【OPS】qwen3-next support triton chunk_gated_delta_rule ops (#4070)

### What this PR does / why we need it?
qwen3-next suppot  triton chunk_gated_delta_rule ops

### co-owners
@OsirisDuan

- vLLM version: v0.11.2

Signed-off-by: shiyuan680 <917935075@qq.com>

vllm_ascend/ops/triton/fla/wy_fast.py

@@ -0,0 +1,131 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
+#
+# This file contains code copied from the flash-linear-attention project.
+# The original source code was licensed under the MIT license and included
+# the following copyright notice:
+# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
+
+# ruff: noqa: E501
+# mypy: ignore-errors
+from typing import Optional, Tuple
+
+import torch
+from vllm.triton_utils import tl, triton
+
+from .utils import prepare_chunk_indices
+
+
+@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
+@triton.jit(do_not_specialize=['T'])
+def recompute_w_u_fwd_kernel(k, v, beta, w, u, A, g, cu_seqlens, chunk_indices,
+                             T, H: tl.constexpr, Hg: tl.constexpr,
+                             K: tl.constexpr, V: tl.constexpr,
+                             BT: tl.constexpr, BK: tl.constexpr,
+                             BV: tl.constexpr, IS_VARLEN: tl.constexpr):
+    T_max = T
+    i_t_o = tl.program_id(0)
+
+    for i_bh in range(H):
+        i_b, i_h = i_bh // H, i_bh % H
+        if IS_VARLEN:
+            i_n, i_t = tl.load(chunk_indices + i_t_o * 2).to(
+                tl.int32), tl.load(chunk_indices + i_t_o * 2 + 1).to(tl.int32)
+            bos, eos = tl.load(cu_seqlens + i_n).to(
+                tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
+            T = eos - bos
+        else:
+            bos, eos = i_b * T, i_b * T + T
+
+        offs_t = tl.arange(0, BT)
+        global_offs_t = i_t * BT + offs_t
+        mask_t = global_offs_t < T
+
+        offs_t_2d = global_offs_t[:, None]
+        offs_bt = tl.arange(0, BT)[None, :]
+        ptr_A = (A + (bos * H + i_h) * BT + offs_t_2d * (H * BT) + offs_bt * 1)
+        mask_A = mask_t[:, None]
+        b_A = tl.load(ptr_A, mask=mask_A, other=0.0).to(tl.float32)
+
+        ptr_g = g + bos + i_h * T_max + global_offs_t
+        b_g = tl.exp(tl.load(ptr_g, mask=mask_t, other=0.0)).to(tl.float32)
+
+        ptr_beta = beta + bos + i_h * T_max + global_offs_t
+        b_beta = tl.load(ptr_beta, mask=mask_t, other=0.0).to(tl.float32)
+
+        for i_v in range(tl.cdiv(V, BV)):
+            offs_v = i_v * BV + tl.arange(0, BV)[None, :]
+            mask_v = (mask_t[:, None]) & (offs_v < V)
+
+            ptr_v = (v + (bos * H + i_h) * V + offs_t_2d * (H * V) +
+                     offs_v * 1)
+            b_v = tl.load(ptr_v, mask=mask_v, other=0.0).to(tl.float32)
+
+            b_vb = (b_v * b_beta[:, None])
+            b_u = tl.dot(b_A, b_vb, allow_tf32=False)
+
+            ptr_u = (u + (bos * H + i_h) * V + offs_t_2d * (H * V) +
+                     offs_v * 1)
+            tl.store(ptr_u, b_u.to(ptr_u.dtype.element_ty), mask=mask_v)
+
+        for i_k in range(tl.cdiv(K, BK)):
+            offs_k = i_k * BK + tl.arange(0, BK)[None, :]
+            mask_k = (mask_t[:, None]) & (offs_k < K)
+            ptr_k = (k + (bos * Hg + i_h // (H // Hg)) * K + offs_t_2d *
+                     (Hg * K) + offs_k * 1)
+            b_k = tl.load(ptr_k, mask=mask_k, other=0.0).to(tl.float32)
+
+            b_kb = (b_k * b_beta[:, None] * b_g[:, None])
+            b_w = tl.dot(b_A, b_kb)
+
+            ptr_w = (w + (bos * H + i_h) * K + offs_t_2d * (H * K) +
+                     offs_k * 1)
+            tl.store(ptr_w, b_w.to(ptr_w.dtype.element_ty), mask=mask_k)
+
+
+def recompute_w_u_fwd(
+    k: torch.Tensor,
+    v: torch.Tensor,
+    beta: torch.Tensor,
+    g_cumsum: torch.Tensor,
+    A: torch.Tensor,
+    cu_seqlens: Optional[torch.LongTensor] = None,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    B, T, Hg, K, V = *k.shape, v.shape[-1]
+    H = v.shape[-2]
+    BT = A.shape[-1]
+
+    chunk_indices = prepare_chunk_indices(cu_seqlens, BT) \
+        if cu_seqlens is not None else None
+    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
+
+    BK = 64
+    BV = 64
+
+    u = torch.empty_like(v)
+    w = k.new_empty(B, T, H, K)
+    beta = beta.transpose(1, 2).contiguous()
+    g_cumsum = g_cumsum.transpose(1, 2).contiguous()
+    recompute_w_u_fwd_kernel[(NT, B)](
+        k=k,
+        v=v,
+        beta=beta,
+        w=w,
+        u=u,
+        A=A,
+        g=g_cumsum,
+        cu_seqlens=cu_seqlens,
+        chunk_indices=chunk_indices,
+        T=T,
+        H=H,
+        Hg=Hg,
+        K=K,
+        V=V,
+        BT=BT,
+        BK=BK,
+        BV=BV,
+        num_warps=4,
+        num_stages=3,
+    )
+    return w, u