[New model] Qwen3-next support (#2917)

### What this PR does / why we need it?
Add Qwen3-next support.

### Does this PR introduce _any_ user-facing change?
Yes, users can use Qwen3 next.
Related doc: https://github.com/vllm-project/vllm-ascend/pull/2916 the
tutorial will be ready in
[here](https://vllm-ascend.readthedocs.io/en/latest/tutorials/multi_npu_qwen3_next.html)

### How was this patch tested?
Doc CI passed

Related: https://github.com/vllm-project/vllm-ascend/issues/2884

Co-Authored-By: Angazenn <supperccell@163.com>
Co-Authored-By: zzzzwwjj <1183291235@qq.com>
Co-Authored-By: MengqingCao <cmq0113@163.com>
Co-Authored-By: linfeng-yuan <1102311262@qq.com>
Co-Authored-By: hust17yixuan <303660421@qq.com>
Co-Authored-By: SunnyLee219 <3294305115@qq.com>
Co-Authored-By: maoxx241 <maoxx241@umn.edu>


- vLLM version: v0.10.2
- vLLM main:
b834b4cbf1

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Angazenn <supperccell@163.com>
Signed-off-by: Your Name <you@example.com>
Signed-off-by: zzzzwwjj <1183291235@qq.com>
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: hust17yixuan <303660421@qq.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
Co-authored-by: Angazenn <supperccell@163.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: zzzzwwjj <1183291235@qq.com>
Co-authored-by: linfeng-yuan <1102311262@qq.com>
Co-authored-by: hust17yixuan <303660421@qq.com>

vllm_ascend/patch/platform/patch_common/__init__.py

@@ -16,4 +16,5 @@
 #
 
 import vllm_ascend.patch.platform.patch_common.patch_distributed  # noqa
+import vllm_ascend.patch.platform.patch_common.patch_mamba_config  # noqa
 import vllm_ascend.patch.platform.patch_common.patch_shared_fused_moe  # noqa

vllm_ascend/patch/platform/patch_common/patch_mamba_config.py

@@ -0,0 +1,97 @@
+# mypy: ignore-errors
+import vllm.model_executor.models.config
+from vllm.logger import init_logger
+from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.models.config import MambaModelConfig
+from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
+from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
+
+
+@classmethod
+def verify_and_update_config(cls, vllm_config) -> None:
+    """
+    Ensure that page size of attention layers is greater than or
+    equal to the mamba layers. If not, automatically set the attention
+    block size to ensure that it is. If the attention page size is
+    strictly greater than the mamba page size, we pad the mamba page size
+    to make them equal.
+
+    Args:
+        vllm_config: vLLM Config
+    """
+    logger = init_logger(__name__)
+    # Enable FULL_AND_PIECEWISE by default
+    MambaModelConfig.verify_and_update_config(vllm_config)
+
+    cache_config = vllm_config.cache_config
+    model_config = vllm_config.model_config
+    parallel_config = vllm_config.parallel_config
+
+    if cache_config.cache_dtype == "auto":
+        kv_cache_dtype = model_config.dtype
+    else:
+        kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
+
+    # get attention page size (for 1 token)
+    attn_page_size_1_token = FullAttentionSpec(
+        block_size=1,
+        num_kv_heads=model_config.get_num_kv_heads(parallel_config),
+        head_size=model_config.get_head_size(),
+        dtype=kv_cache_dtype,
+        use_mla=model_config.use_mla).page_size_bytes
+
+    model_cls, _ = ModelRegistry.resolve_model_cls(
+        model_config.architecture,
+        model_config=model_config,
+    )
+
+    # get mamba page size
+    mamba_page_size = MambaSpec(
+        shapes=model_cls.get_mamba_state_shape_from_config(vllm_config),
+        dtypes=model_cls.get_mamba_state_dtype_from_config(vllm_config),
+        block_size=model_config.max_model_len,
+    ).page_size_bytes
+
+    block_alignment_bytes = 64
+
+    # some attention backends (e.g. FA) only support setting
+    # block size to multiple of 16, so let's suggest a value
+    # that would work (note: FA is currently not compatible
+    # with mamba layers, use FlashInfer instead).
+    attn_block_size = block_alignment_bytes * cdiv(
+        mamba_page_size, block_alignment_bytes * attn_page_size_1_token)
+
+    # override attention block size if either (a) the
+    # user has not set it or (b) the user has set it
+    # too small.
+    if (cache_config.block_size is None
+            or cache_config.block_size < attn_block_size):
+        cache_config.block_size = attn_block_size
+        logger.info(
+            "Setting attention block size to %d tokens "
+            "to ensure that attention page size is >= mamba page size.",
+            attn_block_size)
+
+    # compute new attention page size
+    attn_page_size = \
+        cache_config.block_size * attn_page_size_1_token
+
+    assert attn_page_size >= mamba_page_size
+
+    if attn_page_size == mamba_page_size:
+        # don't need to pad mamba page size
+        return
+
+    # pad mamba page size to exactly match attention
+    if (cache_config.mamba_page_size_padded is None
+            or cache_config.mamba_page_size_padded != attn_page_size):
+        cache_config.mamba_page_size_padded = (attn_page_size)
+        mamba_padding_pct = 100 * (attn_page_size -
+                                   mamba_page_size) / mamba_page_size
+        logger.info(
+            "Padding mamba page size by %.2f%% to ensure "
+            "that mamba page size and attention page size are "
+            "exactly equal.", mamba_padding_pct)
+
+
+vllm.model_executor.models.config.HybridAttentionMambaModelConfig.verify_and_update_config = verify_and_update_config