Feature/video support in random mm dataset (#25963)

Signed-off-by: Eugene Khvedchenia <ekhvedchenia@nvidia.com>
Signed-off-by: Eugene Khvedchenya <ekhvedchenia@nvidia.com>
Co-authored-by: Roger Wang <hey@rogerw.io>

tests/benchmarks/test_random_dataset.py

@@ -359,3 +359,126 @@ def test_random_mm_bucket_config_not_mutated(
         assert len(mm_data) >= 1
         for it in mm_data:
             assert it.get("type") == "image_url"
+
+
+@pytest.mark.benchmark
+def test_random_mm_video_sampling(hf_tokenizer: PreTrainedTokenizerBase) -> None:
+    """Test video sampling functionality in RandomMultiModalDataset."""
+    ds = RandomMultiModalDataset(random_seed=42)
+
+    # Test with video bucket configuration
+    bucket_config = {
+        (64, 64, 1): 0.3,  # Images
+        (64, 64, 8): 0.7,  # Videos
+    }
+
+    limit_mm_per_prompt = {"image": 2, "video": 2}
+
+    samples = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=5,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+    )
+
+    assert len(samples) == 5
+
+    # Check that we have both images and videos
+    video_count = 0
+    image_count = 0
+
+    for s in samples:
+        mm_data = cast(list[dict[str, Any]], s.multi_modal_data)
+        assert len(mm_data) == 1
+
+        item = mm_data[0]
+        if item.get("type") == "video_url":
+            video_count += 1
+            # Verify video URL format
+            url = item.get("video_url", {}).get("url", "")
+            assert url.startswith("data:video/mp4;base64,")
+        elif item.get("type") == "image_url":
+            image_count += 1
+            # Verify image URL format
+            url = item.get("image_url", {}).get("url", "")
+            assert url.startswith("data:image/jpeg;base64,")
+
+    # Should have some videos due to 0.7 probability
+    assert video_count > 0
+    assert image_count > 0
+
+
+@pytest.mark.benchmark
+def test_random_mm_video_only_sampling(hf_tokenizer: PreTrainedTokenizerBase) -> None:
+    """Test sampling with only video buckets."""
+    ds = RandomMultiModalDataset(random_seed=42)
+
+    bucket_config = {
+        (64, 64, 8): 1.0,  # Only videos
+    }
+
+    limit_mm_per_prompt = {"image": 0, "video": 1}
+
+    samples = _collect_mm_samples(
+        ds,
+        hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+    )
+
+    assert len(samples) == 3
+
+    for s in samples:
+        mm_data = cast(list[dict[str, Any]], s.multi_modal_data)
+        assert len(mm_data) == 1
+
+        item = mm_data[0]
+        assert item.get("type") == "video_url"
+        url = item.get("video_url", {}).get("url", "")
+        assert url.startswith("data:video/mp4;base64,")
+
+
+@pytest.mark.benchmark
+def test_random_mm_video_deterministic_sampling(
+    hf_tokenizer: PreTrainedTokenizerBase,
+) -> None:
+    """Test that video sampling is deterministic with same seed."""
+    seed = 123
+    ds_a = RandomMultiModalDataset(random_seed=seed)
+    ds_b = RandomMultiModalDataset(random_seed=seed)
+
+    bucket_config = {
+        (64, 64, 8): 1.0,  # Only videos
+    }
+
+    limit_mm_per_prompt = {"image": 0, "video": 1}
+
+    a = _collect_mm_samples(
+        ds_a,
+        hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+    )
+
+    b = _collect_mm_samples(
+        ds_b,
+        hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+    )
+
+    fa = [_mm_fingerprint_sample(s) for s in a]
+    fb = [_mm_fingerprint_sample(s) for s in b]
+    assert fa == fb

tests/benchmarks/test_random_multimodal_dataset_video.py

@@ -0,0 +1,398 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import base64
+import os
+from tempfile import NamedTemporaryFile
+from typing import Any, cast
+
+import cv2
+import pytest
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+
+from vllm.benchmarks.datasets import RandomMultiModalDataset, SampleRequest
+
+
+@pytest.fixture(scope="session")
+def hf_tokenizer() -> PreTrainedTokenizerBase:
+    """Use a small, commonly available tokenizer."""
+    return AutoTokenizer.from_pretrained("gpt2")
+
+
+@pytest.fixture
+def video_dataset() -> RandomMultiModalDataset:
+    """Create a RandomMultiModalDataset instance for testing."""
+    return RandomMultiModalDataset(random_seed=42)
+
+
+@pytest.mark.benchmark
+def test_generate_synthetic_video_different_seeds():
+    """Test that different seeds produce different videos."""
+    dataset1 = RandomMultiModalDataset(random_seed=123)
+    dataset2 = RandomMultiModalDataset(random_seed=456)
+
+    width, height, num_frames = 64, 48, 8
+
+    video1 = dataset1.generate_synthetic_video(width, height, num_frames)
+    video2 = dataset2.generate_synthetic_video(width, height, num_frames)
+
+    # Videos should be different due to different seeds
+    assert video1["bytes"] != video2["bytes"]
+
+
+@pytest.mark.benchmark
+def test_map_config_to_modality(video_dataset: RandomMultiModalDataset):
+    """Test modality mapping for different configurations."""
+    # Test image configuration (num_frames = 1)
+    assert video_dataset.map_config_to_modality((256, 256, 1)) == "image"
+    assert video_dataset.map_config_to_modality((720, 1280, 1)) == "image"
+
+    # Test video configurations (num_frames > 1)
+    assert video_dataset.map_config_to_modality((256, 256, 8)) == "video"
+    assert video_dataset.map_config_to_modality((720, 1280, 16)) == "video"
+    assert video_dataset.map_config_to_modality((64, 64, 32)) == "video"
+
+    # Test invalid configurations
+    with pytest.raises(ValueError, match="Invalid multimodal item configuration"):
+        video_dataset.map_config_to_modality((256, 256, 0))
+
+    with pytest.raises(ValueError, match="Invalid multimodal item configuration"):
+        video_dataset.map_config_to_modality((256, 256, -1))
+
+
+@pytest.mark.benchmark
+def test_generate_mm_item_video(video_dataset: RandomMultiModalDataset):
+    """Test generating multimodal items for video configurations."""
+    # Test video item generation
+    video_config = (64, 48, 8)  # height, width, num_frames
+    result = video_dataset.generate_mm_item(video_config)
+
+    # Check the result structure matches OpenAI API format
+    assert isinstance(result, dict)
+    assert result["type"] == "video_url"
+    assert "video_url" in result
+    assert "url" in result["video_url"]
+
+    # Check that the URL is a data URL with base64 encoded video
+    url = result["video_url"]["url"]
+    assert url.startswith("data:video/mp4;base64,")
+
+    # Decode and verify the video content
+    base64_data = url.split(",")[1]
+    video_bytes = base64.b64decode(base64_data)
+    assert len(video_bytes) > 0
+
+    # Verify the video can be decoded
+    with NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:
+        temp_path = temp_file.name
+        temp_file.write(video_bytes)
+
+    try:
+        cap = cv2.VideoCapture(temp_path)
+        assert cap.isOpened()
+
+        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+        assert frame_count == 8
+        assert frame_width == 48
+        assert frame_height == 64
+
+        cap.release()
+    finally:
+        if os.path.exists(temp_path):
+            os.unlink(temp_path)
+
+
+@pytest.mark.benchmark
+def test_generate_mm_item_image(video_dataset: RandomMultiModalDataset):
+    """Test generating multimodal items for image configurations."""
+    # Test image item generation
+    image_config = (64, 48, 1)  # height, width, num_frames=1
+    result = video_dataset.generate_mm_item(image_config)
+
+    # Check the result structure matches OpenAI API format
+    assert isinstance(result, dict)
+    assert result["type"] == "image_url"
+    assert "image_url" in result
+    assert "url" in result["image_url"]
+
+    # Check that the URL is a data URL with base64 encoded image
+    url = result["image_url"]["url"]
+    assert url.startswith("data:image/jpeg;base64,")
+
+
+@pytest.mark.benchmark
+def test_generate_mm_item_invalid_config(video_dataset: RandomMultiModalDataset):
+    """Test error handling for invalid configurations."""
+    with pytest.raises(ValueError, match="Invalid multimodal item configuration"):
+        video_dataset.generate_mm_item((256, 256, 0))
+
+
+@pytest.mark.benchmark
+def test_sample_with_video_buckets(
+    video_dataset: RandomMultiModalDataset, hf_tokenizer: PreTrainedTokenizerBase
+):
+    """Test sampling with video bucket configurations."""
+    # Configure bucket with video probability > 0
+    bucket_config = {
+        (64, 64, 1): 0.3,  # Images
+        (64, 64, 8): 0.7,  # Videos
+    }
+
+    limit_mm_per_prompt = {"image": 5, "video": 3}
+
+    samples = video_dataset.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=5,
+        base_items_per_request=2,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    assert len(samples) == 5
+
+    # Check that samples contain both images and videos
+    video_count = 0
+    image_count = 0
+
+    for sample in samples:
+        assert isinstance(sample, SampleRequest)
+        assert sample.multi_modal_data is not None
+        assert isinstance(sample.multi_modal_data, list)
+
+        mm_data = cast(list[dict[str, Any]], sample.multi_modal_data)
+        assert len(mm_data) == 2  # base_items_per_request
+
+        for item in mm_data:
+            if item["type"] == "video_url":
+                video_count += 1
+                # Verify video URL format
+                url = item["video_url"]["url"]
+                assert url.startswith("data:video/mp4;base64,")
+            elif item["type"] == "image_url":
+                image_count += 1
+                # Verify image URL format
+                url = item["image_url"]["url"]
+                assert url.startswith("data:image/jpeg;base64,")
+
+    # Should have some videos due to 0.7 probability
+    assert video_count > 0
+    assert image_count > 0
+
+
+@pytest.mark.benchmark
+def test_sample_video_only_buckets(
+    video_dataset: RandomMultiModalDataset, hf_tokenizer: PreTrainedTokenizerBase
+):
+    """Test sampling with only video buckets."""
+    bucket_config = {
+        (64, 64, 8): 1.0,  # Only videos
+    }
+
+    limit_mm_per_prompt = {"image": 0, "video": 2}
+
+    samples = video_dataset.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    assert len(samples) == 3
+
+    for sample in samples:
+        assert isinstance(sample, SampleRequest)
+        assert sample.multi_modal_data is not None
+        assert isinstance(sample.multi_modal_data, list)
+
+        mm_data = cast(list[dict[str, Any]], sample.multi_modal_data)
+        assert len(mm_data) == 1
+
+        item = mm_data[0]
+        assert item["type"] == "video_url"
+        url = item["video_url"]["url"]
+        assert url.startswith("data:video/mp4;base64,")
+
+
+@pytest.mark.benchmark
+def test_sample_respects_video_limits(
+    video_dataset: RandomMultiModalDataset, hf_tokenizer: PreTrainedTokenizerBase
+):
+    """Test that sampling respects video limits per prompt."""
+    bucket_config = {
+        (64, 64, 8): 1.0,  # Only videos
+    }
+
+    # Set very low video limit
+    limit_mm_per_prompt = {"image": 0, "video": 1}
+
+    samples = video_dataset.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    assert len(samples) == 3
+
+    for sample in samples:
+        mm_data = cast(list[dict[str, Any]], sample.multi_modal_data)
+        assert len(mm_data) <= 1  # Should respect video limit
+
+
+@pytest.mark.benchmark
+def test_sample_mixed_buckets_with_zero_probability(
+    video_dataset: RandomMultiModalDataset, hf_tokenizer: PreTrainedTokenizerBase
+):
+    """Test sampling with mixed buckets including zero probability entries."""
+    bucket_config = {
+        (64, 64, 1): 0.5,  # Images
+        (64, 64, 8): 0.5,  # Videos
+        (128, 128, 16): 0.0,  # Zero probability videos (should be ignored)
+    }
+
+    limit_mm_per_prompt = {"image": 2, "video": 2}
+
+    samples = video_dataset.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=4,
+        base_items_per_request=2,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    assert len(samples) == 4
+
+    # Should only see 64x64 videos, not 128x128 videos
+    for sample in samples:
+        mm_data = cast(list[dict[str, Any]], sample.multi_modal_data)
+        for item in mm_data:
+            if item["type"] == "video_url":
+                # Decode video to verify dimensions
+                url = item["video_url"]["url"]
+                base64_data = url.split(",")[1]
+                video_bytes = base64.b64decode(base64_data)
+
+                with NamedTemporaryFile(suffix=".mp4", delete=False) as temp_file:  # noqa
+                    temp_path = temp_file.name
+                    temp_file.write(video_bytes)
+
+                try:
+                    cap = cv2.VideoCapture(temp_path)
+                    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                    cap.release()
+
+                    # Should be 64x64, not 128x128
+                    assert frame_width == 64
+                    assert frame_height == 64
+                finally:
+                    if os.path.exists(temp_path):
+                        os.unlink(temp_path)
+
+
+@pytest.mark.benchmark
+def test_sample_deterministic_with_videos(hf_tokenizer: PreTrainedTokenizerBase):
+    """Test that sampling with videos is deterministic with same seed."""
+    dataset1 = RandomMultiModalDataset(random_seed=123)
+    dataset2 = RandomMultiModalDataset(random_seed=123)
+
+    bucket_config = {
+        (64, 64, 1): 0.3,  # Images
+        (64, 64, 8): 0.7,  # Videos
+    }
+
+    limit_mm_per_prompt = {"image": 2, "video": 2}
+
+    samples1 = dataset1.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    samples2 = dataset2.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=3,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    assert len(samples1) == len(samples2)
+
+    # Compare multimodal data
+    for s1, s2 in zip(samples1, samples2):
+        assert s1.multi_modal_data == s2.multi_modal_data
+
+
+@pytest.mark.benchmark
+def test_sample_different_seeds_produce_different_videos(
+    hf_tokenizer: PreTrainedTokenizerBase,
+):
+    """Test that different seeds produce different video content."""
+    dataset1 = RandomMultiModalDataset(random_seed=123)
+    dataset2 = RandomMultiModalDataset(random_seed=456)
+
+    bucket_config = {
+        (64, 64, 8): 1.0,  # Only videos
+    }
+
+    limit_mm_per_prompt = {"image": 0, "video": 1}
+
+    samples1 = dataset1.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=2,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    samples2 = dataset2.sample(
+        tokenizer=hf_tokenizer,
+        num_requests=2,
+        base_items_per_request=1,
+        num_mm_items_range_ratio=0.0,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+        bucket_config=bucket_config,
+        input_len=20,
+        output_len=5,
+    )
+
+    # Video content should be different
+    for s1, s2 in zip(samples1, samples2):
+        mm_data1 = cast(list[dict[str, Any]], s1.multi_modal_data)
+        mm_data2 = cast(list[dict[str, Any]], s2.multi_modal_data)
+
+        assert len(mm_data1) == len(mm_data2) == 1
+
+        url1 = mm_data1[0]["video_url"]["url"]
+        url2 = mm_data2[0]["video_url"]["url"]
+
+        assert url1 != url2  # Different video content

vllm/benchmarks/datasets.py

@@ -27,8 +27,10 @@ from copy import deepcopy
 from dataclasses import dataclass
 from functools import cache
 from io import BytesIO
+from tempfile import NamedTemporaryFile
 from typing import Any, cast
 
+import cv2
 import numpy as np
 from PIL import Image
 from transformers import PreTrainedTokenizerBase
@@ -498,9 +500,13 @@ class RandomDataset(BenchmarkDataset):
             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
+        prohibited_tokens = tokenizer.all_special_ids
+        all_tokens = np.arange(vocab_size)
+        allowed_tokens = np.array(list(set(all_tokens) - set(prohibited_tokens)))
+
+        # Generate prefix once
+        prefix_token_ids = self.get_prefix(allowed_tokens, prefix_len)
 
         requests = []
         token_mismatch_total = 0
@@ -513,6 +519,7 @@ class RandomDataset(BenchmarkDataset):
                 input_len=int(input_lens[i]),
                 offset=int(offsets[i]),
                 index=i,
+                allowed_tokens=allowed_tokens,
             )
             token_mismatch_total += token_mismatch
             requests.append(
@@ -553,13 +560,17 @@ class RandomDataset(BenchmarkDataset):
         return requests
 
     def get_prefix(
-        self, tokenizer: PreTrainedTokenizerBase, prefix_len: int
+        self,
+        allowed_tokens: np.ndarray,
+        prefix_len: int,
     ) -> list[int]:
         """
         Get the prefix for the dataset.
         """
         return (
-            self._rng.integers(0, tokenizer.vocab_size, size=prefix_len).tolist()
+            allowed_tokens[
+                self._rng.integers(0, len(allowed_tokens), size=prefix_len)
+            ].tolist()
             if prefix_len > 0
             else []
         )
@@ -623,6 +634,7 @@ class RandomDataset(BenchmarkDataset):
         input_len: int,
         offset: int,
         index: int,
+        allowed_tokens: np.ndarray,
     ) -> tuple[str, int, int]:
         """
         Returns (prompt, total_input_len).
@@ -636,8 +648,11 @@ class RandomDataset(BenchmarkDataset):
         To avoid uncontrolled change of the prompt length,
         the encoded sequence is truncated before being decoded again.
         """
-        # Build the inner sequence by sampling sequentially from the vocab
-        inner_seq = ((offset + index + np.arange(input_len)) % vocab_size).tolist()
+        # Build the inner sequence by sampling
+        # sequentially from the allowed tokens
+        inner_seq = allowed_tokens[
+            (offset + index + np.arange(input_len)) % len(allowed_tokens)
+        ].tolist()
         token_sequence = prefix_token_ids + inner_seq
 
         # Decode, then re-encode and truncate to preserve token count invariants
@@ -772,7 +787,7 @@ class RandomMultiModalDataset(RandomDataset):
 
     Status:
     - Images: supported via synthetic RGB data.
-    - Video: not yet supported (TODO: implement video generation method).
+    - Video: supported via synthetic RGB data.
     - Audio: not yet supported.
 
     Sampling overview:
@@ -782,7 +797,7 @@ class RandomMultiModalDataset(RandomDataset):
        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.
+       `num_frames`=1 as image 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`.
@@ -797,8 +812,7 @@ class RandomMultiModalDataset(RandomDataset):
     """
 
     IS_MULTIMODAL = True
-    # NOTE: video sampling is WIP. Setting it to 0.
-    DEFAULT_LIMIT_MM_PER_PROMPT = {"image": 255, "video": 0}
+    DEFAULT_LIMIT_MM_PER_PROMPT = {"image": 255, "video": 1}
 
     DEFAULT_BASE_ITEMS_PER_REQUEST = 1
     DEFAULT_NUM_MM_ITEMS_RANGE_RATIO = 0.0
@@ -828,12 +842,47 @@ class RandomMultiModalDataset(RandomDataset):
         )
         return Image.fromarray(random_pixels)
 
-    def generate_synthetic_video(self, width: int, height: int, num_frames: int) -> Any:
+    def generate_synthetic_video(
+        self, width: int, height: int, num_frames: int
+    ) -> dict:
         """Generate synthetic video with random values.
 
-        TODO: Finish this method.
+        Creates a video with random pixel values, encodes it to MP4 format,
+        and returns the content as bytes.
         """
-        raise NotImplementedError("Video sampling is WIP.")
+        random_pixels = self._rng.integers(
+            0,
+            256,
+            (num_frames, height, width, 3),
+            dtype=np.uint8,
+        )
+
+        # Create a temporary video file in memory
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        fps = 30  # frames per second
+
+        with NamedTemporaryFile(suffix=".mp4", delete_on_close=False) as temp_file:
+            temp_path = temp_file.name
+
+            # Create video writer
+            video_writer = cv2.VideoWriter(
+                temp_path, fourcc=fourcc, fps=fps, frameSize=(width, height)
+            )
+
+            if not video_writer.isOpened():
+                raise RuntimeError("Failed to create video writer")
+
+            for frame in random_pixels:
+                video_writer.write(frame)
+
+            video_writer.release()
+            temp_file.close()
+
+            # Read the video file content
+            with open(temp_path, "rb") as f:
+                video_content = f.read()
+
+            return {"bytes": video_content}
 
     def map_config_to_modality(self, config: tuple[int, int, int]) -> str:
         """Map the configuration to the modality."""
@@ -1044,16 +1093,6 @@ class RandomMultiModalDataset(RandomDataset):
         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
@@ -1071,9 +1110,24 @@ class RandomMultiModalDataset(RandomDataset):
             bucket_config,
         )
 
-        # Generate prefix once
-        prefix_token_ids = self.get_prefix(tokenizer, prefix_len)
         vocab_size = tokenizer.vocab_size
+        # Can't use tokenizer.all_special_ids since
+        # it returns ONLY ids from special_tokens_map.json
+        # We want to exclude placeholder tokens and all
+        # tokens that indicate start/end of image as it
+        # may break prompt replacement logic.
+        prohibited_tokens = list(
+            tok_id
+            for tok_id, token in tokenizer.added_tokens_decoder.items()
+            if token.special
+        )
+        all_tokens = np.arange(vocab_size)
+        allowed_tokens = np.array(list(set(all_tokens) - set(prohibited_tokens)))
+        logger.debug(
+            "Sampling from %d out of %d (vocab size)", len(allowed_tokens), vocab_size
+        )
+        # Generate prefix once
+        prefix_token_ids = self.get_prefix(allowed_tokens, prefix_len)
         # Add synthetic multimodal items to each request
         mm_requests = []
         token_mismatch_total = 0
@@ -1086,6 +1140,7 @@ class RandomMultiModalDataset(RandomDataset):
                 input_len=int(input_lens[i]),
                 offset=int(offsets[i]),
                 index=i,
+                allowed_tokens=allowed_tokens,
             )
             token_mismatch_total += token_mismatch
             # Get multimodal item iterator for a given request