time

evaluation_wrapper.py

@@ -34,7 +34,7 @@ class VLMModel:
     3. All optimizations are applied in __init__ by calling optimization methods.
     """
     
-    def __init__(self, model_path: str, device: str = "cuda:0"):
+    def __init__(self, model_path: str, device: str = "cpu"):
         """
         Initialize model and apply optimizations.
         

my_patch.py

@@ -249,6 +249,9 @@ def patch_forward(
     video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
         The temporal, height and width of feature shape of each video in LLM.
     """
+    import time
+    start = time.time()
+
     if (input_ids is None) ^ (inputs_embeds is not None):
         raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
@@ -316,34 +319,34 @@ def patch_forward(
             past_key_values=past_key_values,
         )
 
-    # ====== 稀疏采样裁剪：只在 prefill 做（past_key_values is None）=====
-    if past_key_values.get_seq_length() == 0 and visual_pos_masks is not None:
-        # 这些参数你可以通过 kwargs 传入
-        keep_ratio = kwargs.pop("visual_keep_ratio", 0.1)          # 只保留 25% 视觉 token
-        min_keep   = kwargs.pop("min_keep_per_vis", 0)              # 每段视觉最少保留多少（可设比如 16）
-        max_len    = kwargs.pop("truncate_max_len", None)           # 总长度上限（可选）
+    # # ====== 稀疏采样裁剪：只在 prefill 做（past_key_values is None）=====
+    # if past_key_values.get_seq_length() == 0 and visual_pos_masks is not None:
+    #     # 这些参数你可以通过 kwargs 传入
+    #     keep_ratio = kwargs.pop("visual_keep_ratio", 0.1)          # 只保留 25% 视觉 token
+    #     min_keep   = kwargs.pop("min_keep_per_vis", 0)              # 每段视觉最少保留多少（可设比如 16）
+    #     max_len    = kwargs.pop("truncate_max_len", None)           # 总长度上限（可选）
 
-        inputs_embeds, attention_mask, position_ids, visual_pos_masks, deepstack_visual_embeds = sparse_keep_and_gather(
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            visual_pos_masks=visual_pos_masks,
-            deepstack_visual_embeds=deepstack_visual_embeds,
-            keep_ratio=keep_ratio,
-            min_keep_per_vis=min_keep,
-            max_len=max_len,
-        )
+    #     inputs_embeds, attention_mask, position_ids, visual_pos_masks, deepstack_visual_embeds = sparse_keep_and_gather(
+    #         inputs_embeds=inputs_embeds,
+    #         attention_mask=attention_mask,
+    #         position_ids=position_ids,
+    #         visual_pos_masks=visual_pos_masks,
+    #         deepstack_visual_embeds=deepstack_visual_embeds,
+    #         keep_ratio=keep_ratio,
+    #         min_keep_per_vis=min_keep,
+    #         max_len=max_len,
+    #     )
 
-        # cache_position 建议重建为 0..L-1（避免对齐问题）
-        cache_position = torch.arange(
-            inputs_embeds.shape[1], device=inputs_embeds.device, dtype=torch.long
-        ).unsqueeze(0).expand(inputs_embeds.shape[0], -1)
+    #     # cache_position 建议重建为 0..L-1（避免对齐问题）
+    #     cache_position = torch.arange(
+    #         inputs_embeds.shape[1], device=inputs_embeds.device, dtype=torch.long
+    #     ).unsqueeze(0).expand(inputs_embeds.shape[0], -1)
 
-        # rope_deltas 建议也按裁剪后的序列重算（防止不一致）
-        eff_len = attention_mask.sum(dim=1).to(torch.long)  # (B,)
-        max_pos = position_ids.max(dim=0).values.max(dim=1).values  # (B,)
-        self.rope_deltas = (max_pos + 1 - eff_len).unsqueeze(1)
-    # ====== 裁剪结束 ======
+    #     # rope_deltas 建议也按裁剪后的序列重算（防止不一致）
+    #     eff_len = attention_mask.sum(dim=1).to(torch.long)  # (B,)
+    #     max_pos = position_ids.max(dim=0).values.max(dim=1).values  # (B,)
+    #     self.rope_deltas = (max_pos + 1 - eff_len).unsqueeze(1)
+    # # ====== 裁剪结束 ======
 
     outputs = self.language_model(
         input_ids=None,
@@ -357,6 +360,9 @@ def patch_forward(
         **kwargs,
     )
 
+    end = time.time()
+    print('程序运行时间:%s毫秒' % ((end - start)*1000))
+
     return Qwen3VLModelOutputWithPast(
         **outputs,
         rope_deltas=self.rope_deltas,