[pref] qwen3_next add triton ops : fused_sigmoid_gating_delta_rule_update (#4818)

### What this PR does / why we need it?
qwen3_next add fused_sigmoid_gating_delta_rule_update op which fused
fused_gdn_gating+fused_recurrent_gated_delta_rule

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>

tests/e2e/singlecard/test_fused_sigmoid_gating_delta_rule.py

@@ -0,0 +1,65 @@
+import torch
+from vllm.model_executor.layers.fla.ops import fused_recurrent_gated_delta_rule
+from vllm.model_executor.models.qwen3_next import fused_gdn_gating
+
+from vllm_ascend.ops.triton.fla.sigmoid_gating import \
+    fused_sigmoid_gating_delta_rule_update
+
+
+def test_triton_fusion_ops():
+    q = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
+    k = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
+    v = torch.randn(1, 1, 8, 128, dtype=torch.bfloat16).npu()
+    a = torch.tensor([[
+        -2.6094, -0.2617, -0.3848, 2.2656, 3.6250, -0.7383, -1.0938, -0.0505
+    ]]).bfloat16().npu()
+    b = torch.tensor(
+        [[0.4277, 0.8906, 1.6875, 2.3750, 4.1562, 0.3809, 1.0625,
+          3.6719]]).bfloat16().npu()
+    ssm_state = torch.randn(1, 8, 128, 128, dtype=torch.bfloat16).npu()
+    non_spec_state_indices_tensor = torch.tensor([2]).int().npu()
+    non_spec_query_start_loc = torch.tensor([0, 1]).int().npu()
+    a_log = torch.tensor([
+        -2.6875, -3.2031, -3.3438, -2.7812, -3.0625, -4.0312, -5.3750, 5.7188
+    ]).bfloat16().npu()
+    dt_bias = torch.tensor(
+        [-4.7812, -5.0938, -5.5000, 9.4375, 7.6250, -4.3750, -3.0938,
+         0.9688]).bfloat16().npu()
+
+    core_attn_out_non_spec_fused = fused_sigmoid_gating_delta_rule_update(
+        A_log=a_log.contiguous(),
+        dt_bias=dt_bias.contiguous(),
+        q=q.contiguous(),
+        k=k.contiguous(),
+        v=v.contiguous(),
+        a=a.contiguous(),
+        b=b.contiguous(),
+        initial_state_source=ssm_state,
+        initial_state_indices=non_spec_state_indices_tensor,
+        cu_seqlens=non_spec_query_start_loc,
+        use_qk_l2norm_in_kernel=True,
+        softplus_beta=1.0,
+        softplus_threshold=20.0,
+    )
+
+    g, beta = fused_gdn_gating(a_log, a, b, dt_bias)
+    g_non_spec = g
+    beta_non_spec = beta
+    core_attn_out_non_spec_split, last_recurrent_state = (
+        fused_recurrent_gated_delta_rule(
+            q=q,
+            k=k,
+            v=v,
+            g=g_non_spec,
+            beta=beta_non_spec,
+            initial_state=ssm_state,
+            inplace_final_state=True,
+            cu_seqlens=non_spec_query_start_loc,
+            ssm_state_indices=non_spec_state_indices_tensor,
+            use_qk_l2norm_in_kernel=True,
+        ))
+    torch.testing.assert_close(core_attn_out_non_spec_fused,
+                               core_attn_out_non_spec_split,
+                               rtol=1e-02,
+                               atol=1e-02,
+                               equal_nan=True)

vllm_ascend/ops/triton/fla/sigmoid_gating.py

@@ -11,6 +11,7 @@
 
 import os
 
+import torch
 from vllm.triton_utils import tl, tldevice, triton
 
 if os.environ.get('FLA_USE_FAST_OPS', '0') == '1':
@@ -169,3 +170,228 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
             p_ht = ht + (bos + i_t) * stride_final_state_token
         p_ht = p_ht + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
         tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
+
+
+@triton.heuristics({
+    "USE_INITIAL_STATE": lambda args: args["h0_source"] is not None,
+    "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
+})
+@triton.jit(do_not_specialize=["T"])
+def fused_sigmoid_gating_delta_rule_update_kernel(
+    A_log,
+    a,
+    dt_bias,
+    softplus_beta,
+    softplus_threshold,
+    q,
+    k,
+    v,
+    b,
+    o,
+    h0_source,
+    h0_indices,
+    cu_seqlens,
+    scale,
+    T,
+    B: tl.constexpr,
+    H: tl.constexpr,
+    HV: tl.constexpr,
+    K: tl.constexpr,
+    V: tl.constexpr,
+    BK: tl.constexpr,
+    BV: tl.constexpr,
+    USE_INITIAL_STATE: tl.constexpr,
+    USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
+    IS_VARLEN: tl.constexpr,
+):
+    """
+    Fused kernel that combines sigmoid gating computation with recurrent delta rule update.
+    """
+    i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    i_n, i_hv = i_nh // HV, i_nh % HV
+    i_h = i_hv // (HV // H)
+
+    if IS_VARLEN:
+        bos, eos = (
+            tl.load(cu_seqlens + i_n).to(tl.int64),
+            tl.load(cu_seqlens + i_n + 1).to(tl.int64),
+        )
+        all = T
+        T = eos - bos
+    else:
+        bos, eos = i_n * T, i_n * T + T
+        all = B * T
+
+    o_k = i_k * BK + tl.arange(0, BK)
+    o_v = i_v * BV + tl.arange(0, BV)
+
+    p_q = q + (bos * H + i_h) * K + o_k
+    p_k = k + (bos * H + i_h) * K + o_k
+    p_v = v + (bos * HV + i_hv) * V + o_v
+    p_b = b + bos * HV + i_hv
+    p_o = o + ((i_k * all + bos) * HV + i_hv) * V + o_v
+
+    # Gating computation pointers
+    p_A_log = A_log + i_hv
+    p_a = a + bos * HV + i_hv
+    p_dt_bias = dt_bias + i_hv
+
+    mask_k = o_k < K
+    mask_v = o_v < V
+    mask_h = mask_k[:, None] & mask_v[None, :]
+
+    b_h = tl.zeros([BK, BV], dtype=tl.float32)
+    if USE_INITIAL_STATE:
+        idx = tl.load(h0_indices + i_n)
+        # if idx >= 0:
+        tmp0 = tl.where(idx < 0, 0, idx)
+        p_h0 = (h0_source + tmp0 * HV * K * V + i_hv * K * V +
+                o_k[:, None] * V + o_v[None, :])
+        temp1 = tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
+        temp2 = tl.zeros_like(temp1)
+        value0 = tl.where(idx < 0, temp2, temp1)
+        b_h += value0  # tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
+
+    for i in range(0, T):
+        # Load inputs
+        b_q = tl.load(p_q + i * H * K, mask=mask_k, other=0).to(tl.float32)
+        b_k = tl.load(p_k + i * H * K, mask=mask_k, other=0).to(tl.float32)
+        b_v = tl.load(p_v + i * HV * V, mask=mask_v, other=0).to(tl.float32)
+        b_b = tl.load(p_b + i * HV).to(tl.float32)
+
+        # Compute sigmoid gating
+        # Load gating parameters
+        b_A_log = tl.load(p_A_log).to(tl.float32)
+        b_a = tl.load(p_a + i * HV).to(tl.float32)
+        b_dt_bias = tl.load(p_dt_bias).to(tl.float32)
+
+        # Compute g = -exp(A_log) * softplus(a + dt_bias)
+        x = b_a + b_dt_bias
+        beta_x = softplus_beta * x
+        # Apply softplus with numerical stability
+        softplus_x = tl.where(
+            beta_x <= softplus_threshold,
+            (1.0 / softplus_beta) * tl.log(1.0 + tl.exp(beta_x)),
+            x,
+        )
+        b_g = -tl.exp(b_A_log) * softplus_x
+
+        # Compute beta = sigmoid(b)
+        b_beta = 1.0 / (1.0 + tl.exp(-b_b))
+
+        # Apply L2 normalization if enabled
+        if USE_QK_L2NORM_IN_KERNEL:
+            b_q = b_q / (tl.sqrt(tl.sum(b_q * b_q)) + 1e-6)
+            b_k = b_k / (tl.sqrt(tl.sum(b_k * b_k)) + 1e-6)
+
+        b_q = b_q * scale
+
+        # Apply gating to hidden state: h *= exp(g)
+        b_h *= tl.exp(b_g)
+
+        # Delta rule: v -= sum(h * k, dim=0)
+        b_v -= tl.sum(b_h * b_k[:, None], 0)
+
+        # Apply beta gating: v *= beta
+        b_v *= b_beta
+
+        # Update hidden state: h += k[:, None] * v[None, :]
+        b_h += b_k[:, None] * b_v[None, :]
+
+        # Compute output: o = sum(h * q, dim=0)
+        b_o = tl.sum(b_h * b_q[:, None], 0)
+        tl.store(p_o + i * HV * V, b_o.to(p_o.dtype.element_ty), mask=mask_v)
+
+        # # Update pointers for next timestep
+        # p_q += H * K
+        # p_k += H * K
+        # p_o += HV * V
+        # p_v += HV * V
+        # p_b += HV
+        # p_a += HV
+
+    # Store final state back to h0_source with bounds checking
+    if USE_INITIAL_STATE:
+        idx = tl.load(h0_indices + i_n)
+        if idx >= 0:
+            p_h0 = (h0_source + idx * HV * K * V + i_hv * K * V +
+                    o_k[:, None] * V + o_v[None, :])
+            tl.store(p_h0, b_h.to(p_h0.dtype.element_ty), mask=mask_h)
+
+
+def fused_sigmoid_gating_delta_rule_update(
+    A_log: torch.Tensor,
+    a: torch.Tensor,
+    dt_bias: torch.Tensor,
+    softplus_beta: float,
+    softplus_threshold: float,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    b: torch.Tensor,
+    initial_state_source: torch.Tensor,
+    initial_state_indices: torch.Tensor,
+    scale: float = None,
+    use_qk_l2norm_in_kernel: bool = False,
+    cu_seqlens: torch.Tensor = None,
+):
+    """
+    Fused triton implementation of sigmoid gating delta rule update.
+    This function uses a single fused kernel that combines both sigmoid gating computation
+    and the recurrent delta rule update for better performance.
+    """
+    B, T, H, K, V = *k.shape, v.shape[-1]
+    HV = v.shape[2]
+    N = B if cu_seqlens is None else len(cu_seqlens) - 1
+    BK, BV = triton.next_power_of_2(K), min(triton.next_power_of_2(V), 64)
+    NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV)
+    assert NK == 1, "NK > 1 is not supported yet"
+    num_stages = 3
+    num_warps = 1
+
+    if scale is None:
+        scale = k.shape[-1]**-0.5
+    else:
+        assert scale > 0, "scale must be positive"
+
+    o = q.new_empty(NK, *v.shape)
+    grid = (NK, NV, N * HV)
+
+    if not initial_state_indices.is_contiguous():
+        initial_state_indices = initial_state_indices.contiguous()
+    if not initial_state_source.is_contiguous():
+        initial_state_source_contiguous = initial_state_source.contiguous()
+    if not cu_seqlens.is_contiguous():
+        cu_seqlens = cu_seqlens.contiguous()
+
+    fused_sigmoid_gating_delta_rule_update_kernel[grid](
+        A_log=A_log,
+        a=a,
+        dt_bias=dt_bias,
+        softplus_beta=softplus_beta,
+        softplus_threshold=softplus_threshold,
+        q=q,
+        k=k,
+        v=v,
+        b=b,
+        o=o,
+        h0_source=initial_state_source_contiguous,
+        h0_indices=initial_state_indices,
+        cu_seqlens=cu_seqlens,
+        scale=scale,
+        T=T,
+        B=B,
+        H=H,
+        HV=HV,
+        K=K,
+        V=V,
+        BK=BK,
+        BV=BV,
+        USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+    initial_state_source.copy_(
+        initial_state_source_contiguous.view_as(initial_state_source))
+    o = o.squeeze(0)
+    return o

vllm_ascend/patch/__init__.py

@@ -285,3 +285,15 @@
 #    Future Plan:
 #       Remove this patch when vLLM support these operators.
 #
+# ** 15. File: worker/patch_qwen3_next.py**
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `vllm.model_executor.models.qwen3_next.Qwen3NextGatedDeltaNet._forward_core`
+#    Why:
+#       triton ops fused_recurrent_gated_delta_rule and fused_gdn_gating in vLLM perform not good on NPU.
+#    How：
+#       add a new fused triton ops in vLLM with ascend implementation.
+#    Related PR (if no, explain why):
+#       https://github.com/vllm-project/vllm/pull/30860
+#    Future Plan:
+#       Remove this patch when vLLM support these operators.
+#

vllm_ascend/patch/worker/__init__.py

@@ -35,3 +35,4 @@ import vllm_ascend.patch.worker.patch_rope  # noqa
 import vllm_ascend.patch.worker.patch_qwen3_next  # noqa
 import vllm_ascend.patch.worker.patch_qwen3_next_mtp  # noqa
 import vllm_ascend.patch.worker.patch_rejection_sampler  # noqa
+import vllm_ascend.patch.worker.patch_qwen3_next  # noqa

vllm_ascend/patch/worker/patch_qwen3_next.py

@@ -18,14 +18,23 @@
 import torch
 from einops import rearrange
 from torch import nn
+from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import CUDAGraphMode
 from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.fla.ops import (
+    chunk_gated_delta_rule, fused_recurrent_gated_delta_rule)
 from vllm.model_executor.layers.mamba.abstract import MambaBase
-from vllm.model_executor.models.qwen3_next import Qwen3NextGatedDeltaNet
+from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
+    causal_conv1d_fn, causal_conv1d_update)
+from vllm.model_executor.models.qwen3_next import (Qwen3NextGatedDeltaNet,
+                                                   fused_gdn_gating)
 from vllm.triton_utils import triton
+from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
 
 from vllm_ascend.ops.triton.fla.fused_qkvzba_split_reshape import \
     fused_qkvzba_split_reshape_cat
+from vllm_ascend.ops.triton.fla.sigmoid_gating import \
+    fused_sigmoid_gating_delta_rule_update
 
 
 class AscendQwen3Next_GatedDeltaNet(nn.Module, MambaBase):
@@ -101,5 +110,230 @@ class AscendQwen3Next_GatedDeltaNet(nn.Module, MambaBase):
         core_attn_out = rearrange(core_attn_out, "... h d -> ... (h d)")
         output[:num_tokens], _ = self.out_proj(core_attn_out)
 
+    def _forward_core(
+        self,
+        mixed_qkv: torch.Tensor,
+        b: torch.Tensor,
+        a: torch.Tensor,
+        core_attn_out: torch.Tensor,
+    ):
+        """
+        Core attention computation (called by custom op).
+        """
+        forward_context = get_forward_context()
+        attn_metadata: AttentionMetadata = forward_context.attn_metadata
+
+        if attn_metadata is None:
+            # V1 profile run
+            return
+
+        assert isinstance(attn_metadata, dict)
+        attn_metadata = attn_metadata[self.prefix]
+        assert isinstance(attn_metadata, GDNAttentionMetadata)
+        has_initial_state = attn_metadata.has_initial_state
+        spec_query_start_loc = attn_metadata.spec_query_start_loc
+        non_spec_query_start_loc = attn_metadata.non_spec_query_start_loc
+        spec_sequence_masks = attn_metadata.spec_sequence_masks
+        spec_token_indx = attn_metadata.spec_token_indx
+        non_spec_token_indx = attn_metadata.non_spec_token_indx
+        spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor  # noqa: E501
+        non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
+        self_kv_cache = self.kv_cache[forward_context.virtual_engine]
+        conv_state = self_kv_cache[0].transpose(-1, -2)
+        ssm_state = self_kv_cache[1]
+        num_actual_tokens = attn_metadata.num_actual_tokens
+        num_accepted_tokens = attn_metadata.num_accepted_tokens
+
+        mixed_qkv = mixed_qkv[:num_actual_tokens]
+        b = b[:num_actual_tokens]
+        a = a[:num_actual_tokens]
+
+        # 1. Convolution sequence transformation
+        conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
+                                               self.conv1d.weight.size(2))
+
+        if spec_sequence_masks is not None:
+            if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
+                mixed_qkv_spec = mixed_qkv
+                mixed_qkv_non_spec = None
+            else:
+                mixed_qkv_spec = mixed_qkv.index_select(0, spec_token_indx)
+                mixed_qkv_non_spec = mixed_qkv.index_select(
+                    0, non_spec_token_indx)
+        else:
+            mixed_qkv_spec = None
+            mixed_qkv_non_spec = mixed_qkv
+
+        # 1.1: Process the multi-query part
+        if spec_sequence_masks is not None:
+            mixed_qkv_spec = causal_conv1d_update(
+                mixed_qkv_spec,
+                conv_state,
+                conv_weights,
+                self.conv1d.bias,
+                self.activation,
+                conv_state_indices=spec_state_indices_tensor[:, 0]
+                [:attn_metadata.num_spec_decodes],
+                num_accepted_tokens=num_accepted_tokens,
+                query_start_loc=spec_query_start_loc,
+                max_query_len=spec_state_indices_tensor.size(-1),
+                validate_data=False,
+            )
+
+        # 1.2: Process the remaining part
+        if attn_metadata.num_prefills > 0:
+            if mixed_qkv_non_spec is not None:
+                mixed_qkv_non_spec_T = mixed_qkv_non_spec.transpose(0, 1)
+                # - "cache_indices" updates the conv_state cache in positions
+                #   pointed to by "state_indices_tensor"
+                mixed_qkv_non_spec = causal_conv1d_fn(
+                    mixed_qkv_non_spec_T,
+                    conv_weights,
+                    self.conv1d.bias,
+                    activation=self.activation,
+                    conv_states=conv_state,
+                    has_initial_state=has_initial_state,
+                    cache_indices=non_spec_state_indices_tensor,
+                    query_start_loc=non_spec_query_start_loc,
+                    metadata=attn_metadata,
+                ).transpose(0, 1)
+        elif attn_metadata.num_decodes > 0:
+            mixed_qkv_non_spec = causal_conv1d_update(
+                mixed_qkv_non_spec,
+                conv_state,
+                conv_weights,
+                self.conv1d.bias,
+                self.activation,
+                conv_state_indices=
+                non_spec_state_indices_tensor[:attn_metadata.
+                                              num_actual_tokens],
+                validate_data=True,
+            )
+        else:
+            mixed_qkv_non_spec = None
+
+        query_spec, key_spec, value_spec = self.rearrange_mixed_qkv(
+            mixed_qkv_spec)
+        query_non_spec, key_non_spec, value_non_spec = self.rearrange_mixed_qkv(
+            mixed_qkv_non_spec)
+
+        if attn_metadata.num_prefills > 0 or spec_sequence_masks is not None:
+            g, beta = fused_gdn_gating(self.A_log, a, b, self.dt_bias)
+
+            if spec_sequence_masks is not None:
+                if attn_metadata.num_prefills == 0 and attn_metadata.num_decodes == 0:
+                    g_spec = g
+                    beta_spec = beta
+                    g_non_spec = None
+                    beta_non_spec = None
+                else:
+                    g_spec = g.index_select(1, spec_token_indx)
+                    beta_spec = beta.index_select(1, spec_token_indx)
+                    g_non_spec = g.index_select(1, non_spec_token_indx)
+                    beta_non_spec = beta.index_select(1, non_spec_token_indx)
+            else:
+                g_spec = None
+                beta_spec = None
+                g_non_spec = g
+                beta_non_spec = beta
+
+            # 2. Recurrent attention
+
+            # 2.1: Process the multi-query part
+            if spec_sequence_masks is not None:
+                core_attn_out_spec, last_recurrent_state = fused_recurrent_gated_delta_rule(
+                    q=query_spec,
+                    k=key_spec,
+                    v=value_spec,
+                    g=g_spec,
+                    beta=beta_spec,
+                    initial_state=ssm_state,
+                    inplace_final_state=True,
+                    cu_seqlens=spec_query_start_loc[:attn_metadata.
+                                                    num_spec_decodes + 1],
+                    ssm_state_indices=spec_state_indices_tensor,
+                    num_accepted_tokens=num_accepted_tokens,
+                    use_qk_l2norm_in_kernel=True,
+                )
+            else:
+                core_attn_out_spec, last_recurrent_state = None, None
+
+            # 2.2: Process the remaining part
+            if attn_metadata.num_prefills > 0:
+                initial_state = ssm_state[
+                    non_spec_state_indices_tensor].contiguous()
+                initial_state[~has_initial_state, ...] = 0
+                (
+                    core_attn_out_non_spec,
+                    last_recurrent_state,
+                ) = chunk_gated_delta_rule(
+                    q=query_non_spec,
+                    k=key_non_spec,
+                    v=value_non_spec,
+                    g=g_non_spec,
+                    beta=beta_non_spec,
+                    initial_state=initial_state,
+                    output_final_state=True,
+                    cu_seqlens=non_spec_query_start_loc,
+                    head_first=False,
+                    use_qk_l2norm_in_kernel=True,
+                )
+                # Init cache
+                ssm_state[
+                    non_spec_state_indices_tensor] = last_recurrent_state.to(
+                        ssm_state.dtype)
+            elif attn_metadata.num_decodes > 0:
+                core_attn_out_non_spec, last_recurrent_state = (
+                    fused_recurrent_gated_delta_rule(
+                        q=query_non_spec,
+                        k=key_non_spec,
+                        v=value_non_spec,
+                        g=g_non_spec,
+                        beta=beta_non_spec,
+                        initial_state=ssm_state,
+                        inplace_final_state=True,
+                        cu_seqlens=non_spec_query_start_loc[:attn_metadata.
+                                                            num_decodes + 1],
+                        ssm_state_indices=non_spec_state_indices_tensor,
+                        use_qk_l2norm_in_kernel=True,
+                    ))
+            else:
+                core_attn_out_non_spec, last_recurrent_state = None, None
+
+        elif attn_metadata.num_decodes > 0:
+            core_attn_out_non_spec = fused_sigmoid_gating_delta_rule_update(
+                A_log=self.A_log.contiguous(),
+                dt_bias=self.dt_bias.contiguous(),
+                q=query_non_spec.contiguous(),
+                k=key_non_spec.contiguous(),
+                v=value_non_spec.contiguous(),
+                a=a.contiguous(),
+                b=b.contiguous(),
+                initial_state_source=ssm_state,
+                initial_state_indices=non_spec_state_indices_tensor,
+                cu_seqlens=non_spec_query_start_loc,
+                use_qk_l2norm_in_kernel=True,
+                softplus_beta=1.0,
+                softplus_threshold=20.0,
+            )
+
+        # 3. Merge core attention output
+        if spec_sequence_masks is not None and core_attn_out_non_spec is not None:
+            merged_out = torch.empty(
+                (1, num_actual_tokens, *core_attn_out_spec.shape[2:]),
+                dtype=core_attn_out_non_spec.dtype,
+                device=core_attn_out_non_spec.device,
+            )
+            merged_out.index_copy_(1, spec_token_indx, core_attn_out_spec)
+            merged_out.index_copy_(1, non_spec_token_indx,
+                                   core_attn_out_non_spec)
+            core_attn_out[:num_actual_tokens] = merged_out.squeeze(0)
+        elif spec_sequence_masks is not None:
+            core_attn_out[:num_actual_tokens] = core_attn_out_spec.squeeze(0)
+        else:
+            core_attn_out[:num_actual_tokens] = core_attn_out_non_spec.squeeze(
+                0)
+
 
 Qwen3NextGatedDeltaNet.forward = AscendQwen3Next_GatedDeltaNet.forward
+Qwen3NextGatedDeltaNet._forward_core = AscendQwen3Next_GatedDeltaNet._forward_core