Feature/video support in random mm dataset

requirements/bench.txt

@@ -0,0 +1 @@
+opencv-python~=4.12 # We use OpenCV to generate MP4 files for random videos

tests/benchmarks/test_random_dataset.py

@@ -342,3 +342,130 @@ 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