【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>
2025-11-28 20:55:43 +08:00
parent 5447a039b9
commit 1c4a0468ee
13 changed files with 1625 additions and 149 deletions
--- a/vllm_ascend/ops/triton/fla/chunk_scaled_dot_kkt.py
+++ b/vllm_ascend/ops/triton/fla/chunk_scaled_dot_kkt.py
@@ -0,0 +1,147 @@
+# 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
+
+import torch
+from vllm.triton_utils import tl, triton
+
+from .utils import prepare_chunk_indices, safe_exp
+
+
+@triton.heuristics({
+    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
+    'USE_G': lambda args: args['g_cumsum'] is not None,
+})
+@triton.jit(do_not_specialize=['T'])
+def chunk_scaled_dot_kkt_fwd_kernel(
+    k,
+    beta,  # [H, B, T]
+    g_cumsum,  # [H, B, T]
+    A,
+    cu_seqlens,
+    chunk_indices,
+    T,
+    B,
+    H: tl.constexpr,
+    Hg: tl.constexpr,
+    K: tl.constexpr,
+    BT: tl.constexpr,
+    BK: tl.constexpr,
+    IS_VARLEN: tl.constexpr,
+    USE_G: tl.constexpr,
+):
+    bt_stride = B * T
+    i_t_i, _ = tl.program_id(0), tl.program_id(1)
+
+    for i_bh in range(B * H):
+        i_b, i_h = i_bh // H, i_bh % H
+        if IS_VARLEN:
+            i_n, i_t = tl.load(chunk_indices + i_t_i * 2).to(
+                tl.int32), tl.load(chunk_indices + i_t_i * 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
+            i_t = i_t_i
+        o_t = tl.arange(0, BT)
+        o_t_fp32 = o_t.to(tl.float32)
+
+        p_beta = tl.make_block_ptr(beta + i_h * bt_stride + bos, (T, ), (1, ),
+                                   (i_t * BT, ), (BT, ), (0, ))
+        b_beta = tl.load(p_beta, boundary_check=(0, ))
+
+        b_A = tl.zeros([BT, BT], dtype=tl.float32)
+        for i_k in range(tl.cdiv(K, BK)):
+            p_k = tl.make_block_ptr(k + (bos * Hg + i_h // (H // Hg)) * K,
+                                    (T, K), (Hg * K, 1), (i_t * BT, i_k * BK),
+                                    (BT, BK), (1, 0))
+            b_k = tl.load(p_k, boundary_check=(0, 1))
+            b_A += tl.dot(b_k, tl.trans(b_k))
+
+        if USE_G:
+            p_g = tl.make_block_ptr(g_cumsum + i_h * bt_stride + bos, (T, ),
+                                    (1, ), (i_t * BT, ), (BT, ), (0, ))
+            b_g = tl.load(p_g, boundary_check=(0, ))
+            b_g_diff = b_g[:, None] - b_g[None, :]
+            b_A *= safe_exp(b_g_diff)
+
+        b_A *= b_beta[:, None]
+        b_A = tl.where(o_t_fp32[:, None] > o_t_fp32[None, :], b_A, 0)
+        p_A = tl.make_block_ptr(A + (bos * H + i_h) * BT, (T, BT), (BT * H, 1),
+                                (i_t * BT, 0), (BT, BT), (1, 0))
+        tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1))
+
+
+def chunk_scaled_dot_kkt_fwd(
+        k: torch.Tensor,
+        beta: torch.Tensor,
+        g_cumsum: Optional[torch.Tensor] = None,
+        cu_seqlens: Optional[torch.LongTensor] = None,
+        chunk_size: int = 64,
+        output_dtype: torch.dtype = torch.float32) -> torch.Tensor:
+    r"""
+    Compute beta * K * K^T.
+
+    Args:
+        k (torch.Tensor):
+            The key tensor of shape `[B, T, H, K]`.
+        beta (torch.Tensor):
+            The beta tensor of shape `[B, T, H]`.
+        g (torch.Tensor):
+            The cumulative sum of the gate tensor of shape `[B, T, H]`. Default: `None`.
+        gk (torch.Tensor):
+            The cumulative sum of the gate tensor of shape `[B, T, H, K]` applied to the key tensor. Default: `None`.
+        cu_seqlens (torch.LongTensor):
+            The cumulative sequence lengths of the input tensor.
+            Default: None
+        chunk_size (int):
+            The chunk size. Default: 64.
+        output_dtype (torch.dtype):
+            The dtype of the output tensor. Default: `torch.float32`
+
+    Returns:
+        beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size.
+    """
+    B, T, Hg, K = k.shape
+
+    H = beta.shape[-1]
+    BT = chunk_size
+    if cu_seqlens is not None:
+        cu_seqlens = cu_seqlens.cpu()
+        chunk_indices = (prepare_chunk_indices(cu_seqlens, BT)
+                         if cu_seqlens is not None else None)
+        chunk_indices = chunk_indices.npu()
+        cu_seqlens = cu_seqlens.npu()
+    else:
+        chunk_indices = None
+    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
+    A = torch.empty(B, T, H, BT, device=k.device, dtype=output_dtype)
+
+    chunk_scaled_dot_kkt_fwd_kernel[(NT, 1)](
+        k=k,
+        beta=torch.permute(beta, (2, 0, 1)).contiguous(),
+        g_cumsum=torch.permute(g_cumsum, (2, 0, 1)).contiguous(),
+        A=A,
+        cu_seqlens=cu_seqlens,
+        chunk_indices=chunk_indices,
+        T=T,
+        B=B,
+        H=H,
+        Hg=Hg,
+        K=K,
+        BT=BT,
+        BK=128,
+        num_warps=8,
+        num_stages=3,
+        multibuffer=True,
+    )
+    return A