[Kernel] Add fused_sigmoid_gating_delta_rule_update kernel for Qwen3 Next (#35777)

Signed-off-by: Xin Yang <xyangx@amazon.com>

tests/kernels/test_fused_sigmoid_gating_delta_rule.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.fla.ops import (
+    fused_recurrent_gated_delta_rule,
+    fused_sigmoid_gating_delta_rule_update,
+)
+from vllm.platforms import current_platform
+from vllm.utils.torch_utils import set_random_seed
+
+DEVICE = current_platform.device_type
+
+
+@pytest.mark.parametrize("tp_size", [1])
+@pytest.mark.parametrize("num_reqs", [1, 2, 4])
+@pytest.mark.parametrize("num_k_heads", [16])
+@pytest.mark.parametrize("num_v_heads", [32])
+@pytest.mark.parametrize("head_k_dim", [128])
+@pytest.mark.parametrize("head_v_dim", [128])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+def test_fused_sigmoid_gating_delta_rule_update_non_spec(
+    tp_size: int,
+    num_reqs: int,
+    num_k_heads: int,
+    num_v_heads: int,
+    head_k_dim: int,
+    head_v_dim: int,
+    dtype: torch.dtype,
+) -> None:
+    torch.set_default_device(DEVICE)
+    set_random_seed(0)
+    key_dim = head_k_dim * num_k_heads
+    value_dim = head_v_dim * num_v_heads
+    mixed_qkv_dim = (key_dim * 2 + value_dim) // tp_size
+    seq_len = 1  # seq_len is 1 for decode
+    num_tokens = num_reqs * seq_len
+    total_entries = num_tokens * 2
+
+    mixed_qkv = torch.rand(num_tokens, mixed_qkv_dim, dtype=dtype)
+    query, key, value = torch.split(
+        mixed_qkv,
+        [
+            key_dim // tp_size,
+            key_dim // tp_size,
+            value_dim // tp_size,
+        ],
+        dim=-1,
+    )
+    query = query.view(1, num_tokens, num_k_heads, head_k_dim)
+    key = key.view(1, num_tokens, num_k_heads, head_k_dim)
+    value = value.view(1, num_tokens, num_v_heads, head_v_dim)
+
+    A_log = torch.rand(num_v_heads // tp_size, dtype=dtype)
+    dt_bias = torch.rand(num_v_heads // tp_size, dtype=dtype)
+    a = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    b = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    ssm_state = torch.rand(
+        total_entries, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
+    )
+    state_indices = torch.randperm(total_entries, dtype=torch.int32)[:num_tokens]
+    cu_seqlens = torch.arange(0, num_tokens + 1, dtype=torch.int32)
+
+    beta = b.sigmoid()
+    g = -A_log.float().exp() * F.softplus(a.float() + dt_bias)
+    core_attn_out_ref, last_recurrent_state_ref = fused_recurrent_gated_delta_rule(
+        q=query,
+        k=key,
+        v=value,
+        g=g.unsqueeze(0),
+        beta=beta.unsqueeze(0),
+        initial_state=ssm_state.clone(),
+        inplace_final_state=True,
+        ssm_state_indices=state_indices,
+        cu_seqlens=cu_seqlens,
+        use_qk_l2norm_in_kernel=True,
+    )
+
+    core_attn_out, last_recurrent_state = fused_sigmoid_gating_delta_rule_update(
+        A_log=A_log,
+        a=a,
+        b=b,
+        dt_bias=dt_bias,
+        q=query,
+        k=key,
+        v=value,
+        initial_state=ssm_state,
+        inplace_final_state=True,
+        ssm_state_indices=state_indices,
+        cu_seqlens=cu_seqlens,
+        use_qk_l2norm_in_kernel=True,
+    )
+
+    torch.testing.assert_close(core_attn_out, core_attn_out_ref, atol=1e-2, rtol=1e-2)
+    torch.testing.assert_close(
+        last_recurrent_state, last_recurrent_state_ref, atol=1e-2, rtol=1e-2
+    )
+
+
+@pytest.mark.parametrize("tp_size", [1])
+@pytest.mark.parametrize("num_reqs", [1, 2, 4])
+@pytest.mark.parametrize("num_k_heads", [16])
+@pytest.mark.parametrize("num_v_heads", [32])
+@pytest.mark.parametrize("head_k_dim", [128])
+@pytest.mark.parametrize("head_v_dim", [128])
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+def test_fused_sigmoid_gating_delta_rule_update_spec(
+    tp_size: int,
+    num_reqs: int,
+    num_k_heads: int,
+    num_v_heads: int,
+    head_k_dim: int,
+    head_v_dim: int,
+    num_speculative_tokens: int,
+    dtype: torch.dtype,
+) -> None:
+    torch.set_default_device(DEVICE)
+    set_random_seed(0)
+    key_dim = head_k_dim * num_k_heads
+    value_dim = head_v_dim * num_v_heads
+    mixed_qkv_dim = (key_dim * 2 + value_dim) // tp_size
+    num_tokens = num_reqs * (num_speculative_tokens + 1)
+    total_entries = num_tokens * 2
+
+    mixed_qkv = torch.rand(num_tokens, mixed_qkv_dim, dtype=dtype)
+    query, key, value = torch.split(
+        mixed_qkv,
+        [
+            key_dim // tp_size,
+            key_dim // tp_size,
+            value_dim // tp_size,
+        ],
+        dim=-1,
+    )
+    query = query.view(1, num_tokens, num_k_heads, head_k_dim)
+    key = key.view(1, num_tokens, num_k_heads, head_k_dim)
+    value = value.view(1, num_tokens, num_v_heads, head_v_dim)
+
+    A_log = torch.rand(num_v_heads // tp_size, dtype=dtype)
+    dt_bias = torch.rand(num_v_heads // tp_size, dtype=dtype)
+    a = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    b = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    ssm_state = torch.rand(
+        total_entries, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
+    )
+    state_indices = torch.randperm(
+        total_entries,
+        dtype=torch.int32,
+    )[:num_tokens].view(num_reqs, num_speculative_tokens + 1)
+    num_accepted_tokens = torch.randint(
+        1, num_speculative_tokens + 1, (num_reqs,), dtype=torch.int32
+    )
+    cu_seqlens = torch.arange(
+        0, num_tokens + 1, num_speculative_tokens + 1, dtype=torch.int32
+    )
+
+    beta = b.sigmoid()
+    g = -A_log.float().exp() * F.softplus(a.float() + dt_bias)
+    core_attn_out_ref, last_recurrent_state_ref = fused_recurrent_gated_delta_rule(
+        q=query,
+        k=key,
+        v=value,
+        g=g.unsqueeze(0),
+        beta=beta.unsqueeze(0),
+        initial_state=ssm_state.clone(),
+        inplace_final_state=True,
+        ssm_state_indices=state_indices,
+        cu_seqlens=cu_seqlens,
+        num_accepted_tokens=num_accepted_tokens,
+        use_qk_l2norm_in_kernel=True,
+    )
+
+    core_attn_out, last_recurrent_state = fused_sigmoid_gating_delta_rule_update(
+        A_log=A_log,
+        a=a,
+        b=b,
+        dt_bias=dt_bias,
+        q=query,
+        k=key,
+        v=value,
+        initial_state=ssm_state,
+        inplace_final_state=True,
+        ssm_state_indices=state_indices,
+        cu_seqlens=cu_seqlens,
+        num_accepted_tokens=num_accepted_tokens,
+        use_qk_l2norm_in_kernel=True,
+    )
+
+    torch.testing.assert_close(core_attn_out, core_attn_out_ref, atol=1e-2, rtol=1e-2)
+    torch.testing.assert_close(
+        last_recurrent_state, last_recurrent_state_ref, atol=1e-2, rtol=1e-2
+    )

vllm/model_executor/layers/fla/ops/__init__.py

@@ -8,10 +8,12 @@
 # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
 from .chunk import chunk_gated_delta_rule
 from .fused_recurrent import fused_recurrent_gated_delta_rule
+from .fused_sigmoid_gating import fused_sigmoid_gating_delta_rule_update
 from .layernorm_guard import RMSNormGated
 
 __all__ = [
     "RMSNormGated",
     "chunk_gated_delta_rule",
     "fused_recurrent_gated_delta_rule",
+    "fused_sigmoid_gating_delta_rule_update",
 ]

vllm/model_executor/layers/fla/ops/fused_sigmoid_gating.py

+# 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
+
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.heuristics(
+    {
+        "USE_INITIAL_STATE": lambda args: args["h0"] is not None,
+        "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
+        "IS_CONTINUOUS_BATCHING": lambda args: args["ssm_state_indices"] is not None,
+        "IS_SPEC_DECODING": lambda args: args["num_accepted_tokens"] is not None,
+    }
+)
+@triton.jit(do_not_specialize=["N", "T"])
+def fused_sigmoid_gating_delta_rule_update_kernel(
+    A_log,
+    a,
+    b,
+    dt_bias,
+    beta,
+    threshold,
+    q,
+    k,
+    v,
+    o,
+    h0,
+    ht,
+    cu_seqlens,
+    ssm_state_indices,
+    num_accepted_tokens,
+    scale,
+    N: tl.int64,  # num of sequences
+    T: tl.int64,  # num of tokens
+    B: tl.constexpr,
+    H: tl.constexpr,
+    HV: tl.constexpr,
+    K: tl.constexpr,
+    V: tl.constexpr,
+    BK: tl.constexpr,
+    BV: tl.constexpr,
+    stride_init_state_token: tl.constexpr,
+    stride_final_state_token: tl.constexpr,
+    stride_indices_seq: tl.constexpr,
+    stride_indices_tok: tl.constexpr,
+    USE_INITIAL_STATE: tl.constexpr,  # whether to use initial state
+    INPLACE_FINAL_STATE: tl.constexpr,  # whether to store final state inplace
+    USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
+    IS_VARLEN: tl.constexpr,
+    IS_CONTINUOUS_BATCHING: tl.constexpr,
+    IS_SPEC_DECODING: tl.constexpr,
+    IS_KDA: tl.constexpr,
+):
+    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
+
+    if T == 0:
+        # no tokens to process for this sequence
+        return
+
+    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_A_log = A_log + i_hv
+    if not IS_KDA:
+        p_a = a + bos * HV + i_hv
+        p_dt_bias = dt_bias + i_hv
+    else:
+        p_a = a + (bos * HV + i_hv) * K + o_k
+        p_dt_bias = dt_bias + i_hv * K + o_k
+
+    p_b = b + bos * HV + i_hv
+    p_o = o + ((i_k * all + bos) * HV + i_hv) * V + o_v
+
+    mask_k = o_k < K
+    mask_v = o_v < V
+    mask_h = mask_v[:, None] & mask_k[None, :]
+
+    b_h = tl.zeros([BV, BK], dtype=tl.float32)
+    if USE_INITIAL_STATE:
+        if IS_CONTINUOUS_BATCHING:
+            if IS_SPEC_DECODING:
+                i_t = tl.load(num_accepted_tokens + i_n).to(tl.int64) - 1
+            else:
+                i_t = 0
+            # Load state index and check for PAD_SLOT_ID (-1)
+            state_idx = tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to(
+                tl.int64
+            )
+            # Skip if state index is invalid (PAD_SLOT_ID = -1)
+            if state_idx < 0:
+                return
+            p_h0 = h0 + state_idx * stride_init_state_token
+        else:
+            p_h0 = h0 + bos * HV * V * K
+        p_h0 = p_h0 + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
+        b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
+
+    for i_t in range(0, T):
+        b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32)
+        b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32)
+        b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32)
+        b_b = tl.load(p_b).to(tl.float32)
+
+        # If the model is loaded in fp16, without the .float() here, A might be -inf
+        x = tl.load(p_a).to(tl.float32) + tl.load(p_dt_bias).to(tl.float32)
+        softplus_x = tl.where(
+            beta * x <= threshold, (1 / beta) * tl.log(1 + tl.exp(beta * x)), x
+        )
+        b_g = -tl.exp(tl.load(p_A_log).to(tl.float32)) * softplus_x
+
+        # compute beta_output = sigmoid(b)
+        b_beta = tl.sigmoid(b_b.to(tl.float32))
+
+        if USE_QK_L2NORM_IN_KERNEL:
+            b_q = b_q * (tl.rsqrt(tl.sum(b_q * b_q) + 1e-6))
+            b_k = b_k * (tl.rsqrt(tl.sum(b_k * b_k) + 1e-6))
+        b_q = b_q * scale
+        # [BV, BK]
+        if not IS_KDA:
+            b_h *= tl.exp(b_g)
+        else:
+            b_h *= tl.exp(b_g[None, :])
+        # [BV]
+        b_v -= tl.sum(b_h * b_k[None, :], 1)
+        b_v *= b_beta
+        # [BV, BK]
+        b_h += b_v[:, None] * b_k[None, :]
+        # [BV]
+        b_o = tl.sum(b_h * b_q[None, :], 1)
+        tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)
+
+        # keep the states for multi-query tokens
+        if INPLACE_FINAL_STATE:
+            # Load state index and check for PAD_SLOT_ID (-1)
+            final_state_idx = tl.load(
+                ssm_state_indices + i_n * stride_indices_seq + i_t
+            ).to(tl.int64)
+            # Only store if state index is valid (not PAD_SLOT_ID)
+            if final_state_idx >= 0:
+                p_ht = ht + final_state_idx * stride_final_state_token
+                p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
+                tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
+        else:
+            p_ht = ht + (bos + i_t) * stride_final_state_token
+            p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
+            tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
+
+        # 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
+
+
+def fused_sigmoid_gating_delta_rule_update(
+    A_log: torch.Tensor,
+    a: torch.Tensor,
+    b: torch.Tensor,
+    dt_bias: torch.Tensor,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    beta: float = 1.0,
+    threshold: float = 20.0,
+    scale: float = None,
+    initial_state: torch.Tensor = None,
+    inplace_final_state: bool = True,
+    cu_seqlens: torch.LongTensor | None = None,
+    ssm_state_indices: torch.Tensor | None = None,
+    num_accepted_tokens: torch.Tensor | None = None,
+    use_qk_l2norm_in_kernel: bool = False,
+    is_kda: bool = False,
+):
+    """
+    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), 32)
+    NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV)
+    assert NK == 1, "NK > 1 is not supported yet"
+    num_stages = 3
+    num_warps = 4
+
+    if cu_seqlens is not None and q.shape[0] != 1:
+        raise ValueError(
+            f"The batch size is expected to be 1 rather than {q.shape[0]}"
+            f" when using `cu_seqlens`. Please flatten variable-length"
+            f" inputs before processing."
+        )
+    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)
+    if inplace_final_state:
+        final_state = initial_state
+    else:
+        final_state = q.new_empty(T, HV, V, K, dtype=initial_state.dtype)
+
+    stride_init_state_token = initial_state.stride(0)
+    stride_final_state_token = final_state.stride(0)
+
+    if ssm_state_indices is None:
+        stride_indices_seq, stride_indices_tok = 1, 1
+    elif ssm_state_indices.ndim == 1:
+        stride_indices_seq, stride_indices_tok = ssm_state_indices.stride(0), 1
+    else:
+        stride_indices_seq, stride_indices_tok = ssm_state_indices.stride()
+
+    grid = (NK, NV, N * HV)
+    fused_sigmoid_gating_delta_rule_update_kernel[grid](
+        A_log=A_log,
+        a=a.contiguous(),
+        b=b.contiguous(),
+        dt_bias=dt_bias,
+        beta=beta,
+        threshold=threshold,
+        q=q.contiguous(),
+        k=k.contiguous(),
+        v=v.contiguous(),
+        o=o,
+        h0=initial_state,
+        ht=final_state,
+        cu_seqlens=cu_seqlens,
+        ssm_state_indices=ssm_state_indices,
+        num_accepted_tokens=num_accepted_tokens,
+        scale=scale,
+        N=N,
+        T=T,
+        B=B,
+        H=H,
+        HV=HV,
+        K=K,
+        V=V,
+        BK=BK,
+        BV=BV,
+        stride_init_state_token=stride_init_state_token,
+        stride_final_state_token=stride_final_state_token,
+        stride_indices_seq=stride_indices_seq,
+        stride_indices_tok=stride_indices_tok,
+        INPLACE_FINAL_STATE=inplace_final_state,
+        USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
+        IS_KDA=is_kda,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+    o = o.squeeze(0)
+    return o, final_state

vllm/model_executor/models/qwen3_next.py

@@ -34,7 +34,7 @@ from vllm.model_executor.layers.fla.ops import (
     chunk_gated_delta_rule as fla_chunk_gated_delta_rule,
 )
 from vllm.model_executor.layers.fla.ops import (
-    fused_recurrent_gated_delta_rule,
+    fused_sigmoid_gating_delta_rule_update,
 )
 from vllm.model_executor.layers.fla.ops.chunk import l2norm_fwd
 from vllm.model_executor.layers.fused_moe import SharedFusedMoE
@@ -731,42 +731,41 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
             mixed_qkv_non_spec
         )
 
+        if attn_metadata.num_prefills > 0:
             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
+        else:
+            g_non_spec = None
+            beta_non_spec = None
 
         # 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(
+            core_attn_out_spec, last_recurrent_state = (
+                fused_sigmoid_gating_delta_rule_update(
+                    A_log=self.A_log,
+                    a=a,
+                    b=b,
+                    dt_bias=self.dt_bias,
                     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],
+                    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
 
@@ -794,12 +793,14 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
             )
         elif attn_metadata.num_decodes > 0:
             core_attn_out_non_spec, last_recurrent_state = (
-                fused_recurrent_gated_delta_rule(
+                fused_sigmoid_gating_delta_rule_update(
+                    A_log=self.A_log,
+                    a=a,
+                    b=b,
+                    dt_bias=self.dt_bias,
                     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[