FastDeploy/custom_ops/gpu_ops/limit_thinking_content_length_v2.cu

// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "helper.h"
#include "paddle/extension.h"

// status == 0: 正常生成阶段
// status == 1: 替换阶段
// status == 2: 替换结束阶段
// status == 3: 思考结束阶段
__global__ void limit_thinking_content_length_kernel_v2(
    int64_t *next_tokens,
    const int *max_think_lens,
    const int64_t *step_idx,
    int *limit_think_status,
    const int64_t think_end_id,
    const int64_t line_break_id,
    const int bs) {
    int bid = threadIdx.x;
    if (bid >= bs) return;
    // 如果该序列未启用思考功能，则直接返回，默认值为 -1，表示不限制思考长度
    const int max_think_len = max_think_lens[bid];
    if (max_think_len < 0) return;
    int current_limit_think_status = limit_think_status[bid];
    // 如果在回复阶段, 且已经触发停止标志, 则直接返回, 无需多余执行.
    if (current_limit_think_status == 3) {
        return;
    }

    int64_t next_token = next_tokens[bid];
    const int64_t step = step_idx[bid];

    // ======================= 思考阶段控制 =======================
    // 阶段 1: 仍在思考 (status == 0), 检查是否需要强制结束
    // 阶段 2: 在替换 (status == 1), 检查是否替换结束
    if (current_limit_think_status <= 1) {
        // 当开启思考长度控制时，检查是否超时
        if (step == max_think_len) {
            // 强制将当前token替换为结束思考的token
            next_token = line_break_id;
            current_limit_think_status = 1;
        } else if (step == max_think_len + 1) {
            // 强制将当前token替换为结束思考的token
            next_token = think_end_id;
            current_limit_think_status = 1;
        } else if (step == max_think_len + 2) {
            // 强制将当前token替换为结束思考的token
            next_token = line_break_id;
            current_limit_think_status = 1;
        } else if (step == max_think_len + 3) {
            // 强制将当前token替换为结束思考的token
            next_token = line_break_id;
            // 将状态推进到 1, 表示 "正在结束思考"
            current_limit_think_status = 2;
        }
    }
    // ======================= 思考结束处理 =======================
    // 阶段 3: 检查是否已满足结束思考的条件 (status == 0 || status == 2)
    // 这种情况会处理两种场景:
    // 1. status == 0: 模型可能自己生成了 </think>
    // 2. status == 2: 上一阶段强制注入了 \n</think>\n\n
    if (current_limit_think_status == 0) {
        if (next_token == think_end_id) {
            // 确认思考结束，将状态推进到 3 (响应阶段)
            current_limit_think_status = 3;
        }
    }
    if (current_limit_think_status == 2) {
        // 确认思考结束，将状态推进到 3 (响应阶段)
        current_limit_think_status = 3;
    }
    // 写回更新后的 token
    next_tokens[bid] = next_token;
    // 更新全局状态
    limit_think_status[bid] = current_limit_think_status;
}

void LimitThinkingContentLengthV2(const paddle::Tensor &next_tokens,
                                  const paddle::Tensor &max_think_lens,
                                  const paddle::Tensor &step_idx,
                                  const paddle::Tensor &limit_think_status,
                                  const int64_t think_end_id,
                                  const int64_t line_break_id) {
    const int batch_size = next_tokens.shape()[0];
    limit_thinking_content_length_kernel_v2<<<1, 1024>>>(
        const_cast<int64_t *>(next_tokens.data<int64_t>()),
        max_think_lens.data<int>(),
        step_idx.data<int64_t>(),
        const_cast<int *>(limit_think_status.data<int>()),
        think_end_id,
        line_break_id,
        batch_size);
}

PD_BUILD_OP(limit_thinking_content_length_v2)
    .Inputs({"next_tokens", "max_think_lens", "step_idx", "limit_think_status"})
    .Attrs({"think_end_id: int64_t", "line_break_id: int64_t"})
    .Outputs({"next_tokens_out"})
    .SetInplaceMap({{"next_tokens", "next_tokens_out"}})
    .SetKernelFn(PD_KERNEL(LimitThinkingContentLengthV2));