xc-llm-ascend/vllm_ascend/ops/triton/fla/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
# ruff: noqa: E501
# mypy: ignore-errors

import os

from vllm.triton_utils import tl, tldevice, triton

if os.environ.get('FLA_USE_FAST_OPS', '0') == '1':
    div = tldevice.fast_dividef
    exp = tldevice.fast_expf
    log = tldevice.fast_logf
    log2 = tldevice.fast_log2f
else:

    @triton.jit
    def div_normal(x, y):
        return x / y

    div = div_normal
    exp = tl.exp
    log = tl.log
    log2 = tl.log2


@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_recurrent_gated_delta_rule_fwd_kernel(
    q,
    k,
    v,
    g,
    beta,
    o,
    h0,
    ht,
    cu_seqlens,
    ssm_state_indices,
    num_accepted_tokens,
    scale,
    N: tl.constexpr,  # num of sequences
    T: tl.constexpr,  # 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
    IS_BETA_HEADWISE: tl.
    constexpr,  # whether beta is headwise vector or scalar,
    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)

    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:
        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
            p_h0 = h0 + tl.load(ssm_state_indices + i_n * stride_indices_seq +
                                i_t).to(tl.int64) * stride_init_state_token
        else:
            p_h0 = h0 + bos * HV * K * V
        p_h0 = p_h0 + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
        b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)

    for i_t in range(0, T):
        p_q = q + (bos * H + i_h) * K + o_k + H * K * i_t
        p_k = k + (bos * H + i_h) * K + o_k + H * K * i_t
        p_v = v + (bos * HV + i_hv) * V + o_v + HV * V * i_t

        if IS_BETA_HEADWISE:
            p_beta = beta + (bos * HV + i_hv) * V + o_v + HV * V * i_t
        else:
            p_beta = beta + bos * HV + i_hv + HV * i_t

        if not IS_KDA:
            p_g = g + bos * HV + i_hv + HV * i_t
        else:
            p_gk = g + (bos * HV + i_hv + HV * i_t) * K + o_k

        p_o = o + ((i_k * all + bos) * HV + i_hv) * V + o_v + HV * V * i_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_g = tl.load(p_g).to(tl.float32)

        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
        # [BK, BV]
        # b_h *= tl.exp(b_g)
        if not IS_KDA:
            b_g = tl.load(p_g).to(tl.float32)
            b_h *= exp(b_g)
        else:
            b_gk = tl.load(p_gk).to(tl.float32)
            b_h *= exp(b_gk[:, None])
        # [BV]
        b_v -= tl.sum(b_h * b_k[:, None], 0)
        if IS_BETA_HEADWISE:
            b_beta = tl.load(p_beta, mask=mask_v, other=0).to(tl.float32)
        else:
            b_beta = tl.load(p_beta).to(tl.float32)
        b_v *= b_beta
        # [BK, BV]
        b_h += b_k[:, None] * b_v[None, :]
        # [BV]
        b_o = tl.sum(b_h * b_q[:, None], 0)
        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:
            p_ht = ht + tl.load(ssm_state_indices + i_n * stride_indices_seq +
                                i_t).to(tl.int64) * stride_final_state_token
        else:
            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)