[Bugfix] fix qwen3vl expand generation with video

src/transformers/models/qwen3_vl/modeling_qwen3_vl.py

@@ -1535,15 +1535,16 @@ def _expand_inputs_for_generation(
         input_ids: Optional[torch.LongTensor] = None,
         **model_kwargs,
     ) -> tuple[torch.LongTensor, dict[str, Any]]:
-        # Overwritten -- Support for expanding tensors without a batch size dimension
-        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t
+        # Overwritten -- Qwen3VL use timestamps and remove second_per_grid_ts
+        # Support for expanding tensors without a batch size dimension
+        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw
         # pixel_values.shape[0] is sum(seqlen_images for samples)
         # image_grid_thw.shape[0] is sum(num_images for samples)
 
         if expand_size == 1:
             return input_ids, model_kwargs
 
-        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"]
+        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw"]
 
         def _expand_dict_for_generation_visual(dict_to_expand):
             image_grid_thw = model_kwargs.get("image_grid_thw", None)
@@ -1552,6 +1553,17 @@ def _expand_dict_for_generation_visual(dict_to_expand):
                 input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None)
             )
 
+            # video_nums: (batch_size,)
+            # since video_nums is the number of videos in the input dependent on the input_ids(vision_start),
+            # but qwen3vl append vision_start to each frame of each video, so we need to recover the real video_nums according to video_grid_thw
+            if video_grid_thw is not None:
+                cumulative_frame_counts = torch.cumsum(video_grid_thw[:, 0], dim=0)
+                cumulative_token_video_counts = torch.cumsum(video_nums, dim=0)
+                # Find video boundaries in cumulative_frame_counts
+                video_boundary_indices = torch.searchsorted(cumulative_frame_counts, cumulative_token_video_counts)
+                # example: video_boundary_indices = [3, 5] means video_nums = [4, 2]
+                video_nums = torch.diff(torch.cat([-video_boundary_indices.new_ones(1), video_boundary_indices]))
+
             def _repeat_interleave_samples(x, lengths, repeat_times):
                 samples = torch.split(x, lengths)
                 repeat_args = [repeat_times] + [1] * (x.dim() - 1)
@@ -1584,10 +1596,6 @@ def _repeat_interleave_samples(x, lengths, repeat_times):
                     dict_to_expand[key] = _repeat_interleave_samples(
                         dict_to_expand[key], lengths=lengths, repeat_times=expand_size
                     )
-                elif key == "second_per_grid_ts":
-                    dict_to_expand[key] = _repeat_interleave_samples(
-                        dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size
-                    )
             return dict_to_expand
 
         def _expand_dict_for_generation(dict_to_expand):

src/transformers/models/qwen3_vl/modular_qwen3_vl.py

@@ -15,7 +15,7 @@
 """PyTorch Qwen3-VL model."""
 
 from collections.abc import Callable
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 import torch
@@ -1242,6 +1242,101 @@ def prepare_inputs_for_generation(
 
         return model_inputs
 
+    def _expand_inputs_for_generation(
+        self,
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        input_ids: Optional[torch.LongTensor] = None,
+        **model_kwargs,
+    ) -> tuple[torch.LongTensor, dict[str, Any]]:
+        # Overwritten -- Qwen3VL use timestamps and remove second_per_grid_ts
+        # Support for expanding tensors without a batch size dimension
+        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw
+        # pixel_values.shape[0] is sum(seqlen_images for samples)
+        # image_grid_thw.shape[0] is sum(num_images for samples)
+
+        if expand_size == 1:
+            return input_ids, model_kwargs
+
+        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw"]
+
+        def _expand_dict_for_generation_visual(dict_to_expand):
+            image_grid_thw = model_kwargs.get("image_grid_thw", None)
+            video_grid_thw = model_kwargs.get("video_grid_thw", None)
+            image_nums, video_nums = self._get_image_nums_and_video_nums(
+                input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None)
+            )
+
+            # video_nums: (batch_size,)
+            # since video_nums is the number of videos in the input dependent on the input_ids(vision_start),
+            # but qwen3vl append vision_start to each frame of each video, so we need to recover the real video_nums according to video_grid_thw
+            if video_grid_thw is not None:
+                cumulative_frame_counts = torch.cumsum(video_grid_thw[:, 0], dim=0)
+                cumulative_token_video_counts = torch.cumsum(video_nums, dim=0)
+                # Find video boundaries in cumulative_frame_counts
+                video_boundary_indices = torch.searchsorted(cumulative_frame_counts, cumulative_token_video_counts)
+                # example: video_boundary_indices = [3, 5] means video_nums = [4, 2]
+                video_nums = torch.diff(torch.cat([-video_boundary_indices.new_ones(1), video_boundary_indices]))
+
+            def _repeat_interleave_samples(x, lengths, repeat_times):
+                samples = torch.split(x, lengths)
+                repeat_args = [repeat_times] + [1] * (x.dim() - 1)
+                result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0)
+                return result
+
+            for key in dict_to_expand:
+                if key == "pixel_values":
+                    # split images into samples
+                    samples = torch.split(image_grid_thw, list(image_nums))
+                    # compute the sequence length of images for each sample
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "image_grid_thw":
+                    # get the num of images for each sample
+                    lengths = list(image_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "pixel_values_videos":
+                    samples = torch.split(video_grid_thw, list(video_nums))
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "video_grid_thw":
+                    lengths = list(video_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+            return dict_to_expand
+
+        def _expand_dict_for_generation(dict_to_expand):
+            for key in dict_to_expand:
+                if (
+                    key != "cache_position"
+                    and dict_to_expand[key] is not None
+                    and isinstance(dict_to_expand[key], torch.Tensor)
+                    and key not in visual_keys
+                ):
+                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
+            return dict_to_expand
+
+        model_kwargs = _expand_dict_for_generation_visual(model_kwargs)
+
+        if input_ids is not None:
+            input_ids = input_ids.repeat_interleave(expand_size, dim=0)
+
+        model_kwargs = _expand_dict_for_generation(model_kwargs)
+
+        if is_encoder_decoder:
+            if model_kwargs.get("encoder_outputs") is None:
+                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
+            model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
+
+        return input_ids, model_kwargs
+
 
 class Qwen3VLProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {

src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py

@@ -1740,15 +1740,16 @@ def _expand_inputs_for_generation(
         input_ids: Optional[torch.LongTensor] = None,
         **model_kwargs,
     ) -> tuple[torch.LongTensor, dict[str, Any]]:
-        # Overwritten -- Support for expanding tensors without a batch size dimension
-        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t
+        # Overwritten -- Qwen3VLMoe use timestamps and remove second_per_grid_ts
+        # Support for expanding tensors without a batch size dimension
+        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw
         # pixel_values.shape[0] is sum(seqlen_images for samples)
         # image_grid_thw.shape[0] is sum(num_images for samples)
 
         if expand_size == 1:
             return input_ids, model_kwargs
 
-        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"]
+        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw"]
 
         def _expand_dict_for_generation_visual(dict_to_expand):
             image_grid_thw = model_kwargs.get("image_grid_thw", None)
@@ -1757,6 +1758,17 @@ def _expand_dict_for_generation_visual(dict_to_expand):
                 input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None)
             )
 
+            # video_nums: (batch_size,)
+            # since video_nums is the number of videos in the input dependent on the input_ids(vision_start),
+            # but Qwen3VLMoe append vision_start to each frame of each video, so we need to recover the real video_nums according to video_grid_thw
+            if video_grid_thw is not None:
+                cumulative_frame_counts = torch.cumsum(video_grid_thw[:, 0], dim=0)
+                cumulative_token_video_counts = torch.cumsum(video_nums, dim=0)
+                # Find video boundaries in cumulative_frame_counts
+                video_boundary_indices = torch.searchsorted(cumulative_frame_counts, cumulative_token_video_counts)
+                # example: video_boundary_indices = [3, 5] means video_nums = [4, 2]
+                video_nums = torch.diff(torch.cat([-video_boundary_indices.new_ones(1), video_boundary_indices]))
+
             def _repeat_interleave_samples(x, lengths, repeat_times):
                 samples = torch.split(x, lengths)
                 repeat_args = [repeat_times] + [1] * (x.dim() - 1)
@@ -1789,10 +1801,6 @@ def _repeat_interleave_samples(x, lengths, repeat_times):
                     dict_to_expand[key] = _repeat_interleave_samples(
                         dict_to_expand[key], lengths=lengths, repeat_times=expand_size
                     )
-                elif key == "second_per_grid_ts":
-                    dict_to_expand[key] = _repeat_interleave_samples(
-                        dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size
-                    )
             return dict_to_expand
 
         def _expand_dict_for_generation(dict_to_expand):

tests/models/qwen3_vl_moe/test_modeling_qwen3_vl_moe.py

@@ -305,7 +305,6 @@ def test_video_forward(self):
 
 
 @require_torch
-@unittest.skip("The checkpoint is not yet released")
 class Qwen3VLMoeIntegrationTest(unittest.TestCase):
     def setUp(self):
         cleanup(torch_device, gc_collect=True)
@@ -336,6 +335,18 @@ def setUp(self):
                 ],
             }
         ]
+        self.message3 = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "video",
+                        "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4",
+                    },
+                    {"type": "text", "text": "Describe the video in short."},
+                ],
+            }
+        ]
 
     def tearDown(self):
         cleanup(torch_device, gc_collect=True)
@@ -455,6 +466,27 @@ def test_small_model_integration_test_expand(self):
             EXPECTED_DECODED_TEXT,
         )
 
+    @slow
+    def test_small_model_integration_test_expand_with_video(self):
+        model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen3-VL-30B-A3B-Instruct", dtype="auto", device_map="auto"
+        )
+        inputs = self.processor.apply_chat_template(
+            self.message3, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False, num_beams=2, num_return_sequences=2)
+
+        EXPECTED_DECODED_TEXT = [
+            "user\n<0.3 seconds><1.3 seconds><2.4 seconds><3.5 seconds><4.6 seconds><5.6 seconds><6.7 seconds><7.8 seconds><8.9 seconds><9.7 seconds>Describe the video in short.\nassistant\nA baby wearing glasses sits on a bed and flips through a book.",
+            "user\n<0.3 seconds><1.3 seconds><2.4 seconds><3.5 seconds><4.6 seconds><5.6 seconds><6.7 seconds><7.8 seconds><8.9 seconds><9.7 seconds>Describe the video in short.\nassistant\nA baby wearing glasses sits on a bed and flips through the pages of a book."
+        ]  # fmt: skip
+
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
     @slow
     def test_small_model_integration_test_batch_wo_image(self):
         model = Qwen3VLMoeForConditionalGeneration.from_pretrained(