[Misc]feat: adapt to vLLM main (54503ece)

.github/vllm-main-verified.commit

@@ -1 +1 @@
-85c09e9885e346ea1612da30ebff5a75f67d2350
+54503ecec0f3ac31e5ecfc5f28652e4cc42307b5

vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_connector.py

@@ -1108,7 +1108,11 @@ def _get_group_kv_caches(self, group_idx: int, layer_indices: list[int] | None =
         if layer_indices is None:
             _, layer_indices = self.kv_group2layeridx[group_idx]
         layer_index_set = set(layer_indices)
-        num_attn_module = 2 if self.vllm_config.model_config.hf_text_config.model_type == "longcat_flash" else 1
+        num_attn_module = (
+            2
+            if self.vllm_config.model_config.hf_text_config.model_type in ("longcat_flash", "longcat_flash_ngram")
+            else 1
+        )
         from vllm.v1.worker.utils import extract_layer_index
 
         def layer_in_group(layer_name: str) -> bool:
@@ -2081,7 +2085,11 @@ def _build_kv_group2layeridx(self) -> dict[int, tuple[dict[str, Any], list[int]]
         from vllm.v1.worker.utils import extract_layer_index
 
         kv_group2layeridx: dict[int, tuple[dict[str, Any], list[int]]] = {}
-        num_attn_module = 2 if self.vllm_config.model_config.hf_text_config.model_type == "longcat_flash" else 1
+        num_attn_module = (
+            2
+            if self.vllm_config.model_config.hf_text_config.model_type in ("longcat_flash", "longcat_flash_ngram")
+            else 1
+        )
         next_mtp_layer_idx = self.total_layers
         transfer_group_id = 0
         for kv_cache_group_id, group_spec in enumerate(self.kv_cache_config.kv_cache_groups):

vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_layerwise_connector.py

@@ -1336,7 +1336,11 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         if use_kv_buffer:
             self.create_kv_buffer(kv_buffer)
 
-        num_attn_module = 2 if self.vllm_config.model_config.hf_text_config.model_type == "longcat_flash" else 1
+        num_attn_module = (
+            2
+            if self.vllm_config.model_config.hf_text_config.model_type in ("longcat_flash", "longcat_flash_ngram")
+            else 1
+        )
         mtp_layer_name = ""
         for layer_name in kv_caches:
             if "mtp" in layer_name:

vllm_ascend/patch/hunyuan_vl_processor_compat.py

@@ -196,6 +196,66 @@ def call_hf_processor(
     hunyuan_vision.HunYuanVLMultiModalProcessor._call_hf_processor = call_hf_processor
 
 
+def _patch_xdrope_input_positions(hunyuan_vision: Any) -> None:
+    """Guard IndexError when image_start_indices > image_grid_thw entries."""
+
+    def get_xdrope_input_positions(
+        self: Any,
+        input_tokens: list[int],
+        mm_features: list[Any],
+    ) -> Any:
+        from vllm.multimodal.inputs import MultiModalFeatureSpec
+
+        kwargs = MultiModalFeatureSpec.gather_kwargs(
+            mm_features,
+            {"image_grid_thw"},
+        )
+        image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])]
+
+        hf_config = self.config
+        image_start_token_id = hf_config.image_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        xd_num = len(hf_config.rope_scaling["xdrope_section"])
+
+        import torch
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        image_start_indices = torch.argwhere(input_tokens_tensor == image_start_token_id).squeeze(1)
+
+        p_index = torch.arange(len(input_tokens_tensor))
+        w_index = torch.arange(len(input_tokens_tensor))
+        h_index = torch.arange(len(input_tokens_tensor))
+        t_index = torch.arange(len(input_tokens_tensor))
+        for image_index in range(len(image_start_indices)):
+            if image_index >= len(image_grid_thw):
+                continue
+            pos = image_start_indices[image_index] + 2
+            t, h, w = image_grid_thw[image_index]
+            _, llm_grid_h, llm_grid_w = (
+                t,
+                h // spatial_merge_size,
+                w // spatial_merge_size,
+            )
+
+            token_num = (llm_grid_w + 1) * llm_grid_h
+            w_index[pos : pos + token_num].copy_(
+                torch.arange(0, llm_grid_w + 1).reshape(1, -1).expand(llm_grid_h, -1).reshape(-1)
+            )
+            h_index[pos : pos + token_num].copy_(
+                torch.arange(0, llm_grid_h).reshape(-1, 1).expand(-1, llm_grid_w + 1).reshape(-1)
+            )
+            t_index[pos : pos + token_num] = image_index
+
+        if xd_num == 4:
+            llm_positions = torch.stack([p_index, w_index, h_index, t_index])
+        elif xd_num == 3:
+            llm_positions = torch.stack([w_index, h_index, t_index])
+
+        return llm_positions
+
+    hunyuan_vision.HunYuanVLForConditionalGeneration.get_xdrope_input_positions = get_xdrope_input_positions
+
+
 def install_hunyuan_vl_processor_compat() -> None:
     """Align both supported vLLM refs with Transformers 5.13 Hunyuan APIs."""
     # Keep each target's native, image-token-only prompt replacement. The
@@ -208,8 +268,8 @@ def install_hunyuan_vl_processor_compat() -> None:
         _patch_v024_processor_methods(v024_hunyuan_vision)
         return
 
-    if not _remove_stale_registry_entries():
-        return
+    _remove_stale_registry_entries()
     from vllm.model_executor.models import hunyuan_vision as main_hunyuan_vision
 
     _patch_hunyuan_processor_loader(main_hunyuan_vision)
+    _patch_xdrope_input_positions(main_hunyuan_vision)

vllm_ascend/patch/platform/patch_speculative_config.py

@@ -111,7 +111,7 @@ def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
                 "architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
             }
         )
-    if hf_config.model_type == "longcat_flash":
+    if hf_config.model_type in ("longcat_flash", "longcat_flash_ngram"):
         hf_config.model_type = "longcat_flash_mtp"
         n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
         hf_config.update({"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]})

vllm_ascend/quantization/modelslim_config.py

@@ -235,6 +235,11 @@ def _is_fused_moe_layer(layer: torch.nn.Module) -> bool:
         "experts": ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
         "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"],
     },
+    "longcat_flash_ngram": {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "experts": ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
+        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"],
+    },
     "minimax_m2": {
         "qkv_proj": [
             "q_proj",

vllm_ascend/worker/model_runner_v1.py

@@ -3984,7 +3984,7 @@
         else:
             from vllm.v1.worker.utils import bind_kv_cache
 
-            num_attn_module = 2 if self.model_config.hf_text_config.model_type == "longcat_flash" else 1
+            num_attn_module = 2 if self.model_config.hf_text_config.model_type in ("longcat_flash", "longcat_flash_ngram") else 1
             bind_kv_cache(
                 kv_caches,
                 self.compilation_config.static_forward_context,