[Feature]: Support 310P device run qwen2.5/3 dense and qwen2.5vl models (#5776)

### What this PR does / why we need it?
Add basic 310p support. Only dense models work with eager mode now.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>

vllm_ascend/_310p/attention/attention_mask.py

@@ -0,0 +1,98 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch_npu
+
+import vllm_ascend.attention.attention_mask as _base_mask
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, nd_to_nz_spec
+
+_BASE_BUILDER: Callable[[torch.device], Any] = _base_mask.AttentionMaskBuilder
+
+
+def _gen_causal_additive_mask_fp16(max_seq_len: int, device: torch.device) -> torch.Tensor:
+    tril = torch.ones((max_seq_len, max_seq_len), dtype=torch.bool, device=device).tril_()
+    upper = ~tril
+    m = torch.zeros((max_seq_len, max_seq_len), dtype=torch.float16, device=device)
+    m.masked_fill_(upper, float("-inf"))
+    return m
+
+
+def build_splitfuse_attn_mask_310p(attn_metadata, device, *, full_mask_cache=None, full_mask_cache_len=0):
+    qsl = attn_metadata.query_start_loc.detach().to("cpu", dtype=torch.int32)
+    qlens = qsl[1:] - qsl[:-1]
+
+    context_lens = attn_metadata.seq_lens.to(dtype=torch.int32)
+    L = int(context_lens.max().item())
+
+    q_list = qlens.tolist()
+    c_list = context_lens.detach().to("cpu", dtype=torch.int64).tolist()
+    pos_list = [p for ql, cl in zip(q_list, c_list) for p in range(cl - ql, cl)]
+    position = torch.tensor(pos_list, dtype=torch.long, device=device)
+
+    if full_mask_cache is None or full_mask_cache.device != device or full_mask_cache_len < L:
+        tril = torch.ones((L, L), dtype=torch.bool, device=device).tril_()
+        full = torch.zeros((L, L), dtype=torch.float16, device=device)
+        full.masked_fill_(~tril, float("-inf"))
+        full_mask_cache, full_mask_cache_len = full, L
+    else:
+        full = full_mask_cache[:L, :L].contiguous()
+
+    rows = full.index_select(0, position).contiguous()
+    mask = torch_npu.npu_format_cast(nd_to_nz_spec(rows).contiguous(), ACL_FORMAT_FRACTAL_NZ)
+    return mask, full_mask_cache, full_mask_cache_len
+
+
+class _AttentionMaskBuilder310P:
+    """
+    310P adapter:
+      - overrides fp16 causal additive mask generation (use -inf fp16)
+      - delegates all other behaviors to base AttentionMaskBuilder
+      - pooling runner_type is NOT supported on 310P (explicit)
+    """
+
+    def __init__(self, device: torch.device):
+        self._base = _BASE_BUILDER(device)
+
+        self._fp16_mask_cache: torch.Tensor | None = None
+        self._fp16_mask_cached_len: int = 0
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._base, name)
+
+    @property
+    def device(self) -> torch.device:
+        return self._base.device
+
+    def _get_fp16_mask(self, max_seq_len: int) -> torch.Tensor:
+        if self._fp16_mask_cache is None or max_seq_len > self._fp16_mask_cached_len:
+            self._fp16_mask_cache = _gen_causal_additive_mask_fp16(max_seq_len, self.device)
+            self._fp16_mask_cached_len = max_seq_len
+        assert self._fp16_mask_cache is not None
+        return self._fp16_mask_cache[:max_seq_len, :max_seq_len].contiguous()
+
+    def get_attention_mask(self, model_config) -> torch.Tensor:
+        if getattr(model_config, "runner_type", None) == "pooling":
+            raise NotImplementedError("310P does not support runner_type='pooling'")
+        return self._get_fp16_mask(2048)
+
+
+def AttentionMaskBuilder(device: torch.device) -> _AttentionMaskBuilder310P:
+    return _AttentionMaskBuilder310P(device)