standardized format of self.data
        self.data = []

        # Load the JSONL file
        if self.dataset_path.endswith(".jsonl"):
            jsonl_data = pd.read_json(path_or_buf=self.dataset_path, lines=True)

            # check if the JSONL file has a 'prompt' column
            if "prompt" not in jsonl_data.columns:
                raise ValueError("JSONL file must contain a 'prompt' column.")

            # Convert each row to a dictionary and append to self.data
            # This will convert the DataFrame to a list of dictionaries
            # where each dictionary corresponds to a row in the DataFrame.
            # This is the standardized format we want for self.data
            for _, row in jsonl_data.iterrows():
                self.data.append(row.to_dict())
        else:
            raise NotImplementedError(
                "Only JSONL format is supported for CustomDataset."
            )

        random.seed(self.random_seed)
        random.shuffle(self.data)

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        lora_path: Optional[str] = None,
        max_loras: Optional[int] = None,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        skip_chat_template: bool = False,
        **kwargs,
    ) -> list:
        sampled_requests = []
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            prompt = item["prompt"]

            # apply template
            if not skip_chat_template:
                prompt = tokenizer.apply_chat_template(
                    [{"role": "user", "content": prompt}],
                    add_generation_prompt=True,
                    tokenize=False,
                )

            prompt_len = len(tokenizer(prompt).input_ids)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)

        return sampled_requests


# -----------------------------------------------------------------------------
# Sonnet Dataset Implementation
# -----------------------------------------------------------------------------


class SonnetDataset(BenchmarkDataset):
    """
    Simplified implementation of the Sonnet dataset.  Loads poem lines from a
    text file and generates sample requests.  Default values here copied from
    `benchmark_serving.py` for the sonnet dataset.
    """

    DEFAULT_PREFIX_LEN = 200
    DEFAULT_INPUT_LEN = 550
    DEFAULT_OUTPUT_LEN = 150

    def __init__(
        self,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.load_data()

    def load_data(self) -> None:
        if not self.dataset_path:
            raise ValueError("dataset_path must be provided.")
        with open(self.dataset_path, encoding="utf-8") as f:
            self.data = f.readlines()

    def sample(
        self,
        tokenizer,
        num_requests: int,
        prefix_len: int = DEFAULT_PREFIX_LEN,
        input_len: int = DEFAULT_INPUT_LEN,
        output_len: int = DEFAULT_OUTPUT_LEN,
        return_prompt_formatted: bool = False,
        **kwargs,
    ) -> list:
        # Calculate average token length for a poem line.
        tokenized_lines = [tokenizer(line).input_ids for line in self.data]
        avg_len = sum(len(tokens) for tokens in tokenized_lines) / len(tokenized_lines)

        # Build the base prompt.
        base_prompt = "Pick as many lines as you can from these poem lines:\n"
        base_msg = [{"role": "user", "content": base_prompt}]
        base_fmt = tokenizer.apply_chat_template(
            base_msg, add_generation_prompt=True, tokenize=False
        )
        base_offset = len(tokenizer(base_fmt).input_ids)
        if input_len <= base_offset:
            raise ValueError(
                f"'input_len' must be higher than the base prompt length "
                f"({base_offset})."
            )

        # Determine how many poem lines to use.
        num_input_lines = round((input_len - base_offset) / avg_len)
        num_prefix_lines = max(round((prefix_len - base_offset) / avg_len), 0)
        prefix_lines = self.data[:num_prefix_lines]

        samples = []
        while len(samples) < num_requests:
            extra_lines = random.choices(
                self.data, k=num_input_lines - num_prefix_lines
            )
            prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
            msg = [{"role": "user", "content": prompt}]
            prompt_formatted = tokenizer.apply_chat_template(
                msg, add_generation_prompt=True, tokenize=False
            )
            prompt_len = len(tokenizer(prompt_formatted).input_ids)
            if prompt_len <= input_len:
                samples.append(
                    SampleRequest(
                        prompt=prompt_formatted if return_prompt_formatted else prompt,
                        prompt_len=prompt_len,
                        expected_output_len=output_len,
                    )
                )
        return samples


# -----------------------------------------------------------------------------
# BurstGPT Dataset Implementation
# -----------------------------------------------------------------------------


class BurstGPTDataset(BenchmarkDataset):
    """
    Implements the BurstGPT dataset.  Loads data from a CSV file and generates
    sample requests based on synthetic prompt generation. Only rows with Model
    "GPT-4" and positive response tokens are used.
    """

    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        self.load_data()

    def load_data(
        self,
    ):
        if self.dataset_path is None:
            raise ValueError("dataset_path must be provided for loading data.")

        df = pd.read_csv(self.dataset_path)
        # Filter to keep only GPT-4 rows.
        gpt4_df = df[df["Model"] == "GPT-4"]
        # Remove failed requests (where Response tokens is 0 or less).
        gpt4_df = gpt4_df[gpt4_df["Response tokens"] > 0]
        # Sample the desired number of rows.
        self.data = gpt4_df

    def _sample_loaded_data(self, num_requests: int) -> list:
        if num_requests <= len(self.data):
            data = self.data.sample(n=num_requests, random_state=self.random_seed)
        else:
            data = self.data.sample(
                n=num_requests,
                random_state=self.random_seed,
                replace=True,
            )
        # Convert the dataframe to a list of lists.
        return data.values.tolist()

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        max_loras: Optional[int] = None,
        lora_path: Optional[str] = None,
        **kwargs,
    ) -> list[SampleRequest]:
        samples = []
        data = self._sample_loaded_data(num_requests=num_requests)
        for i in range(num_requests):
            input_len = int(data[i][2])
            output_len = int(data[i][3])
            lora_req, tokenizer = self.get_random_lora_request(
                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path
            )
            vocab_size = tokenizer.vocab_size
            # Generate a synthetic prompt: a list of token IDs computed as (i +
            # j) modulo vocab_size.
            token_ids = [(i + j) % vocab_size for j in range(input_len)]
            prompt = tokenizer.decode(token_ids)
            samples.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=input_len,
                    expected_output_len=output_len,
                    lora_request=lora_req,
                )
            )
        return samples


# -----------------------------------------------------------------------------
# HuggingFace Dataset Base Implementation
# -----------------------------------------------------------------------------
class HuggingFaceDataset(BenchmarkDataset):
    """Base class for datasets hosted on HuggingFace."""

    SUPPORTED_DATASET_PATHS: Union[set[str], dict[str, Callable]] = set()

    def __init__(
        self,
        dataset_path: str,
        dataset_split: str,
        dataset_subset: Optional[str] = None,
        **kwargs,
    ) -> None:
        super().__init__(dataset_path=dataset_path, **kwargs)

        self.dataset_split = dataset_split
        self.dataset_subset = dataset_subset
        self.load_data()

    def load_data(self) -> None:
        """Load data from HuggingFace datasets."""
        self.data = load_dataset(
            self.dataset_path,
            name=self.dataset_subset,
            split=self.dataset_split,
            streaming=True,
        )
        self.data = self.data.shuffle(seed=self.random_seed)


# -----------------------------------------------------------------------------
# Conversation Dataset Implementation
# -----------------------------------------------------------------------------


class ConversationDataset(HuggingFaceDataset):
    """Dataset for conversation data with multimodal support."""

    SUPPORTED_DATASET_PATHS = {
        "lmms-lab/LLaVA-OneVision-Data",
        "Aeala/ShareGPT_Vicuna_unfiltered",
    }
    IS_MULTIMODAL = True

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        # Filter examples with at least 2 conversations
        filtered_data = self.data.filter(lambda x: len(x["conversations"]) >= 2)
        sampled_requests = []
        dynamic_output = output_len is None

        for item in filtered_data:
            if len(sampled_requests) >= num_requests:
                break
            conv = item["conversations"]
            prompt, completion = conv[0]["value"], conv[1]["value"]

            prompt_ids = tokenizer(prompt).input_ids
            completion_ids = tokenizer(completion).input_ids
            prompt_len = len(prompt_ids)
            completion_len = len(completion_ids)
            output_len = completion_len if dynamic_output else output_len
            assert isinstance(output_len, int) and output_len > 0
            if dynamic_output and not is_valid_sequence(prompt_len, completion_len):
                continue
            mm_content = process_image(item["image"]) if "image" in item else None
            if enable_multimodal_chat:
                # Note: when chat is enabled the request prompt_len is no longer
                # accurate and we will be using request output to count the
                # actual prompt len and output len
                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests


# -----------------------------------------------------------------------------
# Vision Arena Dataset Implementation
# -----------------------------------------------------------------------------


class VisionArenaDataset(HuggingFaceDataset):
    """
    Vision Arena Dataset.
    """

    DEFAULT_OUTPUT_LEN = 128
    SUPPORTED_DATASET_PATHS = {
        "lmarena-ai/VisionArena-Chat": lambda x: x["conversation"][0][0]["content"],
        "lmarena-ai/vision-arena-bench-v0.1": lambda x: x["turns"][0][0]["content"],
    }
    IS_MULTIMODAL = True

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
            if parser_fn is None:
                raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
            prompt = parser_fn(item)
            mm_content = process_image(item["images"][0])
            prompt_len = len(tokenizer(prompt).input_ids)
            if enable_multimodal_chat:
                # Note: when chat is enabled the request prompt_len is no longer
                # accurate and we will be using request output to count the
                # actual prompt len
                prompt = self.apply_multimodal_chat_transformation(prompt, mm_content)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests


# -----------------------------------------------------------------------------
# Instruct Coder Dataset Implementation
# -----------------------------------------------------------------------------


class InstructCoderDataset(HuggingFaceDataset):
    """
    InstructCoder Dataset.
    https://huggingface.co/datasets/likaixin/InstructCoder

    InstructCoder is the dataset designed for general code editing.  It consists
    of 114,239 instruction-input-output triplets, and covers multiple distinct
    code editing scenario.
    """

    DEFAULT_OUTPUT_LEN = 200  # this is the average default output length
    SUPPORTED_DATASET_PATHS = {
        "likaixin/InstructCoder",
    }

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            prompt = f"{item['instruction']}:\n{item['input']}"
            prompt_len = len(tokenizer(prompt).input_ids)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests


# -----------------------------------------------------------------------------
# MT-Bench Dataset Implementation
# -----------------------------------------------------------------------------


class MTBenchDataset(HuggingFaceDataset):
    """
    MT-Bench Dataset.
    https://huggingface.co/datasets/philschmid/mt-bench

    We create a single turn dataset for MT-Bench.
    This is similar to Spec decoding benchmark setup in vLLM
    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
    """  # noqa: E501

    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
    SUPPORTED_DATASET_PATHS = {
        "philschmid/mt-bench",
    }

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        sampled_requests = []

        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            prompt = item["turns"][0]

            # apply template
            prompt = tokenizer.apply_chat_template(
                [{"role": "user", "content": prompt}],
                add_generation_prompt=True,
                tokenize=False,
            )

            prompt_len = len(tokenizer(prompt).input_ids)
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests


# -----------------------------------------------------------------------------
# AIMO Dataset Implementation
# -----------------------------------------------------------------------------


class AIMODataset(HuggingFaceDataset):
    """
    Dataset class for processing a AIMO dataset with reasoning questions.
    """

    SUPPORTED_DATASET_PATHS = {
        "AI-MO/aimo-validation-aime",
        "AI-MO/NuminaMath-1.5",
        "AI-MO/NuminaMath-CoT",
    }

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        **kwargs,
    ) -> list:
        sampled_requests = []
        dynamic_output = output_len is None

        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            prompt, completion = item["problem"], item["solution"]

            prompt_ids = tokenizer(prompt).input_ids
            completion_ids = tokenizer(completion).input_ids
            prompt_len = len(prompt_ids)
            completion_len = len(completion_ids)
            output_len = completion_len if dynamic_output else output_len
            assert isinstance(output_len, int) and output_len > 0
            if dynamic_output and not is_valid_sequence(
                prompt_len, completion_len, max_prompt_len=2048, max_total_len=32000
            ):
                continue
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=None,
                )
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests


# -----------------------------------------------------------------------------
# Next Edit Prediction Dataset Implementation
# -----------------------------------------------------------------------------


zeta_prompt = """### Instruction:
You are a code completion assistant and your task is to analyze user edits and then rewrite an excerpt that the user provides, suggesting the appropriate edits within the excerpt, taking into account the cursor location.

### User Edits:

{}

### User Excerpt:

{}

### Response:

"""  # noqa: E501


def _format_zeta_prompt(
    sample: dict, original_start_marker: str = "<|editable_region_start|>"
) -> dict:
    """Format the zeta prompt for the Next Edit Prediction (NEP) dataset.

    This function formats examples from the NEP dataset
    into prompts and expected outputs. It could be
    further extended to support more NEP datasets.

    Args:
        sample: The dataset sample containing events,
            inputs, and outputs.
        original_start_marker: The marker indicating the
            start of the editable region. Defaults to
            "<|editable_region_start|>".

    Returns:
        A dictionary with the formatted prompts and expected outputs.
    """
    events = sample["events"]
    input = sample["input"]
    output = sample["output"]
    prompt = zeta_prompt.format(events, input)

    # following the original implementation, extract the focused region
    # from the raw output
    output_start_index = output.find(original_start_marker)
    output_focused_region = output[output_start_index:]
    expected_output = output_focused_region

    return {"prompt": prompt, "expected_output": expected_output}


class NextEditPredictionDataset(HuggingFaceDataset):
    """
    Dataset class for processing a Next Edit Prediction dataset.
    """

    SUPPORTED_DATASET_PATHS = {
        "zed-industries/zeta",
    }
    MAPPING_PROMPT_FUNCS = {
        "zed-industries/zeta": _format_zeta_prompt,
    }

    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int, **kwargs):
        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(self.dataset_path)
        if formatting_prompt_func is None:
            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
        samples = []
        for sample in self.data:
            sample = formatting_prompt_func(sample)
            samples.append(
                SampleRequest(
                    prompt=sample["prompt"],
                    prompt_len=len(tokenizer(sample["prompt"]).input_ids),
                    expected_output_len=len(
                        tokenizer(sample["expected_output"]).input_ids
                    ),
                )
            )
            if len(samples) >= num_requests:
                break
        self.maybe_oversample_requests(samples, num_requests)
        return samples


# -----------------------------------------------------------------------------
# ASR Dataset Implementation
# -----------------------------------------------------------------------------


class ASRDataset(HuggingFaceDataset):
    """
    Dataset class for processing a ASR dataset for transcription.
    Tested on the following set:

    +----------------+----------------------------------------+--------------------------+-----------------------------+
    | Dataset        | Domain                                 | Speaking Style           | hf-subset                   |
    +----------------+----------------------------------------+--------------------------+-----------------------------+
    | TED-LIUM       | TED talks                              | Oratory                  | release1, release2, release3|
    |                |                                        |                          | release3-speaker-adaptation |
    | VoxPopuli      | European Parliament                    | Oratory                  | en, de, it, fr,  ...        |
    | LibriSpeech    | Audiobook                              | Narrated                 | "LIUM/tedlium"              |
    | GigaSpeech     | Audiobook, podcast, YouTube            | Narrated, spontaneous    | xs, s, m, l, xl, dev, test  |
    | SPGISpeech     | Financial meetings                     | Oratory, spontaneous     | S, M, L, dev, test          |
    | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
    +----------------+----------------------------------------+--------------------------+-----------------------------+

    """  # noqa: E501

    SUPPORTED_DATASET_PATHS = {
        "openslr/librispeech_asr",
        "facebook/voxpopuli",
        "LIUM/tedlium",
        "edinburghcstr/ami",
        "speechcolab/gigaspeech",
        "kensho/spgispeech",
    }

    DEFAULT_OUTPUT_LEN = 128
    IS_MULTIMODAL = True

    # TODO Whisper-specific. Abstract interface when more models are supported.
    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
    skip_long_audios: bool = True

    def sample(
        self,
        tokenizer: PreTrainedTokenizerBase,
        num_requests: int,
        output_len: Optional[int] = None,
        **kwargs,
    ) -> list:
        import librosa

        output_len = output_len if output_len is not None else self.DEFAULT_OUTPUT_LEN
        prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
        prompt_len = len(tokenizer(prompt).input_ids)
        sampled_requests = []
        skipped = 0
        for item in self.data:
            if len(sampled_requests) >= num_requests:
                break
            audio = item["audio"]
            y, sr = audio["array"], audio["sampling_rate"]
            duration_s = librosa.get_duration(y=y, sr=sr)
            # Whisper max supported duration
            if self.skip_long_audios and duration_s > 30:
                skipped += 1
                continue

            mm_content = {"audio": (y, sr)}
            sampled_requests.append(
                SampleRequest(
                    prompt=prompt,
                    prompt_len=prompt_len,
                    expected_output_len=output_len,
                    multi_modal_data=mm_content,
                )
            )
        if skipped:
            logger.warning(
                "%d samples discarded from dataset due to"
                " their length being greater than"
                " what Whisper supports.",
                skipped,
            )
        self.maybe_oversample_requests(sampled_requests, num_requests)
        return sampled_requests