From 0c80efd94fb8c17cfc7d1bcb9cdb65f154340994 Mon Sep 17 00:00:00 2001
From: Yuxuan Zhang <2448370773@qq.com>
Date: Thu, 20 Nov 2025 01:32:55 +0800
Subject: [PATCH] GLM-V video segmentation solution adjustment (#28941)

Signed-off-by: zRzRzRzRzRzRzR <2448370773@qq.com>
---
 vllm/model_executor/models/glm4_1v.py | 94 +++++++++++++++++++++++++--
 1 file changed, 90 insertions(+), 4 deletions(-)

diff --git a/vllm/model_executor/models/glm4_1v.py b/vllm/model_executor/models/glm4_1v.py
index 6581bbda6d609..d141e95498064 100644
--- a/vllm/model_executor/models/glm4_1v.py
+++ b/vllm/model_executor/models/glm4_1v.py
@@ -37,7 +37,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
-from transformers import BatchFeature
+from transformers import BatchFeature, Glm4vProcessor
 from transformers.models.glm4v.configuration_glm4v import Glm4vVisionConfig
 from transformers.models.glm4v.image_processing_glm4v import (
     Glm4vImageProcessor,
@@ -1028,7 +1028,7 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
 
         return max(max_frames_per_video, 1)
 
-    def _get_video_second_idx(
+    def _get_video_second_idx_glm4v(
         self, metadata: dict[str, Any], total_frames: int
     ) -> list[int]:
         video_processor = self.get_video_processor()
@@ -1079,6 +1079,83 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
             selected_timestamps.append(timestamps_list[idx])
         return selected_timestamps
 
+    def _get_video_second_idx_glm46v(
+        self, metadata: dict[str, Any], total_frames: int
+    ) -> list[int]:
+        video_processor = self.get_video_processor()
+
+        video_fps = metadata["fps"]
+        meta_frames = metadata.get("total_num_frames", total_frames)
+        max_frame_idx = meta_frames - 1
+        duration = metadata.get("duration", round(max_frame_idx / video_fps) + 1)
+
+        do_sample_frames = metadata.get("do_sample_frames", True)
+        if not do_sample_frames:
+            frame_indices = metadata["frames_indices"]
+        else:
+            DYNAMIC_FPS_THRES = {30: 3, 300: 1, 2400: 0.5}
+            MAX_FRAME_COUNT_DYNAMIC = 640
+            MAX_DURATION = 2400
+
+            effective_duration = min(duration, MAX_DURATION)
+            if effective_duration <= 30:
+                target_fps = DYNAMIC_FPS_THRES[30]
+            elif effective_duration <= 300:
+                target_fps = DYNAMIC_FPS_THRES[300]
+            else:
+                target_fps = DYNAMIC_FPS_THRES[2400]
+
+            temporal_patch_size = getattr(video_processor, "temporal_patch_size", 1)
+            extract_t = int(effective_duration * target_fps * temporal_patch_size)
+            extract_t = min(extract_t, MAX_FRAME_COUNT_DYNAMIC)
+
+            duration_per_frame = 1 / video_fps
+            timestamps = [i * duration_per_frame for i in range(meta_frames)]
+            max_second = int(duration)
+
+            if meta_frames < extract_t:
+                frame_indices = np.linspace(
+                    0, meta_frames - 1, extract_t, dtype=int
+                ).tolist()
+            else:
+                frame_indices = []
+                current_second = 0.0
+                inv_fps = 1 / (temporal_patch_size * target_fps)
+                for frame_index in range(meta_frames):
+                    if timestamps[frame_index] >= current_second:
+                        current_second += inv_fps
+                        frame_indices.append(frame_index)
+                        if current_second >= max_second:
+                            break
+
+            if len(frame_indices) < extract_t:
+                if len(frame_indices) == 0:
+                    start, end = 0, max(meta_frames - 1, 0)
+                else:
+                    start, end = frame_indices[0], frame_indices[-1]
+                frame_indices = np.linspace(start, end, extract_t, dtype=int).tolist()
+            elif len(frame_indices) > extract_t:
+                frame_indices = np.linspace(
+                    0, meta_frames - 1, extract_t, dtype=int
+                ).tolist()
+
+        seen, uniq = set(), []
+        for idx in frame_indices:
+            if idx not in seen:
+                seen.add(idx)
+                uniq.append(idx)
+
+        if len(uniq) & 1:
+            uniq.append(uniq[-1])
+
+        frame_indices = uniq
+        full_second_idxs = [int(idx / video_fps) for idx in frame_indices]
+        timestamps_list = full_second_idxs[::2]
+        selected_timestamps = []
+        for idx in range(len(timestamps_list)):
+            selected_timestamps.append(timestamps_list[idx])
+        return selected_timestamps
+
     def _construct_video_placeholder(
         self,
         video_array: np.ndarray,
@@ -1097,9 +1174,18 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
         merge_length = image_processor.merge_size**2
 
         assert isinstance(grid_thw, torch.Tensor)
-        timestamps = self._get_video_second_idx(metadata, len(video_array))
+        timestamps = (
+            self._get_video_second_idx_glm4v(metadata, len(video_array))
+            if isinstance(hf_processor, Glm4vProcessor)
+            else self._get_video_second_idx_glm46v(metadata, len(video_array))
+        )
+
+        timestamp_format = (
+            "{}" if isinstance(hf_processor, Glm4vProcessor) else "{:.1f} seconds"
+        )
         frames_idx_token = [
-            tokenizer.encode(str(i), add_special_tokens=False) for i in timestamps
+            tokenizer.encode(timestamp_format.format(i), add_special_tokens=False)
+            for i in timestamps
         ]
         T, H, W = grid_thw
         num_tokens_per_frame = int(H * W) // merge_length