Feature/benchmark/random mm data/images (#23119)

Signed-off-by: breno.skuk <breno.skuk@hcompany.ai>

benchmarks/README.md

@@ -59,6 +59,12 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>synthetic</code></td>
     </tr>
+    <tr>
+      <td><strong>RandomMultiModal (Image/Video)</strong></td>
+      <td style="text-align: center;">🟡</td>
+      <td style="text-align: center;">🚧</td>
+      <td><code>synthetic</code> </td>
+    </tr>
     <tr>
       <td><strong>Prefix Repetition</strong></td>
       <td style="text-align: center;">✅</td>
@@ -722,4 +728,75 @@ python benchmarks/benchmark_serving.py \
   --endpoint /v1/chat/completion
 ```
 
+### Synthetic Random Images (random-mm)
+
+Generate synthetic image inputs alongside random text prompts to stress-test vision models without external datasets.
+
+Notes:
+
+- Works only with online benchmark via the OpenAI  backend (`--backend openai-chat`) and endpoint `/v1/chat/completions`.
+- Video sampling is not yet implemented.
+
+Start the server (example):
+
+```bash
+vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
+  --dtype bfloat16 \
+  --max-model-len 16384 \
+  --limit-mm-per-prompt '{"image": 3, "video": 0}' \
+  --mm-processor-kwargs max_pixels=1003520
+```
+
+Benchmark. It is recommended to use the flag `--ignore-eos` to simulate real responses. You can set the size of the output via the arg `random-output-len`.
+
+Ex.1: Fixed number of items and a single image resolutionm, enforcing generation of approx 40 tokens:
+
+```bash
+vllm bench serve \
+  --backend openai-chat \
+  --model Qwen/Qwen2.5-VL-3B-Instruct \
+  --endpoint /v1/chat/completions \
+  --dataset-name random-mm \
+  --num-prompts 100 \
+  --max-concurrency 10 \
+  --random-prefix-len 25 \
+  --random-input-len 300 \
+  --random-output-len 40 \
+  --random-range-ratio 0.2 \
+  --random-mm-base-items-per-request 2 \
+  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
+  --random-mm-bucket-config '{(224, 224, 1): 1.0}' \
+  --request-rate inf \
+  --ignore-eos \
+  --seed 42
+```
+
+The number of items per request can be controlled by passing multiple image buckets:
+
+```bash
+  --random-mm-base-items-per-request 2 \
+  --random-mm-num-mm-items-range-ratio 0.5 \
+  --random-mm-limit-mm-per-prompt '{"image": 4, "video": 0}' \
+  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}' \
+```
+
+Flags specific to `random-mm`:
+
+- `--random-mm-base-items-per-request`: base number of multimodal items per request.
+- `--random-mm-num-mm-items-range-ratio`: vary item count uniformly in the closed integer range [floor(n·(1−r)), ceil(n·(1+r))]. Set r=0 to keep it fixed; r=1 allows 0 items.
+- `--random-mm-limit-mm-per-prompt`: per-modality hard caps, e.g. '{"image": 3, "video": 0}'.
+- `--random-mm-bucket-config`: dict mapping (H, W, T) → probability. Entries with probability 0 are removed; remaining probabilities are renormalized to sum to 1. Use T=1 for images. Set any T>1 for videos (video sampling not yet supported).
+
+Behavioral notes:
+
+- If the requested base item count cannot be satisfied under the provided per-prompt limits, the tool raises an error rather than silently clamping.
+
+How sampling works:
+
+- Determine per-request item count k by sampling uniformly from the integer range defined by `--random-mm-base-items-per-request` and `--random-mm-num-mm-items-range-ratio`, then clamp k to at most the sum of per-modality limits.
+- For each of the k items, sample a bucket (H, W, T) according to the normalized probabilities in `--random-mm-bucket-config`, while tracking how many items of each modality have been added.
+- If a modality (e.g., image) reaches its limit from `--random-mm-limit-mm-per-prompt`, all buckets of that modality are excluded and the remaining bucket probabilities are renormalized before continuing.
+This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`.
+- The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`.
+
 </details>

tests/benchmarks/test_random_dataset.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import random
+from typing import Any, NamedTuple, Optional, cast
+
+import numpy as np
+import pytest
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+from vllm.benchmarks.datasets import (RandomDataset, RandomMultiModalDataset,
+                                      SampleRequest)
+
+
+@pytest.fixture(scope="session")
+def hf_tokenizer() -> PreTrainedTokenizerBase:
+    # Use a small, commonly available tokenizer
+    return AutoTokenizer.from_pretrained("gpt2")
+
+
+class Params(NamedTuple):
+    num_requests: int
+    prefix_len: int
+    range_ratio: float
+    input_len: int
+    output_len: int
+
+
+@pytest.fixture(scope="session")
+def random_dataset_params() -> Params:
+    return Params(num_requests=16,
+                  prefix_len=7,
+                  range_ratio=0.3,
+                  input_len=50,
+                  output_len=20)
+
+
+def _fingerprint_sample(req: SampleRequest) -> tuple[str, int, int]:
+    """Project a SampleRequest into a comparable tuple."""
+    return (req.prompt, req.prompt_len, req.expected_output_len)
+
+
+def _collect_samples(dataset: RandomDataset,
+                    tokenizer: PreTrainedTokenizerBase,
+                     num_requests: int = 16,
+                     prefix_len: int = 7,
+                     range_ratio: float = 0.3,
+                     input_len: int = 50,
+                     output_len: int = 20) -> list[tuple[str, int, int]]:
+    samples = dataset.sample(
+        tokenizer=tokenizer,
+        num_requests=num_requests,
+        prefix_len=prefix_len,
+        range_ratio=range_ratio,
+        input_len=input_len,
+        output_len=output_len,
+    )
+    return [_fingerprint_sample(s) for s in samples]
+
+
+@pytest.mark.benchmark
+def test_random_dataset_same_seed(
+        hf_tokenizer: PreTrainedTokenizerBase,
+        random_dataset_params: Params) -> None:
+    """Same seed should yield identical outputs, even if global RNGs change.
+
+    This guards against accidental reliance on Python's random or np.random
+    in RandomDataset after moving to numpy.default_rng.
+    """
+    p = random_dataset_params
+    common_seed = 123
+    dataset_a = RandomDataset(random_seed=common_seed)
+    dataset_b = RandomDataset(random_seed=common_seed)
+    a = _collect_samples(dataset_a,
+                         hf_tokenizer,
+                         num_requests=p.num_requests,
+                         prefix_len=p.prefix_len,
+                         range_ratio=p.range_ratio,
+                         input_len=p.input_len,
+                         output_len=p.output_len)
+
+    # Perturb global RNG state to ensure isolation
+    random.seed(999)
+    _ = [random.random() for _ in range(100)]
+    np.random.seed(888)
+    _ = [np.random.random() for _ in range(100)]
+
+    b = _collect_samples(dataset_b,
+                         hf_tokenizer,
+                         num_requests=p.num_requests,
+                         prefix_len=p.prefix_len,
+                         range_ratio=p.range_ratio,
+                         input_len=p.input_len,
+                         output_len=p.output_len)
+    assert a == b
+
+@pytest.mark.benchmark
+def test_random_dataset_different_seeds(
+        hf_tokenizer: PreTrainedTokenizerBase,
+        random_dataset_params: Params) -> None:
+    """Different seeds should change outputs with overwhelming likelihood."""
+    p = random_dataset_params
+    seed_a = 0
+    dataset_a = RandomDataset(random_seed=seed_a)
+    a = _collect_samples(dataset_a,
+                         hf_tokenizer,
+                         num_requests=p.num_requests,
+                         prefix_len=p.prefix_len,
+                         range_ratio=p.range_ratio,
+                         input_len=p.input_len,
+                         output_len=p.output_len)
+
+    seed_b = 999
+    dataset_b = RandomDataset(random_seed=seed_b)
+    # Perturb global RNG with same seed as dataset_a to ensure isolation
+    random.seed(seed_a)
+    np.random.seed(seed_a)
+    b = _collect_samples(dataset_b,
+                         hf_tokenizer,
+                         num_requests=p.num_requests,
+                         prefix_len=p.prefix_len,
+                         range_ratio=p.range_ratio,
+                         input_len=p.input_len,
+                         output_len=p.output_len)
+    assert a != b
+
+
+# -----------------------------
+# RandomMultiModalDataset tests
+# -----------------------------
+
+def _mm_fingerprint_sample(
+    req: SampleRequest,
+) -> tuple[str, int, int, int, list[str]]:
+    """Create a compact fingerprint for multimodal samples.
+
+    Includes:
+    - prompt string
+    - prompt_len
+    - expected_output_len
+    - count of multimodal items
+    - per-item type and URL prefix (e.g., 'data:image/jpeg;base64,')
+    """
+    items = req.multi_modal_data or []
+    item_prefixes: list[str] = []
+    for it in items:
+        if isinstance(it, dict) and it.get("type") == "image_url":
+            url = it.get("image_url", {}).get("url", "")
+            # Only keep a short identifying prefix to avoid huge strings
+            item_prefixes.append(f"image:{url[:22]}")
+        elif isinstance(it, dict) and it.get("type") == "video_url":
+            url = it.get("video_url", {}).get("url", "")
+            item_prefixes.append(f"video:{url[:22]}")
+        else:
+            item_prefixes.append("unknown:")
+    return (req.prompt, req.prompt_len, req.expected_output_len, len(items),
+            item_prefixes)
+
+
+def _collect_mm_samples(
+    dataset: RandomMultiModalDataset,
+    tokenizer: PreTrainedTokenizerBase,
+    *,
+    num_requests: int = 8,
+    prefix_len: int = 3,
+    range_ratio: float = 0.0,
+    input_len: int = 20,
+    output_len: int = 5,
+    base_items_per_request: int = 2,
+    num_mm_items_range_ratio: float = 0.0,
+    limit_mm_per_prompt: Optional[dict[str, int]] = None,
+    bucket_config: Optional[dict[tuple[int, int, int], float]] = None,
+    enable_multimodal_chat: bool = False,
+) -> list[SampleRequest]:
+    if limit_mm_per_prompt is None:
+        limit_mm_per_prompt = {"image": 5, "video": 0}
+    if bucket_config is None:
+        bucket_config = {(32, 32, 1): 0.5, (52, 64, 1): 0.5}
+    return dataset.sample(
+        tokenizer=tokenizer,
+        num_requests=num_requests,
+        prefix_len=prefix_len,
+        range_ratio=range_ratio,
+        input_len=input_len,
+        output_len=output_len,
+        base_items_per_request=base_items_per_request,
+        num_mm_items_range_ratio=num_mm_items_range_ratio,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        enable_multimodal_chat=enable_multimodal_chat,
+    )
+
+
+@pytest.mark.benchmark
+def test_random_mm_same_seed(hf_tokenizer: PreTrainedTokenizerBase) -> None:
+    seed = 42
+    ds_a = RandomMultiModalDataset(random_seed=seed)
+    ds_b = RandomMultiModalDataset(random_seed=seed)
+    a = _collect_mm_samples(ds_a, hf_tokenizer)
+    b = _collect_mm_samples(ds_b, hf_tokenizer)
+    fa = [_mm_fingerprint_sample(s) for s in a]
+    fb = [_mm_fingerprint_sample(s) for s in b]
+    assert fa == fb
+
+
+@pytest.mark.benchmark
+def test_random_mm_different_seeds(
+    hf_tokenizer: PreTrainedTokenizerBase,
+) -> None:
+    ds_a = RandomMultiModalDataset(random_seed=0)
+    ds_b = RandomMultiModalDataset(random_seed=999)
+    a = _collect_mm_samples(ds_a, hf_tokenizer)
+    b = _collect_mm_samples(ds_b, hf_tokenizer)
+    fa = [_mm_fingerprint_sample(s) for s in a]
+    fb = [_mm_fingerprint_sample(s) for s in b]
+    assert fa != fb
+
+@pytest.mark.benchmark
+def test_random_mm_respects_limits(
+    hf_tokenizer: PreTrainedTokenizerBase,
+) -> None:
+    ds = RandomMultiModalDataset(random_seed=0)
+    # Requesting 3 items with a per-prompt limit of 1 should error per current
+    # design (dataset refuses to silently clamp below the requested baseline).
+    with pytest.raises(ValueError):
+        _collect_mm_samples(
+            ds,
+            hf_tokenizer,
+            num_requests=12,
+            base_items_per_request=3,
+            num_mm_items_range_ratio=0.0,
+            limit_mm_per_prompt={"image": 1, "video": 0},
+            bucket_config={(32, 32, 1): 1.0},
+        )
+
+
+@pytest.mark.benchmark
+def test_random_mm_zero_prob_entries_are_removed(
+    hf_tokenizer: PreTrainedTokenizerBase,
+) -> None:
+    ds = RandomMultiModalDataset(random_seed=0)
+    # Second bucket has zero probability and should be ignored after
+    # normalization
+    samples = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=6,
+        base_items_per_request=2,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt={"image": 10, "video": 0},
+        bucket_config={(32, 32, 1): 1.0, (52, 64, 1): 0.0},
+    )
+    for s in samples:
+        assert isinstance(s.multi_modal_data, list)
+        typed_mm = cast(list[dict[str, Any]], s.multi_modal_data)
+        for it in typed_mm:
+            assert it.get("type") == "image_url"
+
+
+@pytest.mark.benchmark
+def test_random_mm_zero_items(hf_tokenizer: PreTrainedTokenizerBase) -> None:
+    ds = RandomMultiModalDataset(random_seed=0)
+    samples = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=5,
+        base_items_per_request=0,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt={"image": 5, "video": 0},
+        bucket_config={(32, 32, 1): 1.0},
+    )
+    for s in samples:
+        assert s.multi_modal_data == []
+
+@pytest.mark.benchmark
+def test_random_mm_num_items_per_prompt(
+    hf_tokenizer: PreTrainedTokenizerBase) -> None:
+    ds = RandomMultiModalDataset(random_seed=0)
+    # Fixed number of images per prompt
+    # set num_mm_items_range_ratio to 0.0
+    # TODO: modify video values when video sampling is implemented
+    samples_fixed_items = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=5,
+        base_items_per_request=3,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt={"image": 3, "video": 0},
+        bucket_config={(32, 32, 1): 1.0},
+    )
+    # Must have 5 requests each with 3 mm items per prompt
+    assert len(samples_fixed_items) == 5
+    for s in samples_fixed_items:
+        mm_data = cast(list[dict[str, Any]], s.multi_modal_data)
+        assert len(mm_data) == 3
+        for it in mm_data:
+            assert it.get("type") == "image_url"
+
+
+@pytest.mark.benchmark
+def test_random_mm_bucket_config_not_mutated(
+    hf_tokenizer: PreTrainedTokenizerBase,
+) -> None:
+
+    ds = RandomMultiModalDataset(random_seed=0)
+    # This bucket config is not normalized to sum to 1
+    # and has more buckets than requested images
+    original = {(32, 32, 1): 0.2, (52, 64, 1): 6, (25, 64, 1): 3}
+    # Keep a snapshot to compare after sampling
+    snapshot = dict(original)
+
+    _ = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=4,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt={"image": 1, "video": 0},
+        bucket_config=original,
+    )
+
+    # Ensure the original dict content is unchanged
+    assert original == snapshot
+
+
+    # Vary number of mm items per prompt
+    # set num_mm_items_range_ratio to 0.5
+    samples_varying_items = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=5,
+        base_items_per_request=2,
+        num_mm_items_range_ratio=0.5,
+        limit_mm_per_prompt={"image": 4, "video": 0},
+        bucket_config={(32, 32, 1): 1.0},
+    )
+    # Must have 5 requests each with less than 4 mm items per prompt
+    # but at least 1 mm item per prompt
+    assert len(samples_varying_items) == 5
+    for s in samples_varying_items:
+        mm_data = cast(list[dict[str, Any]], s.multi_modal_data)
+        assert len(mm_data) <= 4
+        assert len(mm_data) >= 1
+        for it in mm_data:
+            assert it.get("type") == "image_url"

vllm/benchmarks/datasets.py

@@ -11,18 +11,21 @@ generation. Supported dataset types include:
   - HuggingFace
   - VisionArena
 """
+import ast
 import base64
 import io
 import json
 import logging
+import math
 import random
 from abc import ABC, abstractmethod
-from collections.abc import Mapping
+from collections.abc import Iterator, Mapping
+from contextlib import suppress
 from copy import deepcopy
 from dataclasses import dataclass
 from functools import cache
 from io import BytesIO
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, Optional, Union, cast
 
 import numpy as np
 from PIL import Image
@@ -114,7 +117,9 @@ class BenchmarkDataset(ABC):
     def apply_multimodal_chat_transformation(
             self,
             prompt: str,
-            mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
+            mm_content: Optional[
+                        Union[MultiModalDataDict, dict, list[dict]]
+                             ] = 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
@@ -122,7 +127,15 @@ class BenchmarkDataset(ABC):
         """
         content = [{"text": prompt, "type": "text"}]
         if mm_content is not None:
-            content.append(mm_content)
+            if isinstance(mm_content, list):
+                content.extend(cast(list[dict[str, Any]], mm_content))
+            elif isinstance(mm_content, dict):
+                content.append(mm_content)
+            else:
+                raise TypeError(  
+                    "Could not process multimodal content of type: " +
+                    f"{type(mm_content)}"  
+                ) 
         return [{"role": "user", "content": content}]
 
     def load_data(self) -> None:
@@ -362,90 +375,536 @@ def process_video(video: Any) -> Mapping[str, Any]:
 
 
 class RandomDataset(BenchmarkDataset):
+    """
+    Synthetic text-only dataset for serving/throughput benchmarks.
+
+    Strategy:
+    - Sample input/output token lengths per request from integer-uniform ranges
+      around configured means (controlled by range_ratio).
+    - Prepend a fixed random prefix of length prefix_len.
+    - Generate the remaining tokens as a reproducible sequence:
+      (offset + index + arange(input_len)) % vocab_size.
+    - Decode then re-encode/truncate to ensure prompt token counts match.
+    - Uses numpy.default_rng seeded with random_seed for reproducible sampling.
+    """
     # 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:
+    def __init__(self, **kwargs) -> None:
         super().__init__(**kwargs)
-        random.seed(self.random_seed)
-        np.random.seed(self.random_seed)
+        # Use numpy's default_rng for deterministic sampling
+        # Do not use random.seed() or np.random.seed() elsewhere in this class.
+        # This ensures that the RNG is isolated from global RNG state.
+        self._rng = np.random.default_rng(self.random_seed)
 
     def sample(
         self,
         tokenizer: PreTrainedTokenizerBase,
         num_requests: int,
+        request_id_prefix: str = "",
         prefix_len: int = DEFAULT_PREFIX_LEN,
         range_ratio: float = DEFAULT_RANGE_RATIO,
         input_len: int = DEFAULT_INPUT_LEN,
         output_len: int = DEFAULT_OUTPUT_LEN,
-        request_id_prefix: str = "",
         **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"
+
+        input_lens, output_lens, offsets = self.get_sampling_params(
+            num_requests, range_ratio, input_len, output_len, tokenizer
         )
 
+        # Generate prefix once
+        prefix_token_ids = self.get_prefix(tokenizer, prefix_len)
         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 [])
+        requests = []
+        for i in range(num_requests):
+            prompt, total_input_len = self.generate_token_sequence(
+                tokenizer=tokenizer,
+                prefix_token_ids=prefix_token_ids,
+                prefix_len=prefix_len,
+                vocab_size=vocab_size,
+                input_len=int(input_lens[i]),
+                offset=int(offsets[i]),
+                index=i,
+            )
+            requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=total_input_len,
+                    expected_output_len=int(output_lens[i]),
+                    request_id=request_id_prefix + str(i),
+                )
+            )
+        return requests
+
+    def get_prefix(
+        self, tokenizer: PreTrainedTokenizerBase, prefix_len: int
+    ) -> list[int]:
+        """
+        Get the prefix for the dataset.
+        """
+        return (
+            self._rng.integers(
+                0, tokenizer.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))
+    def get_sampling_params(
+        self,
+        num_requests: int,
+        range_ratio: float,
+        input_len: int,
+        output_len: int,
+        tokenizer: PreTrainedTokenizerBase,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Get the sampling parameters for the dataset.
+        """
+        # Enforce range_ratio < 1
+        if not (0.0 <= range_ratio < 1.0):
+            raise ValueError("range_ratio must be in [0, 1).")
+        num_special_tokens = int(tokenizer.num_special_tokens_to_add())
+        real_input_len = max(0, int(input_len) - num_special_tokens)
+        # Bounds use floor for low and ceil for high
+        input_low = math.floor(real_input_len * (1 - range_ratio))
+        input_high = math.ceil(real_input_len * (1 + range_ratio))
+        output_low = math.floor(output_len * (1 - range_ratio))
+        output_high = math.ceil(output_len * (1 + range_ratio))
+        # Ensure the lower bound for output length is at least 1 to 
+        # prevent sampling 0 tokens. 
+        output_low = max(output_low, 1)
+
+        if input_low > input_high:
+            raise ValueError(
+                "Invalid input sampling interval: "
+                f"low={input_low} > high={input_high}"
+            )
+        if output_low > output_high:
+            raise ValueError(
+                "Invalid output sampling interval: "
+                f"low={output_low} > high={output_high}"
+            )
 
-        # Add logging for debugging
         logger.info(
             "Sampling input_len from [%s, %s] and output_len from [%s, %s]",
-            input_low, input_high, output_low, output_high)
+            input_low,
+            input_high,
+            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,
+        input_lens = self._rng.integers(input_low, input_high + 1,
+                                           size=num_requests)
+        output_lens = self._rng.integers(output_low, output_high + 1,
+                                            size=num_requests)
+        offsets = self._rng.integers(0, tokenizer.vocab_size, 
                                         size=num_requests)
-        offsets = np.random.randint(0, vocab_size, size=num_requests)
+        return input_lens, output_lens, offsets
 
-        requests = []
+
+    def generate_token_sequence(
+        self,
+        *,
+        tokenizer: PreTrainedTokenizerBase,
+        prefix_token_ids: list[int],
+        prefix_len: int,
+        vocab_size: int,
+        input_len: int,
+        offset: int,
+        index: int,
+    ) -> tuple[str, int]:
+        """
+        Returns (prompt, total_input_len).
+
+        NOTE: 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.
+        """
+        # Build the inner sequence by sampling sequentially from the vocab
+        inner_seq = ((offset + index + np.arange(input_len)) 
+                    % vocab_size).tolist()
+        token_sequence = prefix_token_ids + inner_seq
+
+        # Decode, then re-encode and truncate to preserve token count invariants
+        prompt = tokenizer.decode(token_sequence)
+        total_input_len = prefix_len + int(input_len)
+
+        re_encoded_sequence = tokenizer.encode(
+            prompt, add_special_tokens=False)[:total_input_len]
+        prompt = tokenizer.decode(re_encoded_sequence)
+        total_input_len = len(re_encoded_sequence)
+
+        return prompt, total_input_len
+
+
+# -----------------------------------------------------------------------------
+# MultiModalDataset Implementation
+# -----------------------------------------------------------------------------
+
+class RandomMultiModalDataset(RandomDataset):
+    """
+    Synthetic multimodal dataset (text + images) that extends RandomDataset.
+
+    Status:
+    - Images: supported via synthetic RGB data.
+    - Video: not yet supported (TODO: implement video generation method).
+    - Audio: not yet supported.
+
+    Sampling overview:
+    1) Number of items per request is sampled uniformly from the integer range
+       [floor(n·(1−r)), ceil(n·(1+r))], where n is the base count and r is
+       `num_mm_items_range_ratio` in [0, 1]. r=0 keeps it fixed; r=1 allows 0.
+       The maximum is further clamped to the sum of per-modality limits.
+    2) Each item’s modality and shape is sampled from `bucket_config`, a dict
+       mapping (height, width, num_frames) → probability. We treat 
+       `num_frames`=1 as image and and `num_frames` > 1 as video. 
+       Entries with zero probability are removed and the rest are renormalized 
+       to sum to 1.
+    3) Per-modality hard caps are enforced via `limit_mm_per_prompt`.
+       When a modality reaches its cap, all of its buckets are excluded and the
+       remaining probabilities are renormalized.
+
+    Example bucket configuration:
+    {(256, 256, 1): 0.5, (720, 1280, 1): 0.4, (720, 1280, 16): 0.1}
+      - Two image buckets (`num_frames`=1) and one video bucket 
+      (`num_frames`=16). 
+    OBS.: Only image sampling is supported for now.
+    """
+
+    IS_MULTIMODAL = True
+    # NOTE: video sampling is WIP. Setting it to 0.
+    DEFAULT_LIMIT_MM_PER_PROMPT = {"image": 255, "video": 0}
+
+    DEFAULT_BASE_ITEMS_PER_REQUEST = 1
+    DEFAULT_NUM_MM_ITEMS_RANGE_RATIO = 0.0
+    DEFAULT_MM_ITEM_BUCKET_CONFIG = {
+        (256, 256, 1): 0.5,
+        (720, 1280, 1): 0.5,
+        (720, 1280, 16): 0.0,
+    }
+    DEFAULT_ENABLE_MULTIMODAL_CHAT = False
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+
+
+    def generate_synthetic_image(self, width: int, height: int) -> Image.Image:
+        """Generate synthetic PIL image with random RGB values.
+        
+        NOTE: iid pixel sampling results in worst-case compression 
+        (good for stressing I/O), but very unlike real photos. 
+        We could consider a “low-freq” mode (e.g., noise blur)
+        to emulate network realism instead of max stress.
+        """
+        random_pixels = self._rng.integers(
+            0,
+            256,
+            (height, width, 3),
+            dtype=np.uint8,
+        )
+        return Image.fromarray(random_pixels)
+
+    def generate_synthetic_video(self, width: int, 
+                                    height: int, 
+                                    num_frames: int) -> Any:
+        """Generate synthetic video with random values.
+        
+        TODO: Finish this method.
+        """
+        raise NotImplementedError("Video sampling is WIP.")
+
+    def map_config_to_modality(self, config: tuple[int, int, int]) -> str:
+        """Map the configuration to the modality."""
+        if config[-1] == 1:
+            return "image"
+        elif config[-1] > 1:
+            return "video"
+        else:
+            raise ValueError(f"Invalid multimodal item configuration: {config}")
+
+    def normalize_bucket_config(self, bucket_config: dict[tuple[int, int, int], 
+                                float]) -> dict[tuple[int, int, int], float]:
+        """
+        Remove zero probability entries
+        and normalize the bucket config to sum to 1.
+        """
+        # Raise error if value is negative
+        if any(v < 0 for v in bucket_config.values()):
+            raise ValueError("Bucket config values must be non-negative.")
+        # Remove zero probability entries
+        bucket_config = {k: v for k, v in bucket_config.items() if v > 0}
+        # if bucket config is empty, raise error
+        if not bucket_config:
+            raise ValueError("Got invalid bucket config. "
+                             "Bucket config values must be non-zero.")
+        # Normalize the remaining bucket config to sum to 1
+        total = sum(bucket_config.values())
+        return {k: v / total for k, v in bucket_config.items()}
+
+
+    def generate_mm_item(self, 
+                         mm_item_config: tuple[int, int, int],
+                         ) -> Mapping[str, Any]:
+        """
+        Create synthetic images and videos and 
+        apply process_image/process_video respectively.
+        This follows the OpenAI API chat completions
+        https://github.com/openai/openai-python
+        """
+        
+        if self.map_config_to_modality(mm_item_config) == "image":
+            return process_image(self.generate_synthetic_image(
+                                                            mm_item_config[1],
+                                                            mm_item_config[0]))
+        elif self.map_config_to_modality(mm_item_config) == "video":
+            return process_video(self.generate_synthetic_video(
+                                                            mm_item_config[1], 
+                                                            mm_item_config[0], 
+                                                            mm_item_config[2]))
+        else:
+            raise ValueError(f"Invalid multimodal item configuration: "
+                             f"{mm_item_config}")
+
+
+    def get_mm_item_sampling_params(
+        self,
+        base_items_per_request: int,
+        num_mm_items_range_ratio: float,
+        limit_mm_per_prompt: dict[str, int],
+        bucket_config: dict[tuple[int, int, int], float],
+    ) -> tuple[int, int, dict[str, int], dict[tuple[int, int, int], float]]:
+        """
+        Get the sampling parameters for the multimodal items.
+        """
+        # Enforce num_mm_items_range_ratio <= 1
+        if not (0.0 <= num_mm_items_range_ratio <= 1.0):
+            raise ValueError("num_mm_items_range_ratio must be in [0, 1].")
+
+        # Ensure modalities to sample are in limit_mm_per_prompt
+        for k, v in bucket_config.items():
+            # get modality from bucket config
+            modality = self.map_config_to_modality(k)
+            if modality not in limit_mm_per_prompt:
+                raise ValueError(f"Modality {modality} is not in "
+                                 f"limit_mm_per_prompt: "
+                                 f"{limit_mm_per_prompt.keys()}")
+
+        # Remove zero probability entries 
+        # and normalize bucket config to sum to 1
+        bucket_config = self.normalize_bucket_config(bucket_config)
+        logger.info(
+            "Normalized bucket config: %s", bucket_config,
+        )
+        # Only consider limit per prompt for modalities in bucket config
+        allowed_modalities = {self.map_config_to_modality(cfg) 
+                              for cfg in bucket_config}
+        limit_mm_per_prompt = {
+            k: v for k, v in limit_mm_per_prompt.items() 
+            if k in allowed_modalities}
+        if not limit_mm_per_prompt:
+            raise ValueError("No valid limits for modalities present in "
+                             "bucket_config.")
+
+        logger.info(
+            "Updated mm-limit-per-prompt: %s", limit_mm_per_prompt,
+        )
+
+        # Get max and min num mm items and ensure
+        # it is at most the sum of limit_mm_per_prompt for all modalities
+        max_num_mm_items = min(
+            sum(limit_mm_per_prompt.values()), 
+            math.ceil(base_items_per_request * (1 + num_mm_items_range_ratio))
+        )
+        # Ensure min num mm items is at least 0
+        min_num_mm_items = max(
+            0, 
+            math.floor(base_items_per_request * (1 - num_mm_items_range_ratio))
+        )
+        # Raise error if min num mm items is greater than max num mm items
+        if min_num_mm_items > max_num_mm_items:
+            raise ValueError(f"Min num mm items is greater than max mm items: "
+                             f"{min_num_mm_items} > {max_num_mm_items}")
+        
+        logger.info(
+            "Sampling number of multimodal items from [%s, %s]",
+            min_num_mm_items, max_num_mm_items,
+        )
+
+        return (
+            min_num_mm_items,
+            max_num_mm_items,
+            limit_mm_per_prompt,
+            bucket_config,
+        )
+
+    def get_mm_item_iterator(
+        self,
+        min_num_mm_items: int,
+        max_num_mm_items: int,
+        bucket_config: dict[tuple[int, int, int], float],
+        limit_mm_per_prompt: dict[str, int],
+    ) -> Iterator[tuple[int,int, int]]:
+        """
+        Iterator over the multimodal items for each request
+        whose size is between min_num_mm_items and max_num_mm_items.
+
+        Loop over the bucket config and sample a multimodal item.
+        Loop until the number of multimodal items sampled is equal to 
+        request_num_mm_items or limit of multimodal items per prompt 
+        for all modalities is reached.
+
+        Note:
+        - This function operates on a per-request shallow copy of
+          `bucket_config` (tuple->float). The original dict passed to
+          `sample` is not mutated. If this ever changes, a test
+          is implemented and will fail.
+        """
+        # Get the number of multimodal items to sample
+        request_num_mm_items = int(
+            self._rng.integers(min_num_mm_items, max_num_mm_items + 1)
+        ) 
+        # If request_num_mm_items is 0, yield an empty iterator
+        if request_num_mm_items == 0:
+            return
+        # Initialize modality counters
+        modality_counter = {self.map_config_to_modality(k): 0 
+                            for k in bucket_config}
+        # Copy the bucket config to avoid modifying the original
+        bucket_config_copy = bucket_config.copy()
+        # Loop over the number of multimodal items to sample
+        while sum(modality_counter.values()) < request_num_mm_items:
+            # Sample a multimodal item config
+            mm_item_config = self._rng.choice(list(bucket_config_copy.keys()), 
+                                                p=list(bucket_config_copy.values()))
+            modality = self.map_config_to_modality(mm_item_config)
+            # Check that modality count is less than limit per prompt
+            if modality_counter[modality] < limit_mm_per_prompt[modality]:
+                modality_counter[modality] += 1
+                yield (
+                    mm_item_config
+                )
+            else:
+                # If the counter is greater than the limit per prompt
+                # set all multimodal items of this modality to 0
+                for k, v in bucket_config_copy.items():
+                    if self.map_config_to_modality(k) == modality:
+                        bucket_config_copy[k] = 0
+                # If all configs are 0, break the loop
+                # This should not happen as request_num_mm_items is at most
+                # the sum of limit_mm_per_prompt for all modalities
+                if all(v == 0 for v in bucket_config_copy.values()):
+                    logger.warning("Exhausted all multimodal items "
+                                   "of modality %s",
+                                   modality)
+                    break
+                # Renormalize the bucket config
+                bucket_config_copy = self.normalize_bucket_config(
+                                        bucket_config_copy)
+
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        request_id_prefix: str = "",
+        prefix_len: int = RandomDataset.DEFAULT_PREFIX_LEN,
+        range_ratio: float = RandomDataset.DEFAULT_RANGE_RATIO,
+        input_len: int = RandomDataset.DEFAULT_INPUT_LEN,
+        output_len: int = RandomDataset.DEFAULT_OUTPUT_LEN,
+        limit_mm_per_prompt: dict[str, int] = DEFAULT_LIMIT_MM_PER_PROMPT,
+        base_items_per_request: int = DEFAULT_BASE_ITEMS_PER_REQUEST,
+        num_mm_items_range_ratio: float = DEFAULT_NUM_MM_ITEMS_RANGE_RATIO,
+        bucket_config: dict[tuple[int, int, int], float] = 
+                                        DEFAULT_MM_ITEM_BUCKET_CONFIG,
+        enable_multimodal_chat: bool = DEFAULT_ENABLE_MULTIMODAL_CHAT,
+        **kwargs,
+    ) -> list[SampleRequest]:
+
+        # NOTE: Video sampling is WIP. Raise error if video is in bucket config
+        # and probability is non-zero.
+        if any(self.map_config_to_modality(cfg) == "video" and p > 0 
+                for cfg, p in bucket_config.items()):
+            raise NotImplementedError("Video sampling not implemented; "
+                                      "set its probability to 0.")
+
+        # Get the sampling parameters for the dataset
+        input_lens, output_lens, offsets = self.get_sampling_params(
+            num_requests, range_ratio, input_len, output_len, tokenizer
+        )
+
+        (
+            min_num_mm_items,
+            max_num_mm_items,
+            limit_mm_per_prompt,
+            bucket_config,
+        ) = self.get_mm_item_sampling_params(
+            base_items_per_request,
+            num_mm_items_range_ratio,
+            limit_mm_per_prompt,
+            bucket_config,
+        )
+
+        # Generate prefix once
+        prefix_token_ids = self.get_prefix(tokenizer, prefix_len)
+        vocab_size = tokenizer.vocab_size
+        # Add synthetic multimodal items to each request
+        mm_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.
-            total_input_len = prefix_len + int(input_lens[i])
-            re_encoded_sequence = tokenizer.encode(
-                prompt, add_special_tokens=False)[:total_input_len]
-            prompt = tokenizer.decode(re_encoded_sequence)
-            total_input_len = len(re_encoded_sequence)
-            requests.append(
-                SampleRequest(
+            prompt, total_input_len = self.generate_token_sequence(
+                tokenizer=tokenizer,
+                prefix_token_ids=prefix_token_ids,
+                prefix_len=prefix_len,
+                vocab_size=vocab_size,
+                input_len=int(input_lens[i]),
+                offset=int(offsets[i]),
+                index=i,
+            )
+            # Get multimodal item iterator for a given request
+            mm_item_iterator = self.get_mm_item_iterator(
+                min_num_mm_items,
+                max_num_mm_items,
+                bucket_config,
+                limit_mm_per_prompt,
+            )
+
+            mm_content = cast(list[dict[str, Any]], [
+                self.generate_mm_item(mm_item_config)
+                for mm_item_config in mm_item_iterator
+            ])
+
+            if enable_multimodal_chat:
+                # NOTE: For now this option is only provided for completeness 
+                # given that the serve.py benchmark currently does not use it.
+                mm_chat_prompt: Any = prompt
+                mm_chat_prompt = self.apply_multimodal_chat_transformation(
+                    prompt, mm_content)
+                sample_request = SampleRequest(
+                    prompt=mm_chat_prompt,
+                    prompt_len=total_input_len,
+                    expected_output_len=int(output_lens[i]),
+                    multi_modal_data=None,
+                    request_id=request_id_prefix + str(i),
+                )
+            else:
+                sample_request = SampleRequest(
                     prompt=prompt,
                     prompt_len=total_input_len,
                     expected_output_len=int(output_lens[i]),
+                    multi_modal_data=mm_content,
                     request_id=request_id_prefix + str(i),
-                ))
-        return requests
-
+                )
+            mm_requests.append(sample_request)
+        return mm_requests
 
 # -----------------------------------------------------------------------------
 # ShareGPT Dataset Implementation
@@ -545,8 +1004,8 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
         type=str,
         default="random",
         choices=[
-            "sharegpt", "burstgpt", "sonnet", "random", "hf", "custom",
-            "prefix_repetition"
+            "sharegpt", "burstgpt", "sonnet", "random", "random-mm", "hf", 
+            "custom", "prefix_repetition"
         ],
         help="Name of the dataset to benchmark on.",
     )
@@ -647,6 +1106,98 @@ def add_dataset_parser(parser: FlexibleArgumentParser):
               "input_len * (1 + range_ratio)]."),
     )
 
+    # random multimodal dataset options
+    random_mm_group = parser.add_argument_group(
+        "random multimodal dataset options extended from random dataset")
+    random_mm_group.add_argument(
+        "--random-mm-base-items-per-request",
+        type=int,
+        default=RandomMultiModalDataset.DEFAULT_BASE_ITEMS_PER_REQUEST,
+        help=(
+            "Base number of multimodal items per request for random-mm. "
+            "Actual per-request count is sampled around this base using "
+            "--random-mm-num-mm-items-range-ratio."
+        ),
+    )
+    random_mm_group.add_argument(
+        "--random-mm-num-mm-items-range-ratio",
+        type=float,
+        default=RandomMultiModalDataset.DEFAULT_NUM_MM_ITEMS_RANGE_RATIO,
+        help=(
+            "Range ratio r in [0, 1] for sampling items per request. "
+            "We sample uniformly from the closed integer range "
+            "[floor(n*(1-r)), ceil(n*(1+r))] "
+            "where n is the base items per request. "
+            "r=0 keeps it fixed; r=1 allows 0 items. The maximum is clamped "
+            "to the sum of per-modality limits from "
+            "--random-mm-limit-mm-per-prompt. "
+            "An error is raised if the computed min exceeds the max."
+        ),
+    )
+    random_mm_group.add_argument(
+        "--random-mm-limit-mm-per-prompt",
+        type=json.loads,
+        default=RandomMultiModalDataset.DEFAULT_LIMIT_MM_PER_PROMPT,
+        help=(
+            "Per-modality hard caps for items attached per request, e.g. "
+            "'{\"image\": 3, \"video\": 0}'. The sampled per-request item "
+            "count is clamped to the sum of these limits. When a modality "
+            "reaches its cap, its buckets are excluded and probabilities are "
+            "renormalized."
+            "OBS.: Only image sampling is supported for now."
+        ),
+    )
+
+    def _parse_mm_bucket_config(v: object) -> dict[tuple[int, int, int], float]:
+        # If already a dict (e.g., programmatic call), normalize keys
+        def normalize(d: dict) -> dict[tuple[int, int, int], float]:
+            out: dict[tuple[int, int, int], float] = {}
+            for k, val in d.items():
+                key = k
+                if isinstance(key, str):
+                    with suppress(Exception):
+                        key = ast.literal_eval(key)
+                if not (isinstance(key, tuple) and len(key) == 3
+                        and all(isinstance(x, int) for x in key)):
+                    raise ValueError(
+                        f"Invalid bucket key {k!r}. Expected tuple (H, W, T)."
+                    )
+                out[(int(key[0]), int(key[1]), int(key[2]))] = float(val)
+            return out
+
+        if isinstance(v, dict):
+            return normalize(v)
+        if isinstance(v, str):
+            # Python literal (supports tuple keys)
+            parsed = ast.literal_eval(v)
+            if not isinstance(parsed, dict):
+                raise ValueError("Bucket config must parse to a dict.")
+            return normalize(parsed)
+        raise ValueError("Unsupported value for --random-mm-bucket-config.")
+
+    random_mm_group.add_argument(
+        "--random-mm-bucket-config",
+        type=_parse_mm_bucket_config,
+        default=RandomMultiModalDataset.DEFAULT_MM_ITEM_BUCKET_CONFIG,
+        help=(
+            "The bucket config is a dictionary mapping a multimodal item"
+            "sampling configuration to a probability."
+            "Currently allows for 2 modalities: images and videos. "
+            "An bucket key is a tuple of (height, width, num_frames)"
+            "The value is the probability of sampling that specific item. "
+            "Example: "
+            "--random-mm-bucket-config "
+            "{(256, 256, 1): 0.5, (720, 1280, 1): 0.4, (720, 1280, 16): 0.10} "
+            "First item: images with resolution 256x256 w.p. 0.5"
+            "Second item: images with resolution 720x1280 w.p. 0.4 "
+            "Third item: videos with resolution 720x1280 and 16 frames w.p. 0.1"
+            "OBS.: If the probabilities do not sum to 1, they are normalized."
+            "OBS bis.: Only image sampling is supported for now."
+        ),
+    )
+
+
+
     hf_group = parser.add_argument_group("hf dataset options")
     hf_group.add_argument("--hf-subset",
                           type=str,
@@ -821,6 +1372,22 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
                 range_ratio=args.random_range_ratio,
                 request_id_prefix=args.request_id_prefix,
             ),
+            "random-mm":
+            lambda: RandomMultiModalDataset(
+                random_seed=args.seed, dataset_path=args.dataset_path
+            ).sample(
+                tokenizer=tokenizer,
+                num_requests=args.num_prompts,
+                prefix_len=args.random_prefix_len,
+                range_ratio=args.random_range_ratio,
+                input_len=args.random_input_len,
+                output_len=args.random_output_len,
+                base_items_per_request=args.random_mm_base_items_per_request,
+                limit_mm_per_prompt=args.random_mm_limit_mm_per_prompt,
+                num_mm_items_range_ratio=args.random_mm_num_mm_items_range_ratio,
+                bucket_config=args.random_mm_bucket_config,
+                request_id_prefix=args.request_id_prefix,
+            ),
             "prefix_repetition":
             lambda: PrefixRepetitionRandomDataset(
                 random_seed=args.seed, dataset_path=args.dataset_path
@@ -836,6 +1403,13 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
         }
 
         try:
+            # Enforce endpoint compatibility for multimodal datasets.
+            if args.dataset_name == "random-mm" and args.endpoint_type not in [
+                    "openai-chat"]:
+                raise ValueError(
+                    "Multi-modal content (images) is only supported on "
+                    "'openai-chat' backend."
+                )
             input_requests = dataset_mapping[args.dataset_name]()
         except KeyError as err:
             raise ValueError(f"Unknown dataset: {args.dataset_name}") from err