fix Glm4v batch videos forward

src/transformers/models/glm4v/modeling_glm4v.py

@@ -1052,6 +1052,7 @@ def get_rope_index(
                 device=input_ids.device,
             )
             image_index, video_index = 0, 0
+            video_group_index = 0
             attention_mask = attention_mask.to(total_input_ids.device)
             for i, input_ids in enumerate(total_input_ids):
                 input_ids = input_ids[attention_mask[i] == 1]
@@ -1081,7 +1082,6 @@ def get_rope_index(
 
                 llm_pos_ids_list = []
                 video_frame_num = 1
-
                 for modality_type, start_idx, end_idx in input_type_group:
                     st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
 
@@ -1125,7 +1125,11 @@ def get_rope_index(
                             w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(1, llm_grid_h, -1).flatten()
                             llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx)
 
-                        video_index += 1
+                        video_group_index += 1
+
+                        if video_group_index >= video_grid_thw[video_index][0]:
+                            video_index += 1
+                            video_group_index = 0
 
                         video_frame_num += 1
 
@@ -1174,7 +1178,13 @@ def get_video_features(
                 The temporal, height and width of feature shape of each video in LLM.
         """
         pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
-        video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+        # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames
+        temp_frames_hw = []
+        for t, h, w in video_grid_thw:
+            repeated_row = torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1)
+            temp_frames_hw.append(repeated_row)
+        flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0)
+        video_embeds = self.visual(pixel_values_videos, grid_thw=flattened_video_grid_thw)
         split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
         video_embeds = torch.split(video_embeds, split_sizes)
         return video_embeds

src/transformers/models/glm4v/modular_glm4v.py

@@ -1064,6 +1064,7 @@ def get_rope_index(
                 device=input_ids.device,
             )
             image_index, video_index = 0, 0
+            video_group_index = 0
             attention_mask = attention_mask.to(total_input_ids.device)
             for i, input_ids in enumerate(total_input_ids):
                 input_ids = input_ids[attention_mask[i] == 1]
@@ -1093,7 +1094,6 @@ def get_rope_index(
 
                 llm_pos_ids_list = []
                 video_frame_num = 1
-
                 for modality_type, start_idx, end_idx in input_type_group:
                     st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
 
@@ -1137,7 +1137,11 @@ def get_rope_index(
                             w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(1, llm_grid_h, -1).flatten()
                             llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + st_idx)
 
-                        video_index += 1
+                        video_group_index += 1
+
+                        if video_group_index >= video_grid_thw[video_index][0]:
+                            video_index += 1
+                            video_group_index = 0
 
                         video_frame_num += 1
 
@@ -1173,6 +1177,30 @@ def get_rope_index(
 
             return position_ids, mrope_position_deltas
 
+    def get_video_features(
+        self, pixel_values_videos: torch.FloatTensor, video_grid_thw: Optional[torch.LongTensor] = None
+    ):
+        """
+        Encodes videos into continuous embeddings that can be forwarded to the language model.
+
+        Args:
+            pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+                The tensors corresponding to the input videos.
+            video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+                The temporal, height and width of feature shape of each video in LLM.
+        """
+        pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+        # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames
+        temp_frames_hw = []
+        for t, h, w in video_grid_thw:
+            repeated_row = torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1)
+            temp_frames_hw.append(repeated_row)
+        flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0)
+        video_embeds = self.visual(pixel_values_videos, grid_thw=flattened_video_grid_thw)
+        split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
+        video_embeds = torch.split(video_embeds, split_sizes)
+        return video_embeds
+
     @auto_docstring
     @can_return_tuple
     def forward(
@@ -1664,32 +1692,38 @@ def __call__(
             video_index = 0
             for i in range(len(text)):
                 while self.video_token in text[i]:
-                    num_frames = len(video_grid_thw)
+                    num_frames = video_grid_thw[video_index][0]
                     video_structure = ""
 
                     if hasattr(timestamps, "tolist"):
                         timestamps_list = timestamps.tolist()[0]
                     else:
                         timestamps_list = timestamps[0] if isinstance(timestamps[0], list) else timestamps
+
                     unique_timestamps = []
                     for idx in range(0, len(timestamps_list)):
                         unique_timestamps.append(timestamps_list[idx])
+
                     selected_timestamps = unique_timestamps[:num_frames]
                     while len(selected_timestamps) < num_frames:
                         selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
                     for frame_idx in range(num_frames):
                         timestamp_sec = selected_timestamps[frame_idx]
                         frame_structure = f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec}"
                         video_structure += frame_structure
+
                     text[i] = text[i].replace(self.video_token, video_structure, 1)
+                    num_image_tokens = (
+                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
+                    )
+                    for frame_idx in range(num_frames):
+                        if self.image_token in text[i]:
+                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+
                     video_index += 1
 
-                for frame_idx in range(len(video_grid_thw)):
-                    if self.image_token in text[i]:
-                        num_image_tokens = video_grid_thw[frame_idx].prod() // merge_length
-                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
                 text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])

src/transformers/models/glm4v/processing_glm4v.py

@@ -167,32 +167,38 @@ def __call__(
             video_index = 0
             for i in range(len(text)):
                 while self.video_token in text[i]:
-                    num_frames = len(video_grid_thw)
+                    num_frames = video_grid_thw[video_index][0]
                     video_structure = ""
 
                     if hasattr(timestamps, "tolist"):
                         timestamps_list = timestamps.tolist()[0]
                     else:
                         timestamps_list = timestamps[0] if isinstance(timestamps[0], list) else timestamps
+
                     unique_timestamps = []
                     for idx in range(0, len(timestamps_list)):
                         unique_timestamps.append(timestamps_list[idx])
+
                     selected_timestamps = unique_timestamps[:num_frames]
                     while len(selected_timestamps) < num_frames:
                         selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
                     for frame_idx in range(num_frames):
                         timestamp_sec = selected_timestamps[frame_idx]
                         frame_structure = f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec}"
                         video_structure += frame_structure
+
                     text[i] = text[i].replace(self.video_token, video_structure, 1)
+                    num_image_tokens = (
+                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
+                    )
+                    for frame_idx in range(num_frames):
+                        if self.image_token in text[i]:
+                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+
                     video_index += 1
 
-                for frame_idx in range(len(video_grid_thw)):
-                    if self.image_token in text[i]:
-                        num_image_tokens = video_grid_thw[frame_idx].prod() // merge_length
-                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
                 text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])

src/transformers/models/glm4v/video_processing_glm4v.py

@@ -249,10 +249,6 @@ def _preprocess(
         processed_grids = reorder_videos(processed_grids, grouped_videos_index)
         pixel_values_videos = torch.cat(processed_videos, dim=0)
         video_grid_thw = torch.tensor(processed_grids)
-        total_frames = video_grid_thw[0][0].item()
-        h = video_grid_thw[0][1].item()
-        w = video_grid_thw[0][2].item()
-        video_grid_thw = [[1, h, w] for _ in range(total_frames)]
         data = {
             "pixel_values_videos": pixel_values_videos,
             "video_grid_thw": video_grid_thw,

tests/models/glm4v/test_modeling_glm4v.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Testing suite for the PyTorch GLM-4.1V model."""
 
+import copy
 import gc
 import unittest
 
@@ -236,7 +237,26 @@ def test_multi_gpu_data_parallel_forward(self):
     def test_generate_from_inputs_embeds_with_static_cache(self):
         pass
 
-    # RoPE index doesn't match when using embeddings
+    def test_inputs_embeds(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
+
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["image_grid_thw"]
+
+            wte = model.get_input_embeddings()
+            inputs["inputs_embeds"] = wte(input_ids)
+            with torch.no_grad():
+                model(**inputs)[0]
+
     def test_inputs_embeds_matches_input_ids(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -350,6 +370,44 @@ def test_small_model_integration_test_batch(self):
             EXPECTED_DECODED_TEXT,
         )
 
+    @slow
+    def test_small_model_integration_test_with_video(self):
+        processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking", max_image_size={"longest_edge": 50176})
+        model = Glm4vForConditionalGeneration.from_pretrained(
+            "THUDM/GLM-4.1V-9B-Thinking", torch_dtype=torch.float16, device_map="auto"
+        )
+        questions = ["Describe this video."] * 2
+        video_urls = [
+            "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"
+        ] * 2
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "video",
+                            "video": video_url,
+                        },
+                        {"type": "text", "text": question},
+                    ],
+                }
+            ]
+            for question, video_url in zip(questions, video_urls)
+        ]
+        inputs = processor.apply_chat_template(
+            messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", padding=True
+        ).to(torch_device)
+        output = model.generate(**inputs, max_new_tokens=30)
+        EXPECTED_DECODED_TEXT = [
+            "\n012345Describe this video.\n<think>Got it, let's analyze the video. First, the scene is a room with a wooden floor, maybe a traditional Japanese room with tatami",
+            "\n012345Describe this video.\n<think>Got it, let's analyze the video. First, the scene is a room with a wooden floor, maybe a traditional Japanese room with tatami"
+        ]  # fmt: skip
+        self.assertEqual(
+            processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
     @slow
     def test_small_model_integration_test_expand(self):
         model = Glm4vForConditionalGeneration.from_pretrained(

tests/models/glm4v/test_video_processing_glm4v.py

@@ -228,7 +228,7 @@ def test_call_pytorch(self):
             expected_output_video_shape = self.video_processor_tester.expected_output_video_shape(video_inputs)
             self.assertEqual(list(encoded_videos.shape), expected_output_video_shape)
 
-    @unittest.skip("Skip for now, the test needs adjustment fo GLM-4.1V")
+    @unittest.skip("Skip for now, the test needs adjustment for GLM-4.1V")
     def test_call_numpy_4_channels(self):
         for video_processing_class in self.video_processor_list:
             # Test that can process videos which have an arbitrary number of channels