Support temporal compression for Nemotron-3-VL videos (#36808)

Signed-off-by: Collin McCarthy <cmccarthy@nvidia.com>

vllm/model_executor/models/nano_nemotron_vl.py

@@ -8,6 +8,7 @@
 # --------------------------------------------------------
 
 import copy
+import math
 import warnings
 from abc import abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
@@ -77,6 +78,7 @@ from vllm.renderers import TokenizeParams
 from vllm.sequence import IntermediateTensors
 from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.transformers_utils.configs.radio import RadioConfig
+from vllm.transformers_utils.processors.internvl import get_internvl_target_ratios
 from vllm.transformers_utils.processors.nano_nemotron_vl import (
     AUDIO_CONTEXT,
     IMG_CONTEXT,
@@ -85,7 +87,7 @@ from vllm.transformers_utils.processors.nano_nemotron_vl import (
     BaseNanoNemotronVLProcessor,
     DynamicResolutionImageTiler,
     NanoNemotronVLProcessor,
-    get_internvl_target_ratios,
+    get_video_target_size_and_feature_size,
 )
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
@@ -295,10 +297,13 @@ class NanoNemotronVLProcessingInfo(BaseNanoNemotronVLProcessingInfo):
         max_videos = mm_counts.get("video", 0)
 
         processor = self.get_hf_processor()  # we get the CustomProcessor here
+        T = processor.video_temporal_patch_size
 
         max_image_tokens = self.get_max_image_tokens() * max_images
-        max_total_frames = (seq_len - max_image_tokens) // processor.num_image_token
-        max_frames_per_video = max_total_frames // max(max_videos, 1)
+        tokens_per_tubelet = processor.num_video_token
+        max_total_tubelets = (seq_len - max_image_tokens) // tokens_per_tubelet
+        max_tubelets_per_video = max_total_tubelets // max(max_videos, 1)
+        max_frames_per_video = max_tubelets_per_video * T
         return max(max_frames_per_video, 1)
 
     def get_hf_processor(self, **kwargs: object) -> NanoNemotronVLProcessor:
@@ -589,28 +594,49 @@ class NanoNemotronVLMultiModalProcessor(
             video_num_patches = []
 
         def get_video_replacement_internvl(item_idx: int):
-            feature_size = hf_processor.num_image_token
             video, metadata = mm_items["video"][item_idx]
+            patch_size = hf_processor.config.patch_size
+            downsample_ratio = hf_processor.config.downsample_ratio
+            target_patches = hf_processor.video_target_num_patches
+
+            if target_patches is not None and video is not None and video.shape[0] > 0:
+                orig_h, orig_w = video.shape[1], video.shape[2]
+                _, _, feature_size = get_video_target_size_and_feature_size(
+                    orig_w=orig_w,
+                    orig_h=orig_h,
+                    target_patches=target_patches,
+                    maintain_aspect_ratio=hf_processor.video_maintain_aspect_ratio,
+                    patch_size=patch_size,
+                    downsample_ratio=downsample_ratio,
+                )
+            else:
+                feature_size = hf_processor.num_image_token
             num_patches = video_num_patches[item_idx]
             if num_patches is not None:
                 assert isinstance(num_patches, int)
 
+            T = hf_processor.video_temporal_patch_size
+            if T > 1 and num_patches is not None:
+                num_tubelets = math.ceil(num_patches / T)
+            else:
+                num_tubelets = num_patches
+
             video_pruning_rate = self.info.ctx.get_mm_config().video_pruning_rate
             if video_pruning_rate is not None and video_pruning_rate > 0.0:
                 # Start of EVS-specific code
                 num_tokens = compute_retained_tokens_count(
                     tokens_per_frame=feature_size,
-                    num_frames=num_patches,
+                    num_frames=num_tubelets,
                     q=video_pruning_rate,
                 )
                 # Here we just need placeholders that won't actually be replaced -
                 # we just need to make sure the total number of tokens is correct
                 # assign all tokens to the first frame
-                tokens_per_frame = [num_tokens] + [0] * (num_patches - 1)
+                tokens_per_frame = [num_tokens] + [0] * (num_tubelets - 1)
 
                 # End of EVS-specific code
             else:
-                tokens_per_frame = [feature_size] * num_patches
+                tokens_per_frame = [feature_size] * num_tubelets
 
             frame_duration_ms = int(1000 / metadata["fps"])
             return hf_processor.get_video_repl(
@@ -621,6 +647,7 @@ class NanoNemotronVLMultiModalProcessor(
                 img_start_token_ids=hf_processor._img_start_token_ids,
                 img_end_token_ids=hf_processor._img_end_token_ids,
                 img_context_token_ids=hf_processor._img_context_token_ids,
+                video_temporal_patch_size=T,
             )
 
         if self.info.supports_video:
@@ -745,15 +772,39 @@ class NanoNemotronVLDummyInputsBuilder(
         if self.info.supports_video:
             config = self.info.get_hf_config()
             image_size: int = config.force_image_size
+            processor = self.info.get_hf_processor()
+
+            # When video_target_num_patches is set the per-frame pixel
+            # resolution can exceed image_size.  Use the actual target
+            # dimensions so that profiling sees the correct upper bound.
+            if processor.video_target_num_patches is not None:
+                target_w, target_h, _ = get_video_target_size_and_feature_size(
+                    orig_w=image_size,
+                    orig_h=image_size,
+                    target_patches=processor.video_target_num_patches,
+                    maintain_aspect_ratio=processor.video_maintain_aspect_ratio,
+                    patch_size=config.patch_size,
+                    downsample_ratio=config.downsample_ratio,
+                )
+                video_width, video_height = target_w, target_h
+            else:
+                video_width, video_height = image_size, image_size
+
             target_num_frames = self.info.get_num_frames_with_most_features(
                 seq_len, mm_counts
             )
+            mm_config = self.info.ctx.get_mm_config()
+            if num_frames := mm_config.media_io_kwargs.get("video", {}).get(
+                "num_frames"
+            ):
+                assert num_frames > 0
+                target_num_frames = num_frames
             num_videos = mm_counts.get("video", 0)
             video_overrides = mm_options.get("video")
             dummy_video = {
                 "video": self._get_dummy_videos(
-                    width=image_size,
-                    height=image_size,
+                    width=video_width,
+                    height=video_height,
                     num_frames=target_num_frames,
                     num_videos=num_videos,
                     overrides=video_overrides,
@@ -790,6 +841,9 @@ class NanoNemotronVLDummyInputsBuilder(
 class NemotronH_Nano_VL_V2(
     nn.Module, HasInnerState, IsHybrid, SupportsMultiModal, SupportsMultiModalPruning
 ):
+    requires_sequential_video_encoding = True
+    """Temporarily needed for dynamic res video w/ conv3d, doesn't support bs>1 yet"""
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):
@@ -817,6 +871,11 @@ class NemotronH_Nano_VL_V2(
         self.image_tag_type = config.image_tag_type
         self.video_pruning_rate = multimodal_config.video_pruning_rate
 
+        vision_config = getattr(config, "vision_config", config)
+        self.video_temporal_patch_size: int = getattr(
+            vision_config, "video_temporal_patch_size", 1
+        )
+
         with self._mark_language_model(vllm_config):
             self.language_model = init_vllm_registered_model(
                 vllm_config=vllm_config,
@@ -838,11 +897,12 @@ class NemotronH_Nano_VL_V2(
 
             mlp1 = nn.Sequential(
                 RMSNorm(
-                    hidden_size=vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
+                    hidden_size=vit_hidden_size
+                    * int(round(1 / self.downsample_ratio)) ** 2,
                     eps=1e-5,
                 ),
                 nn.Linear(
-                    vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
+                    vit_hidden_size * int(round(1 / self.downsample_ratio)) ** 2,
                     vision_projection_hidden_size,
                     bias=False,
                 ),
@@ -958,19 +1018,37 @@ class NemotronH_Nano_VL_V2(
         vit_embeds = self.mlp1(vit_embeds)
         return vit_embeds
 
-    def extract_feature(self, pixel_values: torch.Tensor):
+    def extract_feature(
+        self,
+        pixel_values: torch.Tensor,
+        num_frames: int | None = None,
+    ) -> torch.Tensor:
         # Process images in a micro-batch of at most 128 frames per call
-        # This is done on purpose to ensure peak GPU ram usage of huge batch
-        # (namely for really long videos with EVS ON) won't cause any problems
-        # as we don't support chunked prefill for video media
-        micro_batch_size = 128
-        n = pixel_values.shape[0]
+        #   This is done on purpose to ensure peak GPU ram usage of huge batch
+        #   (namely for really long videos with EVS ON) won't cause any problems
+        #   as we don't support chunked prefill for video media
+        # When num_frames is provided and temporal_patch_size > 1, consecutive
+        #   frames are grouped into tubelets — the batch size must be a multiple
+        #   of T so chunk boundaries don't split a tubelet.
+        N, _C, H, W = pixel_values.shape
+
+        T = self.video_temporal_patch_size if num_frames is not None else 1
+        micro_batch_size = 128 - (128 % T)
+        patch_size = self.patch_size
+        H_patches = H // patch_size
+        W_patches = W // patch_size
+
         vit_embeds_list = []
-        for i in range(0, n, micro_batch_size):
-            _, vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
+        for i in range(0, N, micro_batch_size):
+            chunk = pixel_values[i : i + micro_batch_size]
+            if num_frames is not None and T > 1:
+                _, vit_embeds = self.vision_model(chunk, num_frames=chunk.shape[0])
+            else:
+                _, vit_embeds = self.vision_model(chunk)
             vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
-            h = w = int(vit_embeds.shape[1] ** 0.5)
-            vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+            vit_embeds = vit_embeds.reshape(
+                vit_embeds.shape[0], H_patches, W_patches, -1
+            )
             vit_embeds = self.pixel_shuffle(
                 vit_embeds, scale_factor=self.downsample_ratio
             )
@@ -1042,16 +1120,21 @@ class NemotronH_Nano_VL_V2(
     ) -> tuple[torch.Tensor, ...]:
         """Process video input and create final embeddings with video content
         and indicator tokens."""
-        # Get video embeddings using the same processing as images
-        video_embeddings = self._process_image_input(video_input)
+        T = self.video_temporal_patch_size
+
+        if T > 1:
+            video_embeddings = self._extract_video_embeddings_temporal(video_input)
+        else:
+            video_embeddings = self._process_image_input(video_input)
 
         final_video_embeddings: tuple[torch.Tensor, ...] = ()
 
-        image_rows = image_cols = self.config.force_image_size
         downsample_ratio = self.config.downsample_ratio
         patch_size = self.config.patch_size
-        rows = int(image_rows * downsample_ratio // patch_size)
-        cols = int(image_cols * downsample_ratio // patch_size)
+        pixel_values = video_input["pixel_values_flat"]
+        frame_h, frame_w = pixel_values.shape[-2], pixel_values.shape[-1]
+        rows = int(frame_h * downsample_ratio // patch_size)
+        cols = int(frame_w * downsample_ratio // patch_size)
         video_pruning_rate = self.video_pruning_rate
         video_num_frames = video_input["num_patches"].tolist()
         video_frames_indices = video_input["frames_indices"].split(video_num_frames)
@@ -1062,13 +1145,14 @@ class NemotronH_Nano_VL_V2(
             num_frames = video_num_frames[i]
             frames_indices = video_frames_indices[i].tolist()
             frame_duration_ms = video_input["frame_duration_ms"][i].item()
-            assert single_video_embeddings.shape[0] % num_frames == 0
+            num_tubelets = math.ceil(num_frames / T) if T > 1 else num_frames
+            assert single_video_embeddings.shape[0] % num_tubelets == 0
 
             if video_pruning_rate is not None and video_pruning_rate > 0.0:
                 # Start of EVS-specific code
                 retention_mask = compute_retention_mask(
                     single_video_embeddings,
-                    video_size_thw=(num_frames, rows, cols),
+                    video_size_thw=(num_tubelets, rows, cols),
                     spatial_merge_size=1,
                     q=video_pruning_rate,
                 )
@@ -1077,14 +1161,14 @@ class NemotronH_Nano_VL_V2(
                 single_video_embeddings = single_video_embeddings[retention_mask]
 
                 # calculate the actual number of retained tokens per frame
-                retention_mask_thw = retention_mask.reshape(num_frames, rows, cols)
+                retention_mask_thw = retention_mask.reshape(num_tubelets, rows, cols)
                 num_tokens_per_frame = (
                     retention_mask_thw.sum(dim=(1, 2)).long().tolist()
                 )
                 # End of EVS-specific code
             else:
-                feature_size = single_video_embeddings.shape[0] // num_frames
-                num_tokens_per_frame = [feature_size] * num_frames
+                feature_size = single_video_embeddings.shape[0] // num_tubelets
+                num_tokens_per_frame = [feature_size] * num_tubelets
 
             final_video_embeddings += (
                 self._create_final_video_embeddings(
@@ -1092,11 +1176,36 @@ class NemotronH_Nano_VL_V2(
                     num_tokens_per_frame,
                     frames_indices,
                     frame_duration_ms,
+                    video_temporal_patch_size=T,
                 ),
             )
 
         return final_video_embeddings
 
+    def _extract_video_embeddings_temporal(
+        self, video_input: NanoNemotronVLVideoPixelInputs
+    ) -> tuple[torch.Tensor, ...]:
+        """Extract per-video embeddings with temporal compression.
+
+        Each video is processed separately through extract_feature with
+        num_frames, which uses the fixed-resolution temporal path in RADIO
+        (no attention mask, flash attention).
+        """
+        pixel_values = video_input["pixel_values_flat"]
+        num_frames_per_video = video_input["num_patches"].tolist()
+        hidden_size = self.config.text_config.hidden_size
+
+        results: list[torch.Tensor] = []
+        frame_offset = 0
+        for nf in num_frames_per_video:
+            video_frames = pixel_values[frame_offset : frame_offset + nf]
+            frame_offset += nf
+
+            vit_embeds = self.extract_feature(video_frames, num_frames=nf)
+            results.append(vit_embeds.view(-1, hidden_size))
+
+        return tuple(results)
+
     def _process_audio_input(
         self, audio_input: NanoNemotronVLAudioFeatureInputs
     ) -> tuple[torch.Tensor, ...]:
@@ -1134,6 +1243,7 @@ class NemotronH_Nano_VL_V2(
         num_tokens_per_frame: list[int],
         frames_indices: list[int],
         frame_duration_ms: int,
+        video_temporal_patch_size: int = 1,
     ) -> torch.Tensor:
         """Create final embeddings that combine video embeddings with
         text embeddings of indicator tokens.
@@ -1161,6 +1271,7 @@ class NemotronH_Nano_VL_V2(
             img_start_token_ids=self._img_start_token_ids,
             img_end_token_ids=self._img_end_token_ids,
             img_context_token_ids=self._img_context_token_ids,
+            video_temporal_patch_size=video_temporal_patch_size,
         )
 
         # video_repl.full is a list of token IDs
@@ -1207,8 +1318,27 @@ class NemotronH_Nano_VL_V2(
             else:
                 frames_indices = torch.cat([f.flatten() for f in frames_indices], dim=0)
 
-            frame_duration_ms = frame_duration_ms.flatten()
-            expected_h = expected_w = self.config.force_image_size
+            if torch.is_tensor(frame_duration_ms):
+                frame_duration_ms = frame_duration_ms.flatten()
+            else:
+                frame_duration_ms = torch.cat(
+                    [f.flatten() for f in frame_duration_ms], dim=0
+                )
+
+            if (
+                torch.is_tensor(pixel_values_flat_video)
+                and pixel_values_flat_video.ndim == 5
+            ):
+                # batched._reduce_data stacked same-shape videos into
+                # [num_videos, nf, 3, H, W]; unstack back to a list so the
+                # same-H,W cat path below handles it uniformly.
+                pixel_values_flat_video = list(pixel_values_flat_video)
+
+            if not torch.is_tensor(pixel_values_flat_video):
+                pixel_values_flat_video = torch.cat(pixel_values_flat_video, dim=0)
+
+            expected_h = pixel_values_flat_video.shape[-2]
+            expected_w = pixel_values_flat_video.shape[-1]
             num_frames = video_num_patches[0].item()
             resolve_bindings = {"h": expected_h, "w": expected_w, "f": num_frames}
 
@@ -1361,8 +1491,7 @@ class NemotronH_Nano_VL_V2(
 
         self.language_model.load_weights(llm_weights)
         self.vision_model.load_weights(vision_weights)
-        if self.sound_encoder is not None:
-            assert len(sound_weights) > 0
+        if self.sound_encoder is not None and len(sound_weights) > 0:
             self.sound_encoder.load_weights(sound_weights)
 
     def get_vit_model_from_radio_config(self, hf_config):
@@ -1375,12 +1504,23 @@ class NemotronH_Nano_VL_V2(
         image_size = preferred_resolution[0] if preferred_resolution else 224
         patch_size = getattr(hf_config_vision, "patch_size", 16)
 
+        # video_temporal_patch_size and separate_video_embedder are
+        # top-level vision_config attributes, not inside args.
+        video_temporal_patch_size = getattr(
+            hf_config_vision, "video_temporal_patch_size", 1
+        )
+        separate_video_embedder = getattr(
+            hf_config_vision, "separate_video_embedder", True
+        )
+
         radio_config = RadioConfig(
             model_name=model_name,
             image_size=image_size,
             patch_size=patch_size,
             norm_mean=hf_config.norm_mean,
             norm_std=hf_config.norm_std,
+            video_temporal_patch_size=video_temporal_patch_size,
+            separate_video_embedder=separate_video_embedder,
             **hf_config_vision.args,
         )
 

vllm/model_executor/models/radio.py

@@ -123,6 +123,8 @@ class ViTPatchGenerator(nn.Module):
         register_multiple: int | None = None,
         num_registers: int | None = None,
         patch_bias: bool = False,
+        temporal_patch_size: int = 1,
+        separate_video_embedder: bool = True,
         device=None,
         dtype=None,
     ):
@@ -148,6 +150,7 @@ class ViTPatchGenerator(nn.Module):
         self.patch_size = patch_size
         self.abs_pos = abs_pos
         self.embed_dim = embed_dim
+        self.temporal_patch_size = temporal_patch_size
 
         self.num_rows = max_input_dims[0] // patch_size
         self.num_cols = max_input_dims[1] // patch_size
@@ -160,6 +163,21 @@ class ViTPatchGenerator(nn.Module):
             patch_size, embed_dim, bias=patch_bias, **factory
         )
 
+        if temporal_patch_size > 1:
+            if not separate_video_embedder:
+                raise NotImplementedError(
+                    "Only separate_video_embedder=True is supported for"
+                    " temporal compression (temporal_patch_size > 1)"
+                )
+            self.video_embedder = ViTPatchLinear(
+                patch_size,
+                embed_dim,
+                bias=patch_bias,
+                temporal_patch_size=temporal_patch_size,
+                **factory,
+            )
+            self._video_embedder_loaded = False
+
         if abs_pos:
             scale = embed_dim**-0.5
             self.pos_embed = nn.Parameter(
@@ -196,6 +214,60 @@ class ViTPatchGenerator(nn.Module):
             return patches, pos_enc
         return patches
 
+    def forward_video(self, x: torch.Tensor) -> torch.Tensor:
+        """Process video frames with temporal compression.
+
+        Groups T consecutive frames into tubelets before embedding.
+
+        Args:
+            x: [num_frames, 3, H, W] tensor of video frames
+
+        Returns:
+            Embedded patches with temporal compression applied.
+        """
+        if not self._video_embedder_loaded:
+            raise ValueError(
+                "Temporal compression (video_temporal_patch_size > 1) requires "
+                "video_embedder weights, but they were never loaded. "
+                "Ensure the checkpoint was trained with temporal compression."
+            )
+        T = self.temporal_patch_size
+        input_size = x.shape[2:]
+
+        patches = self.im_to_patches(x)  # [N, num_patches, 3*P*P]
+        num_frames, num_spatial, feat_dim = patches.shape
+
+        # Pad to a multiple of T by repeating the last frame so that
+        # all tubelets have exactly T frames.
+        num_pad_frames = (-num_frames) % T
+        if num_pad_frames > 0:
+            last_frame_dup = patches[-1:].expand(num_pad_frames, -1, -1)
+            patches = torch.cat([patches, last_frame_dup], dim=0)
+
+        # Group T frames per tubelet: for each spatial position, concatenate
+        #   features across T consecutive frames; order follows Megatron training
+        num_frames_padded = patches.shape[0]
+        num_tublets = num_frames_padded // T
+        patches = rearrange(
+            patches,
+            "(tubelets frames) spatial feat -> tubelets spatial (frames feat)",
+            tubelets=num_tublets,
+            frames=T,
+            spatial=num_spatial,
+            feat=feat_dim,
+        )
+
+        patches = self.video_embedder(patches)
+
+        patches, pos_enc = self.apply_pos_enc(patches, input_size=input_size)
+
+        patches = self.cls_token(patches)
+
+        patches = self.patch_normalizer(patches)
+        if self.return_pos_enc:
+            return patches, pos_enc
+        return patches
+
     def apply_pos_enc_dynamic(
         self, patches: torch.Tensor, imgs_sizes: list[tuple[int, int]]
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
@@ -381,66 +453,21 @@ class ViTPatchGenerator(nn.Module):
             return pos_embed
 
         if self.cpe_mode:
-            if self.training:
-                min_scale = math.sqrt(0.1)
-                scale = (
-                    torch.rand(batch_size, 1, 1, device=pos_embed.device)
-                    * (1 - min_scale)
-                    + min_scale
-                )
-                aspect_min = math.log(3 / 4)
-                aspect_max = -aspect_min
-                aspect = torch.exp(
-                    torch.rand(batch_size, 1, 1, device=pos_embed.device)
-                    * (aspect_max - aspect_min)
-                    + aspect_min
-                )
-
-                scale_x = scale * aspect
-                scale_y = scale * (1 / aspect)
-                scale_xy = torch.stack([scale_x, scale_y], dim=-1).clamp_(0, 1)
-
-                pos_xy = torch.rand(batch_size, 1, 1, 2, device=pos_embed.device) * (
-                    1 - scale_xy
-                )
+            max_dim = max(input_dims)
+            pos_embed = F.interpolate(
+                pos_embed.float(),
+                size=(max_dim, max_dim),
+                align_corners=False,
+                mode="bilinear",
+            ).to(pos_embed.dtype)
 
-                lin_x = torch.linspace(
-                    0, 1, steps=input_dims[1], device=pos_embed.device
-                )[None, None].expand(batch_size, input_dims[0], -1)
-                lin_y = torch.linspace(
-                    0, 1, steps=input_dims[0], device=pos_embed.device
-                )[None, :, None].expand(batch_size, -1, input_dims[1])
-
-                lin_xy = torch.stack([lin_x, lin_y], dim=-1)
-
-                grid_xy = lin_xy * scale_xy + pos_xy
-
-                # Convert to [-1, 1] range
-                grid_xy.mul_(2).sub_(1)
-
-                pos_embed = F.grid_sample(
-                    pos_embed.float().expand(batch_size, -1, -1, -1),
-                    grid=grid_xy,
-                    mode="bilinear",
-                    padding_mode="zeros",
-                    align_corners=True,
-                ).to(pos_embed.dtype)
-            else:
-                max_dim = max(input_dims)
-                pos_embed = F.interpolate(
-                    pos_embed.float(),
-                    size=(max_dim, max_dim),
-                    align_corners=True,
-                    mode="bilinear",
-                ).to(pos_embed.dtype)
-
-                pos_embed = window_select(pos_embed)
+            pos_embed = window_select(pos_embed)
         else:
             pos_embed = window_select(pos_embed)
 
         if pos_embed.shape[-2:] != input_dims:
             pos_embed = F.interpolate(
-                pos_embed.float(), size=input_dims, align_corners=True, mode="bilinear"
+                pos_embed.float(), size=input_dims, align_corners=False, mode="bilinear"
             ).to(pos_embed.dtype)
 
         pos_embed = pos_embed.flatten(2).permute(0, 2, 1)
@@ -473,9 +500,19 @@ class Im2Patches(nn.Module):
 
 
 class ViTPatchLinear(nn.Linear):
-    def __init__(self, patch_size: int, embed_dim: int, bias: bool = False, **factory):
-        super().__init__(3 * (patch_size**2), embed_dim, bias=bias, **factory)
+    def __init__(
+        self,
+        patch_size: int,
+        embed_dim: int,
+        bias: bool = False,
+        temporal_patch_size: int = 1,
+        **factory,
+    ):
+        super().__init__(
+            3 * temporal_patch_size * (patch_size**2), embed_dim, bias=bias, **factory
+        )
         self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
 
 
 @dataclass(frozen=True, kw_only=True)
@@ -560,6 +597,7 @@ class RadioInternVisionModel(nn.Module):
         max_img_size = int(
             round(config.cpe_max_size / config.patch_size) * config.patch_size
         )
+        self.temporal_patch_size = config.video_temporal_patch_size
         unique_teachers = set(t["name"] for t in config.teachers)
         self.patch_generator = ViTPatchGenerator(
             config.patch_size,
@@ -569,6 +607,8 @@ class RadioInternVisionModel(nn.Module):
             cls_token=True,
             num_cls_tokens=len(unique_teachers) if config.cls_token_per_teacher else 1,
             register_multiple=config.register_multiple,
+            temporal_patch_size=self.temporal_patch_size,
+            separate_video_embedder=config.separate_video_embedder,
         )
 
         self.encoder = RadioVisionEncoder(
@@ -593,33 +633,68 @@ class RadioInternVisionModel(nn.Module):
     def inter_image_mask_metadata(
         self, imgs_sizes: list[tuple[int, int]], device: torch.device
     ) -> MaskMetadata:
+        """Build mask metadata from image pixel sizes. Adds num_skip to each
+        sequence length (cls/register tokens) to match patch generator output."""
         patch_size = self.patch_generator.patch_size
         num_skip = self.patch_generator.num_skip
 
         seq_lens = calc_seq_lens(imgs_sizes, patch_size)
         adjusted = [s + num_skip for s in seq_lens]
+        return self._inter_image_mask_metadata_from_seq_lens(adjusted, device=device)
+
+    def _inter_image_mask_metadata_from_seq_lens(
+        self, seq_lens: list[int], device: torch.device
+    ) -> MaskMetadata:
+        """Build mask metadata from actual sequence lengths (already including
+        cls/register tokens, i.e. patch_count + num_skip per item).
+        Use inter_image_mask_metadata() when you only have imgs_sizes."""
+        assert len(seq_lens) > 0
         cu_seqlens = torch.tensor(
-            list(accumulate(adjusted, initial=0)), dtype=torch.int32, device=device
+            list(accumulate(seq_lens, initial=0)), dtype=torch.int32, device=device
         )
         # Keep max_seqlen on CPU to avoid .item() sync
         # See: https://github.com/vllm-project/vllm/blob/20b6b01/vllm/v1/attention/ops/vit_attn_wrappers.py#L48
-        max_seqlen = torch.tensor(max(adjusted), dtype=torch.int32)
+        max_seqlen = torch.tensor(max(seq_lens), dtype=torch.int32)
         return MaskMetadata(cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
 
     def forward(
         self,
         x: torch.Tensor,
         imgs_sizes: list[tuple[int, int]] | None = None,
+        num_frames: int | None = None,
     ) -> torch.FloatTensor:
-        hidden_states = self.patch_generator(x, imgs_sizes=imgs_sizes)
+        T = self.temporal_patch_size
+
+        # Build packed-sequence metadata for MMEncoderAttention when needed.
         mask_meta = None
-        if imgs_sizes is not None:
-            assert len(imgs_sizes) > 0
-            # Dynamic resolution: process each image as an independent sequence.
-            mask_meta = self.inter_image_mask_metadata(
-                imgs_sizes, device=hidden_states.device
+        packed_batch_size = None  # Original batch size before packing
+
+        if num_frames is not None and T > 1:
+            # Conv3d video: all tubelets have the same sequence length.
+            # Pack [num_tubelets, seq_per_tubelet, hidden] → [1, total, hidden]
+            hidden_states = self.patch_generator.forward_video(x)
+            packed_batch_size, seq_per_tubelet, hidden_dim = hidden_states.shape
+            hidden_states = hidden_states.reshape(1, -1, hidden_dim)
+            mask_meta = self._inter_image_mask_metadata_from_seq_lens(
+                [seq_per_tubelet] * packed_batch_size, device=hidden_states.device
             )
+        else:
+            # Images for any model, or video for non-conv3d model
+            hidden_states = self.patch_generator(x, imgs_sizes=imgs_sizes)
+            if imgs_sizes is not None and len(imgs_sizes) > 1:
+                # Dynamic resolution w/ > 1 image, create attn mask
+                mask_meta = self.inter_image_mask_metadata(
+                    imgs_sizes, device=hidden_states.device
+                )
+
         encoder_outputs = self.encoder(inputs_embeds=hidden_states, mask_meta=mask_meta)
+
+        # Unpack back to original batch shape if we packed for video
+        if packed_batch_size is not None:
+            encoder_outputs = encoder_outputs.reshape(
+                packed_batch_size, seq_per_tubelet, -1
+            )
+
         return encoder_outputs
 
 
@@ -663,8 +738,13 @@ class RadioModel(nn.Module):
         pixel_embeds: torch.Tensor | None = None,
         *,
         imgs_sizes: list[tuple[int, int]] | None = None,
+        num_frames: int | None = None,
     ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
-        y = self.model(pixel_values, imgs_sizes=imgs_sizes)
+        y = self.model(
+            pixel_values,
+            imgs_sizes=imgs_sizes,
+            num_frames=num_frames,
+        )
         return self._extract_final(y, imgs_sizes=imgs_sizes)
 
     def load_weights(self, weights) -> set[str]:
@@ -714,6 +794,9 @@ class RadioModel(nn.Module):
                 weight_loader(param, weight)
                 loaded_params.add(vllm_key)
 
+        if "model.patch_generator.video_embedder.weight" in loaded_params:
+            self.model.patch_generator._video_embedder_loaded = True
+
         return loaded_params
 
     def _extract_final(

vllm/transformers_utils/configs/radio.py

@@ -47,6 +47,14 @@ class RadioConfig(PretrainedConfig):
         teachers: A list of teacher model configurations. Each teacher configuration is
             a dict with keys like "name" and some may have "use_summary".
         cls_token_per_teacher: Whether to use a separate CLS token for each teacher.
+        video_temporal_patch_size: Number of consecutive video frames grouped into
+            a single tubelet for temporal compression. Default 1 (no compression).
+            When > 1, a dedicated video_embedder (3*T*P*P -> hidden) is created
+            alongside the image embedder (3*P*P -> hidden).
+        separate_video_embedder: When True and video_temporal_patch_size > 1, use a
+            dedicated video patch embedder (3*T*P*P -> hidden) separate from the
+            image embedder (3*P*P -> hidden). When False, a single embedder with
+            input size 3*T*P*P is used for both (images are duplicated T times).
     """
 
     model_type = "radio"
@@ -68,6 +76,8 @@ class RadioConfig(PretrainedConfig):
         register_multiple: int | None = None,
         teachers: list[dict[str, Any]] | None = None,
         cls_token_per_teacher: bool = False,
+        video_temporal_patch_size: int = 1,
+        separate_video_embedder: bool = True,
         **kwargs,
     ):
         self.model_name = model_name
@@ -95,4 +105,6 @@ class RadioConfig(PretrainedConfig):
         self.register_multiple = register_multiple
         self.teachers = teachers if teachers is not None else []
         self.cls_token_per_teacher = cls_token_per_teacher
+        self.video_temporal_patch_size = video_temporal_patch_size
+        self.separate_video_embedder = separate_video_embedder
         super().__init__(**kwargs)

vllm/transformers_utils/processors/nano_nemotron_vl.py

@@ -11,6 +11,7 @@ import math
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from dataclasses import dataclass
+from functools import cached_property
 from typing import Any, TypeVar
 
 import einops
@@ -43,6 +44,12 @@ AUDIO_CONTEXT = "<so_embedding>"
 # MAX_FRAMES = 16
 DEFAULT_NUM_TILES = 12
 
+# Configure PIL to handle large images without warnings
+# This prevents DecompressionBombWarning for legitimate large images
+Image.MAX_IMAGE_PIXELS = None  # Disable the limit entirely
+# Alternative: Set a specific higher limit
+# Image.MAX_IMAGE_PIXELS = 300000000  # ~300M pixels
+
 
 def calculate_timestamps(
     indices: list[int] | torch.Tensor,
@@ -138,19 +145,110 @@ def image_to_pixel_values(
     return pixel_values
 
 
+def _compute_aspect_preserving_size(
+    orig_w: int,
+    orig_h: int,
+    target_num_patches: int,
+    patch_size: int,
+    downsample_ratio: float,
+) -> tuple[int, int]:
+    """Compute target pixel dimensions that preserve aspect ratio.
+
+    Mirrors Megatron-LM image_processing.py video frame resizing:
+    target area in patch-grid space is *target_num_patches*, distributed
+    according to the source aspect ratio, then snapped to a multiple of
+    the required divisor (2 for pixel-shuffle).
+    """
+    aspect_wh = orig_w / max(orig_h, 1)
+    ph = round(math.sqrt(target_num_patches / aspect_wh))
+    pw = round(math.sqrt(target_num_patches * aspect_wh))
+    ph = max(ph, 1)
+    pw = max(pw, 1)
+
+    reduction_factor = int(round(1 / downsample_ratio))
+    required_divisor = reduction_factor  # 2 for pixel-shuffle
+    if required_divisor > 1:
+        rem_h = ph % required_divisor
+        rem_w = pw % required_divisor
+        ph_up = ph + (required_divisor - rem_h if rem_h else 0)
+        ph_down = ph - rem_h
+        pw_up = pw + (required_divisor - rem_w if rem_w else 0)
+        pw_down = pw - rem_w
+        if ph_up * pw_up <= target_num_patches:
+            ph, pw = ph_up, pw_up
+        else:
+            ph = max(required_divisor, ph_down)
+            pw = max(required_divisor, pw_down)
+
+    return pw * patch_size, ph * patch_size  # (width, height) in pixels
+
+
+def get_video_target_size_and_feature_size(
+    orig_w: int,
+    orig_h: int,
+    target_patches: int,
+    maintain_aspect_ratio: bool,
+    patch_size: int,
+    downsample_ratio: float,
+) -> tuple[int, int, int]:
+    """Compute target (width, height) and feature_size for video resize and token count.
+
+    Used by video_to_pixel_values (resize) and get_video_replacement_internvl
+    (seq length calc) so both use the same dimensions.
+    """
+    if maintain_aspect_ratio:
+        target_w, target_h = _compute_aspect_preserving_size(
+            orig_w=orig_w,
+            orig_h=orig_h,
+            target_num_patches=target_patches,
+            patch_size=patch_size,
+            downsample_ratio=downsample_ratio,
+        )
+    else:
+        reduction_factor = int(round(1 / downsample_ratio))
+        side = int(math.sqrt(target_patches))
+        side = max(reduction_factor, (side // reduction_factor) * reduction_factor)
+        target_w = side * patch_size
+        target_h = side * patch_size
+
+    feature_size = int((target_h // patch_size) * downsample_ratio) * int(
+        (target_w // patch_size) * downsample_ratio
+    )
+    return target_w, target_h, feature_size
+
+
 def video_to_pixel_values(
     video: npt.NDArray,
     *,
     input_size: int,
-    max_num_tiles: int = 1,
-    use_thumbnail: bool,
+    video_target_num_patches: int | None = None,
+    video_maintain_aspect_ratio: bool = False,
+    patch_size: int = 16,
+    downsample_ratio: float = 0.5,
 ) -> torch.Tensor:
-    assert max_num_tiles == 1, "Video modality always uses one tile"
-
     # (num_frames, H, W, C) -> (num_frames, C, H, W)
     video_tensor = torch.from_numpy(video).permute(0, 3, 1, 2)
 
-    if video_tensor.shape[2] != input_size or video_tensor.shape[3] != input_size:
+    if video_target_num_patches is not None:
+        # Resize to target patch count (aspect-preserving or square).
+        orig_h, orig_w = video_tensor.shape[2], video_tensor.shape[3]
+        target_w, target_h, _ = get_video_target_size_and_feature_size(
+            orig_w=orig_w,
+            orig_h=orig_h,
+            target_patches=video_target_num_patches,
+            maintain_aspect_ratio=video_maintain_aspect_ratio,
+            patch_size=patch_size,
+            downsample_ratio=downsample_ratio,
+        )
+        if video_tensor.shape[2] != target_h or video_tensor.shape[3] != target_w:
+            video_tensor = torch.nn.functional.interpolate(
+                video_tensor,
+                size=(target_h, target_w),
+                mode="bicubic",
+                align_corners=False,
+                antialias=True,
+            )
+    elif video_tensor.shape[2] != input_size or video_tensor.shape[3] != input_size:
         video_tensor = torch.nn.functional.interpolate(
             video_tensor,
             size=(input_size, input_size),
@@ -645,9 +743,9 @@ class BaseNanoNemotronVLProcessor(ABC):
             "which should be a single string"
         )
         parts = [x for x in re.split(r"(<image>)", text[0]) if x]
-        assert parts.count("<image>") == len(pixel_values_lst), (
-            "the number of <image> tokens in the text should be the "
-            "same as the number of images"
+        assert parts.count("<image>") == len(num_tokens_per_image), (
+            f"Expected {len(num_tokens_per_image)} <image> tokens in text "
+            f"but found {parts.count('<image>')}"
         )
 
         for i, (feature_size, num_patches) in enumerate(
@@ -706,6 +804,33 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         self.video_token = video_token
         self.video_pruning_rate = video_pruning_rate
 
+        # Video params live exclusively in vision_config
+        vision_config = getattr(config, "vision_config", config)
+        self.video_temporal_patch_size: int = getattr(
+            vision_config, "video_temporal_patch_size", 1
+        )
+        self.video_maintain_aspect_ratio: bool = getattr(
+            vision_config, "video_maintain_aspect_ratio", False
+        )
+
+        # Resolve video frame target size: exactly one of video_target_num_patches
+        # or video_target_img_size may be set (mirrors Megatron's
+        # DynamicResolutionImageTilingStrategy validation).
+        target_num_patches = getattr(vision_config, "video_target_num_patches", None)
+        target_img_size = getattr(vision_config, "video_target_img_size", None)
+        if target_num_patches is not None and target_img_size is not None:
+            raise ValueError(
+                "Exactly one of video_target_num_patches or "
+                "video_target_img_size must be set, got both"
+            )
+        if target_num_patches is not None:
+            self.video_target_num_patches: int | None = target_num_patches
+        elif target_img_size is not None:
+            base_patches = math.ceil(target_img_size / config.patch_size)
+            self.video_target_num_patches = base_patches * base_patches
+        else:
+            self.video_target_num_patches = None
+
         self.audio_extractor: ParakeetExtractor | None = None
         raw_sound_config = getattr(config, "sound_config", None)
         if raw_sound_config is not None:
@@ -721,6 +846,27 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
             IMG_CONTEXT, add_special_tokens=False
         )
 
+    @cached_property
+    def num_video_token(self) -> int:
+        """Token count per video frame, accounting for video_target_num_patches.
+
+        When video_target_num_patches is set the per-frame feature count
+        differs from the image-based num_image_token.  We use a square
+        dummy (1:1) to compute the feature_size because the dummy video is
+        square and the user confirmed that is acceptable.
+        """
+        if self.video_target_num_patches is not None:
+            _, _, feature_size = get_video_target_size_and_feature_size(
+                orig_w=self.image_size,
+                orig_h=self.image_size,
+                target_patches=self.video_target_num_patches,
+                maintain_aspect_ratio=self.video_maintain_aspect_ratio,
+                patch_size=self.config.patch_size,
+                downsample_ratio=self.config.downsample_ratio,
+            )
+            return feature_size
+        return self.num_image_token
+
     @property
     def supports_video(self) -> bool:
         return self.video_token_id is not None
@@ -738,14 +884,15 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
     def _videos_to_pixel_values_lst(
         self,
         videos: list[npt.NDArray],
-        max_num_tiles: int,
     ) -> list[torch.Tensor]:
         return [
             video_to_pixel_values(
                 video,
                 input_size=self.image_size,
-                max_num_tiles=max_num_tiles,
-                use_thumbnail=self.use_thumbnail,
+                video_target_num_patches=self.video_target_num_patches,
+                video_maintain_aspect_ratio=self.video_maintain_aspect_ratio,
+                patch_size=self.config.patch_size,
+                downsample_ratio=self.config.downsample_ratio,
             )
             for video in videos
         ]
@@ -754,7 +901,6 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         self,
         text: list[str],
         videos: list[tuple[npt.NDArray, dict[str, Any]]],
-        max_num_tiles: int,
     ) -> tuple[list[str], dict[str, Any]]:
         if len(videos) == 0 or not self.supports_video:
             return text, {}
@@ -763,7 +909,6 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         video_metadata_lst = [v[1] for v in videos]
         pixel_values_lst_video = self._videos_to_pixel_values_lst(
             videos_lst,
-            max_num_tiles=max_num_tiles,
         )
 
         # We use frame duration in milliseconds (as integer) to ensure
@@ -788,12 +933,10 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
             "frame_duration_ms": torch.tensor(frame_duration_ms_lst),
         }
 
-        image_size: int = self.config.force_image_size
         patch_size: int = self.config.patch_size
         downsample_ratio = self.config.downsample_ratio
-        tokens_in_single_frame = int(
-            (image_size * image_size // patch_size**2) * (downsample_ratio**2)
-        )
+
+        T = self.video_temporal_patch_size
 
         for pixel_values, video_metadata, frames_indices, frame_duration_ms in zip(
             pixel_values_lst_video,
@@ -802,23 +945,28 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
             frame_duration_ms_lst,
         ):
             num_frames = pixel_values.shape[0]
+            frame_h, frame_w = pixel_values.shape[-2], pixel_values.shape[-1]
+            tokens_in_single_frame = int(
+                (frame_h * frame_w // patch_size**2) * (downsample_ratio**2)
+            )
+            num_tubelets = math.ceil(num_frames / T) if T > 1 else num_frames
 
             if self.video_pruning_rate is not None and self.video_pruning_rate > 0.0:
                 # Start of EVS-specific code
                 num_tokens = compute_retained_tokens_count(
                     tokens_per_frame=tokens_in_single_frame,
-                    num_frames=num_frames,
+                    num_frames=num_tubelets,
                     q=self.video_pruning_rate,
                 )
 
                 # Here we just need placeholders that won't actually be replaced -
                 # we just need to make sure the total number of tokens is correct
                 # assign all tokens to the first frame
-                tokens_per_frame = [num_tokens] + [0] * (num_frames - 1)
+                tokens_per_frame = [num_tokens] + [0] * (num_tubelets - 1)
 
                 # End of EVS-specific code
             else:
-                tokens_per_frame = [tokens_in_single_frame] * num_frames
+                tokens_per_frame = [tokens_in_single_frame] * num_tubelets
 
             video_repl = self.get_video_repl(
                 tokens_per_frame=tokens_per_frame,
@@ -828,6 +976,7 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
                 img_start_token_ids=self._img_start_token_ids,
                 img_end_token_ids=self._img_end_token_ids,
                 img_context_token_ids=self._img_context_token_ids,
+                video_temporal_patch_size=T,
             )
 
             # video_repl.full is a list of token IDs
@@ -908,7 +1057,6 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         text, video_inputs = self._preprocess_video(
             text=text,
             videos=videos,
-            max_num_tiles=1,
         )
 
         text, audio_inputs = self._preprocess_audio(
@@ -962,6 +1110,7 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         img_start_token_ids: list[int],
         img_end_token_ids: list[int],
         img_context_token_ids: list[int],
+        video_temporal_patch_size: int = 1,
     ) -> PromptUpdateDetails[list[int]]:
         """
         Build prompt replacement for a video.
@@ -981,31 +1130,60 @@ class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
         - EVS real (called from get_real_video_repl_for_evs) - different value per frame
         Args:
             tokens_per_frame (list[int]): number of tokens per frame
-            frames_indices (list[int]): frame indices
+                (one per tubelet when T > 1)
+            frames_indices (list[int]): orig. frame indices
+                (one per frame, before tubelet subsampling)
             frame_duration_ms (int): duration of each frame in milliseconds
-            tokenizer (HfTokenizer): tokenizer to use for tokenizing frame separators
+            tokenizer (TokenizerLike): tokenizer to use for tokenizing frame separators
             img_start_token_ids (list[int]): pre-tokenized IMG_START tokens
             img_end_token_ids (list[int]): pre-tokenized IMG_END tokens
             img_context_token_ids (list[int]): pre-tokenized IMG_CONTEXT tokens
+            video_temporal_patch_size (int): temporal patch size for videos
         """
         # TODO: Add support of frame_duration_ms to be None
         # At preprocessing step we should allow absent / metadata without
         # frames_indices field.
         timestamps_enabled = frame_duration_ms is not None
-
-        if timestamps_enabled:
+        T = video_temporal_patch_size
+        num_frames = len(frames_indices)
+
+        if T > 1 and timestamps_enabled:
+            all_timestamps = calculate_timestamps(frames_indices, frame_duration_ms)
+
+            frame_separators = []
+            for group_idx, i in enumerate(range(0, num_frames, T)):
+                group_frames = []
+                for j in range(T):  # Every frame in the group
+                    frame_idx = i + j
+                    if frame_idx < num_frames:
+                        # Valid idx (haven't padded to mult. of T yet)
+                        ts = all_timestamps[frame_idx]
+                        frame_str = "Frame" if j == 0 else "frame"
+                        group_frames.append(
+                            f"{frame_str} {frame_idx + 1} sampled at {ts:.2f} seconds"
+                        )
+                if group_frames:
+                    # Join by `and` if there are >1 frame, otherwise no `and`
+                    # Prepend \n to match training format (except first group)
+                    sep = " and ".join(group_frames) + ": "
+                    if group_idx > 0:
+                        sep = "\n" + sep
+                    frame_separators.append(sep)
+        elif timestamps_enabled:
             timestamps = calculate_timestamps(frames_indices, frame_duration_ms)
 
             assert len(timestamps) == len(tokens_per_frame), (
                 "timestamps and tokens_per_frame must have the same length"
             )
             frame_separators = [
-                f"Frame {i + 1} sampled at {timestamp:.2f} seconds: "
+                ("\n" if i > 0 else "")
+                + f"Frame {i + 1} sampled at {timestamp:.2f} seconds: "
                 for i, timestamp in enumerate(timestamps)
             ]
         else:
             frame_separators = [
-                f"Frame {i + 1}: " for i, _ in enumerate(tokens_per_frame)
+                ("\n" if i > 0 else "") + f"Frame {i + 1}: "
+                for i, _ in enumerate(tokens_per_frame)
             ]
 
         # Tokenize frame separator independently

vllm/v1/worker/gpu_model_runner.py

@@ -420,8 +420,9 @@ class GPUModelRunner(
         self.is_multimodal_raw_input_only_model = (
             model_config.is_multimodal_raw_input_only_model
         )
-        # This will be overridden in load_model()
+        # These will be overridden in load_model()
         self.is_multimodal_pruning_enabled = False
+        self.requires_sequential_video_encoding = False
         # Set to True after init_routed_experts_capturer() completes.
         # Prevents routed experts code from running during profiling/dummy run.
         self.routed_experts_initialized = False
@@ -2625,17 +2626,23 @@ class GPUModelRunner(
         ):
             batch_outputs: MultiModalEmbeddings
 
-            # EVS-related change.
+            # EVS and dynamic res video related change.
             # (ekhvedchenia): Temporary hack to limit peak memory usage when
             # processing multimodal data. This solves the issue with scheduler
             # putting too many video samples into a single batch. Scheduler
             # uses pruned vision tokens count to compare it versus compute
             # budget which is incorrect (Either input media size or non-pruned
             # output vision tokens count should be considered)
+            # dynamic res video for nemotron temporarily uses this hack via
+            # requires_sequential_video_encoding
+            # because it doesn't yet support video batching.
             # TODO(ywang96): Fix memory profiling to take EVS into account and
             # remove this hack.
             if (
-                self.is_multimodal_pruning_enabled
+                (
+                    self.is_multimodal_pruning_enabled
+                    or self.requires_sequential_video_encoding
+                )
                 and modality == "video"
                 and num_items > 1
             ):
@@ -4609,6 +4616,9 @@ class GPUModelRunner(
             and mm_config is not None
             and mm_config.is_multimodal_pruning_enabled()
         )
+        self.requires_sequential_video_encoding = hasattr(
+            self.get_model(), "requires_sequential_video_encoding"
+        )  # Temporary hack for dynamic res video w/o support for bs>1 yet
 
         if (
             is_mixture_of_experts(self.model)