[CI] Fix qwen2.5 vl CI failure (#888)

The [vllm
commit](vllm-project/vllm@67da572)
changed the input and rotary position embedding for qwen 2.5 vl which
break CI. This PR fix the CI failure for qwen2.5 vl in quick

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

Author: wangxiyuan

vllm_ascend/models/qwen2_5_vl.py

@@ -36,9 +36,9 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.qwen2_5_vl import (
     Qwen2_5_VisionAttention, Qwen2_5_VisionBlock, Qwen2_5_VisionPatchEmbed,
-    Qwen2_5_VisionTransformer, Qwen2_5_VLDummyInputsBuilder,
-    Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLMultiModalProcessor,
-    Qwen2_5_VLProcessingInfo)
+    Qwen2_5_VisionRotaryEmbedding, Qwen2_5_VisionTransformer,
+    Qwen2_5_VLDummyInputsBuilder, Qwen2_5_VLForConditionalGeneration,
+    Qwen2_5_VLMultiModalProcessor, Qwen2_5_VLProcessingInfo)
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.multimodal import MULTIMODAL_REGISTRY
 
@@ -152,6 +152,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
+class AscendQwen2_5_VisionRotaryEmbedding(Qwen2_5_VisionRotaryEmbedding):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__(dim, theta)
+        inv_freq = 1.0 / (theta
+                          **(torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.inv_freq = inv_freq
+
+
 class AscendQwen2_5_VisionTransformer(Qwen2_5_VisionTransformer):
 
     def __init__(
@@ -166,6 +175,9 @@ def __init__(
         norm_layer = partial(RMSNorm, eps=norm_eps)
         self.interleaved = interleaved
         self.enable_pad = False
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = AscendQwen2_5_VisionRotaryEmbedding(head_dim //
+                                                                  2)
         self.patch_embed = AscendQwen2_5_VisionPatchEmbed(
             patch_size=vision_config.patch_size,
             temporal_patch_size=vision_config.temporal_patch_size,
@@ -298,6 +310,66 @@ def load_weights(self, weights: Iterable[Tuple[str,
             loaded_params.add(name)
         return loaded_params
 
+    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            ).permute(0, 2, 1, 3).flatten()
+            pos_ids.append(
+                torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def get_window_index(self, grid_thw):
+        window_index: list = []
+        cu_window_seqlens: list = [0]
+        window_index_id = 0
+        vit_merger_window_size = (self.window_size //
+                                  self.spatial_merge_size // self.patch_size)
+
+        for grid_t, grid_h, grid_w in grid_thw:
+            llm_grid_h = grid_h // self.spatial_merge_size
+            llm_grid_w = grid_w // self.spatial_merge_size
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(
+                grid_t, llm_grid_h, llm_grid_w)
+            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+            index_padded = F.pad(index, (0, pad_w, 0, pad_h), 'constant', -100)
+            index_padded = index_padded.reshape(grid_t, num_windows_h,
+                                                vit_merger_window_size,
+                                                num_windows_w,
+                                                vit_merger_window_size)
+            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+                grid_t, num_windows_h * num_windows_w, vit_merger_window_size,
+                vit_merger_window_size)
+            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+            index_padded = index_padded.reshape(-1)
+            index_new = index_padded[index_padded != -100]
+            window_index.append(index_new + window_index_id)
+            cu_seqlens_tmp = seqlens.cumsum(
+                0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
+        window_index = torch.cat(window_index, dim=0)
+        return window_index, cu_window_seqlens
+
     def forward(
         self,
         x: torch.Tensor,
@@ -366,4 +438,37 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             norm_eps=getattr(config, "rms_norm_eps", 1e-6),
             quant_config=self._maybe_ignore_quant_config(quant_config),
             prefix=maybe_prefix(prefix, "visual"),
-        )
+        )
+
+    def _process_image_input(self, image_input) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = image_input["image_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        if image_input["type"] == "image_embeds":
+            image_embeds = image_input["image_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+            image_embeds = self.visual(pixel_values, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each image item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+        return image_embeds.split(sizes.tolist())
+
+    def _process_video_input(self, video_input) -> tuple[torch.Tensor, ...]:
+
+        grid_thw = video_input["video_grid_thw"]
+        assert grid_thw.ndim == 2
+
+        if video_input["type"] == "video_embeds":
+            video_embeds = video_input["video_embeds"].type(self.visual.dtype)
+        else:
+            pixel_values_videos = video_input["pixel_values_videos"].type(
+                self.visual.dtype)
+            video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw)
+
+        # Split concatenated embeddings for each video item.
+        merge_size = self.visual.spatial_merge_size
+        sizes = grid_thw.prod(-1) // merge_size // merge_size
+        return video_embeds.split(sizes.tolist())