Add vllm bench [latency, throughput] CLI commands (#16508)

Signed-off-by: mgoin <mgoin64@gmail.com>

.buildkite/test-pipeline.yaml

@@ -341,6 +341,13 @@ steps:
   commands:
   - bash scripts/run-benchmarks.sh
 
+- label: Benchmarks CLI Test # 10min
+  source_file_dependencies:
+  - vllm/
+  - tests/benchmarks/
+  commands:
+  - pytest -v -s benchmarks/
+
 - label: Quantization Test # 33min
   source_file_dependencies:
   - csrc/

tests/benchmarks/test_latency_cli.py

@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+import subprocess
+
+import pytest
+
+MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+
+
+@pytest.mark.benchmark
+def test_bench_latency():
+    command = [
+        "vllm", "bench", "latency", "--model", MODEL_NAME, "--input-len", "32",
+        "--output-len", "1", "--enforce-eager", "--load-format", "dummy"
+    ]
+    result = subprocess.run(command, capture_output=True, text=True)
+    print(result.stdout)
+    print(result.stderr)
+
+    assert result.returncode == 0, f"Benchmark failed: {result.stderr}"

tests/benchmarks/test_serve_cli.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+import subprocess
+
+import pytest
+
+from ..utils import RemoteOpenAIServer
+
+MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--max-model-len", "1024", "--enforce-eager", "--load-format", "dummy"
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.benchmark
+def test_bench_serve(server):
+    command = [
+        "vllm",
+        "bench",
+        "serve",
+        "--model",
+        MODEL_NAME,
+        "--host",
+        server.host,
+        "--port",
+        str(server.port),
+        "--random-input-len",
+        "32",
+        "--random-output-len",
+        "4",
+        "--num-prompts",
+        "5",
+    ]
+    result = subprocess.run(command, capture_output=True, text=True)
+    print(result.stdout)
+    print(result.stderr)
+
+    assert result.returncode == 0, f"Benchmark failed: {result.stderr}"

tests/benchmarks/test_throughput_cli.py

@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+import subprocess
+
+import pytest
+
+MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+
+
+@pytest.mark.benchmark
+def test_bench_throughput():
+    command = [
+        "vllm", "bench", "throughput", "--model", MODEL_NAME, "--input-len",
+        "32", "--output-len", "1", "--enforce-eager", "--load-format", "dummy"
+    ]
+    result = subprocess.run(command, capture_output=True, text=True)
+    print(result.stdout)
+    print(result.stderr)
+
+    assert result.returncode == 0, f"Benchmark failed: {result.stderr}"

vllm/benchmarks/datasets.py

@@ -0,0 +1,831 @@
+# 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
+
+TODO: Implement CustomDataset to parse a JSON file and convert its contents into
+SampleRequest instances, similar to the approach used in ShareGPT.
+"""
+
+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
+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.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
+
+    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 = image.convert("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
+
+        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(input_len * (1 - range_ratio))
+        input_high = int(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)
+            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
+
+
+# -----------------------------------------------------------------------------
+# 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.")
+
+        try:
+            import pandas as pd
+        except ImportError as e:
+            raise ImportError(
+                "Pandas is required for BurstGPTDataset. Please install it "
+                "using `pip install pandas`.") from e
+
+        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."""
+        try:
+            from datasets import load_dataset
+        except ImportError as e:
+            raise ImportError(
+                "Hugging Face datasets library is required for this dataset. "
+                "Please install it using `pip install datasets`.") from e
+
+        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'
+    }
+
+    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"]
+    }
+
+    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
+
+
+# -----------------------------------------------------------------------------
+# 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

vllm/benchmarks/latency.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Benchmark the latency of processing a single batch of requests."""
+
+import argparse
+import dataclasses
+import json
+import os
+import time
+from pathlib import Path
+from typing import Any, Optional
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+from vllm import LLM, SamplingParams
+from vllm.benchmarks.utils import (convert_to_pytorch_benchmark_format,
+                                   write_to_json)
+from vllm.engine.arg_utils import EngineArgs
+from vllm.inputs import PromptType
+from vllm.sampling_params import BeamSearchParams
+
+
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                     results: dict[str, Any]) -> None:
+    pt_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={"latency": results["latencies"]},
+        extra_info={k: results[k]
+                    for k in ["avg_latency", "percentiles"]})
+    if pt_records:
+        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
+        write_to_json(pt_file, pt_records)
+
+
+def add_cli_args(parser: argparse.ArgumentParser):
+    parser.add_argument("--input-len", type=int, default=32)
+    parser.add_argument("--output-len", type=int, default=128)
+    parser.add_argument("--batch-size", type=int, default=8)
+    parser.add_argument(
+        "--n",
+        type=int,
+        default=1,
+        help="Number of generated sequences per prompt.",
+    )
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument(
+        "--num-iters-warmup",
+        type=int,
+        default=10,
+        help="Number of iterations to run for warmup.",
+    )
+    parser.add_argument("--num-iters",
+                        type=int,
+                        default=30,
+                        help="Number of iterations to run.")
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="profile the generation process of a single batch",
+    )
+    parser.add_argument(
+        "--profile-result-dir",
+        type=str,
+        default=None,
+        help=("path to save the pytorch profiler output. Can be visualized "
+              "with ui.perfetto.dev or Tensorboard."),
+    )
+    parser.add_argument(
+        "--output-json",
+        type=str,
+        default=None,
+        help="Path to save the latency results in JSON format.",
+    )
+    parser.add_argument(
+        "--disable-detokenize",
+        action="store_true",
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
+    )
+
+    parser = EngineArgs.add_cli_args(parser)
+
+
+def main(args: argparse.Namespace):
+    print(args)
+
+    engine_args = EngineArgs.from_cli_args(args)
+
+    # NOTE(woosuk): If the request cannot be processed in a single batch,
+    # the engine will automatically process the request in multiple batches.
+    llm = LLM(**dataclasses.asdict(engine_args))
+    assert llm.llm_engine.model_config.max_model_len >= (
+        args.input_len +
+        args.output_len), ("Please ensure that max_model_len is greater than"
+                           " the sum of input_len and output_len.")
+
+    sampling_params = SamplingParams(
+        n=args.n,
+        temperature=1.0,
+        top_p=1.0,
+        ignore_eos=True,
+        max_tokens=args.output_len,
+        detokenize=not args.disable_detokenize,
+    )
+    print(sampling_params)
+    dummy_prompt_token_ids = np.random.randint(10000,
+                                               size=(args.batch_size,
+                                                     args.input_len))
+    dummy_prompts: list[PromptType] = [{
+        "prompt_token_ids": batch
+    } for batch in dummy_prompt_token_ids.tolist()]
+
+    def llm_generate():
+        if not args.use_beam_search:
+            llm.generate(dummy_prompts,
+                         sampling_params=sampling_params,
+                         use_tqdm=False)
+        else:
+            llm.beam_search(
+                dummy_prompts,
+                BeamSearchParams(
+                    beam_width=args.n,
+                    max_tokens=args.output_len,
+                    ignore_eos=True,
+                ),
+            )
+
+    def run_to_completion(profile_dir: Optional[str] = None):
+        if profile_dir:
+            with torch.profiler.profile(
+                    activities=[
+                        torch.profiler.ProfilerActivity.CPU,
+                        torch.profiler.ProfilerActivity.CUDA,
+                    ],
+                    on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                        str(profile_dir)),
+            ) as p:
+                llm_generate()
+            print(p.key_averages().table(sort_by="self_cuda_time_total"))
+        else:
+            start_time = time.perf_counter()
+            llm_generate()
+            end_time = time.perf_counter()
+            latency = end_time - start_time
+            return latency
+
+    print("Warming up...")
+    for _ in tqdm(range(args.num_iters_warmup), desc="Warmup iterations"):
+        run_to_completion(profile_dir=None)
+
+    if args.profile:
+        profile_dir = args.profile_result_dir
+        if not profile_dir:
+            profile_dir = (Path(".") / "vllm_benchmark_result" /
+                           f"latency_result_{time.time()}")
+        print(f"Profiling (results will be saved to '{profile_dir}')...")
+        run_to_completion(profile_dir=profile_dir)
+        return
+
+    # Benchmark.
+    latencies = []
+    for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
+        latencies.append(run_to_completion(profile_dir=None))
+    latencies = np.array(latencies)
+    percentages = [10, 25, 50, 75, 90, 99]
+    percentiles = np.percentile(latencies, percentages)
+    print(f"Avg latency: {np.mean(latencies)} seconds")
+    for percentage, percentile in zip(percentages, percentiles):
+        print(f"{percentage}% percentile latency: {percentile} seconds")
+
+    # Output JSON results if specified
+    if args.output_json:
+        results = {
+            "avg_latency": np.mean(latencies),
+            "latencies": latencies.tolist(),
+            "percentiles": dict(zip(percentages, percentiles.tolist())),
+        }
+        with open(args.output_json, "w") as f:
+            json.dump(results, f, indent=4)
+        save_to_pytorch_benchmark_format(args, results)

vllm/benchmarks/throughput.py

@@ -0,0 +1,608 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Benchmark offline inference throughput."""
+import argparse
+import dataclasses
+import json
+import os
+import random
+import time
+import warnings
+from typing import Any, Optional, Union
+
+import torch
+import uvloop
+from tqdm import tqdm
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedTokenizerBase)
+
+from vllm.benchmarks.datasets import (AIMODataset, BurstGPTDataset,
+                                      ConversationDataset,
+                                      InstructCoderDataset, RandomDataset,
+                                      SampleRequest, ShareGPTDataset,
+                                      SonnetDataset, VisionArenaDataset)
+from vllm.benchmarks.utils import (convert_to_pytorch_benchmark_format,
+                                   write_to_json)
+from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
+from vllm.entrypoints.openai.api_server import (
+    build_async_engine_client_from_engine_args)
+from vllm.inputs import TextPrompt, TokensPrompt
+from vllm.lora.request import LoRARequest
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import BeamSearchParams
+from vllm.utils import merge_async_iterators
+
+
+def run_vllm(
+    requests: list[SampleRequest],
+    n: int,
+    engine_args: EngineArgs,
+    disable_detokenize: bool = False,
+) -> tuple[float, Optional[list[RequestOutput]]]:
+    from vllm import LLM, SamplingParams
+    llm = LLM(**dataclasses.asdict(engine_args))
+    assert all(
+        llm.llm_engine.model_config.max_model_len >= (
+            request.prompt_len + request.expected_output_len)
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of"
+            " prompt_len and expected_output_len for all requests.")
+    # Add the requests to the engine.
+    prompts: list[Union[TextPrompt, TokensPrompt]] = []
+    sampling_params: list[SamplingParams] = []
+    for request in requests:
+        prompts.append(
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
+            if "prompt_token_ids" in request.prompt else \
+            TextPrompt(prompt=request.prompt,
+                       multi_modal_data=request.multi_modal_data))
+        sampling_params.append(
+            SamplingParams(
+                n=n,
+                temperature=1.0,
+                top_p=1.0,
+                ignore_eos=True,
+                max_tokens=request.expected_output_len,
+                detokenize=not disable_detokenize,
+            ))
+    lora_requests: Optional[list[LoRARequest]] = None
+    if engine_args.enable_lora:
+        lora_requests = [request.lora_request for request in requests]
+
+    use_beam_search = False
+
+    outputs = None
+    if not use_beam_search:
+        start = time.perf_counter()
+        outputs = llm.generate(prompts,
+                               sampling_params,
+                               lora_request=lora_requests,
+                               use_tqdm=True)
+        end = time.perf_counter()
+    else:
+        assert lora_requests is None, "BeamSearch API does not support LoRA"
+        prompts = [request.prompt for request in requests]
+        # output_len should be the same for all requests.
+        output_len = requests[0][2]
+        for request in requests:
+            assert request.expected_output_len == output_len
+        start = time.perf_counter()
+        llm.beam_search(
+            prompts,
+            BeamSearchParams(
+                beam_width=n,
+                max_tokens=output_len,
+                ignore_eos=True,
+            ))
+        end = time.perf_counter()
+    return end - start, outputs
+
+
+def run_vllm_chat(
+        requests: list[SampleRequest],
+        n: int,
+        engine_args: EngineArgs,
+        disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
+    """
+    Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
+    multimodal models as it properly handles multimodal inputs and chat
+    formatting. For non-multimodal models, use run_vllm() instead.
+    """
+    from vllm import LLM, SamplingParams
+    llm = LLM(**dataclasses.asdict(engine_args))
+
+    assert all(
+        llm.llm_engine.model_config.max_model_len >= (
+            request.prompt_len + request.expected_output_len)
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of "
+            "prompt_len and expected_output_len for all requests.")
+
+    prompts = []
+    sampling_params: list[SamplingParams] = []
+    for request in requests:
+        prompts.append(request.prompt)
+        sampling_params.append(
+            SamplingParams(
+                n=n,
+                temperature=1.0,
+                top_p=1.0,
+                ignore_eos=True,
+                max_tokens=request.expected_output_len,
+                detokenize=not disable_detokenize,
+            ))
+    start = time.perf_counter()
+    outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
+    end = time.perf_counter()
+    return end - start, outputs
+
+
+async def run_vllm_async(
+    requests: list[SampleRequest],
+    n: int,
+    engine_args: AsyncEngineArgs,
+    disable_frontend_multiprocessing: bool = False,
+    disable_detokenize: bool = False,
+) -> float:
+    from vllm import SamplingParams
+
+    async with build_async_engine_client_from_engine_args(
+            engine_args, disable_frontend_multiprocessing) as llm:
+        assert all(
+            llm.model_config.max_model_len >= (request.prompt_len +
+                                               request.expected_output_len)
+            for request in requests), (
+                "Please ensure that max_model_len is greater than the sum of"
+                " prompt_len and expected_output_len for all requests.")
+
+        # Add the requests to the engine.
+        prompts: list[Union[TextPrompt, TokensPrompt]] = []
+        sampling_params: list[SamplingParams] = []
+        lora_requests: list[Optional[LoRARequest]] = []
+        for request in requests:
+            prompts.append(
+                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                        multi_modal_data=request.multi_modal_data)
+                if "prompt_token_ids" in request.prompt else \
+                TextPrompt(prompt=request.prompt,
+                           multi_modal_data=request.multi_modal_data))
+            sampling_params.append(
+                SamplingParams(
+                    n=n,
+                    temperature=1.0,
+                    top_p=1.0,
+                    ignore_eos=True,
+                    max_tokens=request.expected_output_len,
+                    detokenize=not disable_detokenize,
+                ))
+            lora_requests.append(request.lora_request)
+
+        generators = []
+        start = time.perf_counter()
+        for i, (prompt, sp,
+                lr) in enumerate(zip(prompts, sampling_params, lora_requests)):
+            generator = llm.generate(prompt,
+                                     sp,
+                                     lora_request=lr,
+                                     request_id=f"test{i}")
+            generators.append(generator)
+        all_gens = merge_async_iterators(*generators)
+        async for i, res in all_gens:
+            pass
+        end = time.perf_counter()
+        return end - start
+
+
+def run_hf(
+    requests: list[SampleRequest],
+    model: str,
+    tokenizer: PreTrainedTokenizerBase,
+    n: int,
+    max_batch_size: int,
+    trust_remote_code: bool,
+    disable_detokenize: bool = False,
+) -> float:
+    llm = AutoModelForCausalLM.from_pretrained(
+        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
+    if llm.config.model_type == "llama":
+        # To enable padding in the HF backend.
+        tokenizer.pad_token = tokenizer.eos_token
+    llm = llm.cuda()
+
+    pbar = tqdm(total=len(requests))
+    start = time.perf_counter()
+    batch: list[str] = []
+    max_prompt_len = 0
+    max_output_len = 0
+    for i in range(len(requests)):
+        prompt = requests[i].prompt
+        prompt_len = requests[i].prompt_len
+        output_len = requests[i].expected_output_len
+        # Add the prompt to the batch.
+        batch.append(prompt)
+        max_prompt_len = max(max_prompt_len, prompt_len)
+        max_output_len = max(max_output_len, output_len)
+        if len(batch) < max_batch_size and i != len(requests) - 1:
+            # Check if we can add more requests to the batch.
+            next_prompt_len = requests[i + 1].prompt_len
+            next_output_len = requests[i + 1].expected_output_len
+            if (max(max_prompt_len, next_prompt_len) +
+                    max(max_output_len, next_output_len)) <= 2048:
+                # We can add more requests to the batch.
+                continue
+
+        # Generate the sequences.
+        input_ids = tokenizer(batch, return_tensors="pt",
+                              padding=True).input_ids
+        llm_outputs = llm.generate(
+            input_ids=input_ids.cuda(),
+            do_sample=True,
+            num_return_sequences=n,
+            temperature=1.0,
+            top_p=1.0,
+            use_cache=True,
+            max_new_tokens=max_output_len,
+        )
+        if not disable_detokenize:
+            # Include the decoding time.
+            tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
+        pbar.update(len(batch))
+
+        # Clear the batch.
+        batch = []
+        max_prompt_len = 0
+        max_output_len = 0
+    end = time.perf_counter()
+    return end - start
+
+
+def save_to_pytorch_benchmark_format(args: argparse.Namespace,
+                                     results: dict[str, Any]) -> None:
+    pt_records = convert_to_pytorch_benchmark_format(
+        args=args,
+        metrics={
+            "requests_per_second": [results["requests_per_second"]],
+            "tokens_per_second": [results["tokens_per_second"]],
+        },
+        extra_info={
+            k: results[k]
+            for k in ["elapsed_time", "num_requests", "total_num_tokens"]
+        })
+    if pt_records:
+        # Don't use json suffix here as we don't want CI to pick it up
+        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
+        write_to_json(pt_file, pt_records)
+
+
+def get_requests(args, tokenizer):
+    # Common parameters for all dataset types.
+    common_kwargs = {
+        "dataset_path": args.dataset_path,
+        "random_seed": args.seed,
+    }
+    sample_kwargs = {
+        "tokenizer": tokenizer,
+        "lora_path": args.lora_path,
+        "max_loras": args.max_loras,
+        "num_requests": args.num_prompts,
+        "input_len": args.input_len,
+        "output_len": args.output_len,
+    }
+
+    if args.dataset_path is None or args.dataset_name == "random":
+        sample_kwargs["range_ratio"] = args.random_range_ratio
+        sample_kwargs["prefix_len"] = args.prefix_len
+        dataset_cls = RandomDataset
+    elif args.dataset_name == "sharegpt":
+        dataset_cls = ShareGPTDataset
+        if args.backend == "vllm-chat":
+            sample_kwargs["enable_multimodal_chat"] = True
+    elif args.dataset_name == "sonnet":
+        assert tokenizer.chat_template or tokenizer.default_chat_template, (
+            "Tokenizer/model must have chat template for sonnet dataset.")
+        dataset_cls = SonnetDataset
+        sample_kwargs["prefix_len"] = args.prefix_len
+        sample_kwargs["return_prompt_formatted"] = True
+    elif args.dataset_name == "burstgpt":
+        dataset_cls = BurstGPTDataset
+    elif args.dataset_name == "hf":
+        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = VisionArenaDataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = InstructCoderDataset
+            common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = ConversationDataset
+            common_kwargs['dataset_subset'] = args.hf_subset
+            common_kwargs['dataset_split'] = args.hf_split
+            sample_kwargs["enable_multimodal_chat"] = True
+        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = AIMODataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+    else:
+        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
+    # Remove None values
+    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
+    return dataset_cls(**common_kwargs).sample(**sample_kwargs)
+
+
+def validate_args(args):
+    """
+    Validate command-line arguments.
+    """
+
+    # === Deprecation and Defaulting ===
+    if args.dataset is not None:
+        warnings.warn(
+            "The '--dataset' argument will be deprecated in the next release. "
+            "Please use '--dataset-name' and '--dataset-path' instead.",
+            stacklevel=2)
+        args.dataset_path = args.dataset
+
+    if not getattr(args, "tokenizer", None):
+        args.tokenizer = args.model
+
+    # === Backend Validation ===
+    valid_backends = {"vllm", "hf", "mii", "vllm-chat"}
+    if args.backend not in valid_backends:
+        raise ValueError(f"Unsupported backend: {args.backend}")
+
+    # === Dataset Configuration ===
+    if not args.dataset and not args.dataset_path:
+        print(
+            "When dataset path is not set, it will default to random dataset")
+        args.dataset_name = 'random'
+        if args.input_len is None:
+            raise ValueError("input_len must be provided for a random dataset")
+
+    # === Dataset Name Specific Checks ===
+    # --hf-subset and --hf-split: only used
+    # when dataset_name is 'hf'
+    if args.dataset_name != "hf" and (
+            getattr(args, "hf_subset", None) is not None
+            or getattr(args, "hf_split", None) is not None):
+        warnings.warn("--hf-subset and --hf-split will be ignored \
+                since --dataset-name is not 'hf'.",
+                      stacklevel=2)
+    elif args.dataset_name == "hf":
+        if args.dataset_path in (
+                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
+                | ConversationDataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
+        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
+                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
+            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
+        else:
+            raise ValueError(
+                f"{args.dataset_path} is not supported by hf dataset.")
+
+    # --random-range-ratio: only used when dataset_name is 'random'
+    if args.dataset_name != 'random' and args.random_range_ratio is not None:
+        warnings.warn("--random-range-ratio will be ignored since \
+                --dataset-name is not 'random'.",
+                      stacklevel=2)
+
+    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
+    # set.
+    if args.dataset_name not in {"random", "sonnet", None
+                                 } and args.prefix_len is not None:
+        warnings.warn("--prefix-len will be ignored since --dataset-name\
+                 is not 'random', 'sonnet', or not set.",
+                      stacklevel=2)
+
+    # === LoRA Settings ===
+    if getattr(args, "enable_lora", False) and args.backend != "vllm":
+        raise ValueError(
+            "LoRA benchmarking is only supported for vLLM backend")
+    if getattr(args, "enable_lora", False) and args.lora_path is None:
+        raise ValueError("LoRA path must be provided when enable_lora is True")
+
+    # === Backend-specific Validations ===
+    if args.backend == "hf" and args.hf_max_batch_size is None:
+        raise ValueError("HF max batch size is required for HF backend")
+    if args.backend != "hf" and args.hf_max_batch_size is not None:
+        raise ValueError("HF max batch size is only for HF backend.")
+
+    if args.backend in {"hf", "mii"} and getattr(args, "quantization",
+                                                 None) is not None:
+        raise ValueError("Quantization is only for vLLM backend.")
+
+    if args.backend == "mii" and args.dtype != "auto":
+        raise ValueError("dtype must be auto for MII backend.")
+    if args.backend == "mii" and args.n != 1:
+        raise ValueError("n must be 1 for MII backend.")
+    if args.backend == "mii" and args.tokenizer != args.model:
+        raise ValueError(
+            "Tokenizer must be the same as the model for MII backend.")
+
+
+def add_cli_args(parser: argparse.ArgumentParser):
+    parser.add_argument("--backend",
+                        type=str,
+                        choices=["vllm", "hf", "mii", "vllm-chat"],
+                        default="vllm")
+    parser.add_argument(
+        "--dataset-name",
+        type=str,
+        choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
+        help="Name of the dataset to benchmark on.",
+        default="sharegpt")
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default=None,
+        help="Path to the ShareGPT dataset, will be deprecated in\
+            the next release. The dataset is expected to "
+        "be a json in form of list[dict[..., conversations: "
+        "list[dict[..., value: <prompt_or_response>]]]]")
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset")
+    parser.add_argument("--input-len",
+                        type=int,
+                        default=None,
+                        help="Input prompt length for each request")
+    parser.add_argument("--output-len",
+                        type=int,
+                        default=None,
+                        help="Output length for each request. Overrides the "
+                        "output length from the dataset.")
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
+                        help="Number of generated sequences per prompt.")
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=1000,
+                        help="Number of prompts to process.")
+    parser.add_argument("--hf-max-batch-size",
+                        type=int,
+                        default=None,
+                        help="Maximum batch size for HF backend.")
+    parser.add_argument(
+        '--output-json',
+        type=str,
+        default=None,
+        help='Path to save the throughput results in JSON format.')
+    parser.add_argument("--async-engine",
+                        action='store_true',
+                        default=False,
+                        help="Use vLLM async engine rather than LLM class.")
+    parser.add_argument("--disable-frontend-multiprocessing",
+                        action='store_true',
+                        default=False,
+                        help="Disable decoupled async engine frontend.")
+    parser.add_argument(
+        "--disable-detokenize",
+        action="store_true",
+        help=("Do not detokenize the response (i.e. do not include "
+              "detokenization time in the measurement)"))
+    # LoRA
+    parser.add_argument(
+        "--lora-path",
+        type=str,
+        default=None,
+        help="Path to the lora adapters to use. This can be an absolute path, "
+        "a relative path, or a Hugging Face model identifier.")
+    parser.add_argument(
+        "--prefix-len",
+        type=int,
+        default=0,
+        help="Number of fixed prefix tokens before the random "
+        "context in a request (default: 0).",
+    )
+    # random dataset
+    parser.add_argument(
+        "--random-range-ratio",
+        type=float,
+        default=0.0,
+        help="Range ratio for sampling input/output length, "
+        "used only for RandomDataset. Must be in the range [0, 1) to define "
+        "a symmetric sampling range "
+        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
+    )
+
+    # hf dtaset
+    parser.add_argument("--hf-subset",
+                        type=str,
+                        default=None,
+                        help="Subset of the HF dataset.")
+    parser.add_argument("--hf-split",
+                        type=str,
+                        default=None,
+                        help="Split of the HF dataset.")
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+
+
+def main(args: argparse.Namespace):
+    if args.tokenizer is None:
+        args.tokenizer = args.model
+    validate_args(args)
+    if args.seed is None:
+        args.seed = 0
+    print(args)
+    random.seed(args.seed)
+    # Sample the requests.
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.tokenizer, trust_remote_code=args.trust_remote_code)
+    requests = get_requests(args, tokenizer)
+    is_multi_modal = any(request.multi_modal_data is not None
+                         for request in requests)
+    request_outputs: Optional[list[RequestOutput]] = None
+    if args.backend == "vllm":
+        if args.async_engine:
+            elapsed_time = uvloop.run(
+                run_vllm_async(
+                    requests,
+                    args.n,
+                    AsyncEngineArgs.from_cli_args(args),
+                    args.disable_frontend_multiprocessing,
+                    args.disable_detokenize,
+                ))
+        else:
+            elapsed_time, request_outputs = run_vllm(
+                requests, args.n, EngineArgs.from_cli_args(args),
+                args.disable_detokenize)
+    elif args.backend == "hf":
+        assert args.tensor_parallel_size == 1
+        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
+                              args.hf_max_batch_size, args.trust_remote_code,
+                              args.disable_detokenize)
+    elif args.backend == "vllm-chat":
+        elapsed_time, request_outputs = run_vllm_chat(
+            requests, args.n, EngineArgs.from_cli_args(args),
+            args.disable_detokenize)
+    else:
+        raise ValueError(f"Unknown backend: {args.backend}")
+
+    if request_outputs:
+        # Note: with the vllm and vllm-chat backends,
+        # we have request_outputs, which we use to count tokens.
+        total_prompt_tokens = 0
+        total_output_tokens = 0
+        for ro in request_outputs:
+            if not isinstance(ro, RequestOutput):
+                continue
+            total_prompt_tokens += len(
+                ro.prompt_token_ids) if ro.prompt_token_ids else 0
+            total_output_tokens += sum(
+                len(o.token_ids) for o in ro.outputs if o)
+        total_num_tokens = total_prompt_tokens + total_output_tokens
+    else:
+        total_num_tokens = sum(r.prompt_len + r.expected_output_len
+                               for r in requests)
+        total_output_tokens = sum(r.expected_output_len for r in requests)
+        total_prompt_tokens = total_num_tokens - total_output_tokens
+
+    if is_multi_modal and args.backend != "vllm-chat":
+        print("\033[91mWARNING\033[0m: Multi-modal request with "
+              f"{args.backend} backend detected. The "
+              "following metrics are not accurate because image tokens are not"
+              " counted. See vllm-project/vllm/issues/9778 for details.")
+        # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
+        # vllm-chat backend counts the image tokens now
+
+    print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
+          f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
+          f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
+    print(f"Total num prompt tokens:  {total_prompt_tokens}")
+    print(f"Total num output tokens:  {total_output_tokens}")
+
+    # Output JSON results if specified
+    if args.output_json:
+        results = {
+            "elapsed_time": elapsed_time,
+            "num_requests": len(requests),
+            "total_num_tokens": total_num_tokens,
+            "requests_per_second": len(requests) / elapsed_time,
+            "tokens_per_second": total_num_tokens / elapsed_time,
+        }
+        with open(args.output_json, "w") as f:
+            json.dump(results, f, indent=4)
+        save_to_pytorch_benchmark_format(args, results)

vllm/entrypoints/cli/benchmark/latency.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+
+from vllm.benchmarks.latency import add_cli_args, main
+from vllm.entrypoints.cli.benchmark.base import BenchmarkSubcommandBase
+from vllm.entrypoints.cli.types import CLISubcommand
+
+
+class BenchmarkLatencySubcommand(BenchmarkSubcommandBase):
+    """ The `latency` subcommand for vllm bench. """
+
+    def __init__(self):
+        self.name = "latency"
+        super().__init__()
+
+    @property
+    def help(self) -> str:
+        return "Benchmark the latency of a single batch of requests."
+
+    def add_cli_args(self, parser: argparse.ArgumentParser) -> None:
+        add_cli_args(parser)
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        main(args)
+
+
+def cmd_init() -> list[CLISubcommand]:
+    return [BenchmarkLatencySubcommand()]

vllm/entrypoints/cli/benchmark/main.py

@@ -1,14 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 import argparse
 
+import vllm.entrypoints.cli.benchmark.latency
 import vllm.entrypoints.cli.benchmark.serve
+import vllm.entrypoints.cli.benchmark.throughput
 from vllm.entrypoints.cli.types import CLISubcommand
 from vllm.utils import FlexibleArgumentParser
 
-# TODO: Add the rest of the benchmark subcommands here,
-# e.g., throughput, latency, etc.
 BENCHMARK_CMD_MODULES = [
+    vllm.entrypoints.cli.benchmark.latency,
     vllm.entrypoints.cli.benchmark.serve,
+    vllm.entrypoints.cli.benchmark.throughput,
 ]
 
 

vllm/entrypoints/cli/benchmark/throughput.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+
+from vllm.benchmarks.throughput import add_cli_args, main
+from vllm.entrypoints.cli.benchmark.base import BenchmarkSubcommandBase
+from vllm.entrypoints.cli.types import CLISubcommand
+
+
+class BenchmarkThroughputSubcommand(BenchmarkSubcommandBase):
+    """ The `throughput` subcommand for vllm bench. """
+
+    def __init__(self):
+        self.name = "throughput"
+        super().__init__()
+
+    @property
+    def help(self) -> str:
+        return "Benchmark offline inference throughput."
+
+    def add_cli_args(self, parser: argparse.ArgumentParser) -> None:
+        add_cli_args(parser)
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        main(args)
+
+
+def cmd_init() -> list[CLISubcommand]:
+    return [BenchmarkThroughputSubcommand()]